Dataset Information

Reducing the search space for causal genetic variants with VASP.

ABSTRACT:

Motivation

Increasingly, cost-effective high-throughput DNA sequencing technologies are being utilized to sequence human pedigrees to elucidate the genetic cause of a wide variety of human diseases. While numerous tools exist for variant prioritization within a single genome, the ability to concurrently analyze variants within pedigrees remains a challenge, especially should there be no prior indication of the underlying genetic cause of the disease. Here, we present a tool, variant analysis of sequenced pedigrees (VASP), a flexible data integration environment capable of producing a summary of pedigree variation, providing relevant information such as compound heterozygosity, genome phasing and disease inheritance patterns. Designed to aggregate data across a sequenced pedigree, VASP allows both powerful filtering and custom prioritization of both single nucleotide variants (SNVs) and small indels. Hence, clinical and research users with prior knowledge of a disease are able to dramatically reduce the variant search space based on a wide variety of custom prioritization criteria.

Availability and implementation

Source code available for academic non-commercial research purposes at https://github.com/mattmattmattmatt/VASP.

SUBMITTER: Field MA

PROVIDER: S-EPMC4495293 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Publications

Reducing the search space for causal genetic variants with VASP.

Field Matthew A MA Cho Vicky V Cook Matthew C MC Enders Anselm A Vinuesa Carola G CG Whittle Belinda B Andrews T Daniel TD Goodnow Chris C CC

Bioinformatics (Oxford, England) 20150308 14

<h4>Motivation</h4>Increasingly, cost-effective high-throughput DNA sequencing technologies are being utilized to sequence human pedigrees to elucidate the genetic cause of a wide variety of human diseases. While numerous tools exist for variant prioritization within a single genome, the ability to concurrently analyze variants within pedigrees remains a challenge, especially should there be no prior indication of the underlying genetic cause of the disease. Here, we present a tool, variant anal ...[more]

PMID: 25755272

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Project description:BACKGROUND:Genome-wide association (GWA) using large numbers of single nucleotide polymorphisms (SNPs) is now a powerful, state-of-the-art approach to mapping human disease genes. When a GWA study detects association between a SNP and the disease, this signal usually represents association with a set of several highly correlated SNPs in strong linkage disequilibrium. The challenge we address is to distinguish among these correlated loci to highlight potential functional variants and prioritize them for follow-up. RESULTS:We implemented a systematic method for testing association across diverse population samples having differing histories and LD patterns, using a logistic regression framework. The hypothesis is that important underlying biological mechanisms are shared across human populations, and we can filter correlated variants by testing for heterogeneity of genetic effects in different population samples. This approach formalizes the descriptive comparison of p-values that has typified similar cross-population fine-mapping studies to date. We applied this method to correlated SNPs in the cholinergic nicotinic receptor gene cluster CHRNA5-CHRNA3-CHRNB4, in a case-control study of cocaine dependence composed of 504 European-American and 583 African-American samples. Of the 10 SNPs genotyped in the r2 > or = 0.8 bin for rs16969968, three demonstrated significant cross-population heterogeneity and are filtered from priority follow-up; the remaining SNPs include rs16969968 (heterogeneity p = 0.75). Though the power to filter out rs16969968 is reduced due to the difference in allele frequency in the two groups, the results nevertheless focus attention on a smaller group of SNPs that includes the non-synonymous SNP rs16969968, which retains a similar effect size (odds ratio) across both population samples. CONCLUSION:Filtering out SNPs that demonstrate cross-population heterogeneity enriches for variants more likely to be important and causative. Our approach provides an important and effective tool to help interpret results from the many GWA studies now underway.

Project description:Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Despite the widespread availability of genome-wide data, existing methods to analyze genetic data still primarily focus on marginal association models, which fall short of fully capturing the polygenic nature of complex traits and elucidating biological causal mechanisms. Here we present a computationally efficient causal inference framework for genome-wide detection of putative causal variants underlying genetic associations. Our approach utilizes summary statistics from potentially overlapping studies as input, constructs in silico knockoff copies of summary statistics as negative controls to attenuate confounding effects induced by linkage disequilibrium, and employs efficient ultrahigh-dimensional sparse regression to jointly model all genetic variants across the genome. Our method is computationally efficient, requiring less than 15 minutes on a single CPU to analyze genome-wide summary statistics. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD) we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline via marginal association testing. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of large-scale genome-wide association studies (GWAS) summary statistics from 2013 to 2022. Results reveal the method's capacity to robustly discover additional loci for polygenic traits beyond conventional GWAS and pinpoint potential causal variants underpinning each locus (on average, 22.7% more loci and 78.7% fewer proxy variants), contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses. We are making the discoveries and software freely available to the community and anticipate that routine end-to-end in silico identification of putative causal genetic variants will become an important tool that will facilitate downstream functional experiments and future research into disease etiology, as well as the exploration of novel therapeutic avenues.

Dataset Information

Reducing the search space for causal genetic variants with VASP.

Motivation

Availability and implementation

Publications

Reducing the search space for causal genetic variants with VASP.

Similar Datasets

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