Biology subjects

Sunyaev, S. R.

Publications and source records attributed to Sunyaev, S. R..

7 recordsLinked to original sources

bioRxiv2018

Applicability of the mutation-selection balance model to population genetics of heterozygous protein-truncating variants in humans

The fate of alleles in the human population is believed to be highly affected by the stochastic force of genetic drift. Estimation of the strength of natural selection in humans generally necessitates a careful modeling of drift including complex effects of the population history and structure. Protein truncating variants (PTVs) are expected to evolve under strong purifying selection and to have a relatively high per-gene mutation rate. Thus, it is appealing to model the population genetics of PTVs under a simple deterministic mutation-selection balance, as has been proposed earlier [1]. Here, we investigated the limits of this approximation using both computer simulations and data-driven approaches. Our simulations rely on a model of demographic history estimated from 33,370 individual exomes of the Non-Finnish European subset of the ExAC dataset [2]. Additionally, we compared the African and European subset of the ExAC study and analyzed de novo PTVs. We show that the mutation-selection balance model is applicable to the majority of human genes, but not to genes under the weakest selection.

genetics↗

bioRxiv2018

Non-parametric polygenic risk prediction using partitioned GWAS summary statistics

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the traits heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for Breast Cancer, Type 2 Diabetes, Inflammatory Bowel Disease and Coronary Heart Disease, all of which have available early intervention or prevention treatments.

bioinformatics↗

bioRxiv2018

Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy

The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remains unclear. We performed a systematic secondary-variant burden analysis of two independent Bardet-Biedl syndrome (BBS) cohorts with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants compared to either population controls or to a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes, a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model paradigm for secondary-variant burden analysis in recessive disorders.

genetics↗

bioRxiv2018

Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies

Genetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of subsignificant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

evolutionary biology↗

bioRxiv2017

Error-prone bypass of DNA lesions during lagging strand replication is a common source of germline and cancer mutations

Spontaneously occurring mutations are of great relevance in diverse fields including biochemistry, oncology, evolutionary biology, and human genetics. Studies in experimental systems have identified a multitude of mutational mechanisms including DNA replication infidelity as well as many forms of DNA damage followed by inefficient repair or replicative bypass. However, the relative contributions of these mechanisms to human germline mutations remain completely unknown. Here, based on the mutational asymmetry with respect to the direction of replication and transcription, we suggest that error-prone damage bypass on the lagging strand plays a major role in human mutagenesis. Asymmetry with respect to transcription is believed to be mediated by the action of transcription-coupled DNA repair (TC-NER). TC-NER selectively repairs DNA lesions on the transcribed strand; as a result, lesions on the non-transcribed strand are preferentially converted into mutations. In human polymorphism we detect a striking similarity between transcriptional asymmetry and asymmetry with respect to replication fork direction. This parallels the observation that damage-induced mutations in human cancers accumulate asymmetrically with respect to the direction of replication, suggesting that DNA lesions are asymmetrically resolved during replication. Re-analysis of XR-seq data, Damage-seq data and cancers with defective NER corroborate the preferential error-prone bypass of DNA lesions on the lagging strand. We experimentally demonstrate that replication delay greatly attenuates the mutagenic effect of UV-irradiation, in line with the key role of replication in conversion of DNA damage to mutations. We conservatively estimate that at least 10% of human germline mutations arise due to DNA damage rather than replication infidelity. The number of these damage-induced mutations is expected to scale with the number of replications and, consequently, paternal age.

biochemistry↗

bioRxiv2017

Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits

Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1-p)], where p is the MAF and negative values of imply larger effect sizes for rare variants. We estimate by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (N = 113,851). All traits produced negative estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true values across traits had mean -0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically significant heterogeneity across traits (P = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10-4 or stronger is necessary to explain the values that we inferred.

genetics↗

bioRxiv2016

Phenotype-specific information improves prediction of functional impact for noncoding variants

Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.

genomics↗