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A Simple Scalable Association Hypothesis Test Combining Gene-wide Evidence From Multiple Polymorphisms.

ABSTRACT: AIMS:In single-nucleotide polymorphism (SNP) scans, SNP-phenotype association hypotheses are tested, however there is biological interpretation only for genes that span multiple SNPs. We demonstrate and validate a method of combining gene-wide evidence using data for high-density lipoprotein cholesterol (HDLC). METHODOLOGY:In a family based study (N=1782 from 482 families), we used 1000 phenotype-permuted datasets to determine the correlation of z-test statistics for 592 SNP-HDLC association tests comprising 14 genes previously reported to be associated with HDLC. We generated gene-wide p-values using the distribution of the sum of correlated z-statistics. RESULTS:Of the 14 genes, CETP was significant (p=4.0×10-5 <0.05/14), while PLTP was significant at the borderline (p=6.7×10-3 <0.1/14). These p-values were confirmed using empirical distributions of the sum of ?2 association statistics as a gold standard (2.9×10-6 and 1.8×10-3, respectively). Genewide p-values were more significant than Bonferroni-corrected p-value for the most significant SNP in 11 of 14 genes (p=0.023). Genewide p-values calculated from SNP correlations derived for 20 simulated normally distributed phenotypes reproduced those derived from the 1000 phenotype-permuted datasets were correlated with the empirical distributions (Spearman correlation = 0.92 for both). CONCLUSION:We have validated a simple scalable method to combine polymorphism-level evidence into gene-wide statistical evidence. High-throughput gene-wide hypothesis tests may be used in biologically interpretable genomewide association scans. Genewide association tests may be used to meaningfully replicate findings in populations with different linkage disequilibrium structure, when SNP-level replication is not expected.

SUBMITTER: Vaidya D

PROVIDER: S-EPMC3969754 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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A Simple Scalable Association Hypothesis Test Combining Gene-wide Evidence From Multiple Polymorphisms.

Vaidya Dhananjay D Yanek Lisa R LR Mathias Rasika A RA Moy Taryn F TF Becker Diane M DM Becker Lewis C LC

British journal of medicine and medical research 20140301 6

<h4>Aims</h4>In single-nucleotide polymorphism (SNP) scans, SNP-phenotype association hypotheses are tested, however there is biological interpretation only for genes that span multiple SNPs. We demonstrate and validate a method of combining gene-wide evidence using data for high-density lipoprotein cholesterol (HDLC).<h4>Methodology</h4>In a family based study (N=1782 from 482 families), we used 1000 phenotype-permuted datasets to determine the correlation of z-test statistics for 592 SNP-HDLC ...[more]

PMID: 24688980

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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.

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A Simple Scalable Association Hypothesis Test Combining Gene-wide Evidence From Multiple Polymorphisms.

Publications

A Simple Scalable Association Hypothesis Test Combining Gene-wide Evidence From Multiple Polymorphisms.

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