Dataset Information

FINEMAP: efficient variable selection using summary data from genome-wide association studies.

ABSTRACT: MOTIVATION:The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. RESULTS:We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects. AVAILABILITY AND IMPLEMENTATION:FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com CONTACT:: christian.benner@helsinki.fi or matti.pirinen@helsinki.fi.

SUBMITTER: Benner C

PROVIDER: S-EPMC4866522 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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FINEMAP: efficient variable selection using summary data from genome-wide association studies.

Benner Christian C Spencer Chris C A CC Havulinna Aki S AS Salomaa Veikko V Ripatti Samuli S Pirinen Matti M

Bioinformatics (Oxford, England) 20160114 10

<h4>Motivation</h4>The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive.<h4>Results</h4>We introduce FINEMAP, a software package to efficiently explore a set of the most important causa ...[more]

PMID: 26773131

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Project description:Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large-scale genome-wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease-related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the "single trait" association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well-characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome-phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual-level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P-value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta-analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta-analysis. All the proposed methods are implemented in a publicly available R package.

Dataset Information

FINEMAP: efficient variable selection using summary data from genome-wide association studies.

Publications

FINEMAP: efficient variable selection using summary data from genome-wide association studies.

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