Thousands of missing variants in the UK BioBank are recoverable by genome realignment

Jia, Tongqiu; Munson, Brenton; Allen, Hana Lango; Ideker, Trey; Majithia, Amit R.

doi:10.1101/868570

Cited by 14 publications

(18 citation statements)

References 16 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We next annotated all variants with Variant Effect Predictor (v97) 57 , extracted variants in exclusively X-linked recessive genes, and retained only the most severe consequence in canonical transcripts. We note that none of these X-linked recessive genes are affected by the recently-reported problem with this UKBB exome data release which is related to mapping errors 63 .…”

Section: Uk Biobank Analysismentioning

confidence: 74%

The contribution of X-linked coding variation to severe developmental disorders

Martin

Gardner

Samocha

et al. 2020

Preprint

View full text Add to dashboard Cite

Over 130 X-linked genes have been robustly associated with developmental disorders (DDs), and X-linked causes have been hypothesised to underlie the higher DD rates in males. We evaluated the burden of X-linked coding variation in 11,046 DD patients, and found a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We developed an improved strategy to detect novel X-linked DDs and identified 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known DD-associated genes. Importantly, we estimated that, in male probands, only 13% of inherited rare missense variants in known DD-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders.

show abstract

Section: Uk Biobank Analysismentioning

confidence: 74%

The contribution of X-linked coding variation to severe developmental disorders

Martin

Gardner

Samocha

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…It has recently been reported that the UK Biobank exome sequencing data is missing variant calls in regions where all reads were assigned MAPQ=0 (for more details, see Jia et al 62 ).…”

Section: Processing Snv/indel Data From Wesmentioning

confidence: 99%

Reduced reproductive success is associated with selective constraint on human genes

Gardner

Neville

Samocha

et al. 2020

Preprint

View full text Add to dashboard Cite

SummaryGenome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants. While genes under the strongest selective constraint are highly enriched for Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known. Here we show that genetic variants that damage these genes reduce reproductive success substantially in males but much less so in females. We present evidence that this reduction is mediated by cognitive and behavioural traits, which renders male carriers of such variants less likely to find mating partners. Our findings represent strong genetic evidence that Darwin’s theory of sexual selection is shaping the gene pool of contemporary human populations. Furthermore, our results suggest that sexual selection can account for about a quarter of all purifying selection acting on human genes.

show abstract

“…In an analysis of this issue 74 , Jia et al compared the number of exome variants per gene identified when using whole-exome sequencing data from the UK Biobank versus using data from gnomAD. They found 641 genes for which the UK Biobank exome data contains no variants whatsoever.…”

Section: Results and Analysismentioning

confidence: 99%

Genetic architecture of complex traits and disease risk predictors

Yong

Raben

Lello

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Using data from the UK Biobank, predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. for the majority of disease conditions studied, a large amount of the variance is accounted for by Snps outside of coding regions. the state of these Snps cannot be determined from exomesequencing data. this suggests that exome data alone will miss much of the heritability for these traits-i.e., existing pRS cannot be computed from exome data alone. We also study the fraction of Snps and of variance that is in common between pairs of predictors. the DnA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. it seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously. Genomic prediction of complex traits and disease risks has advanced considerably thanks to the recent advent of large data sets and improved algorithms. These algorithms range from simple regression, applied to one SNP at a time to estimate statistical significance and effect size (e.g., as used in GWAS), to high dimensional optimization methods such as compressed sensing or sparse learning 1-4. They produce Polygenic Risk Scores (PRS) or Polygenic Scores (PGS): functions that map the state of an individual's DNA at specific locations (SNPs), to a risk score or predicted quantitative trait value. Predictors (PGS or PRS) now exist for a number of important traits and risks, many of which have undergone out-of-sample testing (i.e., validation in groups of individuals not used in training and from other data sets or from separate ancestries) 5-7. The genetic architectures (i.e., the properties of the SNPs activated in the predictors, which are sparse) uncovered vary significantly: the number of SNPs required to capture most of the predictor variance ranges from a few dozen to many thousands. In contrast, traditional Genome Wide Association studies (GWAS) can implicate the entire genome 8,9 , making them unwieldy to analyze. In the case of disease risk, the predictors are already good enough to identify genetic risk outliers. That is, individuals with unusually high (or low) genetic risk of a specific condition. There are many clinical applications for such predictors 5,10-19 (although there is still much work to be done to overcome sampling and algorithmic biases and disparity 20,21). Below we mention two possible future examples. Breast Cancer: Certain variants in the BR...

show abstract

Thousands of missing variants in the UK BioBank are recoverable by genome realignment

Cited by 14 publications

References 16 publications

The contribution of X-linked coding variation to severe developmental disorders

The contribution of X-linked coding variation to severe developmental disorders

Reduced reproductive success is associated with selective constraint on human genes

Genetic architecture of complex traits and disease risk predictors

Contact Info

Product

Resources

About