Category Archives: Genetics

June is National Mens Health Month and the Greater Chattanooga Colon Cancer Foundation (GCCCF) wants to remind everyone that regular colonoscopy screening begins at age 45 unless you have symptoms, a family history, or a genetic risk for colon cancer.

According to the NIH, 5-10% of colorectal cancers are hereditary, and Hamilton County is home to several cancer genetic counselors who help individuals and families learn about their risk factors for developing cancer. Additionally, the Genetic Information Nondiscrimination Act (GINA) of 2008 protects Americans from discrimination based on their genetic information in both health insurance (Title I) and employment (Title II).

GCCCF Board member, Madison Thomason, is a Licensed, Certified Genetic Counselor at CHI Memorial Rees Skillern Cancer Institute. She specializes in oncology and gathers individual and family medical histories to assess the likelihood of a hereditary link and to determine if genetic testing is appropriate

Genetic testing is done by saliva or blood, if someone is deemed appropriate for testing, so not very invasive, said Thomason. Once we have the results, we interpret them in the context of an individuals personal and family histories and determine a plan which could include earlier screenings and/or more frequent screenings. Someone with a genetic risk for colon cancer might start their colonoscopies before age 45, the starting age for someone at average risk. They may also need to have them more often, perhaps as frequently as every 1 or 2 years. In some cases, a preventative surgery or an oral medication may be recommended to lower the chance for cancer. These can be life-saving measures.

Genetic counseling can help people better understand their risk for colon cancer and empower them to take steps to either prevent colon cancer altogether or catch it at an early stage.

The GCCCF works with community partners to raise awareness, provide education and support, and accelerate community understanding that colon and rectal cancers are preventable, treatable, and beatable. To learn more visit http://www.gcccf.org

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Genetics And Colorectal Cancer: When Is The Right Time For A Colonoscopy? - Chattanooga Pulse

On National DNA Day (April 25) this year, NHGRI released a fully revamped version of its popular talking glossary, which included a new name: the Talking Glossary of Genomic and Genetic Terms. First launched in 1998, the talking glossary is one of the most visited sites on genome.gov. Some of the talking glossarys terms receive over 70,000 views per month. This resource is filled with definitions, audio recordings, and illustrations for hundreds of terms. It is one of the premier educational resources offered by NHGRI, and it aims to help users better understand the basics of genomics and genetics.

While reformatted and expanded a few times over the years, the glossarys content had not been comprehensively updated and refined since its creation. The talking glossary targets students, educators, policymakers, healthcare professionals, and the public. Users of the glossary can obtain concise, up-to-date, and accurate definitions, which are presented in a clear, accessible language and a user-friendly format. Terms are defined in both written and oral formats, including a Spanish translation.

To date, the new Talking Glossary of Genomic and Genetic Terms has 222 terms, of which 142 have new accompanying illustrations and 15 have new associated animations. The new color illustrations strive to be clear and concise in a fashion that best conveys the term. Animations are included when it helps the understanding of the term. All of the animations and graphics are readily downloadable for use in other contexts.

The redesigned layout of the talking glossary on genome.gov is based on analytical data and user experience, with the aim of improving performance and reducing user navigation steps. In addition to the ability to browse terms, there is a quick search feature. To help the user better engage with the content, related terms are suggested with each term, and popular terms are featured prominently throughout the resource. The new layout is also designed for cross-browser and cross-device usage.

This new and improved educational resource now features higher sound quality audio recordings from 37 NHGRI experts who served as narrators. In addition to providing the pronunciation and definition of each term, the narrator in each case provides a personal commentary (or riff) about the term. As expected, the glossary now contains more modern terms that represent recent advances in genomics. In addition, there are several relevant terms related to social science. In developing the updated talking glossary, an effort was made to eliminate stigmatizing language from any of the definitions.

Because the talking glossary is one of the most popular educational resources on genome.gov, NHGRI is committed to keeping it current. Additional terms, illustrations, and animations will be added in the coming months.

The NHGRI Talking Glossary of Genomic and Genetic Terms is only one of a collection of remote-learning resources to help make genomics and genetics more accessible and understandable to a wide array of audiences. For additional materials, see all of NHGRIs educational resources.

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NHGRI releases new and improved Talking Glossary of Genomic and Genetic Terms - National Human Genome Research Institute

A toddler with a genetic condition so rare that only two other kids in the United Kingdom have been diagnosed with it is having her hair shaved off for her own safety.

Sydney Miller, 2, is described by her mom as just like a newborn baby due to Primrose syndrome, which impacts only 50 children around the world.

Mom-of-five Stacie Miller, 36, said Sydney showed no signs of being any different from other babies when she was born March 18, 2020, just before the first pandemic lockdown.

The first sign that something was up was that Sydney could not open one of her eyes, prompting fears she may have been born without an eyeball.

She was diagnosed with Horner syndrome, where one eye is blue and one is brown, and an indicator of an underlying condition, which sparked a string of appointments as doctors tried to work out what was wrong.

A doctor who treated Sydney had been to a seminar on Primrose syndrome, which is so rare that even health care professionals have to read about it on Google.

Sydney is non-verbal and her parents do not know if she will ever talk.

In January, she was given two hearing aids because her inner ear has not fully developed, meaning she has hearing loss.

She cannot crawl or pull herself up, so mom Stacie has to carry her around, but she only weighs 20 pounds, as much as a 6-month-old baby. Stacie said children with the syndrome bear a physical resemblance to each other in a way similar to Down syndrome.

Stacie and husband Stuart, 47, are self-employed and run a wedding car business in Dunbar, East Lothian, Scotland.

Sydney is now starting to attend an additional-needs nursery where she has an occupational therapist and a physiotherapist on-site and health care workers around.

Stacie said the family is unsure what the future holds due to the rarity of the illness, which was initially feared to be cancer.

Stacie said: "When Sydney was born she didn't look any different to any of my other children.

"As the weeks went on she wouldn't open one of her eyes and it was thought it might be a blocked tear duct.

"Then they thought she might be missing an eyeball.

"She was diagnosed with Horner Syndrome which means one eye is blue and one is brown and it makes her sweat on one side of her body, sometimes she will be red and white.

"She was sent for an MRI scan which ruled out neuroblastama, then they started doing genetic testing.

"A genetics doctor who had seen her had been at a seminar on Primrose Syndrome, and he said before the tests came back that he thought that was what it was.

"It is very very rare, there are only 50 children with it worldwide."

Due to a pituitary cyst, Sydney may need hormone therapy when she gets older.

Stacie added: "She is non-verbal and we don't know if she will be able to talk.

"She can't crawl and she can't stand.

"There is so little research into Primrose Syndrome because it is such a rare illness, the main characteristic is muscle wastage and scoliosis and autism."

The couple realized Sydney's hair was posing a danger to the child when Stacie took a photo of her and realized there were bald patches on her head.

Stacie began to notice Sydney was pulling strands of hair from her scalp, which later came out in clumps.

She then began chewing it and it became entangled in the feeding button she has in her tummy.

Her hair will soon be shaved off to stop the risk of choking, amid hopes she will grow out of the desire to pull it out - described as "looking for feedback," which also includes high-pitched screaming and banging her heels until they are bruised.

Sydney has an unusually high pain threshold, and when her full set of teeth emerged at 6 months old, she wasn't bothered by teething.

However, she was going through a pacifier a day as she couldn't stop munching on them, and her parents feared they posed a choking risk.

Her full diagnosis is of Primrose syndrome, Horners syndrome, Harlequin syndrome, partial agenisis of the corpus collosum (brain undeveloped before birth), pituitary cyst, macrocephaly (abnormally large head), bilateral hearing loss mild/moderate, global development delay, and non-verbal and non-mobile.

Sydney requires gastrostomy, or G-button feeding.

Stacie said: "She is really happy, she never cries unless she is really unwell.

"When she smiles it lights up the room.

"It is hard to predict because we don't know what to expect.

"All the health professionals have had to Google Primrose Syndrome so we are very limited with what we actually know, it was only discovered in the 80s."

Sydney was treated at the Edinburgh Sick Kids Hospital, and will start nursery school when she is 3.

Stacie said: "Covid was really tough as only one parent was allowed in hospital and I was trying to feed back to Sydney's dad what I'd been told."

She said the family includes Sydney in all their activities, and will be taking her to Turkey on vacation in July.

She has been to the cinema and to music festivals, along with siblings Carson, 7, Pree, 12, Bradley, 14, and Mackenzie, 15.

They are looking forward to being able to move to a bigger house, as the three-bed home where they live is cramped with Sydney's equipment and has 21 steps in front.

Stacie said: "If she had had a better-known condition it would have been easier.

"Nobody knows day to day what her needs will be.

"She's very small for a child her age and she curls up, she's like a newborn baby."

The family is fundraising for another toddler, Flora Gentleman, 3, who needs cancer treatment abroad.

Stacie said: "Everybody is so interested in Sydney, she's got so many followers on Facebook.

"I've set up a Facebook just for her so people can keep up to date with her condition and treatments."

This story was provided to Newsweek by Zenger News.

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Toddler Has Genetic Condition So Rare That Doctors Had to Google It - Newsweek

BOSTON and LAUSANNE, Switzerland, June 1, 2022 /PRNewswire/ -- SOPHiA GENETICS (Nasdaq: SOPH), a leader in data-driven medicine, today announced three abstracts accepted for poster presentation and one for online publication at the 2022 American Society of Clinical Oncology (ASCO) Annual Meeting taking place June 3-7 in Chicago. SOPHiA GENETICS and GE Healthcare will also be hosting an Innovation Symposium on Monday, June 6th from 6:30 8:00 pm to present how the companies are working together to deliver on the promise of integrated cancer medicine by bringing global insights across multiple diagnostic modalities to clinical and biopharma customers.

"These high-impact ASCO contributions from SOPHiA GENETICS and collaborators demonstrate how our multimodal technology and solutions help drive novel insights and enhance oncology discoveries," said Dr. Philippe Menu, Chief Medical Officer at SOPHiA GENETICS. "By utilizing our data-driven medicine approach and by applying our AI and machine learning algorithms to real-world multimodal data sets, SOPHiA GENETICS has the potential to help inform treatment decisions at the individual patient level for cancer patients globally. I am really excited to attend ASCO to share how our mission to democratize data-driven medicine is helping transform cancer care."

An overview of the four accepted SOPHiA GENETICS abstracts at ASCO 2022 are included below. The full abstracts will be published in the Meeting Proceedings, an online supplement of the Journal of Clinical Oncology.

About SOPHiA GENETICSSOPHiA GENETICS (Nasdaq: SOPH) is a health care technology company dedicated to establishing the practice of data-driven medicine as the standard of care and for life sciences research. It is the creator of the SOPHiA DDM Platform, a cloud-based SaaS platform capable of analyzing data and generating insights from complex multimodal data sets and different diagnostic modalities. The SOPHiA DDM Platform and related solutions, products and services are currently used by more than 790 hospital, laboratory, and biopharma institutions globally. For more information, visit SOPHiAGENETICS.COM, or connect on Twitter, LinkedIn and Instagram. Where others see data, we see answers.

SOPHiA GENETICS products are for Research Use Only and not for use in diagnostic procedures, unless specified otherwise. 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.

SOURCE SOPHiA GENETICS

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SOPHiA GENETICS Announces Three Poster Presentations and One Online Publication Accepted at the 2022 American Society of Clinical Oncology Annual...

Colorado State University researchers are partnering with the American Hereford Association to support cattle producers and the beef industry in finding sustainable solutions to environmental and economic challenges.

The new research aims to enhance understanding of genetic differences in seedstock relative to enteric methane production and nitrogen excretion while identifying selection tools that can help reduce beef's carbon and environmental footprint.

"We're excited to begin this cooperative research agreement with Colorado State University,"says Jack Ward, executive vice president of the American Hereford Association, one of the largest beef breed associations in the United States. "It leverages decades of research and data collected by AHA members aimed at characterizing genetics associated with production efficiency, which plays a key role in environmental and economic sustainability."

Environmental and economic challengesDirect emissions from the animal agriculture sector account for 3.8% of U.S. greenhouse gas emissions, according to the Environmental Protection Agency. Enteric methane accounts for approximately 27% of methane emissions in the U.S.

Methane emission, as a genetic trait in cattle, appears to be moderately heritable with genetic correlations to economically relevant production traits, such as measures of growth, dry matter intake and various estimates of feed efficiency.

Worldwide attention is also focusing more intently on nitrogen a byproduct of rumen fermentation. Previous research suggests genetics play a significant role in nitrogen excretion by cattle, and when selected for, an individual animal's environmental footprint can be reduced.

"We know genetic improvement of our industry is driven by gains made in the seedstock sector. One only needs to look at changes in carcass meat yield and quality over the last two decades to realize the potential for improvements in seedstock genetics to transform the entire beef industry,"says Animal Sciences Professor Mark Enns, a beef cattle geneticist and key member of the research team.

Sustainable solutions"Often, we hear criticism leveled at the beef industry regarding greenhouse gas emissions and the impact of cattle on the environment, but with little context,"Enns says. "Cattle also sequester carbon and contribute to environmental health. This project will contribute to the beef cattle industry's goal of demonstrating carbon neutrality by 2040."

Given the Hereford breed's inherent genetic advantages associated with production efficiency, Ward says documenting the relationship between traits associated with efficiency and greenhouse gas emissions is logical next step for the breed and the industry.

"Beef industry stakeholders including the National Cattlemen's Beef Association have committed to improving the environmental impact of U.S. cattle production. This project aims to develop a selection tool for the American Hereford Association and the broader cattle industry that helps producers identify genetics that will have reduced greenhouse gas emissions without sacrificing animal productivity,"says Kim Stackhouse-Lawson, director of CSU AgNext, a pioneering research collaborative developing sustainable solutions for animal agriculture.

By leveraging existing animal performance data and monitoring animal emissions, Stackhouse-Lawson explains the goal is to identify genetic traits that influence environmental emissions from individual animals and then develop selection indices that can be used to reduce the environmental impact of cattle, while maintaining, and ideally improving economic returns to producers.

"This project will also position the American Hereford Association as a sustainability leader in the beef industry through the development of genetic selection tools that can identify and inform breeders of genetics that meet climate goals without sacrificing quality, performanceand efficiency," says Stackhouse-Lawson.

Further, Enns notes the project has potential to pave new paths of revenue for cattle producers. These could include such things as verified sustainable production claims, in addition to commonly discussed carbon credits.

Supporting the beef industryThe U.S. beef cattle industry has a long history of demonstrating extraordinary gains in efficiency over time, using genetics, technology and management to produce more beef with fewer cows and less land.

"This research will help us identify ways to magnify the gains the industry has already achieved," Ward says.

"CSU is involved in this project because we are passionate about beef production and the beef industry, and the societal benefits it brings from the upcycling of human-inedible plant materials and byproducts into high-quality protein,"Enns says. "From a genetic improvement standpoint, CSU has a long history of new trait development and delivery of selection tools to the industry. As such, we feel we have much to contribute in this realm, striving to produce cattle that meet consumer demands, yet have a smaller environmental footprint."

Source: Colorado State University, whichis solely responsible for the information provided, and wholly owns the information. Informa Business Media and all its subsidiaries are not responsiblefor any of the contentcontained in this information asset.

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CSU partners with American Hereford Association on genetics research - Beef Magazine

Taller people have an increased risk of peripheral neuropathy, as well as skin and bone infections, but a lower risk of heart disease, high blood pressure and high cholesterol, according to the worlds largest study of height and disease.

A persons height raises and reduces their risk of a variety of diseases, according to the research led by Sridharan Raghavan of the Rocky Mountain Regional VA Medical Center in the US. The findings are published in the journal PLOS Genetics.

Height has been a factor associated with multiple common conditions, ranging from heart disease to cancer. But scientists have struggled to determine whether being tall or short is what puts people at risk, or if factors that affect height, such as nutrition and socioeconomic status, are actually to blame.

In the study, researchers set out to remove these confounding factors by looking separately at connections between various diseases and a persons actual height, and connections to their predicted height based on their genetics.

The team used data from the VA Million Veteran Program, including genetic and health information from more than 200,000 white adults and more than 50,000 black adults. The study looked at more than 1,000 conditions and traits, making it the largest study of height and disease to date.

The results confirmed previous findings from smaller studies that being tall is linked to a higher risk of atrial fibrillation and varicose veins, and a lower risk of coronary heart disease, high blood pressure and high cholesterol.

Researchers also uncovered new associations between being taller and a higher risk of peripheral neuropathy, which is caused by damage to nerves on the extremities, as well as skin and bone infections such as leg and foot ulcers.

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The researchers now believe height may be a previously unrecognised risk factor for several common diseases. However, they cautioned that more studies were needed to clarify some of the findings, and future work would benefit from studying a more diverse international population.

We found evidence that adult height may impact over 100 clinical traits, including several conditions associated with poor outcomes and quality of life peripheral neuropathy, lower extremity ulcers, and chronic venous insufficiency, said Raghavan. We conclude that height may be an unrecognised non-modifiable risk factor for several common conditions in adults.

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More skin infection, less heart disease: study reveals how being tall affects health - The Guardian

ITEM 5.02 Departure of Directors or Certain Officers; Election of Directors;Appointment of Certain Officers; Compensatory Arrangements of Certain Officers.

(b) and (c)

(e)

ITEM 5.07 Submissions of Matters to a Vote of Security Holders

The following is a brief description of each matter submitted to a vote at theAnnual Meeting, as well as the number of votes cast for and against and thenumber of abstentions and broker non-votes with respect to each matter.

Proposal No. 1: Election of Directors

Proposal No. 3: Approval, on an Advisory Basis, of the Compensation of Our NamedExecutive Officers, as Disclosed in the Proxy Statement

--------------------------------------------------------------------------------

ITEM 9.01 Financial Statements and Exhibits.

(+) Management contract or compensatory plan arrangement

--------------------------------------------------------------------------------

Edgar Online, source Glimpses

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MYRIAD GENETICS INC : Change in Directors or Principal Officers, Submission of Matters to a Vote of Security Holders, Financial Statements and...

1. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation  Nature.com
2. A Nutritious Diet May Reduce Diabetes Risk, Regardless of Genetics  Verywell Health
3. Worldwide analysis 'a major step' in understanding genetics of type 2 diabetes  Medical Economics
4. Huge study of diverse populations advances understanding of type 2 diabetes  Science Daily
5. CCMB research shows population- specific genetic susceptibility to Type-2 Diabetes  The Hindu
6. View Full Coverage on Google News

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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation - Nature.com

DNM filtering in 100,000 Genomes Project

We analysed DNMs called in 13,949 parentoffspring trios from 12,609 families from the rare disease programme of the 100,000 Genomes Project. The rare disease cohort includes individuals with a wide array of diseases, including neurodevelopmental disorders, cardiovascular disorders, renal and urinary tract disorders, ophthalmological disorders, tumour syndromes, ciliopathies and others. These are described in more detail in previous publications60,61. The cohort was whole-genome sequenced at around 35 coverage and variant calling for these families was performed through the Genomics England rare disease analysis pipeline. The details of sequencing and variant calling have been previously described61. DNMs were called by the Genomics England Bioinformatics team using the Platypus variant caller62. These were selected to optimize various properties, including the number of DNMs per person being approximately what we would expect, the distribution of the VAF of the DNMs to be centred around 0.5 and the true positive rate of DNMs to be sufficiently high as calculated from examining IGV plots. The filters applied were as follows:

Genotype is heterozygous in child (1/0) and homozygous in both parents (0/0).

Child read depth (RD)>20, mother RD>20, father RD>20.

Remove variants with >1 alternative read in either parent.

VAF>0.3 and VAF<0.7 for child.

Remove SNVs within 20bp of each other. Although this is probably removing true MNVs, the error mode was very high for clustered mutations.

Removed DNMs if child RD>98 (ref. 14).

Removed DNMs that fell within known segmental duplication regions as defined by the UCSC (http://humanparalogy.gs.washington.edu/build37/data/GRCh37GenomicSuperDup.tab).

Removed DNMs that fell in highly repetitive regions (http://humanparalogy.gs.washington.edu/build37/data/GRCh37simpleRepeat.txt).

For DNM calls that fell on the X chromosome, these slightly modified filters were used:

For DNMs that fell in PAR regions, the filters were unchanged from the autosomal calls apart from allowing for both heterozygous (1/0) and hemizygous (1) calls in males.

For DNMs that fell in non-PAR regions the following filters were used:

For males: RD>20 in child, RD>20 in mother, no RD filter on father.

For males: the genotype must be hemizygous (1) in child and homozygous in mother (0/0).

For females: RD>20 in child, RD>20 in mother, RD>10 in father.

To identify individuals with hypermutation in the DDD study, we started with exome-sequencing data from the DDD study of families with a child with a severe, undiagnosed developmental disorder. The recruitment of these families has been described previously63: families were recruited at 24 clinical genetics centres within the UK National Health Service and the Republic of Ireland. Families gave informed consent to participate, and the study was approved by the UK Research Ethics Committee (10/H0305/83, granted by the Cambridge South Research Ethics Committee, and GEN/284/12, granted by the Republic of Ireland Research Ethics Committee). Sequence alignment and variant calling of SNVs and indels were conducted as previously described. DNMs were called using DeNovoGear and filtered as described previously12,64. The analysis in this paper was conducted on a subset (7,930 parentoffspring trios) of the full current cohort, which was not available at the start of this research.

In the DDD study, we identified 9 individuals out of 7,930 parentoffspring trios with an increased number of exome DNMs after accounting for parental age (7-17 exome DNMs compared to an expected number of ~2). These were subsequently submitted along with their parents for PCR-free whole-genome sequencing at >30x mean coverage using Illumina 150bp paired end reads and in house WSI sequencing pipelines. Reads were mapped with bwa (v0.7.15)65. DNMs were called from these trios using DeNovoGear64 and were filtered as follows:

Child RD>10, mother RD>10, father RD>10.

Alternative allele RD in child of >2.

Filtered on strand bias across parents and child (p-value>0.001, Fishers exact test).

Removed DNMs that fell within known segmental duplication regions as defined by the UCSC (http://humanparalogy.gs.washington.edu/build37/data/GRCh37GenomicSuperDup.tab).

Removed DNMs that fell in highly repetitive regions (http://humanparalogy.gs.washington.edu/build37/data/GRCh37simpleRepeat.txt).

Allele frequency in gnomAD<0.01.

VAF<0.1 for both parents.

Removed mutations if both parents have >1 read supporting the alternative allele.

Test to see whether VAF in the child is significantly greater than the error rate at that site as defined by error sites estimated using Shearwater66.

Posterior probability from DeNovoGear>0.00781 (refs. 12,64).

Removed DNMs if the child RD>200.

After applying these filters, this resulted in 1,367 DNMs. All of these DNMs were inspected in the Integrative Genome Viewer67 and removed if they appeared to be false-positives. This resulted in a final set of 916 DNMs across the 9 trios. One out of the nine had 277 dnSNVs genome wide, whereas the others had expected numbers (median, 81 dnSNVs).

To phase the DNMs in both 100kGP and DDD, we used a custom script that used the following read-based approach to phase a DNM. This first searches for heterozygous variants within 500bp of the DNM that was able to be phased to a parent (so not heterozygous in both parents and offspring). We next examined the reads or read pairs that included both the variant and the DNM and counted how many times we observed the DNM on the same haplotype of each parent. If the DNM appeared exclusively on the same haplotype as a single parent then that was determined to originate from that parent. We discarded DNMs that had conflicting evidence from both parents. This code is available on GitHub (https://github.com/queenjobo/PhaseMyDeNovo).

To assess the effect of parental age on germline-mutation rate, we ran the following regressions on autosomal DNMs. These and subsequent statistical analyses were performed primarily in R (v.4.0.1). On all (unphased) DNMs, we ran two separate regressions for SNVs and indels. We chose a negative binomial generalized linear model (GLM) here as the Poisson was found to be overdispersed. We fitted the following model using a negative Binomial GLM with an identity link where Y is the number of DNMs for an individual:

E(Y)=0+1paternal age+2maternal age

For the phased DNMs we fit the following two models using a negative binomial GLM with an identity link where Ymaternal is the number of maternally derived DNMs and Ypaternal is the number of paternally derived DNMs:

E(Ypaternal)=0+1paternal age

E(Ymaternal)=0+1maternal age

To identify individuals with hypermutation in the 100kGP cohort, we first wanted to regress out the effect of parental age as described in the parental age analysis. We then looked at the distribution of the studentized residuals and then, assuming these followed a t distribution with N3 degrees of freedom, calculated a t-test P value for each individual. We took the same approach for the number of indels except, in this case, Y would be the number of de novo indels.

We identified 21 individuals out of 12,471 parentoffspring trios with a significantly increased number of dnSNVs genome wide (P<0.05/12,471tests). We performed multiple quality control analyses, which included examining the mutations in the Integrative Genomics Browser for these individuals to examine DNM calling accuracy, looking at the relative position of the DNMs across the genome and examining the mutational spectra of the DNMs to identify any well-known sequencing error mutation types. We identified 12 that were not truly hypermutated. The majority of false-positives (10) were due to a parental somatic deletion in the blood, increasing the number of apparent DNMs (Supplementary Fig. 7). These individuals had some of the highest numbers of DNMs called (up to 1,379 DNMs per individual). For each of these 10 individuals, the DNM calls all clustered to a specific region in a single chromosome. In this same corresponding region in the parent, we observed a loss of heterozygosity when calculating the heterozygous/homozygous ratio. Moreover, many of these calls appeared to be low-level mosaic in that same parent. This type of event has previously been shown to create artifacts in CNV calls and is referred to as a loss of transmitted allele event68. The remaining two false-positives were due to bad data quality in either the offspring or one of the parents leading to poor DNM calls. The large number of DNMs in these false-positive individuals also led to significant underdispersion in the model so, after removing these 12 individuals, we reran the regression model and subsequently identified 11 individuals who appeared to have true hypermutation (P<0.05/12,459tests).

Mutational signatures were extracted from maternally and paternally phased autosomal DNMs, 24 controls (randomly selected), 25 individuals (father with a cancer diagnosis before conception), 27 individuals (mother with a cancer diagnosis before conception) and 12 individuals with hypermutation that we identified. All DNMs were lifted over to GRCh37 before signature extraction (100kGP samples are a mix of GRCh37 and GRCh38) and, through the liftover process, a small number of 100kGP DNMs were lost (0.09% overall, 2 DNMs were lost across all of the individuals with hypermutation). The mutation counts for all of the samples are shown in Supplementary Table 1. This was performed using SigProfiler (v.1.0.17) and these signatures were extracted and subsequently mapped on to COSMIC mutational signatures (COSMIC v.91, Mutational Signature v.3.1)19,40. SigProfiler defaults to selecting a solution with higher specificity than sensitivity. A solution with 4 de novo signatures was chosen as optimal by SigProfiler for the 12 individuals with germline-hypermutated genomes. Another stable solution with five de novo signatures was also manually deconvoluted, which has been considered as the final solution. The mutation probability for mutational signature SBSHYP is shown in Supplementary Table 3.

We compared the extracted signatures from these individuals with hypermutation with a compilation of previously identified signatures caused by environmental mutagens from the literature. The environmental signatures were compiled from refs. 24,51,52. Comparison was calculated as the cosine similarity between the different signatures.

We compiled a list of DNA-repair genes that were taken from an updated version of the table in ref. 69 (https://www.mdanderson.org/documents/Labs/Wood-Laboratory/human-dna-repair-genes.html). These can be found in Supplementary Table 4. These are annotated with the pathways that they are involved with (such as nucleotide-excision repair, mismatch repair). A rare variant is defined as those with an allele frequency of <0.001 for heterozygous variants and those with an allele frequency of <0.01 for homozygous variants in both the 1000 Genomes as well as across the 100kGP cohort.

The A135T variant of MPG was generated by site-directed mutagenesis and confirmed by sequencing both strands. The catalytic domain of WT and A135T MPG was expressed in BL21(DE3) Rosetta2 Escherichia coli and purified as described for the full-length protein70. Protein concentration was determined by absorbance at 280nm. Active concentration was determined by electrophoretic mobility shift assay with 5-FAM-labelled pyrolidine-DNA48 (Extended Data Fig. 8). Glycosylase assays were performed with 50mM NaMOPS, pH7.3, 172mM potassium acetate, 1mM DTT, 1mM EDTA, 0.1mgml1 BSA at 37C. For single-turnover glycosylase activity, a 5'-FAM-labelled duplex was annealed by heating to 95C and slowly cooling to 4C (Extended Data Fig. 9). DNA substrate concentration was varied between 10nM and 50nM, and MPG concentration was maintained in at least twofold excess over DNA from 25nM to 10,000nM. Samples taken at timepoints were quenched in 0.2M NaOH, heated to 70C for 12.5min, then mixed with formamide/EDTA loading buffer and analysed by 15% denaturing polyacrylamide gel electrophoresis. Fluorescence was quantified using the Typhoon 5 imager and ImageQuant software (GE). The fraction of product was fit by a single exponential equation to determine the observed single-turnover rate constant (kobs). For Hx excision, the concentration dependence was fit by the equation kobs=kmax[E]/(K1/2+[E]), where K1/2 is the concentration at which half the maximal rate constant (kmax) was obtained and [E] is the concentration of enzyme. It was not possible to measure the K1/2 for A excision using a fluorescence-based assay owing to extremely tight binding71. Multiple turnover glycosylase assays were performed with 5nM MPG and 1040-fold excess of substrate (Extended Data Fig. 8).

To estimate the fraction of germline mutation variance explained by several factors, we fit the following negative binomial GLMs with an identity link. Data quality is likely to correlate with the number of DNMs detected so, to reduce this variation, we used a subset of the 100kGP dataset that had been filtered on some base quality control metrics by the Bioinformatics team at GEL:

We then included the following variables to try to capture as much of the residual measurement error which may also be impacting DNM calling. In brackets are the corresponding variable names used in the models below:

Mean coverage for the child, mother and father (child mean RD, mother mean RD, father mean RD)

Proportion of aligned reads for the child, mother and father (child prop aligned, mother prop aligned, father prop aligned)

Number of SNVs called for child, mother and father (child snvs, mother snvs, father snvs)

Median VAF of DNMs called in child (median VAF)

Median Bayes Factor as outputted by Platypus for DNMs called in the child. This is a metric of DNM quality (median BF).

The first model only included parental age:

E(Y)=0+1paternal age+2maternal age

The second model also included data quality variables as described above:

$$begin{array}{cc}E(Y),= & {beta }_{0}+{beta }_{1}{rm{paternal; age}}+{beta }_{2}{rm{maternal; age}}\ & +{beta }_{3}{rm{child; mean; RD}}+{beta }_{4}{rm{mother; mean; RD}}\ & +{beta }_{5}{rm{father; mean; RD}}+{beta }_{6}{rm{child; prop; aligned}}\ & +{beta }_{7}{rm{mother; prop; aligned}}+{beta }_{8}{rm{father; prop; aligned}}\ & +{beta }_{9}{rm{childs; nvs}}+{beta }_{10}{rm{mother; snvs}}+{beta }_{11}{rm{father; snvs}}\ & +{beta }_{12}{rm{median; VAF}}+{beta }_{13}{rm{median; BF}}end{array}$$

The third model included a variable for excess mutations in the 11 confirmed individuals with hypermutation (hm excess) in the 100kGP dataset. This variable was the total number of mutations subtracted by the median number of DNMs in the cohort (65), Yhypermutatedmedian(Y) for these 11 individuals and 0 for all other individuals.

$$begin{array}{cc}E(Y),= & {beta }_{0}+{beta }_{1}{rm{paternal; age}}+{beta }_{2}{rm{maternal; age}}\ & +{beta }_{3}{rm{child; mean; RD}}+{beta }_{4}{rm{mother; mean; RD}}\ & +{beta }_{5},{rm{father; mean; RD}}+{beta }_{6}{rm{child; prop; aligned}}\ & +{beta }_{7}{rm{mother; prop; aligned}}+{beta }_{8}{rm{father; prop; aligned}}\ & +{beta }_{9}{rm{child; snvs}}+{beta }_{10}{rm{mother; snvs}}+{beta }_{11}{rm{father; snvs}}\ & +{beta }_{12}{rm{median; VAF}}+{beta }_{13}{rm{median; BF}}+{beta }_{14}{rm{hm; excess}}end{array}$$

The fraction of variance (F) explained after accounting for Poisson variance in the mutation rate was calculated in a similar way to in ref. 1 using the following formula:

$$F={rm{pseudo}},{R}^{2}frac{1-underline{Y}}{{rm{Var}}(Y)}$$

McFaddens pseudo R2 was used here as a negative binomial GLM was fitted. We repeated these analyses fitting an ordinary least squares regression, as was done in ref. 1, using the R2 and got comparable results. To calculate a 95% confidence interval, we used a bootstrapping approach. We sampled with a replacement 1,000 times and extracted the 2.5% and 97.5% percentiles.

We fit eight separate regressions to assess the contribution of rare variants in DNA-repair genes (compiled as described previously). These were across three different sets of genes: variants in all DNA-repair genes, variants in a subset of DNA-repair genes that are known to be associated with base-excision repair, MMR, NER or a DNA polymerase, and variants within this subset that have also been associated with a cancer phenotype. For this, we downloaded all ClinVar entries as of October 2019 and searched for germline pathogenic or likely pathogenic variants annotated with cancer55. We tested both all non-synonymous variants and just PTVs for each set. To assess the contribution of each of these sets, we created two binary variables per set indicating a presence or absence of a maternal or paternal variant for each individual, and then ran a negative binomial regression for each subset including these as independent variables along with hypermutation status, parental age and quality-control metrics as described in the previous section.

We downsampled from the full cohort to examine how the estimates of the fraction of variance in the numberof DNMs explained by paternal age varied with sample number. We first simulated a random sample as follows 10,000 times:

Randomly sample 78 trios (the number of trios in ref. 1.)

Fit ordinary least squares of E(Y)=0+1paternal age.

Estimated the fraction of variance (F) as described in ref. 1.

We found that the median fraction explained was 0.77, with a s.d. of 0.13 and with 95% of simulations fallings between 0.51 and 1.00.

To identify parents who had received a cancer diagnosis before the conception of their child, we examined the admitted patient care hospital episode statistics of these parents. There were no hospital episode statistics available before 1997, and many individuals did not have any records until after the birth of the child. To ensure that comparisons were not biased by this, we first subset to parents who had at least one episode statistic recorded at least two years before the childs year of birth. Two years before the childs birth was our best approximation for before conception without the exact child date of birth. This resulted in 2,891 fathers and 5,508 mothers. From this set we then extracted all entries with ICD10 codes with a C prefix, which corresponds to malignant neoplasms, and Z85, which corresponds to a personal history of malignant neoplasm. We defined a parent as having a cancer diagnosis before conception if they had any of these codes recorded 2 years before the childs year of birth. We also extracted all entries with ICD10 code Z511, which codes for an encounter for antineoplastic chemotherapy and immunotherapy.

Two fathers of individuals with hypermutation who we suspect had chemotherapy before conception did not meet these criteria as the father of GEL_5 received chemotherapy for treatment for systemic lupus erythematosus and not cancer and, for the father of GEL_8, the hospital record personal history of malignant neoplasm was entered after the conception of the child (Supplementary Table 5).

To compare the number of dnSNVs between the group of individuals with parents with and without cancer diagnoses, we used a Wilcoxon test on the residuals from the negative binomial regression on dnSNVs correcting for parental age, hypermutation status and data quality. To look at the effect of maternal cancer on dnSNVs, we matched these individuals on maternal and paternal age with sampling replacement with 20 controls for each of the 27 individuals. We found a significant increase in DNMs (74 compared to 65 median dnSNVs, P=0.001, Wilcoxon Test).

For this analysis, we started with the same subset of the 100kGP dataset that had been filtered as described in the analysis of the impact of rare variants in DNA-repair genes across the cohort (see above). To ensure variant quality, we subsetted to variants that have been observed in genomes from gnomAD (v.3)72. These were then filtered by ancestry to parentoffspring trios where both the parents and child mapped on to the 1000 Genomes GBR subpopulations. The first 10 principal components were subsequently included in the heritability analyses. To remove cryptic relatedness, we removed individuals with an estimated relatedness of >0.025 (using GCTA grm-cutoff, 0.025). This resulted in a set of 6,352 fathers and 6,329 mothers. The phenotype in this analysis was defined as the residual from the negative binomial regression of the number of DNMs after accounting for parental age, hypermutation status and several data quality variables, as described when estimating the fraction of DNM count variation explained (see above). To estimate heritability, we ran GCTA GREML-LDMS on two linkage disequilibrium stratifications and three MAF bins (0.0010.01, 0.010.05, 0.051)56. For mothers, this was run with the --reml-no-constrain option because it would otherwise not converge (Supplementary Table 9).

Further information on research design is available in theNature Research Reporting Summary linked to this paper.

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Genetic and chemotherapeutic influences on germline hypermutation - Nature.com

The project will analyse risks from Sano Genetics data from 3,000 UK adults suffering from long COVID

PrecisionLife has announced a partnership with Sano Genetics a genetic research platform enabling patients to participate in ethical research projects. It is hoped that the move will accelerate the understanding of long-term COVID-19 impacts.

The project will analyse Sano Genetics data from 3,000 UK adults suffering from long COVID symptoms, using PrecisionLifes proprietary combinatorial analytics platform to identify risk-factors and potential drug targets.

It is estimated that 5-30% of COVID-19 patients will go on to have long-term complications and with over 500 million people worldwide confirmed as having been infected the need for better diagnostics and treatments is of utmost importance.

Under the terms of the collaboration, Sano Genetics will provide access to its long COVID patient population dataset to PrecisionLife for analysis.

Dr Patrick Short, CEO and co-founder of Sano Genetics, said: Learning to live with COVID-19 and manage its health consequences has long term public health and economic implications. An estimated 1.7 million people in the UK have reported experiences of long COVID, with symptoms lasting longer than four weeks.

Understanding how our genetics influence our response to COVID-19 is key to better protecting vulnerable people and developing effective treatments. PrecisionLifes analysis of Sano Genetics data will enable this deep biological understanding.

Dr Steve Gardner, CEO of PrecisionLife, explained: Long COVID is a major public health issue. Most sufferers have no clear path for engaging with the healthcare system, as diagnosis is uncertain and the complex symptoms and causes of the disease are not yet fully understood. In our 2020 study, we noted a range of cardiovascular, immunological and neurological changes in COVID-19 patients, and want to understand whether these are transient or permanent.

"We are confident that this study into the long-term effects of SARS-CoV-2 infection, working in partnership with Sano Genetics, will deliver valuable insights to enable a better understanding of long COVID vulnerabilities and ultimately ensure that personalised treatments are directed towards those patients that need them most, he added.

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PrecisionLife and Sano Genetics partnership will help identify treatments for long COVID - PharmaTimes