Category Archives: Genetics

INTRODUCTION

Interplanetary space is populated by densely ionizing particle radiation not naturally present on Earth (1). Life on Earth has evolved under the protection of a geomagnetic field, which deflects high-charge, high-energy (HZE) ions; however, the constant flux of HZE ions in deep space is essentially impossible to shield, making astronaut exposures inevitable (2).

In the absence of human epidemiological data for exposures to HZE radiation, uncertainties surround the cancer risk estimates for space flight crews that venture beyond low Earth orbit. The current NASA space radiation cancer risk model is built largely upon epidemiological data from the survivors of the Hiroshima and Nagasaki atomic bombings, a cohort of individuals exposed predominantly to -rays (35), a form of photon radiation. One key assumption in this NASA model is that the spectra of tumor types, and their biologic behaviors, will be similar for individuals exposed to ionizing radiation, whether particle or photon. However, notable physical differences exist between ionizing photon and particle radiation, and these physical differences translate to unique ionization and damage patterns at the molecular, cellular, and tissue levels. HZE ion exposures produce spatially clustered DNA double-strand breaks, along with other DNA lesions in close proximity to break sites (6). In contrast, -rays produce sparse ionization events that are random in spatial distribution and less likely to have additional DNA lesions immediately adjacent to the break sites. Other assumptions in the model are that radiogenic tumors are no more lethal than their sporadic counterparts and that females are at greater risk for radiogenic cancers than males (7).

In assessing cancer risks to astronauts, the premise that HZE ion exposures increase the risk for the same types of tumors that arise in human populations exposed to -rays is supported by the few animal studies of HZE ion carcinogenesis conducted to date (8). These studies, conducted on genetically homogeneous animals, have demonstrated that tumor types arising in HZE ionirradiated animals are the same as those that occur spontaneously in these animals or following exposure to photon radiation (8). However, all previous data are from either inbred mice (9, 10) or rats (11), F1 hybrid mice (12, 13), or rat stocks with limited genetic heterogeneity (11, 1416), and the tumor types that arise in inbred rodents are determined, in very large part, by their genetic background. Therefore, the spectrum of tumors that might arise in a genetically diverse population exposed to HZE ions is unknown.

With the emergence of multiparent outbreeding strategies that produce highly recombinant mouse populations with allelic variants from multiple founder strains (1719), it is possible to model the effects of population diversity in carcinogenesis studies by minimizing the overwhelming effects of genetic background and increasing the phenotypic repertoire available within a test population. These populations also allow for high-precision genetic mapping (18, 20). Quantitative trait locus (QTL) mapping is a powerful forward-genetics approach that allows for unbiased testing of genetic variants that may influence gene-environment interactions for radiation effects (21, 22). Highly recombinant populations were constructed for the purpose of mapping complex traits, and QTL can often be resolved to megabase resolution (1820). In addition, complete sequence information can be used on genotyped individuals by imputing the substantial genomic resources available for the founder strains.

Studying tumors that arise in irradiated, genetically diverse mouse populations presents a unique opportunity to test key assumptions of the NASA risk model, particularly whether HZE ions induce the same tumors by the same mechanisms as -rays. If so, the current practice of extrapolating human epidemiological data from individuals exposed to -rays to astronauts exposed to HZE ions would be a valid approach for risk calculation in the space radiation environment.

To study the effects of HZE ion irradiation in a genetically heterogeneous population, 1850 HS/Npt stock mice (23) of both sexes were genotyped for 77,808 single-nucleotide polymorphism (SNPs) and exposed to (i) 0.4 gray (Gy) of 28Si ions (240 MeV/n) [linear energy transfer (LET), 80 keV/m; = 0.031 particles/m2] or (ii) 56Fe ions (600 MeV/n) (LET, 181 keV/m; = 0.014 particles/m2), (iii) 3 Gy of 137Cs -rays, or (iv) sham irradiation. We chose 56Fe ions because of their high abundance in galactic cosmic radiation (GCR) and because their high charge (Z = +26) makes them particularly damaging (24). The 28Si ions were selected because their LET more closely approximates the dose average LET of secondary fragments generated by GCR penetrating an aluminum spacecraft hull (25). The mice were monitored daily until they reached 800 days of age or became moribund. Comprehensive necropsies were performed on each mouse and involved all organ systems. Each detected lesion was characterized histologically by a board-certified veterinary pathologist. Tumors were the predominant cause of morbidity and mortality for both HZE ionirradiated (n = 622) and -rayirradiated (n = 615) populations as well as for the population of unirradiated mice (n = 613). Overall life span was significantly reduced for irradiated populations (Fig. 1A), which can be attributed to the increased incidence and decreased median survival for radiation-induced tumors. For irradiated mice, populations exposed to 0.4-Gy HZE ions had increased survival times compared to mice exposed to 3.0 Gy of -rays (Fig. 1A). Although these doses seem disparate, their selection is based on preliminary dose-response studies (26), which reveal that 0.4 Gy of HZE ions and 3.0-Gy -rays are each maximally tumorigenic.

Overall survival for HS/Npt mice, plotted as Kaplan-Meier survival, is presented for each exposure group (A). The incidence of specific tumor histotypes (B) and median survival times for these tumors (C) are plotted for each exposure group, which demonstrates that certain tumor types occur at an increased frequency following exposures to radiation of specific qualities and survival times in irradiated mice are decreased for some tumor types. The incidence of specific tumor histotypes within HS/Npt families is plotted for unirradiated (D), -rayirradiated (E), and HZE ionirradiated families (F) and demonstrates that specific tumor types often occur at very high incidence within some families and not at all in others, indicating heritability of tumor susceptibility. Furthermore, adjacent families are more closely related, and tumor incidences, for example, family 23 and adjacent families, have a high incidence of B cell lymphoma. The 47 HS/Npt families are arranged along the x axis (D to F).

A wide variety of tumor diagnoses [82 distinct tumor histotypes (table S1)] were observed in HS/Npt mice. Although most of these tumor types were rare, 18 histotypes were observed at incidences greater than 1%. Overall, the spectra of tumor histotypes produced in genetically diverse populations exposed to HZE ions and -rays were similar (Fig. 1B). Furthermore, tumor types induced by radiation were generally similar to those arising spontaneously in HS/Npt mice; however, radiation-exposed populations demonstrated decreased median survival times associated with tumor development (Fig. 1C and figs. S7 to S22) and increased incidences for specific tumor types, such as leukemias and Harderian gland adenocarcinomas, following radiation (Fig. 1B). The structure of the HS/Npt population can be divided into families that consist of mice more closely related to one another. Many tumor histotypes show high incidences within some families but are absent or rare in others (Fig. 1, D to F), which is consistent with genetic susceptibility to certain tumor types. Furthermore, certain tumorsparticularly lymphomas, pulmonary adenocarcinomas, hepatocellular carcinomas, Harderian gland tumors, and myeloid leukemiasdemonstrate a periodicity in tumor incidence (Fig. 1, D to F) where adjacent families often display similar incidences, which could be predicted on the basis of the circular breeding design used to generate HS/Npt, in which adjacent families are more related to one another than families further removed.

Although the tumor spectra are similar for each irradiated population, the different radiation qualities demonstrate varied efficiencies for producing specific tumor histotypes. -rayirradiated mice were at greater risk for myeloid leukemia, T cell lymphoma, pituitary tumors, and ovarian granulosa cell tumors than unirradiated mice; HZE ionirradiated mice demonstrated an intermediate susceptibility to these histotypes (Fig. 1B). For Harderian gland tumors, thyroid tumors, hepatocellular carcinomas, and sarcomas, HZE ion and -rayirradiated mice were at a similarly and significantly increased risk compared to unirradiated controls (fig. S7 to S22).

NASA permissible exposure limits for radiation limit the number of days an astronaut can spend in space based on modeled cancer risk. These limits are different for men and women (27) due primarily to epidemiological data that indicate that women are at greater risk for radiogenic cancers than men due to their longer life spans and susceptibility to specific cancer types, such as lung, ovarian, and breast carcinomas. Female HS/Npt mice have longer life spans than males (P = 2.7 106, log-rank test), with unirradiated females living 43 days longer (686.1 days), on average, than males (643.2 days) (fig. S1A). In contrast, no survival difference is observed between -rayirradiated females and males (P = 0.51) or HZE ionirradiated females and males (P = 0.06), indicating that female HS/Npt mice are more susceptible to radiation-induced morbidities and mortalities than males (fig. S1, B and C). Irradiated female mice had increased incidences of (i) ovarian tumors, (ii) mammary tumors, (iii) central nervous system tumors (pituitary adenomas, choroid plexus tumors, and ependymomas), (iv) diffuse large B cell and lymphoblastic B cell lymphomas, (v) osteosarcomas, and (vi) leiomyosarcomas (fig. S1D). Female mice were at lower risk for radiogenic lung cancer (fig. S1D and table S1), which is a major contributor to limiting flight time for female astronauts. Modeling risk by sex in humans has been confounded by different smoking rates between men and women in the atomic bomb survivor cohort (28).

To determine whether the genetic variants that increase tumor susceptibility following -ray irradiation also increase tumor susceptibility following HZE ion irradiation, genome-wide association mapping was performed for 18 tumor types in which there was an incidence of greater than 1%. Genomes were reconstructed for each mouse using a probabilistic model to predict founder haplotypes from high-density genotype data (18). Reconstructed genomes represent the unique accumulation of meiotic events for each individual and form a scaffold for the imputation of known sequencing information from the eight parental inbred strains. Polygenic covariance among related individuals is of significant concern in multiparent crosses and was corrected for during QTL mapping with a kinship term (18, 29). Mapping was performed for each phenotype using both a generalized linear mixed-effects model and proportional hazards regression model with the aforementioned kinship to adjust for polygenic covariance between related mice. To determine the significance thresholds for a model in which no QTL is present, the phenotypes were permuted, the regression model was run, and the maximum statistic was retained from each permutation (30). The 95% significance threshold was minimally variable between phenotypes with a mean threshold of log(P) > 5.8, and this value was used to identify significant associations. This is consistent with the estimated 0.05 Bonferroni genome-wide corrected threshold of log(P) > 6.0, which is considered overly conservative for QTL mapping (30).

At least one QTL was identified for 13 of the 18 tumor phenotypes examined. For tumor incidence, 35 QTL were identified with an average confidence interval of 3.4 Mb (table S2). For QTL at the 95% confidence threshold, effect sizes average 3.7% of the phenotypic variance with a range of 0.75 to 7.46%. For most of the tumors, the genetic architecture was complex with multiple QTL individually explaining a small proportion of the total variance. Although loci with moderate effects on the phenotype were most common, 11 large effect QTL were observed for seven tumor histotypes, with effect sizes greater than 5% (table S2).

To determine potential effects of genetic variants on tumor latency following irradiation, mapping was also performed using proportional hazards regression model (table S3) and 38 QTL were identified for 12 tumor types. QTL associated with tumor survival times mirrored those identified for tumor incidence, indicating that the genetic variants that control susceptibility to radiation-induced tumors also determine latencies.

Neoplasia is a binomially distributed trait, and therefore, the power to detect significant associations is primarily dependent on tumor incidence and QTL effect size. This leads to important considerations for the ultimate goal of this analysis, which is to determine similarities between QTL for specific neoplasms in populations exposed to different qualities of radiation. For some tumor types, a significant peak was observed in one exposure group with a suggestive peak present at the same locus in the alternative exposure group. We speculate that the reason certain radiation qualities produce only suggestive QTL for certain tumor phenotypes is likely due to decreased mapping power as a result of the variation in incidence between groups. In these cases, if the peak was more significant when combining radiation groups, the QTL was considered significant for all irradiated animals regardless of radiation quality.

Thyroid tumors are a well-known radiation-induced entity for both humans and mice; however, relatively little is known about genetic variants that increase susceptibility to this disease in mice. In HS/Npt mice, spontaneous thyroid adenomas occurred at relatively low frequencies and had a uniformly late onset, with tumors occurring between 700 and 800 days of age (Fig. 2A). In contrast, thyroid tumors arising in HZE ion or -rayexposed mice occur with significantly earlier onsets, with tumors arising as early as 250 days of age (Fig. 2A).

Thyroid follicular adenoma Kaplan-Meier survival estimate (A) along with genome-wide association plots for thyroid adenoma in HZE ionirradiated, -rayirradiated, HZE ion and -rayirradiated, and unirradiated mice (B) and an expanded plot for chromosome 2 (C), which contains the most significant association locus; gray lines indicate 95% (upper line) and 90% confidence (lower line) for log10(P values). Genome-wide association results reveal significant results in HZE ion and -rayirradiated mice that are further bolstered by combining the groups. The top panel of (D) shows strains that contribute the reference allele for the SNPs highlighted in red in the middle panel, indicated by vertical lines (D); the C57BL/6J strain contributes an allele that differs significantly from the other seven strains. The middle panel shows the log10(P value) of each SNP in the interval (D); the most significant SNPs are highlighted in red, and the bottom panel lists genes within the QTL interval. Genes that contain splice site, missense, or stop-related SNPs are colored red (D). Resample model averaging was performed within chromosome 2 to compare the distribution of peak log10(P values) for each exposure group (E); there is broad overlap for HZE- and -rayirradiated mice, and grouping all irradiated mice together further narrows the distribution of peak log10(P values). Mbp, megabase pair.

Association mapping reveals a significant 3.4-Mb interval on chromosome 2 for HZE ionexposed animals (Fig. 2, B and C). The same locus is identified in the -rayirradiated population if the significance threshold is decreased to a level at which 30% of identified QTL will be false positives. Combining both irradiated populations markedly increases the significance of the QTL identified on chromosome 2. The QTL interval (119 to 125 Mb) contains 39,179 SNPs (Sanger Mouse Genomes, REL-1505) and 142 genes (Ensembl version 85) (Fig. 2D). Within the QTL region, the C57BL/6J parental strain contains an introgression from the Mus musculus musculus genome (31); we found that HS/Npt mice carrying the C57BL/6J haplotype at the QTL have increased thyroid tumor incidence regardless of whether they are exposed to HZE ions or -rays.

To further explore the possibility that the QTL identified on chromosome 2 controls susceptibility following -ray and HZE ion exposures, we used a nonparametric resample model averaging procedure (32) across the entire chromosome to identify genomic loci that consistently reappear in resampled populations. Briefly, genome scans are repeated for each new dataset created, in which some individuals may be sampled more than once and some not at all (32). Resample model averaging consistently identifies the same locus for all groups of mice, regardless of radiation exposure (Fig. 2E). Furthermore, the resample model averaging procedure identifies the same locus for tumors arising spontaneously (Fig. 2E). Data from this tumor phenotype indicate that the same inheritable genetic variants contribute to an individuals risk of developing thyroid cancer, regardless of radiation exposure.

Acute myeloid leukemia (AML) is another common radiation-induced tumor in both mice and humans (33, 34). In concordance with previous studies conducted with inbred mice (26), -ray exposures in HS/Npt mice are more efficient at inducing AML than HZE ion exposures. In our -irradiated mice, 15.6% (96 of 615) developed AML compared to 2.9% (18 of 622) of those exposed to HZE ions and 1.6% (10 of 613) of unirradiated mice. AML median survival times were similar for all groups (Fig. 3A). Association mapping revealed a significant QTL for the -irradiated population on chromosome 2 that reached the 95% confidence threshold (Fig. 3, B and C), but no QTL was observed for the HZE ionexposed population, in which the incidence of AML was much lower. However, when grouping HZE ion and -rayirradiated mice together, the same QTL was significantly bolstered (Fig. 3B). If the susceptibility alleles identified at this locus were only contributing to disease following -ray irradiation and were, therefore, randomly distributed among the affected mice in the HZE ionexposed group, then we would expect the log10(P values) to decrease when combining -irradiated mice; however, the log10(P value) for this locus significantly increases when repeating the mapping procedure included all irradiated mice.

(A) Kaplan-Meier plots for myeloid leukemia demonstrate similar median survival estimates for myeloid leukemia between groups. (B) Genome-wide association procedures identify a narrow QTL on chromosome 2; two gray lines indicate 95% (upper line) and 90% confidence (lower line) for log10(P values). Expanded mapping results are depicted in (C) along with contributing strains for the reference allele. The A/J, AKR/J, C57BL/6J, DBA/2J, and LP/J strains contribute alleles that differ from the other strains, indicated by vertical lines in the top panel (C). The middle panel shows the log10(P value) of each SNP in the interval. The most significant SNPs are highlighted in red. The bottom panel shows the genes in the QTL interval. Genes that contain splice site, missense, or stop-related SNPs are indicated in red. Copy number results for Spi1 and Asxl1 in splenic samples from mice diagnosed with myeloid leukemia are plotted by exposure group (D).

Radiation-induced AML is a well-characterized disease in mice (10, 35, 36) and is most commonly the result of a radiation-induced minimally deleted region on chromosome 2 containing the PU.1 gene (current murine nomenclature, Spi1) and a recurrent point mutation that inactivates the remaining Spi1 allele (37). Figure 3C depicts mouse chromosome 2 with the positions of the QTL identified in our irradiated mice and the Spi1 gene. To test the hypothesis that AMLs occurring in HZE ionexposed animals will contain the same molecular aberrations know to occur in AML arising in -rayexposed mice, the copy number for Spi1 was investigated in leukemia samples to assess for deletions. As expected, most of the leukemias occurring in -rayexposed mice had a deletion in one copy of Spi1. In contrast, Spi1 deletions in spontaneously occurring AML were less common (Fig. 3D). Similar to -rayirradiated mice, leukemias that developed in mice exposed to HZE ions, although fewer in number, also have an increased incidence of Spi1 deletion. This finding indicates that AML arises by similar molecular mechanisms following exposures to HZE ions or -rays.

Because the QTL identified on chromosome 2 is approximately 60 Mb from the commonly deleted region containing Spi1 and because radiation-induced deletions can be notoriously large, we considered the possibility that the identified QTL was also deleted in these leukemias, resulting in loss of one copy of the QTL region. To test this hypothesis, we determined the copy number for a gene located at distal to the QTL support interval, Asxl1. As expected, we found that Asxl1 was not deleted in any sample in which Spi1 was not deleted; however, in 69% of cases with a Spi1 deletion, Asxl1and presumably the entire QTL regionwas also deleted (Fig. 3D). This demonstrates that most of the radiation-induced AML cases arose from progenitor cells haploinsufficient for the entire QTL region.

HZE ion and, to a lesser extent, -ray irradiation were particularly effective in inducing Harderian gland tumors at the doses used in this study, which was expected on the basis of extensive published radiation quality data on these tumors (8, 38). In the HZE ionirradiated group, Harderian gland tumors were observed in 22.7% (221 of 622) of mice and 3.2% (20 of 622) were malignant. In the -irradiated group, 15.3% (94 of 615) of mice developed Harderian gland tumors and 2.7% (17 of 615) were malignant. In contrast, spontaneous Harderian gland tumors occurred in only 4.1% (25 of 613) of unirradiated mice and 0.7% (4 of 613) were malignant. Despite the differences in tumor incidences following irradiation, median survival times for Harderian gland adenocarcinoma were similar for all groups (HZE ion, 582 days; -ray, 571 days; and unirradiated mice, 571 days).

Two QTL were observed for Harderian gland adenocarcinomas in HZE ionirradiated mice, one on chromosome 4 and another on chromosome 9 (Fig. 4A). The 1.7-Mb interval identified on chromosome 4 (Fig. 4B) is similar to previously discussed QTL regions in that combining both irradiated populations markedly increases the significance of this locus, which suggests that this QTL is associated with Harderian gland adenocarcinoma susceptibility in both HZE ion and -rayirradiated mice. In contrast, a 2.3-Mb QTL interval on chromosome 9 is observed only in HZE ionirradiated mice, and the locus is absent when combining all irradiated mice and repeating the mapping procedure (Fig. 4C). To further evaluate these QTL, resample model averaging was performed within chromosomes 4 and 9 to determine the distribution of peak log10(P values) along each chromosome. For chromosome 4, there is substantial spatial overlap identified in peak log10(P value) associations in the HZE ionexposed population and the -rayirradiated population, and the HZE ion and -rayirradiated population yields the most consistent identification of the QTL region (Fig. 4D). In contrast, although nearly all identified peak log10(P values) were identified in the 2.3-Mb QTL interval on chromosome 9 for HZE ionirradiated mice, the distributions of peak log10(P values) for other exposure groups do not substantially overlap and are widely distributed along the chromosome (Fig. 4E). The resample model averaging results indicate that while the chromosome 4 QTL contributes to susceptibility to Harderian gland adenocarcinomas in both HZE ion and -rayirradiated populations, the QTL identified on chromosome 9 appears to only be involved in Harderian adenocarcinoma susceptibility following HZE ion exposures.

Genome-wide association plots for Harderian gland adenocarcinoma (A) for HZE ionirradiated, -rayirradiated, HZE ion and -rayirradiated, and unirradiated mice; two gray lines indicate 95% (upper line) and 90% confidence (lower line) for log10(P values). Chromosome 4, which is expanded in (B), reveals a significant QTL associated with HZE ion irradiation, which is further increased significantly when grouping all irradiated mice (HZE ion and -ray irradiated) together, which indicated that the genetic variants in this location are important for Harderian gland adenocarcinoma following exposures to either HZE ion or -ray irradiation. In contrast, chromosome 9, which is expanded in (C), reveals a significant QTL associated only with HZE ion irradiation; this locus is absent when grouping all irradiated mice (HZE ion and -ray irradiated) together, which suggests that the allele(s) present in this region may only play a role for HZE ioninduced tumors. Resample model averaging was performed within chromosomes containing significant QTL. There is significant spatial overlap identified on chromosome 4 for peak log10(P value) associations in the HZE ionexposed population, the -rayirradiated population, and the HZE ion and -rayirradiated population that demonstrates the most consistent identification of the QTL region (D). In contrast, although nearly all identified peak log10(P values) were identified in the chromosome 9 QTL interval for HZE ion irradiated mice, the peak log10(P values) for other exposure groups are widely distributed along the chromosome (E).

In addition to looking for similarities between individual, selected QTL for HZE ion and -rayexposed populations, we also sought a more holistic method in which entire genome-wide association results could be compared between groups in an unsupervised process. We used hierarchical clustering to create cluster dendrograms using entire genome-wide scans for a given phenotype. By considering results from genome-wide associations, rather than individualized peaks observed within genome-wide associations, we submit for comparison not only highly significant QTL regions but also the numerous loci detected with lower confidence.

Unsupervised hierarchical clustering of genome scans creates significant clustering events that often occur for the same histotype regardless of radiation exposure (Fig. 5A). Multiple tumor histotypesincluding mammary adenocarcinoma, thyroid adenoma, and hepatocellular carcinomacluster by histotype, regardless of radiation exposure. To demonstrate and validate the methodology of QTL clustering, genome-wide scans for coat colors in each treatment group are evaluated and coat color genome-wide scans cluster together, as expected (Fig. 5B). These results further support the hypothesis that host genetic factors are highly important in determining risk of radiation carcinogenesis, whether following HZE ion or -ray exposures.

(A) Unsupervised hierarchical clustering of genome-wide association scans for tumor phenotypes reveals that the most significant clustering events often occur for the same histotype regardless of radiation exposure; these include mammary adenocarcinoma, thyroid adenoma, and hepatocellular carcinoma. (B) As expected, clustering genome scans for coat color demonstrates the expected results: that genome scans cluster together despite exposure group. The green line represents the 99% confidence level of the most significant dendrogram heights by permutations (log10 values permuted with genetic markers) to determine a distribution of dendrogram heights under the null hypothesis that no associations exist (C), demonstrating that the observed clusters are highly unlikely to occur randomly.

Permissible exposure limits for astronauts are based on the risk of death from cancer rather than cancer development, and the incidence to mortality conversion used in the risk calculation uses spontaneously occurring cancers in the U.S. population. Thus, there is an assumption that radiogenic tumors are no more lethal than spontaneous tumors. To determine whether tumors that arise following HZE ion exposure are more malignant than their counterparts arising in unirradiated or -rayirradiated mice, metastatic disease was characterized for each group. Pulmonary metastases were consistently observed in cases of hepatocellular carcinoma, Harderian gland adenocarcinoma, osteosarcoma, and ovarian granulosa cell tumor. Metastases were no more frequent in irradiated animals than in controls, and there was no significant difference in metastatic incidence between HZE ionirradiated mice and -rayirradiated mice (fig. S5A), and pulmonary metastatic density is similar between groups (fig. S5, B to D).

Tumor latency following irradiation was compared between exposure groups using survival statistics. Differences in tumor latency in this context indicate a decrease in time for tumor initiation or promotion. Since radiation is efficient at both initiation and promotion, decreased latencies are expected for irradiated population. Tumor progression is not evaluated, and our results therefore do not demonstrate whether tumors arising in irradiated individuals are more likely to progress rapidly than those arising spontaneously. As expected, tumors arising in both HZE ion and -rayirradiated mice show significantly decreased latencies in comparison to the unirradiated population (fig. S7 to S22). However, HZE ions did not further decrease latencies when compared to -rayirradiated mice.

Carcinogenesis as a result of space radiation exposure is considered the primary impediment to human space exploration (2). Compared to forms of radiation found naturally on Earth, including x-rays, -rays, and particles, HZE ions in space are much more difficult to shield (2) and have a distinct ionization pattern that aligns along dense track structures, resulting in clustered damage to chromatin (6). Because HZE ions, a highly penetrating component of GCRs, are not amenable to shielding (28, 29), exposure risks are inherent to manned missions in interplanetary space, but estimating the risk associated with this unique form of particle radiation is complicated by the essential lack of data for human exposures (28). As a substitute, human exposure data from other forms of ionizing radiation, primarily -ray (35) photon radiation, are used in cancer risk models with the assumption that photon and particle radiation have qualitatively comparable biological effects.

Animal models are a vital component in determining the validity of the extrapolation of human terrestrial radiation exposure data to exposures that will occur in astronauts in the space radiation environment. To date, carcinogenesis studies designed to evaluate the effects of HZE ions have used rodents with limited genetic heterogeneity (916). The advantage of removing genetic variability in animal models is the consequent decrease in phenotypic variability, which allows for fewer individuals to detect potential environmental effects on phenotype; the disadvantage is that strain-specific responses in genetically identical populations are significant and can obscure the variability that one might expect in a diverse population, such as humans. By using a genetically diverse population with a wide range of tumor susceptibilities, the spectra of tumors that occur following exposures to particle and photon radiation can be compared. The results of this study indicate that the spectrum of tumor histotypes observed in a genetically diverse population exposed to particle radiation is not unique to that observed in a population exposed to photon radiation or to the tumor spectrum observed in an unirradiated population. Despite the similarities observed in tumor spectra following radiation exposures, the radiation qualities and doses used for this study have unique efficiencies at producing specific tumor types, and while this work demonstrates that the underlying genetics of susceptibility can be similar for tumorigenesis following both high- and low-LET radiation, further work is necessary to define risks for specific tumor histotypes based on exposures.

This study uses a highly recombinant mouse population (HS/Npt stock) that is genetically diverse and designed for genome mapping (1921, 23), a forward-genetics approach that allows for an unbiased search of the entire genome for genetic associations. In contrast, genetically engineered mouse models rely on a reverse-genetics approach in which a given gene is first altered and the resulting phenotypes are then characterized. Studies using forward-genetics are most informative in populations that contain abundant genetic and phenotypic diversity. HS/Npt mice are a multiparent cross derived from eight inbred strains (A/J, AKR/J, BALBc/J, CBA/J, C3H/HeJ, C57BL/6J, DBA/2J, and LP/J); each individual contains a unique mosaic of founder haplotypes and a high degree of heterozygosity, and recombination events become increasingly dense with each generation. Our population of HS/Npt mice was obtained from generation 71 of circular outbreeding. Creating these populations is not trivial and has been a central goal of communities involved in genetics research over the past few decades, resulting in the creation of rodent populations ideal for genome mapping (1820, 3942).

Genome mapping allows the discovery of QTL associated with susceptibility to complex traits, such as radiogenic cancers; this approach is uniquely suited to comparing inheritable risk factors for cancers following exposures to unique carcinogens, such as particle and photon radiation. In broader terms, this work demonstrates the utility of highly recombinant mouse models created for genetic mapping in carcinogenesis studies, an application that has not been previously attempted. Mapping QTL in carcinogenesis studies provides inherent challenges due to the structure of binomial data, potential confounding causes of death following irradiation and aging, the fundamental stochastic nature of radiation tumorigenesis, and incomplete penetrance of potential allelic variants. Despite these challenges, we were able to map QTL for 13 neoplastic subtypes and many of these identified loci are previously unidentified.

At the doses used in this study, HZE ions appear to be less effective than -rays in inducing precursor T cell lymphoblastic lymphoma (pre-T LL) and ovarian tubulostromal adenomas and granulosa cell tumors. This may be due to a combination of dose inhomogeneity in HZE ionirradiated tissues and the major role cell killing plays in the etiology of these specific tumors. pre-T LL can be prevented by transplanting irradiated mice with unirradiated syngeneic bone marrow cells or by shielding some of their bone marrow during irradiation (43, 44). The underlying mechanism by which unirradiated bone marrow cells suppress lymphomagenesis may involve a cell competition process by which older T cell progenitors resident in the thymus are normally replaced by fresh progenitors that immigrate from the bone marrow. Radiation kills these fresh bone marrow cells or reduces their fitness, which, in turn, prolongs the time that older T cell progenitors already in the thymus survive and self-renew. This, along with the increased proliferative cycles of the older T cell progenitors needed to maintain production of mature T cells, results in a corresponding increase in the oncogenic mutations that they accumulate and a concomitant increase in lymphomagenesis (45). Replenishing dead or damaged bone marrow cells by transplantation or preventing their damage through shielding suppresses lymphomagenesis.

At the 3-Gy dose of -rays used in this study, all of the bone marrow cells are uniformly irradiated. This is not the case for HZE particle radiation. The average diameter of a murine bone marrow cell nucleus is around 6 m (46). At the fluence of HZE ions used in this study, the probability that a 6-m-diameter nucleus will be traversed by a 28Si ion and a 56Fe particle is 0.88 and 0.40, respectively. On the basis of a Poisson distribution, the probabilities of a nucleus not being traversed at all are 0.41 and 0.67 for 28Si and 56Fe irradiation, respectively. Thus, many of the T cell progenitors in the bone marrow are not irradiated (although they receive a small dose from -rays). These cells should exert a protective effect similar to transplanting unirradiated bone marrow cells or shielding some of the bone marrow during irradiation, rendering HZE ions less efficient for lymphomagenesis. Given that most of the pre-T LL in the HZE ionirradiated group are likely spontaneous, it is expected that they cluster more closely to spontaneous pre-T LL than to -rayinduced pre-T LL.

The mechanism leading to murine tumors of ovarian surface epithelium origin is well understood. Loss of primordial follicle oocytes by radiation-induced apoptosis results in a decrease in estrogen production, which, in turn, leads to elevated levels of follicle-stimulating hormone (FSH) and luteinizing hormone (LH) in the circulation. FSH and LH drive proliferation of ovarian surface epithelium cells (47). Ovarian tumors can be induced in some animal models by artificially manipulating levels of these hormones (4749). Irradiated mice can be protected from tubulostromal adenomas and granulosa cell tumors by shielding one ovary during irradiation or by transplanting the mice with an unirradiated ovary (50, 51); these interventions protect some oocytes and thereby maintain proper regulation of FSH and LH levels.

Assuming that the target cells are primordial follicle oocytes with a diameter of 12 m, the probabilities of no traversals are 0.2 for 56Fe and 0.03 for 28Si at the 0.4-Gy dose used here. The probabilities for one or fewer traversals are 0.52 for 56Fe and 0.14 for 28Si. Whether a sufficient number of follicles survive at 0.4 Gy to account for the observed ovarian tumor sparing is unknown. Mishra and colleagues (52) observed a dose-dependent decrease in primordial stage follicles in C57BL/6 mice 8 weeks after irradiation with 56Fe ions (600 MeV/n). Sixteen percent of the follicles survived at the 0.3-Gy dose, and normal levels of serum FSH and LH were present; at 0.5 Gy, only 1% of the follicles survived and an increase in serum FSH was observed. Caution is needed in using Mishras results in interpreting our own since we used mice with different genetic backgrounds and the FSH and LH levels in the 0.3 Gyirradiated mice may increase relative to unirradiated controls if time points beyond 8 weeks are assayed. In any event, microdosimetric effects should be incorporated into any risk model for tumors in which cell killing plays a prominent role.

The location of the chromosome 2 QTL in a region frequently deleted in radiogenic AMLs may be happenstance, but there are scenarios in which its chromosomal location would be crucial to its function. One possibility is that the polymorphism increases the frequency of AML-associated chromosome 2 deletions in irradiated hematopoietic cells by controlling the spatial confirmation of the chromosome such that the proximal and distal deletion breakpoints are in close proximity to one another (46). This type of proximity mechanism has been evoked to explain recurrent chromosomal rearrangements seen in radiation-induced papillary thyroid carcinoma and some spontaneous cancers (53, 54). In this scenario, the QTL could be a structural polymorphism (e.g., segmental duplication or interstitial telomeric sequence), which would affect chromosomal conformation, yielding a different conformation in susceptible mouse strains than resistant strains. Structural polymorphisms are easily missed in the assembly of the strain-specific genomic sequences used for mapping studies, so we would be unaware of its existence. A second possibility is that the polymorphism is in a gene needed for myeloid progenitor cell survival. Mouse strains resistant to myeloid leukemia would have a hypomorphic allele of this gene. If one copy is lost (i.e., through radiation-induced deletion), then the remaining copy would be insufficient for cell survival. Thus, in mouse strains resistant to radiogenic AML, a chromosome 2 deletion, which is the first step in radiation leukemogenesis, is a lethal event and leukemogenesis is thereby halted. Susceptible strains would have a fully functional allele of the gene, so that if one copy is deleted, the remaining copy maintains cell viability, allowing further leukemogenic events to occur (46). A caveat to both the chromosome conformation and haploinsufficiency scenarios is that the chromosome 2 deletions mapped in radiogenic AMLs from the F1 progeny of AML-susceptible CBA/H mice and AML-resistant C57BL/6 mice do not occur preferentially in the CBA/H origin chromosome (55). However, in that study, only 10 tumors were informative. In addition, susceptibility to radiogenic AML is multigenic, so it is possible that the difference in susceptibility between the CBA/H and C57BL/6 strains is not due to the chromosome 2 QTL.

HZE ions seem particularly effective in inducing Harderian gland tumors at the doses used in this study. This result was expected on the basis of extensive published radiation quality data on these tumors (8, 38). The mechanism responsible for higher tumorigenic efficacy of HZE ions relative to -rays is unknown; however, we have identified a QTL associated with Harderian gland adenocarcinoma following HZE ion exposures that does not appear to lend susceptibility to the same tumor following -ray exposures (Fig. 4C). Furthermore, HZE ioninduced Harderian gland adenomas and adenocarcinomas cluster away from spontaneous and -rayinduced Harderian gland tumors (Fig. 5), indicating non-overlap of some of the susceptibility loci. There are data that suggest that HZE ion irradiation has an effect on tumor promotion that -ray irradiation lacks. The observation is that pituitary isografts, which result in elevated levels of pituitary hormones, enhance the induction of Harderian gland tumors and decrease their latency in mice irradiated with -rays or fission neutrons but do not increase tumor prevalence in mice irradiated with 56Fe ions (600 MeV/n) (12). This would explain the high relative biological effectiveness (RBE) for 56Fe ions. It would also render QTLs that act in the promotion of -ray and spontaneous tumors irrelevant to HZE ioninduced tumors.

The use of unsupervised clustering on genome-wide association results is a novel approach to search for shared tumorigenic mechanisms between radiogenic and spontaneous tumors or between tumors induced by different radiation qualities. Potentially, the results could be used to inform risk modeling. For example, using the 99% confidence interval as a cutoff, thyroid adenomas, pituitary tumors, osteosarcomas, B cell lymphoblastic leukemia, mammary tumors, and hepatocellular carcinomas cluster by histotypes regardless of whether they arose in HZE ionirradiated or -rayirradiated mice. Of these, the incidences of thyroid tumors, pituitary tumors, and osteosarcomas are significantly increased following exposures to either HZE ions or -rays. Taking pituitary adenoma as an example, these findings suggest that it would be reasonable to extrapolate the risk of HZE ioninduced pituitary adenoma as a multiple of -rayinduced pituitary adenoma risk (i.e., using a relative risk model). Because there were too few spontaneous pituitary adenomas to position them on the dendrogram, we cannot determine whether the risk of HZE ioninduced pituitary adenoma could reasonably be modeled on the basis of the incidence of the spontaneous tumor. Another pattern of association is observed for Harderian gland adenoma and follicular B cell lymphoma in which, at the 99% confidence interval, spontaneous tumors cluster with -rayinduced tumors but not with HZE ioninduced tumors. There are a number of ways that this could occur. Three possibilities are as follows: (i) HZE ions act through a tumorigenic mechanism different from that of spontaneous and -rayinduced tumors. (2) HZE ions bypass the need for one or more of the genetically controlled steps required for spontaneous and -rayinduced tumors, and (iii) there are multiple pathways to tumor formation, and HZE ion irradiation forces tumorigenesis through only one (or a subset) of them. Harderian gland tumors may fall into the second possibility. As described earlier, observations on mice receiving pituitary isografts before irradiation suggest that HZE ions may have Harderian gland tumor promotion effects that -rays lack. If so, the QTL controlling those effects would be inconsequential in the tumorigenesis of HZE ioninduced Harderian gland tumors, and those tumors would cluster away from their spontaneous and -rayinduced counterparts. Whether a relative risk model, an absolute risk model, or a combination of the two would be most appropriate in Harderian gland tumor risk calculations would depend on which of the above possibilities is most accurate.

NASA seeks to limit the risk of exposure-induced death (REID) from radiogenic cancer to below 3% (56). For multiple missions aboard the International Space Station (flown in solar minimum conditions), the model projects that males will exceed permissible exposure limits at 24 months and females, at 18 months; women are considered at greater risk for radiogenic cancers than men because of longer life spans and increased susceptibility to specific cancer types, including lung, ovarian, and breast carcinomas. Because the 3% REID is derived from the upper 95% confidence interval for the risk estimate (57), decreasing the uncertainty for space radiationinduced cancers can significantly increase the flight time allowed for astronauts. The 95% confidence interval surrounding the risk estimates not only primarily reflects uncertainties in our understanding of HZE ions but also includes uncertainties surrounding dose-rate effects, transfer of risk between human populations, space dosimetry, and errors in the existing human epidemiology data. Concerning sex predilections, our results also demonstrate a sex difference in carcinogenesis risk, where female mice are at greater risk for radiogenic cancers than males, following either HZE ion or -ray exposures. These results are consistent with the current NASA model to calculate cancer risk from space radiation exposures (5).

Whether genotypic assays of radiosensitivity can improve the precision of risk assessment in humans will depend on a number of factors. One is the extent to which heritable sequence variants determine cancer risk from HZE ion exposures. HZE ion radiation exposures result in more complex molecular lesions that are less amenable to repair (58). Thus, it could be argued that sequence variants that result in subtle differences in DNA repair and damage response pathways would have a lesser impact on HZE ion radiation carcinogenesis. However, this work demonstrates that genetic susceptibility does indeed have a significant role in tumorigenesis following HZE ion exposures. Personalized approaches to cancer risk assessments may eventually allow for greater reductions in uncertainties when generating space radiation cancer risk estimates (28).

There are limitations to a mouse carcinogenesis study comparing acute -ray and HZE ion exposures. First, for cost efficiency and logistics reasons, a single dose was used for each radiation quality: 3.0 Gy for -ray exposures and 0.4 Gy for HZE ion exposures. Preliminary studies have demonstrated that these doses produce the maximum tumor incidence in inbred strains (24). Because tumor susceptibility and association mapping were the primary goals of this study, doses were chosen with the goal of generating the greatest tumor incidences and, therefore, the greatest power to detect significant QTL. However, caution must be taken when comparing the two single-dose groups, as it is impossible to untangle dose responses in such a study. An additional benefit of the selected doses is that 0.4 Gy of HZE ions represents a realistic dose, received over 20 to 30 months, for a flight crew traveling to Mars. Second, the applicability of these findings to human populations is limited, as rodents serve only as models of carcinogenesis.

The results presented here indicate that host genetic factors dictate risk for tumor development following radiation exposures, regardless of radiation quality. Therefore, at a population level, risks can be extrapolated from terrestrial exposures to the space radiation environment and at an individual level, and humans harboring susceptibility alleles for radiation-induced tumors developed on Earth are also likely at increased risk in space.

Male and female HS/Npt mice (n = 1850) were generated from breeding pairs obtained from Oregon Health and Sciences University (Portland, OR). The mice were group-housed (five mice of the same sex per cage) in a climate-controlled facility at 70F (21.1C) with free access to food (Teklad global rodent diet 2918) and sterile water and a 12-hour light cycle. Mice were shipped to Brookhaven National Laboratories (Upton, NY) where they were exposed to accelerator-produced HZE ions at the NASA Space Radiation Laboratory at 7 to 12 weeks of age. HS/Npt stock mice of both sexes were exposed to 0.4 Gy of 28Si ions (240 MeV/n) (n = 308) or 56Fe ions (600 MeV/n) (n = 314), 3 Gy of 137Cs -rays (n = 615), or sham irradiated (n = 622). Following irradiation exposure or sham irradiations, mice were returned to Colorado State University (Fort Collins, CO) and monitored twice daily for the duration of the study. The mice were evaluated for cancer development until they reached 800 days of age or became moribund. All animal procedures were approved by the Colorado State University Institutional Animal Use and Care Committee.

This study uses a highly recombinant mouse population (HS/Npt stock) that is genetically diverse and designed for genome mapping (1921, 23). HS/Npt mice are a multiparent cross derived from eight inbred strains (A/J, AKR/J, BALBc/J, CBA/J, C3H/HeJ, C57BL/6J, DBA/2J, and LP/J); each individual contains a unique mosaic of founder haplotypes and a high degree of heterozygosity, and recombination events become increasingly dense with each generation. Our population of HS/Npt mice was obtained from generation 71 of circular outbreeding.

DNA was isolated from tail biopsies taken from each mouse at 9 to 10 weeks of age. DNA was extracted and purified (QIAGEN, catalog no. 69506) according to the manufacturers instructions. GeneSeek (Lincoln, NE) performed genotyping assays using the Mega Mouse Universal Genotyping Array (MegaMUGA) (59) for a total of 1878 mice (including 28 inbred mice representing the founder strains). The MegaMUGA is built on the Illumina Infinium platform and consists of 77,808 single-nucleotide polymorphic markers that are distributed throughout the genome with an average spacing of 33 kb.

The heterogeneous stock mice are descendants of eight inbred founder strains. For each mouse, allele calls from the MegaMUGA array were used to calculate descent probabilities using a hidden Markov model (HMM), in which the hidden states were the founder strains and the observed data were the genotypes. The HMM generates probabilistic estimates of the diplotype state(s) for each marker locus and produces a unique founder haplotype mosaic for each mouse (18).

For this lifetime carcinogenesis study, all disease states were interpreted within the context of a systematic pathologic evaluation directed by board-certified veterinary pathologists (E.F.E. and D.A.K.). Structured necropsy and tissue collection protocols were followed for each mouse and involved photodocumentation of all gross lesions, collection of frozen tumor material, and preservation of tumor material in RNAlater. All tissues were grossly evaluated for all mice. To evaluate brain tissues and Harderian glands, craniums were decalcified for 48 hours in Formical-4 (StatLab, McKinney, TX 75069, product 1214) and five coronal sections of the skull were reviewed for each mouse. All gross lesions were evaluated microscopically and fixed in 10% neutral-buffered formalin and paraffin-embedded, and 5-m sections were stained with hematoxylin and eosin (H&E) and evaluated by a veterinary pathologist. For mice with solid tumors, all lung fields were examined histologically to detect the presence or absence of micrometastases. Tumor nomenclature was based on consensus statements produced by the Society of Toxicologic Pathology for mouse tumors (www.toxpath.org/inhand.asp). Representative histologic images routinely stained with H&E are presented in figs. S2 (A to E) and S3 (A and B).

Tissue microarrays were constructed to immunophenotype and subcategorize lymphoid neoplasms, which were the most commonly diagnosed tumors in irradiated and unirradiated HS/Npt mice. Identification of tissue sampling regions was performed by a veterinary pathologist. For each case, duplicate cores were taken from multiple anatomic locations (lymph nodes, spleen, thymus, etc.). Thirteen tissue microarrays were created, each of which contained six cores of control tissue at one corner of the array (haired skin, spleen, thymus, or liver); these control tissues were present in a unique combination and allowed for (i) orientation of the resulting sections, (ii) verification that the slide matched the block, and (iii) positive controls for immunohistochemistry. Figure S3D illustrates one tissue microarray as well as the resulting immunohistochemistry results for one thymic lymphoma (fig. S3E) and a core containing normal spleen (fig. S3F). Immunohistochemistry for T cell identification was performed using a rabbit monoclonal, anti-CD3 (SP7) antibody obtained from Abcam (ab16669; 1:300). Immunohistochemistry for B cell identification was performed using two rabbit monoclonal antibodies: an anti-CD45 antibody (ab10558; 1:1000) and an anti-PAX5 antibody (ab140341; 1:50). All immunohistochemistry was performed on a Leica BOND-MAX autostainer with the Leica BOND Polymer Refine Red Detection system (Leica DS9390, Newcastle Upon Tyne, UK). In addition to defining the immunophenotype, lymphomas were characterized according to the Mouse Model of Human Cancer Consortiums Bethesda protocols (60). For these protocols, anatomic location is important for the final diagnosis, and therefore, lymph node involvement was used from necropsy reports when necessary. Additional features included cell size, nuclear size, chromatic organization, and mitotic figure frequency, and the presence or absence of a leukemic phase was defined by bone marrow involvement within the sternum or femur. The most common lymphoma subtypes (fig. S4A) were evaluated for survival (fig. S4B), and pre-T LL typically presented with early-onset and large thymic masses.

Droplet digital polymerase chain reaction (ddPCR) was performed on cases of AML to assess deletion status via copy number variation for two genes: Spi1 and Asxl1. These genes are both located on chromosome 2 at base pair locations 91,082,390 to 91,115,756 for Spi1 and 153,345,845 to 153,404,007 for Asxl1. To establish a reference for normal diploid copy number in each AML sample, the copy number of H2afx was also determined. H2afx is located on chromosome 9, and deletions in this region have not been reported in murine AML. Bio-Rad PrimePCR probes were used for all assays as follows: Asxl1 ddPCR probe (dMmuCPE5100268), Spi1 ddPCR probe (dMmuCPE5094900), and H2afx ddPCR probe (dMmuCPE5104287). Ratios were created between the test gene and the reference gene (Spi1:H2afx and Asxl1:H2afx) to determine copy number with the assumption that the reference gene would not be deleted or amplified. Ideally, ratios of 1:1 represent equal copy numbers for both the test gene and the reference gene, and ratios of 1:2 represent a deletion in one copy of the test gene. However, since the tumor samples contained neoplastic cells as well as stromal cells and other cells, the ideal 1:2 ratio was not commonly observed. This is because stromal cells, which occur at unknown proportions in each tumor and which should not have chromosomal deletions, artificially increase ratios for tumor samples in which a deletion is indeed present. To account for stromal cell contamination, a cutoff ratio of 3:4 was established. Tumor samples with ratios below 3:4 were considered to have a deletion in one copy of the test gene.

For cases in which a solid tumor was identified, a standard section containing all lung lobes was processed and evaluated histologically. In cases where pulmonary metastases were observed, whole-slide scanning was performed at 200 magnification using an Olympus VS120-S5 and the OlyVIA software suite (www.olympusamerica.com/) to generate images for quantification of metastatic density (fig. S5). An analysis software, ImageJ (https://imagej.nih.gov/ij/), was used to quantify the total area of normal lung and the total area of metastatic foci (fig. S5). Metastatic density is reported as a percentage of the total metastasis area divided by the total lung area.

Association mapping was performed using a mixed-effects regression model with sex and cohort as fixed effects and a random-effects term to adjust for relatedness between mice by computing a matrix of expected allele sharing of founder haplotypes for each pair of mice (22). Three statistical models were fit to account for the wide range of trait distributions in this study. A generalized linear regression model was fit for binomial distributions, such as neoplasia. Cox regression analysis was incorporated to model time-to-event distributions to evaluate genetic contributions to tumor latency. Following genome-wide association analyses, resample model averaging methods were used to identify QTL that are consistently reproduced within subsamples of the mapping population.

Thresholds were determined using a permutation procedure in which the genotypes were fixed and the phenotype values were rearranged randomly within each sex. The distribution of the maximum negative log(P value) of association under the null hypothesis that no associations exist (null model) was determined for each genome scan with permuted data. One thousand permutations were performed for each phenotype in each radiation exposure group, simulating effects arising from covariates, the linkage disequilibrium structure of the genome, and effects due to phenotype distribution. A threshold was defined as an estimate of the genome-wide significance for which a type I statistical error will occur at a given frequency (29). Confidence intervals for each QTL were determined by nonparametric resample model averaging procedures using bootstrap aggregation with replacement. In this procedure, the mapping population is sampled to create a new dataset in which some individuals may be omitted and some may appear multiple times (30), and the locus with peak significance is recorded. Resampling is repeated 200 times for each phenotype to determine a 95% confidence interval for a given QTL. Effect sizes were calculated using the Tjur method for association mapping with logistic regression and pseudo-R2 for mapping with Cox proportional hazard regression. Statistical significance for each model was assessed using a permutation strategy to randomize genotypes via resampling without replacement and maintaining covariates. Permutation analysis was performed (1000 tests) for each trait and exposure group to generate estimations of genome-wide significance thresholds. As genome scans with hundreds of thousands of imputed SNPs are computationally intensive, parallel computing was essential and accomplished using spot instances of resizable Elastic Compute Cloud hosting resources.

Comparisons were made between whole-genome scans using Pearson correlations as a similarity measure with clustering based on average linkage. Significance of clustering results was estimated with 10,000 random permutations of the dataset (log10 values permuted with genetic markers) to determine a distribution of dendrogram heights under the null hypothesis that no associations exist. Each permutated dataset simulates a null distribution of the maximally significant clustering based on a randomly assorted set of P values for each genomic locus.

Bootstrap aggregation is a resample model averaging procedure that has been demonstrated to produce highly accurate estimates of QTL in structured populations (32). The procedure is relatively simple: for a genome-wide association study (GWAS) of n individuals, a sampling of n draws is obtained, with replacement, from the observed individuals to form a new dataset in which some individuals are omitted and some appear multiple times. For each new dataset created this way, an estimate of the QTL location is calculated. This process is repeated many times and is the basis for determining a confidence interval for a given result. The use of bootstrap procedures is commonly used this way to estimate QTL support intervals in experimental crosses; however, this statistical method can potentially be applied to other areas of QTL research, including comparative QTL mapping.

When an identical QTL is observed for two distinct traits, one explanation is that a single gene is involved for two distinct biologic processes, also known as pleiotropy. This was sometimes assumed in early mouse QTL studies that resulted in coincident loci for distinct traits. Another possibility, however, is that two distinct genetic variants are present in close proximity, each independently contributing to the two phenotypes. Because the two hypothetical genetic variants happen to be in close proximity, they are difficult to distinguish in low-resolution mapping studies. Using resample model averaging in highly recombinant mice is proposed to best differentiate precise locations of the QTL; if the same markers were repeatedly identified, then the case for pleiotropy was strengthened. For comparative QTL mapping in tumorigenesis studies, nonparametric resample model averaging could similarly be leveraged to identify whether the same QTL renders an individual susceptible to distinct environmental carcinogens. One significant advantage to using bootstrap procedures to detect potential coincident loci is that comparisons can be made between groups based on the identification of a highly significant QTL identified in only one exposure group (e.g., at a false-positive rate of 1 per 20 scans). This QTL may be present in the alternative exposure group, but at lower confidence (e.g., at a false-positive rate of 1 per 10 scans), and therefore discarded in a typical GWAS. A diagrammatic representation of the comparative QTL bootstrap procedure is presented in fig. S6. Because the resultant genetic positions derived from bootstrapping are composed of the most significant locus for each resampling regardless of the significance level for the mapping procedure, comparisons can be drawn between QTL that might have been discarded on the basis of the stringent statistical demands of an assay involving hundreds of thousands of independent tests. Using this procedure on thyroid tumors demonstrates that the same loci are consistently identified whether exposed to particle or photon irradiation (Fig. 2E). Using the comparative QTL procedure described, it can be determined whether an individuals cancer risk from one carcinogen will be predictive of that individuals cancer risk to another carcinogen. The application of this procedure is well illustrated by the space radiation problem, where much is known about -ray exposures and little is known about space radiation exposures.

In addition to looking for similarities between individual selected QTL for HZE ion and -rayexposed populations, we also sought a more holistic method in which entire genome scans could be compared between groups in an unsupervised process. By using entire genome scans, we submit for comparison not only highly significant regions but also the numerous loci detected with lower confidence. To determine similarity of genetic association profiles for all phenotypes and to detect possible coincident QTL, clustering procedures were used to compare genome-wide association scans between different radiation exposure groups. To demonstrate and validate the methodology of QTL clustering, genome-wide scans for coat colors in each treatment group are evaluated (Fig. 5B). As expected, genome-wide scans for coat color are unaffected by radiation exposures, and therefore, clustering is based entirely on coat phenotype rather than radiation exposure group. Using the same procedure for neoplasia indicates that tumor types often clustered together as well, regardless of radiation exposure (Fig. 5A). Genome scans for thyroid tumors and mammary adenocarcinomas in radiation-exposed groups and all hepatocellular carcinoma genome scans cluster together. This finding supports the hypothesis that host genetic factors are more important in determining neoplasm incidence than radiation exposure type. Unlike other statistic procedures, such as regression models, clustering lacks a response variable and is not routinely performed as a formal hypothesis test. Therefore, determining the significance of a clustering result can be problematic, as no consensus method exists for cluster validation. Permutation analysis provides the distribution of clustering results that will randomly occur from a given dataset; this can then be used as a baseline from which to determine a significance level on a given dendrogram tree [green line in Fig. 5 (A to C)]. While the overall validity of a given cluster can be accomplished by cluster permutation analysis, no method is identified to estimate the number of clusters that should be present in a dataset. Furthermore, methods to determine the significance of specific subset of objects clustering together do not exist; in such cases, the permutation threshold is likely overly stringent.

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A new model aims to predict genetic changes that have unintended consequences in trees that researchers genetically modify.

It could pave the way for more efficient research in the fields of both genetic modification and forestry.

Researchers want to genetically modify trees for a variety of applications, from biofuels to paper production. They also want to steer clear of modifications to one gene that result in unexpected changes to other genes.

The research at issue focuses on lignin, a complex material found in trees that helps to give trees their structure. It is, in effect, what makes wood feel like wood.

Whether you want to use wood as a biofuel source or to create pulp and paper products, there is a desire to modify the chemical structure of lignin by manipulating lignin-specific genes, resulting in lignin that is easier to break down, says corresponding author Cranos Williams, an associate professor of electrical and computer engineering at North Carolina State University. However, you dont want to make changes to a trees genome that compromise its ability to grow or thrive.

The researchers focused on a tree called Populus trichocarpa, which is a widely used model organismmeaning that scientists who study genetics and tree biology spend a lot of time studying P. trichocarpa.

Previous research generated models that predict how independent changes to the expression of lignin genes impacted lignin characteristics, says Megan Matthews, first author of the paper, a former PhD student at NC State, and a current postdoctoral researcher at the University of Illinois.

These models, however, do not account for cross-regulatory influences between the genes. So, when we modify a targeted gene, the existing models do not accurately predict the changes we see in how non-targeted genes are being expressed. Not capturing these changes in expression of non-targeted genes hinders our ability to develop accurate gene-modification strategies, increasing the possibility of unintended outcomes in lignin and wood traits.

To address this challenge, we developed a model that was able to predict the direct and indirect changes across all of the lignin genes, capturing the effects of multiple types of regulation. This allows us to predict how the expression of the non-targeted genes is impacted, as well as the expression of the targeted genes, Matthews says.

Another of the key merits of this work, versus other models of gene regulation, is that previous models only looked at how the RNA is impacted when genes are modified, Matthews says. Those models assume the proteins will be impacted in the same way, but thats not always the case. Our model is able to capture some of the changes to proteins that arent seen in the RNA, or vice versa.

This model could be incorporated into larger, multi-scale models, providing a computational tool for exploring new approaches to genetically modifying tree species to improve lignin traits for use in a variety of industry sectors.

In other words, by changing one gene, researchers can accidentally mess things up with other genes, creating trees that arent what they want. The new model can help researchers figure out how to avoid that.

The paper appears in the journal PLOS Computational Biology. Support for the work came from the National Science Foundation and a National Physical Science Consortium Graduate Fellowship.

Source: NC State

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Model warns of genetic modification gone awry in trees - Futurity: Research News

This week, we're profiling five very special Maritime families who have children with rare genetic disorders. This is the fourth article.Read more about the series by Lifestyles editor Jen Taplinhere

Ivy Stewart cant move her face, but she smiles with her eyes.

The adorable blond, wispy-haired two-year-old has big blue eyes, pink toddler cheeks and a tiny little mouth. She also has Moebius syndrome, which means theres no movement in the muscles in her face.

She doesnt blink. Instead, her eyes roll up and back every few minutes.

She loves everything, says her mom, Emily.

Even with not having expression, she finds other ways to express herself. With Moebius syndrome they say smile with your heart, and we say she smiles with her heart or her eyes.

Its a busy morning at Ronald McDonald House in Halifax as the Stewart family Emily, Craig and their three kids: Olive, 5; Ivy, 2; and Levi, 11 months preparesfor the long drive back to their hometown of Woodstock, N.B.

Ivy is sitting in a high chair, draining a package of apple sauce while her baby brother cuddles mom and big sister attacks a colouring page.

Its a five-hour drive but a 45-minute air ambulance ride from their home to the IWK Health Centre in Halifax. They should know, theyve had to take Ivy by air ambulance three times in her short life.

One airlift when she was little, they had to do an IV from her head because her veins were so small. They had to poke her quite a few times because her veins kept blowing, said Emily.

Airlifts are always emotional, she adds.

The last one was in January because of a breathing problem. Ivy has small airways and a respiratory infection can turn dangerous fast. They were back in Halifax in February for a tonsillectomy so that the next time she has swelling in her throat, there will be more room.

Making the trip to Halifax is something theyve grown used to since their second child was born. It was at the 20-week ultrasound when their doctor in New Brunswick told them their baby had club feet, was very small and had a lemon-shaped skull.

Ivy didnt cry when she was born. She wasnt breathing much either because she couldnt open her mouth.

Emily and Craig didnt have the time to process the situation in those first few days.

It was Day 4 and I got discharged (from the hospital) but she was still in, says Emily.

Someone came behind me with their baby and I just remember that moment I broke down and cried because that was the moment we realized we were leaving without our baby.

For Craig, that breakdown came after weeks of daily hospital visits.

Thats when I was able to comprehend everything and get it through my brain, he says.

As their baby grew, they noticed when she cried there was no expression on her face. When Ivy was two months old, they had an appointment with the genetics clinic, where they started the process of testing and waiting months for the results to come back.

Then at six months she got respiratory syncytial virus, a serious respiratory illness.

She was in the hospital in Fredericton, struggling to breathe while having undiagnosed seizures. She was airlifted to the IWK, where she saw specialists who ended up diagnosing Ivy with Moebius syndrome, a rare genetic disorder.

To see the little blessings in things like that is how we have to take it with ourselves. Even though its an emotional experience to look back on it, the blessing from it is we came out with all the new doctors that she needed, Emily said.

It was just a relief, that feeling to just have an answer. And then when we had an answer, we had a path to move ahead.

Having a diagnosis meant getting Ivy on seizure medications that made a big difference (she hasnt had a big seizure since July), and setting up a care regimen that involves eye drops once an hour and ointment three times a day. Shes eating now, but she was mostly fed through a gastrostomy tube until she was 18 months old.

Doctors at Toronto Sick Kids have developed a smile surgery for kids with Moebius when theyre four or five years old, and Ivy is considered a good candidate. Surgeons will take a muscle from her thigh and attach it to her jaw. Through physiotherapy, shell learn to activate it.

Until then, weve been showing her how to push her fingers up and make a smile, which is something we learned from other parents, Emily says.

Connecting with parents of children with Moebius syndrome or other rare genetic disorders makes a big difference.

Its huge because you dont know what to expect, and in general people can sympathize but they dont really understand the extent of everything, Craig says.

It was actually really nice to meet some other families and talk to other people that either had similar experiences or very close to being the same.

Adds Emily: Even if youre not going through the same thing, everyone can relate to the hospital life.

Read more about our series here.

Part One, The Gardiner family:Nature chose her. Toddler faces 40+ surgeries in her lifetimePart Two, The Langille family:Little Georgia has the rarest of disorders. We just do the best we can.Part Three, The Jacksons:I know my little girl is in there; Truro family lives with heartbreaking uncertainties

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One in a million: Rare genetic disorder means this toddler smiles with her eyes - TheChronicleHerald.ca

A remote area of Argentina has been home to curly Criollo horses for centuries. Curly coats were unknown among the horses that the Spanish took across the Atlantic in the 1500s. Where did this curly coat come from? Is a previously unknown genetic mutation behind it? Curly horse enthusiast Dr Mitch Wilkinson traveled south from the United States in the first steps towards getting answers.

All horses are special and all horses have a fascinating history, but the mysterious curly-coated horses around the world have a truly intriguing history and science which I have had the good fortune to be involved with for the past two decades.

I recently traveled to Patagonia at the invitation of Dr Gerardo Rodriguez and his wife, Andrea, to gather hair follicles and whole blood samples for analysis by Professor Emeritus, Dr Gus Cothran at Texas A&M Universitys Equine Genetics Laboratory, under the direction of Dr Rytis Juris.

We traveled to a remote part of Argentina and stayed in the village of Maquinchao in the Rio Negro province of Patagonia.

Maquinchao is located near the Somuncura Plateau. This almost completely uninhabited area was declared a National Protected Area by the Argentine government in 1993.

The Somuncura Plateau is the home of unique species of plants and animals found nowhere else on Earth, and as you have probably already guessed, it is the home of a unique type of wild curly horse.

To be clear, the gene mutation which causes the distinctive winter curly coat on these Argentine equines is different than the mutations that cause curly coats in North American horses.

The two populations do have a strong Spanish heritage in common, but these wild curly horses of the Somuncura Plateau are unique, rare, and have curly winter coats caused by a different mutation. There is a possibility they are unique to the region and found no other place on earth.

Geologists describe the Somuncura Plateau, where these horses live as an Island of volcanic rock on dry land. It covers about 15,000 square kilometres. It is almost completely uninhabited.

One of the unique animals found on or near the Somuncura Plateau is the guanaco.

The guanaco is a medium-sized camelid related to South American llamas and alpacas. It is more distantly related to the Dromedary and Bactrian camels found in Africa, the Middle East, and Asia.Camels, like horses, began their evolution in North America 35 million years ago.

North American camels survived until the more recent geological past, but sadly are now gone. Their extinction in North America is still a mystery like that of the horse.

However, four species of camel managed to survive until the present in South America. They are llamas, alpacas, vicunas, and guanacos.

Another animal is a large, flightless bird distantly related to the ostrich and emu. It is the called the rhea, and is a native inhabitant to the Somuncura Plateau.

In this area, the species of rhea known as Darwins rhea runs across the volcanic landscape eating grasshoppers and lizards.

The gauchos traditionally hunt rheas on horseback with bolas. The rhea is a species native to Patagonia. Both rheas and guanacos share the Somuncura Plateau with the wild horses.

However, to tell the story of these horses, we must go back several hundred years to the Spanish re-introduction of the horse to South America.

In 1535, Don Pedro de Mendoza, the conquistador and governor of the Spanish colony which would eventually become Argentina, was given the task by the king of Spain to administer and form a colony within the lands of the La Plata River region claimed by the Spanish crown in central South America. Mendozas administrative charge included the drainage area of the La Plata River and the new colony of Buenos Aires to which Mendoza brought both work and fine warhorses directly from Spain.

Initially, there were about 100 horses imported. The stallions were especially fine animals, who came directly from breeding farms in Cadiz, Spain.

After half a dozen years of mistreatment of the native people by Spanish colonists, Buenos Aires was destroyed and burned in 1540 by indigenous tribes.

The Spanish fled the area and left somewhere between 12 to 45 horses behind.

Records do not indicate what type of horses were abandoned. Some may have been common workhorses, while others may have been fine warhorses and gentlemens mounts. It is thought they were probably a mixture of both.

Under favorable conditions on the pampas, the escaped horses adapted and reproduced portentously. The descendants formed herds of hundreds of thousands of wild horses known as baguales.

The Spanish did not return until 1580.

In the 40-year absence of the Spanish, two remarkable things happened to the abandoned horses. They multiplied and some developed curly winter coats.

Currently, studies of undisturbed wild horse populations in Australia show that a herd of feral horses under favorable environmental conditions have about a 25% yearly growth rate.

A very conservative yearly growth rate of 20% would mean that if 50 horses were left behind in Buenos Aires in 1540, the number would have grown to 91 individuals by 1543 and 369 individuals by 1550.

Because of the exponential growth pattern, by 1580, 40 years later, the number of horses is estimated to be in the thousands. Even if only 12 horses were left by the Spanish, there could have been up to 36,000 free-roaming horses upon their return. This is exactly what the second wave of Spanish conquistadors observed upon their return in 1580, but something unusual was also observed.

The Spanish observed that some of the feral horses had developed a curly coat. Horses with curly winter coats were unknown in Spain.

The term Criollo was applied to humans and animals. It originally referred to individuals of pure-bred Spanish ancestry born in the Americas. With the return of the Spanish to the Rio Plata area, new horses from Spain were imported and these were mixed with the now feral Spanish horses to become the Criollo horse of South America. Both in the wild and in domestication, Criollo horses thrived in Argentina, Uruguay, and Southern Brazil.

Records show that in 1739, the Spanish explorers Cabrera and Solanet wrote in their journals that they had observed curly-coated wild horses mixed within the wild horse populations of Argentina and Brazil. This was two centuries after the Spanish re-introduction of the horse to South America.

Forty years later, in 1781, about the time the United States was fighting England for its independence, a Spanish military engineer and naturalist named Felix de Azara was assigned to the Rio Plata area.Azara was to spend 20 years in South America, from 1781 to 1801. During this time, he extensively explored this part of South America from Buenos Aires.

Azaras excellent drawings of plants and animals along with his written accounts were some of the first literary works to reach the general public about the wonders of South America. In 1801, his observations were published in Paris in book form under the title Quadrupeds de Paraguay. Within the pages of the book, he describes his observations of curly-coated horses seen within some of the wild herds he witnessed.

Eighty years later in 1886, Charles Darwin in his publication The Variation of Animals and Plants under Domestication, cited Azaras observations of horses.

Darwin used the curly-coated Spanish Criollo horses as observed by Azara to be a classic example of adaption by natural selection.

Darwin reasoned that a random mutation occurred within the feral population that resulted in a type of horse favored by natural selection to thrive in this New World environment. Darwin was undoubtedly correct.

On Darwins two voyages to South America, he traveled extensively by horseback. He was disappointed he never observed curly-coated horses in wild herds. Had they disappeared?

In 2010, a curly horse breeder in Texas, Angie Gaines, was contacted by Dr Gerardo Rodriguez about curly-coated horses he had found and started breeding. Dr Rodriguez is a former military officer and a veterinarian. He had captured the original horses on the Somuncura Plateau.

Gerardo was introduced to curly horses by an Indian gentleman, Enrique Piehincura, who was living an isolated existence near a water hole on the Somuncura plateau. Unfortunately, due to global warming, the water has disappeared. Enrique was forced to move and the fate of the wild horses that included curly horses is unknown. They seem to have disappeared into the Somuncura.

There were challenges in collecting samples of blood and hair follicles from the horses of Dr Rodriguez during our expedition south. Some of the horses had been handled quite a bit while others were quite fearful. Thanks to the excellent horse handling skill of the gauchos present, all the curly horses were sampled without injury. As you can see, there were some intense moments.

Raw blood samples were placed in thermos bottles and shipped by international carriers to Texas A&M University Equine Genetics lab for analysis.

At the time of the writing of this article, the gene mutation that creates the curly winter coat in these Patagonian curly horses has not yet been identified.

In early 2020, a preliminary dendrogram was produced using the PHYLIPanalysis Program. PHYLIP stands for PhylogenyInferencePackage. This package of 35 programs was developed by the University of Washington for the purpose of inferring the genetic relationships between animals and constructing genetic relationship trees or dendrograms.

In this case, 15 microsatellite markers were used to compare the Patagonian curly horses to other known breeds. The results from the Patagonian curly horses were totally unexpected.

The limited number of individual horses sampled hampered the results. However, the preliminary results show that instead of these horses being related to the iconic horses of Southern Spain like the Andalusian and the Lusitano, they were related to the smaller Celtic type horses located in the Northern Iberian Peninsula.

The Patagonian curly horses were genetically close to the Mexican Galiceno horse, which is thought to be a descendent of Northern Spains Celtic type horses brought to the Yucatan by Cortez during his conquest of the Aztec nation.

The Galiceno horse is a much smaller horse than the Patagonian curly horses, standing between 12 to 13.2 hands. There are fewer than 100 Galiceno horses in the United States and Mexico.

Other relationships were established by the dendrogram. Patagonian curly horses have a close association with ancestors of the Galiceno horse, which are the Portuguese Garrano and the Spanish Galician and Asturcon.

Because of isolation and different human and environmental pressures on the population, the horses of the Somuncura, whether curly coated or not, differ from the typical Argentine Criollo. They may represent an older Criollo type.

The fact that the Patagonian curly horses are not small; they range from 14 hands to 16 hands, show that other larger types of horses have bred into the population making them different from their Northern Spanish Celtic ancestors.

The Spanish Celtic horses are small horses ranging from 12 to 13 hands, but still considered to be horses. These Spanish horses traditionally have been used as work and pack animals. In every case, the Northern Spanish horses are declining in number and are considered endangered.

The Patagonian curly horses retain enough of their Northern Spanish Celtic genetics to pair close to these horse breeds.

Current efforts are being made to ask the Argentine governments help in preserving what is left of the unique curly-coated horses of the Somuncura Plateau. Lets hope they are successful.

More on curly horses from Mitch Wilkinson

About the author

Dr. Mitch Wilkinson has been a lifelong horse enthusiast. After receiving a bachelors degree in chemistry and professional dental degrees, he earned a post-doctoral masters degree from Baylor University in biology. Currently, Dr. Wilkinson is Chairman of the Curly Mustang Association and Vice-Chair of the International Curly Horse Organizations Research Department and the current coordinator of research for the Sulphur Springs Mustang Registry.

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Seeking genetic answers about the mysterious curly horses of Patagonia - Horsetalk

GeneDx, a global leader in genomics andpatient testing, is celebrating its remarkable 20th anniversary throughout themonth of March.

The Gaithersburg, Maryland company has played an important role in the history of genetic sequencing and the rise of the BioHealth Capital Region as a global biohealth cluster. GeneDx was the very first company to commercially offer NGS (Next Generation Sequencing) testing in a CLIA (Clinical Laboratory Improvement Amendments) lab and has been at the leading edge of genetic sequencing and testing for two decades. The companys whole exome sequencing program and comprehensive testing capabilities are world-renowned.

In its storied 20 yearhistory, GeneDx has provided genetic testing to patients in over 55 countries.The company is known globally as world-class experts in rare and ultra-rarediseases.

In 2000, GeneDx was founded by former National Institutes of Health (NIH) scientists Dr. Sherri Bale and Dr. John Compton. These two genomics experts and thought leaders started GeneDx to complete an important mission: To provide rare and ultra-rare disease patients and families with diagnostic services that were not commercially available at that time.

Prior to launching GeneDx, Bale spent 16 years at NIH, the last nine as Head of the Genetic Studies Section in the Laboratory of Skin Biology. She has been a pioneer during her storied career, publishing over 140 papers, chapters and books in the field. Her 35-year career includes deep experience in clinical, cytogenetic, and molecular genetics research.

Before partnering with Bale to form GeneDx, Compton was an investigator at the Jackson Laboratory, and for the last nine years as a senior scientist in the Genetics Studies Section at the NIH. Comptons work on the molecular genetics of inherited skin disease and expertise in laboratory methodology is known throughout the world. Compton has remarkable experience in the development and application of molecular biological techniques to answer questions about genetics and epidermal differentiation.

GeneDx, like manysuccessful BHCR life science companies, had a humble start, operating initiallyout of the Technology Development Center incubator. Just six years later,GeneDx was acquired by BioreferenceLabs for approximately $17M.

From there, the companylaunched its first array CGH (Comparative Genomic Hybridization) or aCGH testin 2007. An array CGH is also called microarray analysis, which is a atechnique enabling high-resolution, genome-wide screening of segmental genomiccopy number variations (NIH). By 2008, GeneDx had launched its Cardiology NextGeneration Sequencing Panel and by 2011 the company had commercialized itsneurology testing program. In 2012, GeneDx launched its Whole Exome Sequencing (XomeDx) for which it has become so well known in the genomicfield. A year later its Inherited Cancer Panels hit the market. 2018 saw thecompany achieve a significant milestone when it announced ithad performed clinical Exome Sequencing on more than 100,000 individuals.

Both Bale and Comptonhave since retired and GeneDx is currently led by Chief Medical Officer Dr. Gabriele Richard;Chief Innovation Officer Kyle Retterer, MS;Rhonda Brandon, MS

Chief InformationOfficer; and Dr. Sean Hofherr, FACMG, CLIA Laboratory Director & ChiefScientific Officer.

GeneDx has come a longway from its incubator headquarters over the past two decades. With over 450employees, the company continues to deliver on its mission to provide crucialdiagnostic genetic testing capabilities to patients and families across theglobe.

Happy Anniversary GeneDX. Heres to many more.

Over the past 8 years, Chris has grown BioBuzz into a respected brand that is recognized for its community building, networking events and news stories about the local biotech industry. In addition, he runs a Recruiting and Marketing Agency that helps companies attract top talent through a blended model that combines employer branding and marketing services together with a high powered recruiting solution.

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GeneDx Celebrates 20 Year History as Pioneer In Genetic Sequencing and Testing - BioBuzz

The Distaff Gospels, a collection of medieval Old Wives Tales, warns that if a pregnant woman eats hare shes likely to have a baby with a cleft palate, while eating fish heads leads to a trout pout. While these ideas certainly arent supported by modern science, there is still plenty of confusion surrounding genetics todayfor example, the idea that an inherited disease is the result of something bad happening in the family, that mutations are always bad, or that looking more like one parent than the other means youve inherited more of their genes.

Geneticist Kat Arney explores some of the myths and misconceptions about genetics, genomics and inheritance, in partnership with the Genomics Education Programme.

Genetic testsand increasingly, more detailed genomic analysisare providing an unprecedented amount of information about the underlying genetic variations and alterations that affect health. The pace at which genomic data and technologies are coming into the clinic is impressive. At the same time, it can leave patients, the public and healthcare providers feeling overwhelmed and trying to figure out what it all means.

Laura Boyes, Consultant Genetic Counselor for the West Midlands, explains where we get our ideas about inheritance from, and how they shape our family relationships. She also talks about the need to normalize the idea of genetic variation: nobody has a perfect genome, and we are all carrying our own unique alterations.

Anna Middleton, Head of Society and Ethics Research at the Wellcome Genome Campus in Cambridge, discusses whether media portrayals of genetics are helpful or harmful, and whether scientists should get worked up about bad science in the movies.

Finally, Arney speaks with Michelle Bishop, the Education Lead for the Genomics Education Programme, about the importance of providing accurate and understandable information about genomics, and the need for educators and healthcare professionals to keep up to date with advances in this fast-moving field.

The Genomics Education Programme is running a week of action from the 16th to the 20th March 2020, designed to raise awareness about the impact of genomics in healthcare and what we can all do to tackle some of the myths and misconceptions that are out there. Following @genomicsedu and #GenomicsConversation on Twitter or visit genomicseducation.hee.nhs.uk for more information.

Full transcript, links and references available online atGeneticsUnzipped.com

Genetics Unzippedis the podcast from the UKGenetics Society,presented by award-winning science communicator and biologistKat Arneyand produced byFirst Create the Media.Follow Kat on Twitter@Kat_Arney,Genetics Unzipped@geneticsunzip,and the Genetics Society at@GenSocUK

Listen to Genetics Unzipped onApple Podcasts(iTunes)Google Play,Spotify,orwherever you get your podcasts

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Podcast: Can you inherit more than half your genes from one parent? Debunking genomic myths and misconceptions - Genetic Literacy Project

Please note that this article includes an image of human remains.

A friend of mine with Central American, Southern European, and West African ancestry is lactose intolerant. Drinking milk products upsets her stomach, and so she avoids them. About a decade ago, because of her low dairy intake, she feared that she might not be getting enough calcium, so she asked her doctor for a bone density test. He responded that she didnt need one because blacks do not get osteoporosis.

My friend is not alone. The view that black people dont need a bone density test is a longstanding and common myth. A 2006 study in North Carolina found that out of 531 African American and Euro-American women screened for bone mineral density, only 15 percent were African American womendespite the fact that African American women made up almost half of that clinical population. A health fair in Albany, New York, in 2000, turned into a ruckus when black women were refused free osteoporosis screening. The situation hasnt changed much in more recent years.

Meanwhile, FRAX, a widely used calculator that estimates ones risk of osteoporotic fractures, is based on bone density combined with age, sex, and, yes, race. Race, even though it is never defined or demarcated, is baked into the fracture risk algorithms.

Lets break down the problem.

First, presumably based on appearances, doctors placed my friend and others into a socially defined race box called black, which is a tenuous way to classify anyone.

Race is a highly flexible way in which societies lump people into groups based on appearance that is assumed to be indicative of deeper biological or cultural connections. As a cultural category, the definitions and descriptions of races vary. Color lines based on skin tone can shift, which makes sense, but the categories are problematic for making any sort of scientific pronouncements.

Second, these medical professionals assumed that there was a firm genetic basis behind this racial classification, which there isnt.

Third, they assumed that this purported racially defined genetic difference would protect these women from osteoporosis and fractures.

The view that black people dont need a bone density test is a longstanding and common myth.

Some studies suggest that African American womenmeaning women whose ancestry ties back to Africamay indeed reach greater bone density than other women, which could be protective against osteoporosis. But that does not mean being blackthat is, possessing an outward appearance that is socially defined as blackprevents someone from getting osteoporosis or bone fractures. Indeed, this same research also reports that African American women are more likely to die after a hip fracture. The link between osteoporosis risk and certain racial populations may be due to lived differences such as nutrition and activity levels, both of which affect bone density.

But more important: Geographic ancestry is not the same thing as race. African ancestry, for instance, does not tidily map onto being black (or vice versa). In fact, a 2016 study found wide variation in osteoporosis risk among women living in different regions within Africa. Their genetic risks have nothing to do with their socially defined race.

When medical professionals or researchers look for a geneticcorrelateto race, they are falling into a trap: They assume thatgeographic ancestry, which does indeed matter to genetics, can be conflated with race, which does not. Sure, different human populations living in distinct places may statistically have different genetic traitssuch as sickle cell trait (discussed below)but such variation is about local populations (people in a specific region), not race.

Like a fish in water, weve all been engulfed by the smog of thinking that race is biologically real. Thus, it is easy to incorrectly conclude that racial differences in health, wealth, and all manner of other outcomes are the inescapable result of genetic differences.

The reality is that socially defined racial groups in the U.S. and most everywhere else do differ in outcomes. But thats not due to genes. Rather, it is due to systemic differences in lived experience and institutional racism.

Communities of color in the United States, for example, often have reduced access to medical care, well-balanced diets, and healthy environments. They are often treated more harshly in their interactions with law enforcement and the legal system. Studies show that they experience greater social stress, including endemic racism, that adversely affects all aspects of health. For example, babies born to African American women are more than twice as likely to die in their first year than babies born to non-Hispanic Euro-American women.

Systemic racism leads to different health outcomes for various populations. The infant mortality rate, for example, for African American infants is double that for European Americans. Kelly Lacy/Pexels

As a professor of biological anthropology, I teach and advise college undergraduates. While my students are aware of inequalities in the life experiences of different socially delineated racial groups, most of them also think that biological races are real things. Indeed, more than half of Americans still believe that their racial identity is determined by information contained in their DNA.

For the longest time, Europeans thought that the sun revolved around the Earth. Their culturally attuned eyes saw this as obvious and unquestionably true. Just as astronomers now know thats not true, nearly all population geneticists know that dividing people into races neither explains nor describes human genetic variation.

Yet this idea of race-as-genetics will not die. For decades, it has been exposed to the sunlight of facts, but, like a vampire, it continues to suck bloodnot only surviving but causing harm in how it can twist science to support racist ideologies. With apologies for the grisly metaphor, it is time to put a wooden stake through the heart of race-as-genetics. Doing so will make for better science and a fairer society.

In 1619, the first people from Africa arrived in Virginia and became integrated into society. Only after African and European bond laborers unified in various rebellions did colony leaders recognize the need to separate laborers. Race divided indentured Irish and other Europeans from enslaved Africans, and reduced opposition by those of European descent to the intolerable conditions of enslavement. What made race different from other prejudices, including ethnocentrism (the idea that a given culture is superior), is that it claimed that differences were natural, unchanging, and God-given. Eventually, race also received the stamp of science.

Swedish taxonomist Carl Linnaeus divided humanity up into racial categories according to his notion of shared essences among populations, a concept researchers now recognize has no scientific basis. Wikimedia Commons

Over the next decades, Euro-American natural scientists debated the details of race, asking questions such as how often the races were created (once, as stated in the Bible, or many separate times), the number of races, and their defining, essential characteristics. But they did not question whether races were natural things. They reified race, making the idea of race real by unquestioning, constant use.

In the 1700s, Carl Linnaeus, the father of modern taxonomy and someone not without ego, liked to imagine himself as organizing what God created. Linnaeus famously classified our own species into races based on reports from explorers and conquerors.

The race categories he created included Americanus, Africanus, and even Monstrosus (for wild and feral individuals and those with birth defects), and their essential defining traits included a biocultural mlange of color, personality, and modes of governance. Linnaeus described Europeaus as white, sanguine, and governed by law, and Asiaticus as yellow, melancholic, and ruled by opinion. These descriptions highlight just how much ideas of race are formulated by social ideas of the time.

In line with early Christian notions, these racial types were arranged in a hierarchy: a great chain of being, from lower forms to higher forms that are closer to God. Europeans occupied the highest rungs, and other races were below, just above apes and monkeys.

So, the first big problems with the idea of race are that members of a racial group do not share essences, Linnaeus idea of some underlying spirit that unified groups, nor are races hierarchically arranged. A related fundamental flaw is that races were seen to be static and unchanging. There is no allowance for a process of change or what we now call evolution.

There have been lots of efforts since Charles Darwins time to fashion the typological and static concept of race into an evolutionary concept. For example, Carleton Coon, a former president of the American Association of Physical Anthropologists, argued in The Origin of Races (1962) that five races evolved separately and became modern humans at different times.

One nontrivial problem with Coons theory, and all attempts to make race into an evolutionary unit, is that there is no evidence. Rather, all the archaeological and genetic data point to abundant flows of individuals, ideas, and genes across continents, with modern humans evolving at the same time, together.

In this map, darker colors correspond to regions in which people tend to have darker skin pigmentation. Reproduced with permission from Dennis ONeil.

A few pundits such as Charles Murray of the American Enterprise Institute and science writers such as Nicholas Wade, formerly of The New York Times, still argue that even though humans dont come in fixed, color-coded races, dividing us into races still does a decent job of describing human genetic variation. Their position is shockingly wrong. Weve known for almost 50 years that race does not describe human genetic variation.

In 1972, Harvard evolutionary biologist Richard Lewontin had the idea to test how much human genetic variation could be attributed to racial groupings. He famously assembled genetic data from around the globe and calculated how much variation was statistically apportioned within versus among races. Lewontin found that only about 6 percent of genetic variation in humans could be statistically attributed to race categorizations. Lewontin showed that the social category of race explains very little of the genetic diversity among us.

Furthermore, recent studies reveal that the variation between any two individuals is very small, on the order of one single nucleotide polymorphism (SNP), or single letter change in our DNA, per 1,000. That means that racial categorization could, at most, relate to 6 percent of the variation found in 1 in 1,000 SNPs. Put simply, race fails to explain much.

In addition, genetic variation can be greater within groups that societies lump together as one race than it is between races. To understand how that can be true, first imagine six individuals: two each from the continents of Africa, Asia, and Europe. Again, all of these individuals will be remarkably the same: On average, only about 1 out of 1,000 of their DNA letters will be different. A study by Ning Yu and colleagues places the overall difference more precisely at 0.88 per 1,000.

The circles in this diagram represent the relative size and overlap in genetic variation in three human populations. The African population circle (blue) is largest because it contains the most genetic diversity. Genetic diversity in European (orange) and Asian (green) populations is a subset of the variation in Africa. Reproduced by permission of the American Anthropological Association.Adapted from the original, which appeared in the book RACE.Not for sale or further reproduction.

The researchers further found that people in Africa had less in common with one another than they did with people in Asia or Europe. Lets repeat that: On average, two individuals in Africa are more genetically dissimilar from each other than either one of them is from an individual in Europe or Asia.

Homo sapiens evolved in Africa; the groups that migrated out likely did not include all of the genetic variation that built up in Africa. Thats an example of what evolutionary biologists call the founder effect, where migrant populations who settle in a new region have less variation than the population where they came from.

Genetic variation across Europe and Asia, and the Americas and Australia, is essentially a subset of the genetic variation in Africa. If genetic variation were a set of Russian nesting dolls, all of the other continental dolls pretty much fit into the African doll.

What all these data show is that the variation that scientistsfrom Linnaeus to Coon to the contemporary osteoporosis researcherthink is race is actually much better explained by a populations location. Genetic variation is highly correlated to geographic distance. Ultimately, the farther apart groups of people are from one another geographically, and, secondly, the longer they have been apart, can together explain groups genetic distinctions from one another. Compared to race, those factors not only better describe human variation, they invoke evolutionary processes to explain variation.

Those osteoporosis doctors might argue that even though socially defined race poorly describes human variation, it still could be a useful classification tool in medicine and other endeavors. When the rubber of actual practice hits the road, is race a useful way to make approximations about human variation?

When Ive lectured at medical schools, my most commonly asked question concerns sickle cell trait. Writer Sherman Alexie, a member of the Spokane-Coeur dAlene tribes, put the question this way in a 1998 interview: If race is not real, explain sickle cell anemia to me.

In sickle cell anemia, red blood cells take on an unusual crescent shape that makes it harder for the cells to pass through small blood vessels. Mark Garlick/Science Photo Library/AP Images

OK! Sickle cell is a genetic trait: It is the result of an SNP that changes the amino acid sequence of hemoglobin, the protein that carries oxygen in red blood cells. When someone carries two copies of the sickle cell variant, they will have the disease. In the United States, sickle cell disease is most prevalent in people who identify as African American, creating the impression that it is a black disease.

Yet scientists have known about the much more complex geographic distribution of sickle cell mutation since the 1950s. It is almost nonexistent in the Americas, most parts of Europe and Asiaand also in large swaths of Northern and Southern Africa. On the other hand, it is common in West-Central Africa and also parts of the Mediterranean, Arabian Peninsula, and India. Globally, it does not correlate with continents or socially defined races.

In one of the most widely cited papers in anthropology, American biological anthropologist Frank Livingstone helped to explain the evolution of sickle cell. He showed that places with a long history of agriculture and endemic malaria have a high prevalence of sickle cell trait (a single copy of the allele). He put this information together with experimental and clinical studies that showed how sickle cell trait helped people resist malaria, and made a compelling case for sickle cell trait being selected for in those areas. Evolution and geography, not race, explain sickle cell anemia.

What about forensic scientists: Are they good at identifying race? In the U.S., forensic anthropologists are typically employed by law enforcement agencies to help identify skeletons, including inferences about sex, age, height, and race. The methodological gold standards for estimating race are algorithms based on a series of skull measurements, such as widest breadth and facial height. Forensic anthropologists assume these algorithms work.

Skull measurements are a longstanding tool in forensic anthropology. Internet Archive Book Images/Flickr

The origin of the claim that forensic scientists are good at ascertaining race comes from a 1962 study of black, white, and Native American skulls, which claimed an 8090 percent success rate. That forensic scientists are good at telling race from a skull is a standard trope of both the scientific literature and popular portrayals. But my analysis of four later tests showed that the correct classification of Native American skulls from other contexts and locations averaged about two incorrect for every correct identification. The results are no better than a random assignment of race.

Thats because humans are not divisible into biological races. On top of that, human variation does not stand still. Race groups are impossible to define in any stable or universal way. It cannot be done based on biologynot by skin color, bone measurements, or genetics. It cannot be done culturally: Race groupings have changed over time and place throughout history.

Science 101: If you cannot define groups consistently, then you cannot make scientific generalizations about them.

Wherever one looks, race-as-genetics is bad science. Moreover, when society continues to chase genetic explanations, it misses the larger societal causes underlying racial inequalities in health, wealth, and opportunity.

To be clear, what I am saying is that human biogenetic variation is real. Lets just continue to study human genetic variation free of the utterly constraining idea of race. When researchers want to discuss genetic ancestry or biological risks experienced by people in certain locations, they can do so without conflating these human groupings with racial categories. Lets be clear that genetic variation is an amazingly complex result of evolution and mustnt ever be reduced to race.

Similarly, race is real, it just isnt genetic. Its a culturally created phenomenon. We ought to know much more about the process of assigning individuals to a race group, including the category white. And we especially need to know more about the effects of living in a racialized world: for example, how a societys categories Race is real, it just isnt genetic. Its a culturally created phenomenon.and prejudices lead to health inequalities. Lets be clear that race is a purely sociopolitical construction with powerful consequences.

It is hard to convince people of the dangers of thinking race is based on genetic differences. Like climate change, the structure of human genetic variation isnt something we can see and touch, so it is hard to comprehend. And our culturally trained eyes play a trick on us by seeming to see race as obviously real. Race-as-genetics is even more deeply ideologically embedded than humanitys reliance on fossil fuels and consumerism. For these reasons, racial ideas will prove hard to shift, but it is possible.

Over 13,000 scientists have come together to formand publicizea consensus statement about the climate crisis, and that has surely moved public opinion to align with science. Geneticists and anthropologists need to do the same for race-as-genetics. The recent American Association of Physical Anthropologists Statement on Race & Racism is a fantastic start.

In the U.S., slavery ended over 150 years ago and the Civil Rights Law of 1964 passed half a century ago, but the ideology of race-as-genetics remains. It is time to throw race-as-genetics on the scrapheap of ideas that are no longer useful.

We can start by getting my friendand anyone else who has been deniedthat long-overdue bone density test.

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Race Is Real, But It's Not Genetic - SAPIENS

Mar 12th 2020

THE ATOMIC bomb convinced politicians that physics, though not readily comprehensible, was important, and that physicists should be given free rein. In the post-war years, particle accelerators grew from the size of squash courts to the size of cities, particle detectors from the scale of the table top to that of the family home. Many scientists in other disciplines looked askance at the money devoted to this big science and the vast, impersonal collaborations that it brought into being. Some looked on in envy. Some made plans.

The idea that sequencing the whole human genome might provide biology with some big science of its own first began to take root in the 1980s. In 1990 the Human Genome Project was officially launched, quickly growing into a global endeavour. Like other fields of big science it developed what one of the programmes leaders, the late John Sulston, called a tradition of hyperbole. The genome was Everest; it was the Apollo programme; it was the ultimate answer to that Delphic injunction, know thyself. And it was also, in prospect, a cornucopia of new knowledge, new understanding and new therapies.

By the time the completion of a (rather scrappy) draft sequence was announced at the White House in 2000, even the politicians were drinking the Kool-Aid. Tony Blair said it was the greatest breakthrough since antibiotics. Bill Clinton said it would revolutionise the diagnosis, prevention and treatment of most, if not all, human diseases. In coming years, doctors increasingly will be able to cure diseases like Alzheimers, Parkinsons, diabetes and cancer by attacking their genetic roots.

Such hype was always going to be hard to live up to, and for a long time the genome project failed comprehensively, prompting a certain Schadenfreude among those who had wanted biology kept small. The role of genetics in the assessment of peoples medical futures continued to be largely limited to testing for specific defects, such as the BRCA1 and BRCA2 mutations which, in the early 1990s, had been found to be responsible for some of the breast cancers that run in families.

To understand the lengthy gap between the promise and the reality of genomics, it is important to get a sense of what a genome really is. Although sequencing is related to an older technique of genetic analysis called mapping, it produces something much more appropriate to the White House kitchens than to the Map Room: a recipe. The genes strung out along the genomes chromosomesbig molecules of DNA, carefully packedare descriptions of lifes key ingredients: proteins. Between the genes proper are instructions as to how those ingredients should be used.

If every gene came in only one version, then that first human genome would have been a perfect recipe for a person. But genes come in many varietiesjust as chilies, or olive oils, or tinned anchovies do. Some genetic changes which are simple misprints in the ingredients specification are bad in and of themselvesjust as a meal prepared with fuel oil instead of olive oil would be inedible. Others are problematic only in the context of how the whole dish is put together.

The most notorious of the genes with obvious impacts on health were already known before the genome was sequenced. Thus there were already tests for cystic fibrosis and Huntingtons disease. The role of genes in common diseases turned out to be a lot more involved than many had naively assumed. This made genomics harder to turn into useful insight.

Take diabetes. In 2006 Francis Collins, then head of genome research at Americas National Institutes of Health, argued that there were more genes involved in diabetes than people thought. Medicine then recognised three such genes. Dr Collins thought there might be 12. Today the number of genes with known associations to type-2 diabetes stands at 94. Some of these genes have variants that increase a persons risk of the disease, others have variants that lower that risk. Most have roles in various other processes. None, on its own, amounts to a huge amount of risk. Taken together, though, they can be quite predictivewhich is why there is now an over-the-counter genetic test that measures peoples chances of developing the condition.

In the past few years, confidence in sciences ability to detect and quantify such genome-wide patterns of susceptibility has increased to the extent that they are being used as the basis for something known as a polygenic risk score (PRS). These are quite unlike the genetic tests people are used to. Those single-gene tests have a lot of predictive value: a person who has the Huntingtons gene will get Huntingtons; women with a dangerous BRCA1 mutation have an almost-two-in-three chance of breast cancer (unless they opt for a pre-emptive mastectomy). But the damaging variations they reveal are rare. The vast majority of the women who get breast cancer do not have BRCA mutations. Looking for the rare dangerous defects will reveal nothing about the other, subtler but still possibly relevant genetic traits those women do have.

Polygenic risk scores can be applied to everyone. They tell anyone how much more or less likely they are, on average, to develop a genetically linked condition. A recently developed PRS for a specific form of breast cancer looks at 313 different ways that genomes vary; those with the highest scores are four times more likely to develop the cancer than the average. In 2018 researchers developed a PRS for coronary heart disease that could identify about one in 12 people as being at significantly greater risk of a heart attack because of their genes.

Some argue that these scores are now reliable enough to bring into the clinic, something that would make it possible to target screening, smoking cessation, behavioural support and medications. However, hope that knowing their risk scores might drive people towards healthier lifestyles has not, so far, been validated by research; indeed, so far things look disappointing in that respect.

Assigning a PRS does not require sequencing a subjects whole genome. One just needs to look for a set of specific little markers in it, called SNPs. Over 70,000 such markers have now been associated with diseases in one way or another. But if sequencing someones genome is not necessary in order to inspect their SNPs, understanding what the SNPs are saying in the first place requires that a lot of people be sequenced. Turning patterns discovered in the SNPs into the basis of risk scores requires yet more, because you need to see the variations in a wide range of people representative of the genetic diversity of the population as a whole. At the moment people of white European heritage are often over-represented in samples.

The first genome cost, by some estimates, $3bn

The need for masses of genetic information from many, many human genomes is one of the main reasons why genomic medicine has taken off rather slowly. Over the course of the Human Genome Project, and for the years that followed, the cost of sequencing a genome fell quicklyas quickly as the fall in the cost of computing power expressed through Moores law. But it was falling from a great height: the first genome cost, by some estimates, $3bn. The gap between getting cheaper quickly and being cheap enough to sequence lots of genomes looked enormous.

In the late 2000s, though, fundamentally new types of sequencing technology became available and costs dropped suddenly (see chart). As a result, the amount of data that big genome centres could produce grew dramatically. Consider John Sulstons home base, the Wellcome Sanger Institute outside Cambridge, England. It provided more sequence data to the Human Genome Project than any other laboratory; at the time of its 20th anniversary, in 2012, it had produced, all told, almost 1m gigabytesone petabyteof genome data. By 2019, it was producing that same amount every 35 days. Nor is such speed the preserve of big-data factories. It is now possible to produce billions of letters of sequence in an hour or two using a device that could easily be mistaken for a chunky thumb drive, and which plugs into a laptop in the same way. A sequence as long as a human genome is a few hours work.

As a result, thousands, then tens of thousands and then hundreds of thousands of genomes were sequenced in labs around the world. In 2012 David Cameron, the British prime minister, created Genomics England, a firm owned by the government, and tasked initially with sequencing 100,000 genomes and integrating sequencing, analysis and reporting into the National Health Service. By the end of 2018 it had finished the 100,000th genome. It is now aiming to sequence five million. Chinas 100,000 genome effort started in 2017. The following year saw large-scale projects in Australia, America and Turkey. Dubai has said it will sequence all of its three million residents. Regeneron, a pharma firm, is working with Geisinger, a health-care provider, to analyse the genomes of 250,000 American patients. An international syndicate of investors from America, China, Ireland and Singapore is backing a 365m ($405m) project to sequence about 10% of the Irish population in search of disease genes.

Genes are not everything. Controls on their expressionepigentics, in the jargonand the effects of the environment need to be considered, too; the kitchen can have a distinctive effect on the way a recipe turns out. That is why biobanks are being funded by governments in Britain, America, China, Finland, Canada, Austria and Qatar. Their stores of frozen tissue samples, all carefully matched to clinical information about the person they came from, allow study both by sequencing and by other techniques. Researchers are keen to know what factors complicate the lines science draws from genes to clinical events.

Today various companies will sequence a genome commercially for $600-$700. Sequencing firms such as Illumina, Oxford Nanopore and Chinas BGI are competing to bring the cost down to $100. In the meantime, consumer-genomics firms will currently search out potentially interesting SNPs for between $100 and $200. Send off for a home-testing kit from 23andMe, which has been in business since 2006, and you will get a colourful box with friendly letters on the front saying Welcome to You. Spit in a test tube, send it back to the company and you will get inferences as to your ancestry and an assessment of various health traits. The health report will give you information about your predisposition to diabetes, macular degeneration and various other ailments. Other companies offer similar services.

Plenty of doctors and health professionals are understandably sceptical. Beyond the fact that many gene-testing websites are downright scams that offer bogus testing for intelligence, sporting ability or wine preference, the medical profession feels that people are not well equipped to understand the results of such tests, or to deal with their consequences.

An embarrassing example was provided last year by Matt Hancock, Britains health minister. In an effort to highlight the advantages of genetic tests, he revealed that one had shown him to be at heightened risk of prostate cancer, leading him to get checked out by his doctor. The test had not been carried out by Britains world-class clinical genomics services but by a private company; critics argued that Mr Hancock had misinterpreted the results and consequently wasted his doctors time.

23andMe laid off 14% of its staff in January

He would not be the first. In one case, documented in America, third-party analysis of genomic data obtained through a website convinced a woman that her 12-year-old daughter had a rare genetic disease; the girl was subjected to a battery of tests, consultations with seven cardiologists, two gynaecologists and an ophthalmologist and six emergency hospital visits, despite no clinical signs of disease and a negative result from a genetic test done by a doctor.

At present, because of privacy concerns, the fortunes of these direct-to-consumer companies are not looking great. 23andMe laid off 14% of its staff in January; Veritas, which pioneered the cheap sequencing of customers whole genomes, stopped operating in America last year. But as health records become electronic, and health advice becomes more personalised, having validated PRS scores for diabetes or cardiovascular disease could become more useful. The Type 2 diabetes report which 23andMe recently launched looks at over 1,000 SNPs. It uses a PRS based on data from more than 2.5m customers who have opted to contribute to the firms research base.

As yet, there is no compelling reason for most individuals to have their genome sequenced. If genetic insights are required, those which can be gleaned from SNP-based tests are sufficient for most purposes. Eventually, though, the increasing number of useful genetic tests may well make genome sequencing worthwhile. If your sequence is on file, many tests become simple computer searches (though not all: tests looking at the wear and tear the genome suffers over the course of a lifetime, which is important in diseases like cancer, only make sense after the damage is done). If PRSs and similar tests come to be seen as valuable, having a digital copy of your genome at hand to run them on might make sense.

Some wonder whether the right time and place to do this is at birth. In developed countries it is routine to take a pinprick of blood from the heel of a newborn baby and test it for a variety of diseases so that, if necessary, treatment can start quickly. That includes tests for sickle-cell disease, cystic fibrosis, phenylketonuria (a condition in which the body cannot break down phenylalanine, an amino acid). Some hospitals in America have already started offering to sequence a newborns genome.

Sequencing could pick up hundreds, or thousands, of rare genetic conditions. Mark Caulfield, chief scientist at Genomics England, says that one in 260 live births could have a rare condition that would not be spotted now but could be detected with a whole-genome sequence. Some worry, though, that it would also send children and parents out of the hospital with a burden of knowledge they might be better off withoutespecially if they conclude, incorrectly, that genetic risks are fixed and predestined. If there is unavoidable suffering in your childs future do you want to know? Do you want to tell them? If a child has inherited a worrying genetic trait, should you see if you have it yourselfor if your partner has? The ultimate answer to the commandment know thyself may not always be a happy one.

This article appeared in the Technology Quarterly section of the print edition under the headline "Welcome to you"

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Genomics took a long time to fulfil its promise - The Economist

Progressive Genetics is suspending its manual milk recording service from 12:00pm tomorrow, Tuesday, March 17, due to the ongoing developments with Covid-19.

Taking measures to prevent the spread of the novel coronavirus, the agricultural services firm sent out a text to customers of its manual milk recording service earlier today, Monday, March 16, to inform them of the development.

The manual milk recording will be suspended for a two-week period and is expected to resume on Monday, March 30, according to the company.

Speaking to AgriLand about the decision, Progressive Genetics milk recording manager Stephen Connolly explained: We have to be responsible.

We want to protect our staff, our contractors and our farmers. Thats whats most important.

The manager assured that Electronic do it yourself (EDIY) milk recording will continue over the two-week period, adding:

We have a protocol in place to minimise contact with the farmer and if a farmer is under pressure with a [somatic] cell count issue or anything like that we will get EDIY staff to drop bottles out so that the farmer can do samples themselves, if there is a spike in cell count.

Commenting on the suspension, Connolly said: It is unfortunate and regrettable, but you need a bit of common sense. We do need to put best practice in place and then hopefully after the next two weeks we can get back manual milk recording.

We all have to play our part. Its trying to minimise everything as much as possible. We all need to do our bit, whether it be Progressive Genetics or farmers or the public, just to minimise the risk.

The manager reiterated that EDIY services remain in place, adding that strict protocols are being adhered to regarding minimising contact and disinfecting equipment between farms.

If a farmer has a problem, we will get bottles out to them for milk recording and cell count; we wont leave anyone in the lurch.

Were available to be contacted in the office or our supervisors are available to be contacted if farmers have any issues or anything like that well be on call.

Its just unfortunate. Its a challenge but we have to put common sense and peoples safety before anything else, Connolly concluded.

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Progressive Genetics to suspend manual milk recording due to Covid-19 - Agriland

Any given morning,Megan Roche, MD, is probably out running -- but we're not talking about a standard 5K. Roche is the2016 USA Track and Field ultrarunner and sub-ultrarunner of the year, a five-time national ultrarunning champion, a North American Mountain Running Champion and six-time member of the U.S. world ultrarunning team.

When she's not scaling muddy mountains or competing in races up to 50 miles long, Roche is working on her PhD in epidemiology, after completing a medical degree at Stanford in 2018. Her research enables her to continue running, coaching andwriting about runningwith her husband, a fellow ultramarathon winner, all while delving into the science of athletic performance.

She slowed down long enough to talk with me about her love of running and science, and how these two passions shape her career path.

How did you become interested in running and taking on longer distances?

I always knew I loved running. I played field hockey in college and then I took a fifth year to run track. From there, it was just a natural progression. I love nature and time out on trails, so running longer distances just means covering more ground in beautiful places. Plus, I enjoy the physiology element of longer-distance running. I think there's a lot of different variables that go into the longer distances, like fueling, the mental mindset and metering out your effort.

Do you think about what's happening in your body while running longer races?

I do sometimes. But honestly, when it hurts, I try to turn that off and just have a completely blank brain. After the fact, it's fun to go through and think about the different cellular processes that are going on as your body is going through that pain and putting out power. Even though it's unpleasant, it's a really beautiful element of human physiology that we can push the body to its limits.

How do you balance a sport and a profession that are both so time-intensive?

I get almost all my training done in the early morning. I'm a morning person, which helps. When I run or exercise it actually makes me more time efficient -- I feel like I need that energy release. Getting in the training is a way to prime my brain for the rest of the day. I probably spend about 13 or 14 hours a week training, so in the grand scheme of things, these are just hours that make me more productive down the road.

Does your running impact your research and vice versa?

It definitely does. One of my research focuses is genetic predictors of sports injury in athletes, working withStuart Kim, PhD. Some of that research involves genetic consulting with athletes and oftentimes training questions come up.

Another study I'm working on is the Healthy Runner Project withMichael Fredericson, MD;Emily Kraus, MD, andKristin Sainani, MD, PhD. There, we're looking at stress fracture rates in Stanford track and field athletes, and looking at preventing bone stress injuries, primarily through a nutrition intervention and making sure that athletes have sufficient energy availability. Being able to connect with the research participants as athletes is helpful. I also apply Healthy Runner research in my work as a running coach and in my writing.

Have you tested your own genetics?

I have. Fortunately, they're actually pretty good, in terms of injury markers. I did rupture my high hamstring tendons, recently, so I will be searching for a hamstring marker down the road.

What are you most proud of in your life thus far?

For me, the decision not to go to residency was one that was very difficult. Heading into medical school, I was interested in being an orthopedic surgeon, but I realized that it just wasn't conducive to all the other things I have going on in life.

I'm proud of being able to step off that path, being okay with taking a "career swerve" and ultimately finding what I love. Every morning I wake up, and I'm so excited to do the science and the running that I do with inspiring mentors and people that I care about. I'm proud of the decisions that got me to that point and grateful for the balance that I've found.

Photo by Daphne Sashin

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She's an ultrarunning champion, studying the genetics of sports injury - Scope