Category Archives: Genetics

The Hospital will use SOPHiA DDM to enhance its testing and research of blood cancers

BOSTON and ROLLE, Switzerland, June 13, 2024 /PRNewswire/ -- SOPHiA GENETICS (Nasdaq: SOPH), a cloud-native healthcare technology company and a leader in data-driven medicine, today announced that Kepler Uniklinikum, Austria's second largest hospital, is live on SOPHiA GENETICS' platform. The hospital will use the SOPHiA DDM Platform to advance its next-generation sequencing (NGS) testing and diagnostics of blood-related cancers.

Kepler Uniklinikum, which has 1,800 beds, is the central healthcare provider for Upper Austria. The hospital will implement the SOPHiA DDM Platform across its medical and chemical laboratory locations to deepen its in-house NGS testing capabilities and expand its offerings to its patients, specifically for those faced with blood cancers and disorders.

Cancer is the second most common cause of death in Austria, with about 42,000 people diagnosed with cancer each year.1 Additionally, on a global scale, blood cancers are the fifth most common type of cancer in the world.2 Advances in diagnostics and treatment of blood cancers depend on timely, cost-effective, and reliable sequencing data. The SOPHiA DDM Platform uses NGS to target key variants from FFPE, blood, or bone marrow samples, helping lead to fast and accurate detection of variants associated with the disease. The SOPHiA DDM Platform is specifically designed to compute a wide array of genomic variants and continually hones its machine learning algorithms to detect genomic variants associated with rare and challenging cases.

"Kepler Uniklinikum's implementation of the SOPHiA DDM Platform will help progress the use of data-driven medicine throughout Austria by ensuring their patients receive the most advanced and accurate testing," said Kevin Puylaert, Managing Director, EMEA, SOPHiA GENETICS. "This testing will not only help the local population but will provide valuable insights and data to support others using the SOPHiA DDM Platform around the world."

The SOPHiA DDM Platform delivers results that are nearly 100 percent reproducible to provide consistent inter- and intra-run results, ensuring stable and trustworthy sequencing data.

For more information on SOPHiA GENETICS, visit SOPHiAGENETICS.COM, or connect on LinkedIn.

About SOPHiA GENETICS SOPHiA GENETICS (Nasdaq: SOPH) is a cloud-native healthcare technology company on a mission to expand access to data-driven medicine by using AI to deliver world-class care to patients with cancer and rare disorders across the globe. It is the creator of the SOPHiA DDM Platform, which analyzes complex genomic and multimodal data and generates real-time, actionable insights for a broad global network of hospital, laboratory, and biopharma institutions. For more information, visit SOPHiAGENETICS.COM and connect with us on LinkedIn.

SOPHiA DDM for Lymphoid Malignancies is available as a CE-IVD product for In Vitro Diagnostic Use in Europe and Turkey. The information in this press release is about products that may or may not be available in different countries and, if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please contact support@sophiagenetics.com to obtain the appropriate product information for your country of residence.

SOPHiA GENETICS Forward-Looking Statements: This press release contains statements that constitute forward-looking statements. All statements other than statements of historical facts contained in this press release, including statements regarding our future results of operations and financial position, business strategy, products, and technology, as well as plans and objectives of management for future operations, are forward-looking statements. Forward-looking statements are based on our management's beliefs and assumptions and on information currently available to our management. Such statements are subject to risks and uncertainties, and actual results may differ materially from those expressed or implied in the forward-looking statements due to various factors, including those described in our filings with the U.S. Securities and Exchange Commission. No assurance can be given that such future results will be achieved. Such forward-looking statements contained in this press release speak only as of the date hereof. We expressly disclaim any obligation or undertaking to update these forward-looking statements contained in this press release to reflect any change in our expectations or any change in events, conditions, or circumstances on which such statements are based, unless required to do so by applicable law. No representations or warranties (expressed or implied) are made about the accuracy of any such forward-looking statements.

1 https://ascopost.com/issues/october-25-2022/incidence-and-cancer-related-mortality-in-austria/ 2 https://www.worldwidecancerresearch.org/news-opinion/2022/september/blood-cancer-everything-you-need-to-know/

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SOPHiA GENETICS Announces Kepler Uniklinikum is Live on SOPHiA DDM Platform - BioSpace

Media Advisory

Wednesday, June 12, 2024

A genomic analysis co-led by NIH suggests that the DNA a woman is born with may influence how her cells respond to chromosomal abnormalities acquired with aging.

Researchers have identified inherited genetic variants that may predict the loss of one copy of a womans two X chromosomes as she ages, a phenomenon known as mosaic loss of chromosome X, or mLOX. These genetic variants may play a role in promoting abnormal blood cells (that have only a single copy of chromosome X) to multiply, which may lead to several health conditions, including cancer. The study, co-led by researchers at the National Institutes of Healths (NIH) National Cancer Institute, was published June 12, 2024, in Nature.

To better understand the causes and effects of mLOX, researchers analyzed circulating white blood cells of nearly 900,000 women across eight biobanks, of whom 12% had the condition. The researchers identified 56 common genetic variantslocated near genes associated with autoimmune diseases and cancer susceptibilitythat influenced whether mLOX developed. In addition, rare variants in a gene known as FBXO10 were associated with a doubling in the risk of mLOX.

In women with mLOX, the investigators also identified a set of inherited genetic variants on the X chromosome that were more frequently observed on the retained X chromosome than on the one that was lost. These variants could one day be used to predict which copy of the X chromosome is retained when mLOX occurs. This is important because the copy of the X chromosome with these variants may have a growth advantage that could elevate the womans risk for blood cancer.

The researchers also looked for associations of mLOX with more than 1,200 diseases and confirmed previous findings of an association with increased risk of leukemia and susceptibility to infections that cause pneumonia.

The scientists suggest that future research should focus on how mLOX interacts with other types of genetic variation and age-related changes to potentially alter disease risk.

Mitchell Machiela, Sc.D., M.P.H., Division of Cancer Epidemiology and Genetics, National Cancer Institute

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection appears June 12, 2024, in Nature.

About the National Cancer Institute (NCI):NCIleads the National Cancer Program and NIHs efforts to dramatically reduce the prevalence of cancer and improve the lives of people with cancer. NCI supports a wide range of cancer research and training extramurally through grants and contracts. NCIs intramural research program conducts innovative, transdisciplinary basic, translational, clinical, and epidemiological research on the causes of cancer, avenues for prevention, risk prediction, early detection, and treatment, including research at the NIH Clinical Centerthe worlds largest research hospital. Learn more about the intramural research done in NCIs Division of Cancer Epidemiology and Genetics. For more information about cancer, please visit the NCI website atcancer.govor call NCIs contact center at 1-800-4-CANCER (1-800-422-6237).

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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Inherited genetic factors may predict the pattern of X chromosome loss in older women - National Institutes of Health (NIH) (.gov)

Roth Gregory, A. et al. Global burden of cardiovascular diseases and risk factors, 19902019. J. Am. Coll. Cardiol. 76, 29823021 (2020).

Article CAS PubMed PubMed Central Google Scholar

Khera, A. V. & Kathiresan, S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat. Rev. Genet. 18, 331344 (2017).

Article CAS PubMed PubMed Central Google Scholar

Chen, Z. & Schunkert, H. Genetics of coronary artery disease in the post-GWAS era. J. Intern. Med. 290, 980992 (2021).

Article PubMed Google Scholar

Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 18031815 (2022).

Article CAS PubMed PubMed Central Google Scholar

Tcheandjieu, C. et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. 28, 16791692 (2022).

Article CAS PubMed PubMed Central Google Scholar

Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581594 (2013).

Article CAS PubMed Google Scholar

Plenge, R. M. Disciplined approach to drug discovery and early development. Sci. Transl. Med. 8, 349ps15 (2016).

Article PubMed Google Scholar

Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942948 (2021).

Article CAS PubMed Google Scholar

Do, R. et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102106 (2015).

Article CAS PubMed Google Scholar

Yao, K. et al. Exome sequencing identifies rare mutations of LDLR and QTRT1 conferring risk for early-onset coronary artery disease in Chinese. Natl Sci. Rev. 9, nwac102 (2022).

Article CAS PubMed PubMed Central Google Scholar

Khera, A. V. et al. Gene sequencing identifies perturbation in nitric oxide signaling as a nonlipid molecular subtype of coronary artery disease. Circ. Genom. Precis. Med. 15, e003598 (2022).

Article CAS PubMed PubMed Central Google Scholar

Martin, S. S. et al. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation 149, e347e913 (2024).

Article PubMed Google Scholar

Maddox, T. M. et al. Nonobstructive coronary artery disease and risk of myocardial infarction. JAMA 312, 17541763 (2014).

Article CAS PubMed PubMed Central Google Scholar

Park, D. W. et al. Extent, location, and clinical significance of non-infarct-related coronary artery disease among patients with ST-elevation myocardial infarction. JAMA 312, 20192027 (2014).

Article CAS PubMed Google Scholar

Forrest, I. S. et al. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. Lancet 401, 215225 (2023).

Article PubMed Google Scholar

Petrazzini, B. O. et al. Coronary risk estimation based on clinical data in electronic health records. J. Am. Coll. Cardiol. 79, 11551166 (2022).

Article PubMed PubMed Central Google Scholar

Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 10971103 (2021).

Article CAS PubMed Google Scholar

Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 21902191 (2010).

Article CAS PubMed PubMed Central Google Scholar

Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat. Genet. 48, 314317 (2016).

Article CAS PubMed Google Scholar

Zhou, W. et al. Efficiently controlling for casecontrol imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 13351341 (2018).

Article CAS PubMed PubMed Central Google Scholar

Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906908 (2018).

Article CAS PubMed PubMed Central Google Scholar

Nikpay, M. et al. A comprehensive 1,000 genomesbased genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 11211130 (2015).

Article CAS PubMed PubMed Central Google Scholar

Tarugi, P. et al. Molecular diagnosis of hypobetalipoproteinemia: an ENID review. Atherosclerosis 195, e19e27 (2007).

Article CAS PubMed Google Scholar

Ference, B. A. et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N. Engl. J. Med. 375, 21442153 (2016).

Article CAS PubMed Google Scholar

Schmidt, A. F. et al. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study. Lancet Diabetes Endocrinol. 5, 97105 (2017).

Article CAS PubMed PubMed Central Google Scholar

Lotta, L. A. et al. Association between low-density lipoprotein cholesterollowering genetic variants and risk of type 2 diabetes: a meta-analysis. JAMA 316, 13831391 (2016).

Article CAS PubMed PubMed Central Google Scholar

Benn, M., Nordestgaard, B. G., Grande, P., Schnohr, P. & Tybjrg-Hansen, A. PCSK9R46L, low-density lipoprotein cholesterol levels, and risk of ischemic heart disease: 3 independent studies and meta-analyses. J. Am. Coll. Cardiol. 55, 28332842 (2010).

Article CAS PubMed Google Scholar

Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311D1320 (2021).

Article CAS PubMed Google Scholar

Thomas, D. G., Wei, Y. & Tall, A. R. Lipid and metabolic syndrome traits in coronary artery disease: a Mendelian randomization study. J. Lipid Res. 62, 100044 (2021).

Article CAS PubMed PubMed Central Google Scholar

Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417425 (2015).

Article CAS PubMed PubMed Central Google Scholar

Schrodi, S. J. The impact of diagnostic code misclassification on optimizing the experimental design of genetic association studies. J. Healthc. Eng. 2017, 7653071 (2017).

Article PubMed PubMed Central Google Scholar

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203209 (2018).

Article CAS PubMed PubMed Central Google Scholar

Klarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49, 13921397 (2017).

Article CAS PubMed PubMed Central Google Scholar

Honigberg, M. C. et al. Premature menopause, clonal hematopoiesis, and coronary artery disease in postmenopausal women. Circulation 143, 410423 (2021).

Article PubMed Google Scholar

Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 12191224 (2018).

Article CAS PubMed PubMed Central Google Scholar

Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

Article PubMed PubMed Central Google Scholar

Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 113 (2010).

Article Google Scholar

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 13471358 (2019).

Article PubMed Google Scholar

Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 1822 (2002).

Google Scholar

Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 126 (2008).

Article Google Scholar

Grn, B., Kosmidis, I. & Zeileis, A. Extended beta regression in R: shaken, stirred, mixed, and partitioned. J. Stat. Softw. 48, 125 (2012).

Article Google Scholar

McCaw, Z. R., Lane, J. M., Saxena, R., Redline, S. & Lin, X. Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies. Biometrics 76, 12621272 (2020).

Article CAS PubMed PubMed Central Google Scholar

Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514518 (2019).

Article CAS PubMed PubMed Central Google Scholar

Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248249 (2010).

Article CAS PubMed PubMed Central Google Scholar

Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 38123814 (2003).

Article CAS PubMed PubMed Central Google Scholar

Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 15531561 (2009).

Article CAS PubMed PubMed Central Google Scholar

Schwarz, J. M., Cooper, D. N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361362 (2014).

Article CAS PubMed Google Scholar

Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence Kernel association test. Am. J. Hum. Genet. 89, 8293 (2011).

Article CAS PubMed PubMed Central Google Scholar

Liu, Y. et al. ACAT: a fast and powerful P value combination method for rare-variant analysis in sequencing studies. Am. J. Hum. Genet. 104, 410421 (2019).

Article CAS PubMed PubMed Central Google Scholar

McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

Article PubMed PubMed Central Google Scholar

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Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for ... - Nature.com

A recent study published in Molecular Psychiatry reveals that certain genetic traits inherited from Neanderthals may significantly contribute to the development of autism. This groundbreaking research shows that specific Neanderthal genetic variants can influence autism susceptibility, suggesting a link between our ancient relatives and modern neurodevelopmental conditions.

The study was motivated by the longstanding curiosity about how archaic human DNA, particularly from Neanderthals, influences modern human health. Homo neanderthalensis, commonly known as Neanderthals, are our closest known cousins on the hominin tree of life. It is estimated that populations of European and Asian descent have about 2% Neanderthal DNA, a remnant from interbreeding events that occurred when anatomically modern humans migrated out of Africa around 47,000 to 65,000 years ago.

While previous studies have identified Neanderthal genetic contributions to traits like immune function, skin pigmentation, and metabolism, the role of these ancient genes in brain development and neurodevelopmental conditions like autism has remained largely unexplored. In their new study, the researchers aimed to fill that gap by investigating whether Neanderthal DNA is more prevalent in autistic individuals compared to non-autistic controls.

Autism is a neurodevelopmental condition characterized by challenges in social interaction, communication, and repetitive behaviors or restricted interests. The severity and specific manifestations of these traits can vary widely among individuals. Given that autism is characterized by distinct patterns in brain connectivity, the researchers sought to better understand whether these patterns could be linked to Neanderthal DNA.

I should state that I am on the autism spectrum myself. Ive been involved with the online autistic and neurodiversity communities since circa 2003. I was a moderator on the well known forum, Wrong Planet, under the handle, Sophist, and later developed my own autism website called Gestalt, explained study author Emily Casanova, an assistant professor of neuroscience at Loyola University New Orleans and creator of the website Science Over a Cuppa.

So, Ive been interested in autism and figuring out what makes it tick for a long time. In the last decade, Ive focused more on the genetics side, but Ive also had an ongoing interest in evolutionary biology. For many years, that latter interest was just a hobby, but in recent years Ive begun working more on things like studying the evolution of autism genes and just trying to understand how a lot of these developmentally-related genes evolve over time.

You may be wondering what that has to do with Neanderthals! Well, one of the topics Ive been studying is how hybridization (the coming-together of two species) influences the offspring over subsequent generations, Casanova continued. Hybridization has a tendency to shake things up genetically not just because youre splicing two species together in an additive process but because some genetic variants dont always work so well when theyre suddenly thrown together in a single genome.

Variations tend to have partners they like to travel with over generations and when sexual recombination splits them apart, sometimes that can create some new problems in the offspring. Interestingly, this process may also be a stimulus for more rapid evolution following hybridization.

So, Im very interested in these Neanderthal variants not just in understanding how they may influence autism susceptibility but even how they might have guided our own subsequent brain evolution over the last 50,000 years, she explained. I dont think its a coincidence that many of the variants implicated in autism are also implicated in human intelligence, so I find that possibility fascinating.

The research team utilized whole exome sequencing (WES) data from the Simons Foundation Powering Autism Research (SPARK) Database, focusing on autistic individuals and their unaffected siblings. They compared these groups against individuals from the Genotype-Tissue Expression (GTEx) and 1000 Genomes (1000G) databases. Specifically, the researchers examined single nucleotide polymorphisms (SNPs) derived from Neanderthals, which are variations in a single DNA building block.

The researchers found that autistic individuals had a higher prevalence of rare Neanderthal-derived genetic variants compared to non-autistic controls. These rare variants, which occur in less than 1% of the population, were significantly enriched in the genomes of autistic individuals across three major ethnic groups: black non-Hispanic, white Hispanic, and white non-Hispanic.

I know a lot of people are going to read the headline and immediately assume that autistic people have more Neanderthal DNA than non-autistic people that theyre somehow more Neanderthal,' Casanova told PsyPost. I wouldnt say I blame them for the assumption, especially when the Neanderthal Theory of Autism had already been proposed and popularized by Leif Ekblad, an autistic independent researcher, as far back as 2001. This idea made its way around the online autistic community in the early 2000s and served partly as inspiration for Ekblads Aspie Quiz, which has continued to be one of the most popular online autism-related quizzes.

Our results are a little more nuanced than autistic people are just more Neanderthal. For background, the human genome is made up of over 3 billion nucleotide pairs. The vast majority of our genomes is pretty identical to one another. But theres a few places in the human genome that are sites of variation.

Neanderthal DNA provides some of that variation and some of those variants are common (1% or more of the population has that particular variant) or they can be rare (less than 1% has that variant), Casanova explained. In our study, weve found that autistic people, on average, have more rare Neanderthal variants, not that they have more Neanderthal DNA in general. That means that while not all Neanderthal DNA is necessarily influencing autism susceptibility, a subset is.

In contrast to the rare variants, the study found that common Neanderthal-derived variants were less prevalent in black non-Hispanic and white Hispanic autistic individuals compared to controls. Common variants are those present in 1% or more of the population. This finding was not observed in white non-Hispanic autistic individuals, who did not show significant differences in common Neanderthal DNA compared to controls or unaffected siblings.

The researchers also identified specific clinical associations between Neanderthal-derived variants and autism-related traits. For example, a particular SNP (rs112406029) in the SLC37A1 gene was significantly associated with epilepsy in white non-Hispanic autistic individuals. This variant was more common in autistic individuals with epilepsy than in those without, and was even more prevalent in those with a family history of the condition.

Similar associations were found in other ethnic groups, linking certain Neanderthal variants to traits such as intellectual disability, language delay, and language regression. These findings suggest that Neanderthal-derived genetic variants may not only contribute to autism susceptibility but also influence specific comorbid conditions and traits.

I was rather surprised that many of the Neanderthal-derived variants we found that were associated with autism dramatically varied by ethnic group, Casanova said. In hindsight, I suppose that shouldnt be so surprising, but it does mean that a lot of these weak variants that are playing roles in autism are influenced by the background genome, which varies by ethnicity.

So, one variant may be strongly linked with autism in black Americans, while that same variant doesnt appear to be playing a measurable role in white Hispanics and non-Hispanics. To me it suggests that our tendency to white wash genetics and ignore variants that arent implicated across all genetic backgrounds means that were missing out on a lot of important genetic factors.

The findings have significant implications for our understanding of autism and its genetic underpinnings. By highlighting the role of ancient Neanderthal DNA, the research opens new avenues for exploring how hybridization events between archaic and modern humans have shaped neurodevelopmental conditions.

In this current study, we only investigated the parts of the genome that contain protein-coding genes (known as the exome), Casanova noted. In the next phase, we plan on looking at the entire genome, since theres a lot of interesting regulatory material thats contained in those regions thats undoubtedly influencing when and how genes are expressed. We also plan on including the Denisovan genome in our next phase of study to see if that DNA may be playing roles in autism in people with Asian/Native American backgrounds.

Some people in the autistic community get uncomfortable with genetics studies, the researcher added. In part, this is rooted in fears related to eugenics. The autistic community is well aware of how prenatal genotyping of Down Syndrome has led to abortion in about 30% of those cases. But I would just like to assure people that these Neanderthal-derived variants are also occurring in people, especially family members, without autism. So, while identifying these susceptibility factors may help us build a fuller picture of autism and its very complex roots, this knowledge cannot be used to aid in eugenics or similar agendas.

The study, Enrichment of a subset of Neanderthal polymorphisms in autistic probands and siblings, Rini Pauly, Layla Johnson, F. Alex Feltus, and Emily L. Casanova.

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Ancient Neanderthal DNA found to influence autism susceptibility - PsyPost

Mutations are changes in the molecular letters that make up the DNA code, the blueprint for all living cells. Some of these changes can have little effect, but others can lead to diseases, including cancer. Now, a new study introduces an original technique, HiDEF-seq, that can accurately detect the early molecular changes in DNA code that precede mutations.

The study authors say their techniqueHairpin Duplex Enhanced Fidelity Sequencingcould advance our understanding of the basic causes of mutations in both healthy cells and in cancer, and how genetic changes naturally accumulate in human cells as people age.

Led by a team of researchers at NYU Langone Health, with collaborators across North America and in Denmark, the work helps to resolve the earliest steps in how mutations occur in DNA.

The new study is based on the understanding that DNA is made up of two strands of molecular letters, or bases. Each strand is composed of four types of letters: adenine (A), thymine (T), guanine (G), and cytosine (C). The bases of each strand pair with bases in the other strand in a specific pattern, with As pairing with Ts and Gs pairing with Cs. This allows the DNA code to be replicated and passed down accurately from one generation of cells to the next. Importantly, mutations are changes in the DNA code that are present in both strands of DNA. For example, a base pair of G and C, with a G on one strand paired with a C on the other strand, can mutate to an A and T base pair.

However, researchers say, most mutations have their origins in DNA changes that are present in only one of the two DNA strands, and these single-strand changes, such as a mismatched G and T base pair, cannot be accurately identified using previous testing techniques. These changes can occur when a DNA strand is not copied correctly during replication, as when a cell divides into two cells, or when one of the two DNA strands is damaged by heat or by other chemicals in the body. If these single-strand DNA changes are not repaired by the cell, then the changes are at risk of becoming permanent double-strand mutations.

Published in the journal Nature online June 12, the study showed that the HiDEF-seq technique detected double-strand mutations with extremely high accuracy, with an estimate of one recording error per 100 trillion base pairs analyzed. Moreover, HiDEF-seq detected changes in the DNA letter code while they were present on just one of the two strands of DNA, before they become permanent double-strand mutations.

Our new HiDEF-seq sequencing technique allows us to see the earliest fingerprints of molecular changes in DNA when the changes are only in single strands of DNA, said senior study author Gilad D. Evrony, MD, PhD, a core member of the Center for Human Genetics and Genomics at NYU Grossman School of Medicine.

Because people with genetic syndromes linked to cancer are known to have higher rates of mutations in their cells than people with no cancer predisposition, researchers began their experiments by describing the DNA changes in healthy cells from people with these syndromes. Specifically, investigators worked with healthy cells from people with polymerase proofreadingassociated polyposis (PPAP), a hereditary condition linked to an increased risk for colorectal cancer, and congenital mismatch repair deficiency (CMMRD), another hereditary condition that increases the likelihood of several cancers in children.

Using HiDEF-seq, researchers found a higher number of single-strand DNA changes in their cells, such as a T paired with a C in place of the original G paired with a C, than in the cells from people who did not have either syndrome. Moreover, the pattern of these single-strand changes was similar to the pattern observed in the double-strand DNA mutations for people with either syndrome.

Subsequent experiments were performed in human sperm, which are known to have among the lowest double-strand mutation rates of any human cell type. Researchers found that the pattern of chemical damage, called cytosine deamination, observed by HiDEF-seq in single stands of DNA in sperm, closely matched the damage observed in blood DNA intentionally damaged by heat. This, the researchers say, suggests that the two patterns of chemical damage to DNA, one natural and the other induced by external forces, occur through a similar process.

Our study lays the foundation for using the HiDEF-seq technique in future experiments to transform our understanding of how DNA damage and mutations arise, said Dr. Evrony, who is also an assistant professor in the Department of Pediatricsand the Department of Neuroscience and Physiology at NYU Grossman School of Medicine. Single-strand changes in DNA occur continually as cells divide and multiply, and while layers of repair mechanisms fix most changes, some get through and become mutations.

Our long-term goal is to use HiDEF-seq to create a comprehensive catalogue of single-strand DNA mismatch and damage patterns that will help explain the known double-strand mutation patterns, said Dr. Evrony. In the future, we hope to combine profiling of single-strand DNA lesions, as obtained from HiDEF-seq, with the lesions resulting double-strand mutations to better understand and monitor the everyday effects on DNA from environmental exposures.

Geneticists estimate that there are approximately 12 billion bases or individual DNA letters that can be damaged or mismatched in each human cell, as there are two copies of the genetic code, with one copy inherited from each parent. Each of these copies comprise a double-stranded DNA spanning 3 billion base pairs. Dr. Evrony says that every base position in the genetic code is likely damaged or mutated at some point during an individuals lifetime in at least some cells.

Funding for the study was provided by National Institutes of Health grants UG3NS132024, R21HD105910, DP5OD028158, T32AG052909, F32AG076287, and P30CA016087. Additional funding support was provided by the Sontag Foundation, the Pew Foundation, and the Jacob Goldfield Foundation.

Dr. Evrony and NYU have a patent application pending on the HiDEF-seq method.

Dr. Evrony owns equity in the DNA-sequencing companies Illumina, Pacific Biosciences, and Oxford Nanopore Technologies, some of whose products were adapted for use in this study. All of these arrangements are being managed in accordance with the policies and practices of NYU Langone Health.

Besides Dr. Evrony, other NYU Langone researchers involved in this study are co-lead authors Mei-Hong Liu and Benjamin Costa and co-authors Emilia Bianchini, Una Choi, Rachel Bandler, Marta Gronska-Peski, Adam Schwing, Zachary Murphy, Caitlin Loh, and Tina Truong.

Other study co-investigators include Emilie Lassen, Daniel Rosenkjaer, and Anne-Bine Skytte, at the Cryos International Sperm and Egg Bank in Copenhagen, Denmark; Shany Picciotto and Jonathan Shoag, at Case Western Reserve University in Cleveland; Vanessa Bianchi, Lucie Stengs, Melissa Edwards, Nuno Miguel Nunes, and Uri Tabori, at the Hospital for Sick Children in Toronto; Randall Brand, at the University of Pittsburgh; Tomi Pastinen, at Childrens Mercy Kansas City in Missouri; and Richard Wagner, at the Universit de Sherbrooke in Canada.

David March Phone: 212-404-3528 David.March@NYULangone.org

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New Technique Reveals Earliest Signs of Genetic Mutations - NYU Langone Health

The Hospital will use SOPHiA DDM to enhance its testing and research of blood cancers

BOSTON and ROLLE, Switzerland, June 13, 2024 /PRNewswire/ -- SOPHiA GENETICS (Nasdaq: SOPH), a cloud-native healthcare technology company and a leader in data-driven medicine,today announced that Kepler Uniklinikum, Austria's second largest hospital, is live on SOPHiA GENETICS' platform. The hospital will use the SOPHiA DDMPlatform to advance its next-generation sequencing (NGS) testing and diagnostics of blood-related cancers.

Kepler Uniklinikum, which has 1,800 beds, is the central healthcare provider for Upper Austria. The hospital will implement the SOPHiA DDMPlatform across its medical and chemical laboratory locations to deepen its in-house NGS testing capabilities and expand its offerings to its patients, specifically for those faced with blood cancers and disorders.

Cancer is the second most common cause of death in Austria, with about 42,000 people diagnosed with cancer each year.1 Additionally, on a global scale, blood cancers are the fifth most common type of cancer in the world.2 Advances in diagnostics and treatment of blood cancers depend on timely, cost-effective, and reliable sequencing data. The SOPHiA DDM Platform uses NGS to target key variants from FFPE, blood, or bone marrow samples, helping lead to fast and accurate detection of variants associated with the disease. The SOPHiA DDM Platform is specifically designed to compute a wide array of genomic variants and continually hones its machine learning algorithms to detect genomic variants associated with rare and challenging cases.

"Kepler Uniklinikum's implementation of the SOPHiA DDM Platform will help progress the use of data-driven medicine throughout Austria by ensuring their patients receive the most advanced and accurate testing," said Kevin Puylaert, Managing Director, EMEA, SOPHiA GENETICS. "This testing will not only help the local population but will provide valuable insights and data to support others using the SOPHiA DDM Platform around the world."

The SOPHiA DDM Platform delivers results that are nearly 100 percent reproducible to provide consistent inter- and intra-run results, ensuring stable and trustworthy sequencing data.

For more information on SOPHiA GENETICS, visitSOPHiAGENETICS.COM, or connect on LinkedIn.

About SOPHiA GENETICS SOPHiA GENETICS (Nasdaq: SOPH) is a cloud-native healthcare technology company on a missionto expand access to data-driven medicine by using AI to deliver world-class care to patients withcancer and rare disordersacross the globe. It is the creator of the SOPHiA DDM Platform,whichanalyzes complex genomic and multimodal data and generates real-time, actionableinsights for a broad global network of hospital, laboratory, and biopharma institutions.For more information, visitSOPHiAGENETICS.COM and connect with us onLinkedIn.

SOPHiADDM for Lymphoid Malignancies is available as a CE-IVD product for In Vitro Diagnostic Use in Europe and Turkey. The information in this press release is about products that may or may not be available in different countries and, if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please contact [emailprotected] to obtain the appropriate product information for your country of residence.

SOPHiA GENETICS Forward-Looking Statements:This press release contains statements that constitute forward-looking statements. All statements other than statements of historical facts contained in this press release, including statements regarding our future results of operations and financial position, business strategy, products, and technology, as well as plans and objectives of management for future operations, are forward-looking statements. Forward-looking statements are based on our management's beliefs and assumptions and on information currently available to our management. Such statements are subject to risks and uncertainties, and actual results may differ materially from those expressed or implied in the forward-looking statements due to various factors, including those described in our filings with the U.S. Securities and Exchange Commission. No assurance can be given that such future results will be achieved. Such forward-looking statements contained in this press release speak only as of the date hereof. We expressly disclaim any obligation or undertaking to update these forward-looking statements contained in this press release to reflect any change in our expectations or any change in events, conditions, or circumstances on which such statements are based, unless required to do so by applicable law. No representations or warranties (expressed or implied) are made about the accuracy of any such forward-looking statements.

1 https://ascopost.com/issues/october-25-2022/incidence-and-cancer-related-mortality-in-austria/ 2 https://www.worldwidecancerresearch.org/news-opinion/2022/september/blood-cancer-everything-you-need-to-know/

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SOPHiA GENETICS Announces Kepler Uniklinikum is Live on SOPHiA DDM Platform - PR Newswire

T cells: BMTs double-edged sword

Bone marrow transplants, also known as blood stem cell transplants or hematopoietic stem cell transplants (HST), revolutionized blood cancer treatment. After a conditioning regimen wipes out as many blood cancer cells as possible and also wiping out healthy blood stem cells transplant recipients receive healthy new donor bone marrow or blood stem cells. These take root in the bone marrow and grow new oxygen-carrying red blood cells and infection-fighting white blood cells.

With the stem cells come mature donor immune cells, including T cells,a specialized type of immune cell critical both to a bone marrow transplants success in treating cancer, and the driving force behind GVHD.

T cells use a specialized molecule, called a T-cell receptor, or TCR, to read bits of proteins pinned to the surface of cells by a molecular peg. These protein-peg complexes act as bulletins of a cells health status, and T cells are trained to leave healthy cells alone.

But when a T cell is dumped in a new environment, they can misread these bulletins. These T cells may attack a BMT recipients healthy tissue, even as some donor T cells kill off lingering tumor cells and prevent relapse.

To help ensure that donor T cells understand the messages theyre reading, hematologists try to ensure that the pegs theyll see in a recipient match the pegs theyre used to seeing in their home turf, the donor. If they are well-matched, T cells are more likely to understand the message that their new host is self and safe.

This is called tissue typing.

But tissue typing isnt perfect. The pegs, called HLA, for human leukocyte antigen (or MHC, for major histocompatibility complex) are among the most variable genes humans have. And each person has several MHC genes, each with its own dizzying variety. On top of this, the little protein messages cradled by MHC molecules can vary between recipient and donor just enough to send a "danger!" signal to donor T cells (even when MHC genes match perfectly).

Right now, treatments for GVHD, like corticosteroids, muffle the anti-tumor cells along with those driving GVHD.

Its the holy grail of transplant: separating GVHD from the graft-vs.-leukemia effect, Yeh said. I wanted to understand how to improve GVHD from the standpoint of the individual T cells.

He hoped that if he could identify the GVHD-promoting T cells, he would be able to devise strategies to remove them, leaving only the leukemia-targeting cells behind.

Still working from the assumption that recipient and donor genetics would be the key to solving this dilemma, Yeh performed twin transplant studies. In these, bone marrow from one donor mouse is split apart and transplanted into two genetically identical recipient mice.

Yeh and Hill expected that, faced with the same genetic milieu, the same T cells that respond to the new environment would expand in each recipient. (TCRs are even more variable than MHCs: each new T cell builds a bespoke TCR that is one of more than a quadrillion possible TCRs.)

By comparing the donor TCRs in each recipient, the scientists expected it would be possible to find the recipient-targeting TCRs that could drive GVHD.

But the T cells confounded them. In each recipient, a pool of T cells with certain TCRs would expand, suggesting that they were responding to something in their new environment.

Even in completely identical donor and recipients, theres almost no overlap in which T cells expanded, Hill said. This is shocking given how we currently think about alloreactivity [the T-cell response to MHC variants].

But an individual may have billions of different TCRs floating around some found on only a single T cell. Yeh, working with Fred Hutch computational biologist Phil Bradley, PhD, developed mathematical models to confirm that the lack of overlap didnt reflect the chance that each twin had received a different set of rare TCRs.

Yeh found that antibiotic treatment (two weeks prior to one week after transplant), but not total-body irradiation, reduced the pool of T cells that expanded after transplant. Previous work had suggested that the microbiome can influence GVHD through general immune mechanisms. What if the T cells were responding to the microbes through their TCRs?

Yeh took advantage of the fact that mice bred in different facilities have different microbiome makeups. He gave a bone marrow transplant to two sets of recipient mice (with higher and lower levels of a certain bacterium). To the donor bone marrow, he added T cells genetically engineered to carry TCRs that detect the bacterium.

He found that not only did the bacterium-targeting T cells expand more in the mice with higher levels of the bacterium, but also exacerbated GVHD lethality. Yeh further showed that GVHD does not originate with the bacterium-targeting T cells.

By themselves they dont do too much, he said.

Excerpt from:
Secret to tamping down GVHD may lie in microbial genetics - Fred Hutchinson Cancer Center

Integrative psychiatric research consortium 2012 (iPSYCH2012) cohort

The iPSYCH2012 cohort is a well-documented extensively cited cohort24. In short, individuals born between 1981 and 2005 (n=1,472,762) were considered for ascertainment, representing the entire population of Denmark born in that timeframe. Of these 30,000 were randomly sampled, regardless of (psychiatric) disorder status, to create an unbiased population representative control group. Using information ascertained from the Danish Civil31,32, National Patient33 and/or Psychiatric Central Research Registers34 57,764 design cases were selected with indications of clinical diagnoses of mental health disorders. In total, 87,764 individuals were selected to form the cohort. Indications are based on International Classification of Disease (ICD) codes representing the clinical diagnosis associated with an instance of care provided at one of many psychiatric facilities throughout Denmark. The following six case groups as having at least one indication with the corresponding ICD1035 (or equivalent ICD836) codes were defined: attention deficit hyperactivity disorder (ADHD: F90.0), anorexia nervosa (AN: F50.0, F50.1), autism spectrum disorder (ASD: F84.0, F84.1, F84.5, F84.8, F84.9), affective disorder (AFF: F30F39), bipolar disorder (BD: F30-31), and schizophrenia (SCZ: F20). Of all selected individuals a dried neonatal heel prick blood spot was obtained from the Danish Neonatal Screening Biobank37. Individuals were removed when no blood spots could be obtained. The use of this data is according to the guidelines provided by the Danish Scientific Ethics Committee, the Danish Health Data Authority, the Danish Data Protection Agency, and the Danish Neonatal Screening Biobank Steering Committee. For each dried bloodspot the DNA was extracted and amplified followed by genotyping using the Infinium PsychChip v1.0 array24. Of 9714 bloodspots not DNA could successfully be genotyped and therefore the individuals were excluded from the study. A subset of good quality SNPs were phased into haplotypes using SHAPEIT338 and imputed using Impute239 with reference haplotypes from the 1000 genomes project phase 340. Genotypes were checked for imputation quality (INFO>0.2), Hardy-Weinberg equilibrium (HWE; p<110-6), association with genotyping wave (p<5108), association with imputation batch (p<5108), differing imputation quality between subjects with and without psychiatric diagnoses (p<1106), and minor allele frequency (MAF>0.01). Finally, we extracted unrelated individuals of European ancestry leaving 77,082 individuals.

The Danish Civil Registration System was established in 1986 and contains detailed information pertaining to sex, date of birth, parental links, and continuously updated information on vital status (e.g., migration or death) for all individuals alive and living in Denmark for the past seventy years31,32. The Danish National Patient Register includes the full medical records of all individuals treated at Danish hospitals (inpatient department) since January 1, 1977, as well as in outpatient clinics since 1 January 1994 (or occasionally since 1995)33. The register was updated in 2002 to also include individuals treated in hospitals outside of Denmark and treatments not covered under the Danish health insurance agreement at private healthcare facilities. Finally, the Danish Psychiatric Central Research Register contains data on admissions to psychiatric inpatient facilities up to and including 1994. Following 1994, the register was extended to include outpatient contacts in psychiatric departments34. As of April 2017, the civil register contained 9,851,330 individuals, the national patient registers 8,065,597 individuals, and the psychiatric register 1,005,068 individuals. All individuals were born between January 1, 1858, and April 21, 2017. All registers contained a unique personal identification number given to all individuals living in Denmark, therefore allowing for accurate linking across the different registers. By Danish law, informed consent is not required for register-based studies and no compensation was provided. This work is based on Danish register data that are not publicly available due to privacy protection, including the General Data Protection Regulation (GDRP). Only Danish research environments are granted authorisation. Foreign researchers can, however, get access to data under Danish research environment authorisation. Further information on data access can be found at https://www.dst.dk/en/TilSalg/Forskningsservice or by contacting the senior corresponding authors.

The Swedish Total Population Register (TPR), started in 1968 and continuously updated, holds information on all individuals who are residents of Sweden. It contains information on birth, death, name change, marital status, family relationships and migration within Sweden as well as to and from other countries41. Multi-Generation Register (MGR)42 is part of TPR and contains information on all residents in Sweden who were born in 1932 or later and alive in 1961 (index persons), together with their parents. Familial linkage (i.e., parental information) is available for more than 95% of individuals who died before 1968, about 60% of those died between 1968 and 1990, and more than 90% of those alive in 1991. The Swedish Inpatient Register was launched in 1964 (psychiatric diagnoses from 1973) but complete coverage was reached in 1987. It includes discharge diagnoses, dates of hospital admission and discharge, and has a coverage of at least 71% of all residents for somatic care discharge in 1982 and 86% of all psychiatric care in 1973. Since 2001, this register also covers outpatient43. The individually unique National Registration Number was used to link data from all the registers. All Swedish-born residents were followed for any cardiometabolic and mental disorders from birth until emigration or death from 1973 to 2016. By Swedish law, informed consent is not required for register-based studies and no compensation was provided. The use of Swedish data was approved by the regional ethics review board in Stockholm, Sweden with DNR 2012/1814-31/4. Data from Swedish registers are not available for sharing due to policies and regulations in Sweden. Swedish register data are available to all researchers through applications at Statistics Sweden (SCB, https://www.scb.se/en/) and The National Board of Health and Welfare (Socialstyrelsen, https://www.socialstyrelsen.se/)

By Danish and Swedish law, consent to use register data for register-based studies is not required.

We defined the six MDs, namely attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), autism spectrum disorders (ASD), affective disorders (AFF), bipolar disorder (BD), and schizophrenia (SCZ), and cardiometabolic disorders, using information from the Danish and Swedish Patient Register. Mental disorders were previously defined and used for GWAS analysis of iPSYCH 2012 data by Schork et al. 2019. These disorders represent the most well-documented, well-known and most common mental disorders occurring in the population. The cardiometabolic disorders were selected based on a.) common in the population i.e., high prevalence or b.) less common prevalence i.e., low prevalence and c.) selected disorder had available GWAS summary statistics in any publicly available repository. Note, that AFF includes two main diagnosis, BD and major depressive disorder. Individuals with at least one hospital visit concerning these disorders (primary or secondary diagnosis) were considered cases with MD or CMD. Individuals diagnosed with SCZ, BD, or AFF before age 10 were removed from the analysis, as the validity of such a diagnosis is considered clinically unreliable. ICD 8 codes were used until 1993 and ICD 10 codes were used since 1994 in Denmark; ICD 8 codes were used until 1986, ICD 9 codes were used during 19871996, and ICD 10 codes were used since 1997 in Sweden (Supplementary data9). To minimise the effect of left-handed censuring we removed individuals born outside of Denmark and Sweden as these individuals may have been diagnosed in another country. By doing so we excluded both Danish/Swedish citizens as well as individuals migrating to Denmark and Sweden. No information is recorded regarding terms such as race, ancestry, or ethnicity. However, both Denmark and Sweden are predominantly of white-European ancestry with relatively recent large migration patterns from non-European countries therefore we assume that we extracted mostly individuals of white-European ancestry and indirectly removed individuals of non-European ancestry when filtering on country of birth.

A total of 15 cardiometabolic GWAS summary statistics including stroke (subtypes)44, CAD45, aneurysms46 and HF47 were obtained through multiple public repositories. GWAS summary statistics containing participants of the VA Million Veterans Programme (e.g., T2D48, venous thromboembolism49, and peripheral artery disease50) were provided after approval was granted by the National Institute of Health (project #26508). GWAS summary statistics for ADHD51, AN52, ASD53, BD54, and MDD55 excluding iPSYCH participants (except SCZ56 which does not contain iPSYCH samples) were kindly provided through their respective PGC consortium. iPSYCH only GWAS summary statistics for MDs25 were downloaded from internal iPSYCH servers and are available on request. The full list of all cardiometabolic- and mental disorder GWAS summary statistics used is shown in Supplementary Data6.

All GWAS summary statistics were uniformly cleaned using internal software57. First, for each GWAS summary statistic, we inferred the genome build by mapping SNPs to dbSNP build 151 using GRCh38, GRCHh35, GRCh36, and GRCh37 genomic coordinates. The version with the highest number of mapped SNPs was inferred as the build of the original GWAS. Next, a second mapping step uses the inferred build to simultaneously map and liftover the position and chromosome coordinate to the GRCh37 version of dbSNP, which adds information about reference and alternative alleles. RSids were used when chromosome and base pair information were not available. The reference allele of dbSNP corresponds to the reference allele of the reference genome. The allele directions were flipped making the effect allele the reference allele. Effect scores (e.g., beta coefficients, odds ratios, and z-scores) were adjusted accordingly. Finally, multi-allelic, allele mismatched, and strand ambiguous SNPs alongside SNPs with duplicated positions, missing test statistics, and indels were removed57.

SNP based heritability (({{{{{{rm{h}}}}}}}_{{{{{{rm{SNP}}}}}}}^{2})) and genetic correlations (rg SNP) between all cleaned MD and CMD GWAS summary statistics were estimated using linkage-disequilibrium score regression (LDSC)58,59 version 1.0.1 using authors protocols.

We estimated the cumulative incidence of all MDs and CMDs, which can be interpreted as the number of cases happening before a specific age. The cumulative incidences were estimated for the general population, individuals with one or more full siblings diagnosed with the same disorder, and individuals with one or more parents diagnosed with the cross-disorder (e.g., the cumulative incidence of ADHD for individuals with at least one parent diagnosed with type-2 diabetes). We expected the distribution of individuals into these three categories to be associated with birth year. Thus, to control for substantial changes over time in the underlying incidence, diagnoses (e.g., shifting of ICD systems), data availability, and registration (e.g., use of inpatient diagnoses up to 1995/2000 and in- and out-patient diagnoses subsequently), all cumulative incidences were estimated stratifying on the year of birth using the Nelson-Aalen estimator, which can utilise censored, competing risks, and incomplete data19. Next, we estimated the additive heritability (h2) and genetic correlation (rg) under the liability threshold model based on the cumulative incidence as a function of pedigree relatedness following procedures described by Wray and Gottesman21,60,61. In short, the liability threshold model assumes that disease liability underlying the disease status is normally distributed, Z~N(0,1), and individuals with the disorder must therefore have surpassed a liability threshold62,63. Given the normal distribution theory, the liability threshold of a given disorder can be estimated from the population that are affected in their lifetime (lifetime risk). All analyses were done in R v4.2.1 using the cmprsk v2.2 package.

Using the full available register data (no restriction of birth year), the heritability of liability of disorders was calculated by deriving the general population- (e.g., risk of ADHD in the population) and full-sibling familial risk (e.g., risk of ADHD when having a full-sibling with ADHD) cumulative incidences for individuals born in the same calendar year (e.g., 1965, 1966, till 2016). Here, we use the cumulative incidence (general population and full-sibling risk) at the last observed time point as estimates of the proportion of the population born in the same calendar year that is affected in their lifetime resulting in estimates of heritability (Eqs.1 and 2) for individuals born in the same calendar year (({h}_{{year; of; birth}}^{2})).

$${{{{{rm{Heritability}}}}}},({{{{{{rm{h}}}}}}}^{2})=frac{T-{T}_{R}sqrt{left(1-left(1-T/iright)left({T}^{2}-{T}_{R}^{2}right)right)}}{{a}_{R}left(i+left(i-Tright){T}_{R}^{2}right)}$$

(1)

$${{{{{rm{s}}}}}}.{{{{{rm{e; }}}}}}left({{{{{{rm{h}}}}}}}^{2}right)=frac{1}{{a}_{R}}sqrt{left[frac{{K}^{2}}{{y}^{2}}{left(frac{1}{i}-{a}_{R}{h}^{2}left(i-Tright)right)}^{2}+frac{{K}_{R}^{2}}{{i}^{2}{y}_{R}^{2}}right]}$$

(2)

Where T=Liability threshold of the disease in the general population, TR=liability threshold of the disease based on affected family members, i=mean liability of disease in the population calculated as i=y/K; where K is the lifetime probability of disease in the population and y the height of the normal curve at threshold T, aR=additive genetic relationship between relatives, KR=the lifetime probability of disease in individuals with affected family members. Note that all estimates are derived for individuals born in the same calendar year.

In contrast to the h2 estimation, for the genetic correlation, we restricted the birth window to individuals born between 1981 and 2005, using medical records up to 2012. The rg between disorders was calculated by deriving: the general population risk for both disorders (e.g., ADHD and T2D) and parent-offspring cross disorder familial risk (e.g., risk of ADHD when having a parent with T2D) cumulative incidences for individuals born in the same calendar year (e.g., 1981,1982 till 2005). In line with the h2 estimation, we used the cumulative incidence at the last observed time point for each birth year for all three cumulative incidence functions (general population risk and cross-disorder familial risk). Using the h2 of both disorders previously obtained we derived estimates of genetic correlations (Eqs.3 and 4) per year of birth (({r}_{g,{year; of; birth}})).

$${{{{{rm{Genetic}}}}; {{{rm{correlation}}}}; }}({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}}})=frac{left(frac{{T}_{c}-{T}_{{R}_{c}}sqrt{1 , - , left(1 , - , {T}_{f}/{i}_{f}right)left({T}_{f}^{2} , - , {T}_{{R}_{c}}^{2}right)}}{{a}_{R}left({i}_{f}+left({i}_{f}-{T}_{f}right){T}_{{R}_{c}}^{2}right)}right)}{sqrt{{h}_{c}^{2}{h}_{f}^{2}}}$$

(3)

$${{{{{rm{s}}}}}}.{{{{{rm{e; }}}}}}left({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}}}right)=frac{frac{1}{{a}_{R}}sqrt{left[frac{{K}_{f}^{2}}{{y}_{f}^{2}}{left(frac{i}{{i}_{f}}-{a}_{R}{r}_{{cf}}{h}_{c}{h}_{f}left({i}_{f}-{T}_{f}right)right)}^{2}+frac{1}{{i}_{f}^{2}}left(frac{{K}_{{R}_{c}}^{2}}{{y}_{{R}_{c}}^{2}}+frac{{K}_{c}^{2}}{{y}_{c}^{2}}right)right]}}{sqrt{{h}_{c}^{2}{h}_{f}^{2}}}$$

(4)

Where Tc and Tf=liability threshold of disease c and f in the general population, ({T}_{{R}_{c}})=liability threshold of disease c in individuals with relatives with disease f, if=mean liability of disease f in the population, aR=additive genetic relationship between relatives, ({h}_{c}^{2}{{; and; h}}_{f}^{2})=heritability of diseases c and f, Kf is the lifetime probability of disease f in the general population. Note that all estimates arederived for individuals born in the same calendar year.

We obtain overall h2 and rg estimates by weighing the individual ({h}_{{year; of; birth}}^{2}) and ({r}_{g,{year; of; birth}}) by the inverse of their sampling variance (Eqs.5 and 6) using a random-effects model.

$${{{{{{rm{IVW}}}}}}}_{{{{{{rm{random}}}}}}}=frac{{sum }_{k=1}^{K}{hat{theta }}_{k} , {w}_{k}^{*}}{{sum }_{k=1}^{K}{w}_{k}^{*}};{{{{{{rm{w}}}}}}}_{{{{{{rm{k}}}}}}}^{*}=frac{1}{{s}_{k}^{2}+{r}^{2}}$$

(5)

$$s.e.; left({{{{{{rm{IVW}}}}}}}_{{{{{{rm{random}}}}}}}right) ,=, sqrt{frac{1}{{sum }_{k=1 , }^{K}{w}_{k}^{*}}}$$

(6)

Where K = numbers of estimates, ({s}_{k}^{2}) = variance of estimate k, r2 = the variance of the distribution of true effect sizes, ({hat{theta }}_{k}) = point estimate k, and ({w}_{k}^{*}) = random-effects weight.

Under a bivariate liability threshold model, the phenotypic correlation (rP) between two traits can be broken down to its (additive) genetic- and non-genetic factors. This allows us to quantify and understand the contribution of the estimated genetic correlation and heritability to the level of comorbidity between MDs and CMDs, i.e., hazard ratios) reported by Momen et al. 1 which uses the same Danish register data.

$${{{{{rm{Relative}}}}; {{{rm{risk}}}}}},left({{{{{rm{RR}}}}}}right)=frac{{1-{{{{{rm{e}}}}}}}^{left({{{{{rm{HR}}}}}}times log left(1-{{{{{rm{r}}}}}}right)right)}}{{{{{{rm{r}}}}}}}$$

(7)

Where HR =hazard ratio reported by Moment et al. 2020, and r = rate of the disorder in the reference group derived by weighting the individual estimates (1981-2005 using medical records up to 2012) by the inverse of their sampling variances.

$${{{{{rm{Odds}}}}; {{{rm{ratio}}}}}},left({{{{{rm{OR}}}}}}right)=frac{left(1-{{{{{rm{p}}}}}}right)times {{{{{rm{RR}}}}}}}{1-left({{{{{rm{RR}}}}}}times {{{{{rm{p}}}}}}right)}$$

(8)

Where p = incidence of the disorder in the nonexposed group (here p=r) and RR = the calculated relative risk.

$${{{{{rm{Phenotypic}}}}; {{{rm{correlation}}}}}},({r}_{p})=frac{{{{{{{rm{OR}}}}}}}^{frac{{{{{{rm{pi }}}}}}}{4}}-1}{{{{{{{rm{OR}}}}}}}^{frac{{{{{{rm{pi }}}}}}}{4}}+1}$$

(9)

Where OR = odds ratio estimated as a function of relative risk.

The phenotypic correlation can be expressed as the function of the genetic and non-genetic component

$${{{{{rm{Phenotypic}}}}; {{{rm{correlation}}}}}},left({r}_{p}right)={r}_{g}sqrt{{h}_{c}^{2}{h}_{f}^{2}}+{r}_{e}sqrt{left(1-{h}_{c}^{2}right)left(1-{h}_{f}^{2}right){{{{{rm{;}}}}}}}$$

$${{{{{rm{Genetic}}}}; {{{rm{component}}}}}},({{{{{rm{G}}}}}})={{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}}}times sqrt{{{{{{{rm{h}}}}}}}_{{{{{{rm{c}}}}}}}^{2} {times {{{{{rm{h}}}}}}_{{{{{rm{f}}}}}}^{2}}}$$

(10)

$${{{{{rm{Non}}}}}}-{{{{{rm{genetic}}}}; {{{rm{component}}}}}},({{{{{rm{E}}}}}})={r}_{p}-G$$

(11)

Where ({r}_{p}) = tetrachoric correlation derived from the HR, rg = the genetic correlation estimates and re the environmental correlation estimates between disorder c and f, h2 the heritability estimates for c and f. 95% CIs for G and E were derived using both the upper and lower 95% CIs of rg and rp. Note to estimate GSNP and ESNP replace rg and h2 estimates by SNP based estimates rg SNP and h2SNP.

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Quantifying the relative importance of genetics and environment on the comorbidity between mental and ... - Nature.com

We report genome-wide SNP data for nine individuals from the ST site Rostovka, new data for two BOO individuals, and shotgun genome data for five already published BOO individuals (Table1). We performed 1240k SNP22,23 and mitochondrial genome captures on the nine individuals from ROT, and the two new BOO individuals, as well as Y-chromosomal capture24 on just the males. Lastly, we generated shotgun sequence data for five published BOO individuals, including one 40 covered individual (Fig.1a, Table1, Supplementary Data1). Of the newly analyzed individuals, eight ROT individuals were genetically male and one was female, while both new BOO individuals were female. Biological relatedness among the newly reported individuals was estimated using READ25, Pairwise Mismatch Rate (PMR), KIN26, and lcMLkin27 (Supplementary Data25). Based on consistent results across these analyses, we identified a pair of second-degree relatives (ROT011 and ROT015), both of whom are males carrying Y-haplogroup C2a, and could either represent a grandson/grandparent, a nephew/uncle pair or paternal half-siblings, consistent with overlapping radiocarbon dates for both individuals (Table1). A second-degree related pair was also found among the BOO individuals (BOO004-BOO005).

We generated a radiocarbon date for individual BOO004, whose genome was shotgun sequenced to 40 coverage (Table1). The radiocarbon date (MAMS-57646) was determined to be 335125 BP, or 1735-1538 calBC (2) after calibration with OxCal 4.428, and 1504-1220 calBC (2) when correcting for a potential freshwater reservoir effect using the Marine 20 curve28. The corrected date is an approximation because we do not know the extent of fish consumption in the BOO individuals.

We used smartPCA29 to perform a principal component analysis (PCA) of modern-day reference populations from Eurasia and the Americas, onto which the ROT and the BOO individuals were projected (Fig.2a, b). When assessing the genetic structure of Eurasian populations, plotting PC1 vs. PC2 (Fig.2b) allows us to separate west and east Eurasian populations from the Native American groups, while plotting PC1 vs PC3 (Fig.2a) distinguishes the major Eurasian ecological zones30,31. When plotting PC1 vs PC2, the ANE ancestry cline becomes apparent including individuals from Afontova Gora, Malta1, Botai, West Siberian hunter-gatherers (WSHG), and others. ROT individuals vary along the ANE ancestry cline, while the BOO form a tight cluster within the variation seen at ROT. ROT and BOO individuals fall on the Eurasian PCA (PC1 vs PC3), mainly along a genetic cline of present-day populations that occupy the ecological forest-tundra zone (after Jeong et al. 31; Fig.2a), which coincides with the distribution of modern-day Uralic speaking groups and represents the Siberian ancestry variation. BOO individuals form a tighter and more homogeneous cluster, in the middle of the cline between Eastern_Siberia_LNBA and the EEHG, that can be seen with both the PCA and the ADMIXTURE analyses, in line with what has been previously reported17. By contrast, the ROT individuals are genetically more heterogenous and spread on a triangle (Fig.2b) between the Western Steppe Middle to Late Bronze Age cluster (e.g. Sintashta_MLBA32), Eastern_Siberia_LNBA and WSHG individuals, which is also visible in the results from unsupervised ADMIXTURE (k=10) (Fig.2c, Supplementary Fig.4).

a Principal component analysis plot with newly typed (colored symbols with black outline) and published (no outline) ancient individuals projected onto modern variation calculated using modern Eurasian and North American populations from AADR v44.371. Modern populations are shown as gray circles and modern Uralic speaking groups as open circles. Ancient reference individuals are listed under Published ancient data, and the new individuals are listed under This study. PC1 vs PC3 are plotted, which reveals three genetic clines (labeled in Italics) between Western and Eastern Eurasian populations; b PCA results for PC1 vs PC2; c Unsupervised ADMIXTURE results (k=10) of a representative subset of the relevant populations and sample names shown in the PCA plot. WSHG West-Siberian Hunter-Gatherers, EEHG Eastern European Hunter-Gatherers, WHG Western Hunter-Gatherers, LNBA Late Neolithic/Bronze Age, MLBA Middle/Late Bronze Age.

We performed Y-haplogroup (Y-hg) typing of the ROT males using the YMCA method24 (Table1) and identified two individuals who carry Y-hg R1a (ROT003: R1a-M417 and ROT016: R1a-Z645), one of the most widely distributed Y-hgs in Eurasia33. However, both individuals could be R1a-Z645, since ROT003 does not have coverage on either ancestral or derived ISOGG list SNPs downstream of R1a-M417. Generally, due to their geographic distribution, these R1a sub-lineages are thought to represent the eastward movement of Corded Ware-, and Fatyanovo-associated groups. ROT002, the individual with the highest proportion of north Siberian ancestry, was assigned to Y-hg N1a (N-L392). This Y-hg has also been found in two BOO individuals17. Lineage N-L392 is one of the most common in present-day Uralic populations which highlights the potential importance of Y-hg N-L392 in the dissemination of proto-Uralic. One of the male individuals (ROT004) was assigned to haplogroup Q1b (Q-M346), which is found throughout Asia, including in several Turkic speaking populations, e.g.,Tuvinians, Todjins, Altaians, Sojots, and the Mongolian-speaking Kalmyk population34. ROT017 carries Y-hg Q1b (Q-L53), which is also common among present-day Turkic speakers across Eurasia. The branch Q-YP4004 includes Central Asian Q-L53(xL54) lineages and one ancient Native American individual from Lovelock Cave in Nevada35, while the oldest Q-L53 individual is irk040 (Baikal Neolithic, 4846 BP)36. The lineage C2a-L1373, carried by ROT011, is found at high frequency in Central Asian populations, North Asia and the Americas. Lastly, ROT006 carries Y-hg R1b (R1b-M73), a sister-clade of R1b-M269, which is common in the Caucasus, Siberia, Mongolia, and Central Asia today34. Overall, the Y-hg lineage diversity of male ROT individuals is consistent with the heterogeneous nature of the ST37.

We also identified a large diversity in the mitochondrial haplogroups (mt-hg) among ROT (Table1), including mt-hgs that are found commonly in east Eurasia (A10, C1, C4, G2a1)38,39,40,41 and in west Eurasia (H1, H101, U5a, R1b, R1a)42,43. Consistently, the individual ROT002 with the highest affinity to Siberia_LNBA and carrying the Y-hg N-L392 also carries a mt-hg G2a1 commonly found in Eastern Eurasia. Analogously, individual ROT003 who carries Sintashta_MLBA-like ancestry and the Corded Ware-derived Y-hg R1a1a1, is also a carrier of the R1a1a mt-hg commonly found in west Eurasia.

We used F-statistics44 to formally assess the relationship of the ROT and BOO individuals with each other, and with different modern and ancient reference individuals and populations. First, we performed outgroup f3-statistics of the form f3(Mbuti; test, modern) to test for the affinity of each ROT and BOO individual with modern world-wide populations (Supplementary Fig.5, Supplementary Data6). The f3-statistics results mirror the distribution of the samples in the PCA and ADMIXTURE analyses, wherein the individuals with higher proportions of Eastern_Siberia_LNBA ancestry (e.g. ROT002) show a greater affinity to modern-day Siberian and Uralic-speaking populations, such as Nganasan, Evenk, Negidal, Nanai, and Ulchi (Supplementary Fig.5A), whereas the individuals with more Sintashta-like Western_Steppe_MLBA ancestry (e.g., ROT003) are closer to modern-day (North) Europeans, including Norwegian, Belarusian, Lithuanian, Scottish and Icelandic individuals (Supplementary Fig.5B). Comparisons with ancient groups/individuals using f3(Mbuti; test, ancient) showed a similar trend (Supplementary Fig.5). ROT002 on the eastern end of the Eurasian cline shares more genetic drift with Eastern_Siberia_LNBA, Russia Ust Belaya Neolithic, and Mongolia Early Iron Age individuals (Supplementary Fig.5A). By contrast, ROT003, the westernmost individual in the Eurasian PCA space, has the highest affinity to Lithuania early Middle Neolithic Narva, Russia Sintashta, Kazakhstan Georgievsky Middle Bronze Age, Russia Poltavka, and Serbia Mesolithic individuals (Supplementary Fig.5B). Similar trends can be observed for BOO, wherein the modern Uralic-speaking populations, such as Nganasan and Selkup, are among the tests with the highest f3- statistics. The ancient individuals most closely related to BOO areEEHG,WSHG, Botai and Tarim Early/Middle Bronze Age (EMBA) individuals carrying high levels of ANE ancestry (Supplementary Fig.5JR).

Based on the geographic location of the sites, we tested whether ROT and BOO individuals retained more local ANE ancestry compared to contemporaneous groups from similar general geographic area, time period, and archeological affiliation, using f4-statistics of the form f4(X, test; WSHG, Mbuti) where X stands for ROT and BOO individuals, and test populations include Okunevo, Tarim_EMBA_1, Sintashta_MLBA, and Eastern_Siberia_LNBA (Fig.3). This test allowed us to identify groups that form a clade with ROT and BOO, and cases where ROT and BOO may have additional affinity to ANE ancestry represented here by WSHG from Russia as the best spatial and temporal proxy. We find that ROT and BOO individuals carry excess affinity to ANE when compared to Eastern_Siberia_LNBA (Fig.3a) and Russia MLBA Sintashta (Fig.3c), except for ROT002 and ROT003. All BOO individuals are symmetrically related to the Okunevo Bronze Age group indicating no additional affinity to ANE (Fig.3b). However, we see more heterogeneity in ROT, with some individuals having significantly more, and others significantly less genetic affinity to WSHG compared to Okunevo (Fig.3b). All but one individual (ROT013) have significantly less ANE ancestry compared to Tarim EMBA (Fig.3d). The general observations from f4-statistics formally confirm the PCA results (Fig.2), where ROT individuals vary in their location with regards to WSHG, i.e., ANE ancestry affinity, while the BOO individuals are more homogeneous.

f4-statistics testing for excess WSHG ancestry in ROT and BOO individuals with respect to a Yakutia Lena 4780-2490 (Siberia_LNBA), b Okunevo, c Russia MLBA Sintashta, and d Tarim EMBA1. Significantly non-zero f4-statistics (|Z|>3) are shown in color, and non-significant f4-statistics are shown in gray. All error bars indicate 3 standard errors. X denotes the individuals given on the y-axis.

The genetic profile of BOO individuals is intriguing, when compared to present-day individuals of the same geographic area of Scandinavia and western Russia (Fig.2). However, the cultural affiliation of the BOO individuals remains poorly understood. Based on pairwise outgroup-f3-statistics with different ancient populations from Scandinavia, Anatolia_N, and Sintashta_MLBA, the BOO and ROT individuals separate from the rest of the ancient populations (Supplementary Fig.6). The f3- and f4-statistics together show a non-local genetic origin for the BOO individuals, with no substantial levels of early European farmer ancestry, which thus excludes contact with contemporary and genetic contribution towards subsequent Scandinavian groups.

Lastly, we performed qpAdm analysis to formally test for and quantify the admixture proportions of potential source populations for ROT and BOO individuals (Fig.4, Supplementary Data7). Here, we successfully modeled the ROT individuals as a mix of three sources (Eastern_Siberia_LNBA, Sintashta_MLBA, and WSHG), except for ROT002, which we modeled instead as a two-source mixture of mainly Eastern_Siberia_LNBA ancestry and a smaller proportion of EEHG-like ancestry that could be represented by either Sintashta_MLBA, WSHG, or EEHG, and ROT003 which we modeled with Sintashta_MLBA as single source (Fig.4b). We also tested whether ROT individuals could be modeled as a two-way mixture of the Eastern_Siberia_LNBA ancestry and either Sintashta_MLBA or WSHG as sources, however, this combination of ancestries did not result in consistently plausible model fits, compared to the combination of all three ancestries (Fig.4ac). By contrast, BOO individuals could not be modeled using either the combination of all three ancestry sources (Eastern_Siberia_LNBA, Sintashta_MLBA, and WSHG), or just a two-way mixture (Fig.4a, c, Supplementary Data7). However, replacing WSHG with EEHG as the putative local hunter-gatherer ancestry substrate and using Eastern_Siberia_LNBA as a second source provided good model fits (Fig.4d, Supplementary Data8). Importantly, all BOO individuals, except for BOO001, could also be modeled as a mixture of ROT002 and EEHG (Fig.4e, f, Supplementary Data8) suggesting, together with the results from the outgroup f3-statistics (Supplementary Fig.6), that BOO individuals may represent a subset of the diversity present in ROT.

a qpAdm models using Eastern Siberia LNBA, Russia MLBA Sintashta, and WSHG as sources; b Models with Eastern Siberia LNBA and Sintashta as sources; c Models with Eastern Siberia LNBA and WSHG as sources; d Models with Eastern Siberia LNBA and EEHG as sources; e Models with Eastern Siberia LNBA and EEHG; f Models with ROT002 and EEHG. Corresponding p-values for each analysis are shown to the right of each row. Models with p-values<0.05 are grayed out, and the models with negative ancestry proportions are indicated as Not feasible.

To investigate distant biological relatedness among the BOO individuals, we first imputed the genomes using GLIMPSE45 with the 1000G dataset46 as a reference panel (ROT individuals are below the required coverage threshold for imputation). Based on the identification of haplotype blocks of certain lengths that are shared between individuals, i.e. identical by descent47, we confirmed the above identified 2nd-degree related pair (BOO004-BOO005), and also found two third-degree related pairs (BOO003-BOO004 and BOO003-BOO005), as well as multiple pairs potentially related in the fourth-fifth-degree (Supplementary Data9). The observation that the BOO individuals are distantly related to each other explains the relative homogeneity seen in the sample compared to ROT. According to the archeological context, two pairs of biologically related individuals were buried in the same grave: third-degree related pair BOO003 (burial 16, sepulture 1, female) and BOO004 (burial 16, sepulture 3, male); and one 4th/5th-degree related pair BOO005 (burial 17, sepulture 3, female) and BOO009 (burial 17, sepulture 4, female)18.

We also tested for IBD sharing between BOO and published individuals who are broadly contemporaneous and geographically close, including Tarim_EMBA48, Okunevo42, Sintashta_MLBA32, EEHG49, Botai42, Yamnaya42, Easter_Siberia_LNBA36, and others (Fig.5a, Supplementary Data9). We found three shared IBD fragments (1422cM) between BOO individuals and Sintashta_MLBA individuals (Supplementary Data9), potentially suggesting shared ancestors as recent as approximately 500750 years, and most likely reflecting the shared EEHG ancestry that is present in both groups.

a IBD sharing between BOO and published data. Shared IBD chunks between 12 and 30cM in length are shown. The total IBD length shared is indicated by the color of the square, and population designation is shown on the y-axis. b HapROH output for BOO, ROT and relevant contemporaneous populations. Runs of homozygosity (ROH) are plotted by population for individuals with more than 400k SNPs on the 1240k panel. ROH segments are colored according to their binned lengths.

To investigate the underlying population structure, general parental background relatedness, and effective population sizes, we used HapROH50 to analyze runs-of-homozygosity (ROH) in the genomes of the BOO individuals together with a set of published individuals with more than 400k SNPs on the 1240k panel. We compared BOO to geographically and genetically close individuals from the Eurasian forest-tundra-steppe area, associated with Okunevo, Sintashta_MLBA, EEHG (UOO), Eastern_Siberia_LNBA, Tarim EMBA, and Fatyanovo cultures (Fig.5b). We also included two ROT individuals with more than 200k SNPs, but these results should be interpreted with caution. The ROH results of BOO individuals suggests that this early Metal Age group had a relatively small effective population size of ~2N=800, and one of the individuals (BOO006) appears to be an offspring of second cousins. Tarim EBMA, Okunevo, and Eastern_Siberia_LNBA groups also seemed to have relatively small effective population sizes, while Fatyanovo and Sintashta-associated groups potentially had larger effective population sizes (Fig.5b). In comparison, ROT individuals show similar ROH profiles to the populations they are closely related to, based on the PCA and F-statistics, i.e., ROT002 resembles the Eastern Siberian LNBA, and ROT017 the BOO individuals (Fig.5b).

High-coverage shotgun data from BOO004 allowed us to perform demographic modeling to investigate North Eurasian genetic ancestry and the nature of the admixture of the Eastern and Western Eurasian sources found in BOO individuals using a site-frequency spectrum (SFS) modeling-based method called momi251. We included published data from representative North Eurasian populations, both preceding and contemporaneous to BOO. We also used DATES v.75352 to estimate the date of the admixture event in BOO individuals between the EEHG and Eastern_Siberia_LNBA sources to be 17.981.06 generations ago, or around 500 calendar years prior to the mean radiocarbon date of BOO, assuming a generation time of 29 years53 (Supplementary Fig.7). This results in an approximate date of admixture ~4086 or ~3800 years ago when the marine reservoir correction is taken into account.

After an incremental build-up of our momi2 model (Supplementary Note4, Supplementary Data1012, Supplementary Tables16, Supplementary Figs.812) and including three admixture events, our final model estimated the split times between Africans (Yoruba, YRI) and Eurasians (Loschbour) 87,790 years ago (95% CI 85,25091,040), and between Western Eurasians (Loschbour) and Eastern Eurasians (CHB) at 53,010 years ago (95% CI 49,20055,540). The divergence between the lineage leading to the Eastern Siberia LNBA and CHB was found to be 21,580 years ago (95% CI 18,60024,810). We then modeled gene flow from the lineage leading to CHB to the EEHG at 9.4% (95% CI 4.4%14.7%). The effective population size Ne for Eastern Siberia LNBA was found to be 1690 (95% CI 13802020), and the population size for EEHG - 2470 (95% 19303790). The gene flow event from EEHG to East Siberian LNBA was modeled at 12.5% (95% CI 7.77%15.7%). These gene flow events are in line with the shared ANE ancestry history in both lineages. We estimated a recent admixture for BOO individuals (95% confidence interval (CI) 37784357 years ago), with substantial gene flow (39.8%; 95% CI 34.944.4%) from Eastern Eurasians (represented here by Eastern Siberia LNBA). Importantly, the mixture proportions are consistent with the results from qpAdm, and the date estimates overlap with those from DATES. The population size estimated for BOO (Ne=235, 95% CI 118441) from momi2 (Fig.6, Supplementary Data10) is at the smaller end of the estimate obtained from hapROH (2N between 400 and 800 individuals, Fig.6), which is likely an effect of momi2 not taking into account inbreeding via the analysis of the runs of homozygosity.

Momi2 demographic model for BOO004 using shotgun sequencing data from published ancient and modern individuals. Point estimates of the final model are shown in blue; results for 100 nonparametric bootstraps are shown in gray. The sampling times of populations are indicated by circles and population size estimates by the thickness of branches. The y-axis is linear below 10,000 years ago, and logarithmic above it. See Supplementary Data10 for specific parameter values. YRI Yoruban, CHB Han Chinese.

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Bronze age Northern Eurasian genetics in the context of development of metallurgy and Siberian ancestry ... - Nature.com

By Olivia Trani

Women are twice as likely as men to develop post-traumatic stress disorder, but the factors contributing to this disparity have largely remained unsettled. A research team led by Virginia Commonwealth University and Lund University in Sweden conducted the largest twin-sibling study of PTSD to date to shed light on how genetics may play a role. Their results, published Tuesday in the American Journal of Psychiatry, are the first to demonstrate that women have a higher genetic risk for the disorder compared with men.

By analyzing health data from over 16,000 twin pairs and 376,000 sibling pairs, the research team discovered that heritability for PTSD was 7 percentage points higher in women (35.4%) than in men (28.6%). They also found evidence that the genes that make up the heritable risk for PTSD vary between the two sexes.

The researchers say their findings could inform strategies for PTSD prevention and intervention following a traumatic event, as well as help address stigmas related to womens mental health.

Women are at higher risk for developing PTSD than men, even when controlling for the type of trauma, income level, social support and other environmental factors. Some of the theories as to why that is have frankly been unkind to women, such as attributing the sex difference to a weakness or lack of ability to cope, said Ananda B. Amstadter, Ph.D., a professor in the VCU School of Medicines departments of Psychiatry and Human and Molecular Genetics and lead author of the study. I think this study can help move the narrative that people can have an inherited biological risk for PTSD, and that this genetic risk is greater in women.

Nearly 70% of the global population are exposed to at least one traumatic event in their lifetime, such as physical or sexual assault, a motor vehicle accident, exposure to combat or a natural disaster. About 6% of those who are exposed to trauma develop PTSD. Amstadters research focuses on understanding the conditions that might increase or decrease a persons risk of experiencing PTSD, particularly how a persons genes impact their risk.

If you think of risk for PTSD like a pie chart, were trying to better understand what factors make up the pieces of this pie, she said. Some of the risk is influenced by a persons environment, such as the experiences they have while growing up. On the other hand, some of the risk will be influenced by the genes they inherit from their parents.

Previous research has looked into how genes influence the likelihood of developing PTSD, but the study conducted by Amstadter and her colleagues is the first of its kind to investigate how genetic risk varies by sex.

For this project, the research team examined anonymized clinical data from Swedish population-based registries. Their analysis consisted of more than 400,000 pairs of twins or siblings born up to two years apart in Sweden between 1955 and 1980. Studies on twins and siblings, because of their genetic similarities, can help researchers determine how a persons genes influence their risk for mental illnesses.

Every time a person within this age group interacts with Swedens health care system, whether thats visiting their primary care doctor, filling a prescription or going to the hospital, that information is recorded in their national registries. This kind of data is a really powerful tool for addressing questions related to genetic risk for medical conditions, Amstadter said. Prior PTSD studies involving twins and siblings have typically only included a few thousand individuals. Because our sample size was so large in comparison, we were able to make calculations with a higher degree of certainty.

Through statistical modeling, the researchers calculated how much a persons genetic makeup influenced their likelihood of developing PTSD following a traumatic event. In finding that PTSD was 35.4% heritable in women but only 28.6% heritable in men, they demonstrated that women have a higher biological risk for PTSD.

Their models also revealed that the genes associated with PTSD were highly correlated (0.81) but not entirely the same between men and women. This suggests that the genetic underpinnings of sex hormones, like testosterone, estrogen and progesterone, may be involved in the development of PTSD. The research team is collaborating with the Psychiatric Genomics Consortium to identify the molecular genetic variants that may contribute to sex-specific pathways of risk.

Amstadter conducted the research at the Virginia Institute for Psychiatric and Behavioral Genetics at VCU alongside co-authors Shannon Cusack, Ph.D., a postdoctoral scholar; and Kenneth Kendler, M.D., the institutes director, professor of psychiatry and eminent scholar. They collaborated with Lund University co-authors Sara Lnn, Ph.D.; Jan Sundquist, M.D., Ph.D.; and Kristina Sundquist, M.D., Ph.D.

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Women have a higher genetic risk for PTSD according to study by VCU and Swedish researchers - VCU Health