Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

Abstract

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation-and distancebased traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics. cs.ucla.edu/followupSNPs.

Colocalization of GWAS and eQTL Signals Detects Target Genes

“…In this section, we use simulated datasets in order to assess the accuracy of our method. We simulated summary statistics utilizing the multivariate normal distribution (MVN) that is utilized 8 in previous studies [18,[29][30][31][32]. More details on simulated data are provided in Section 3.4.2.…”

A unifying framework for summary statistic imputation

“…The second class of methods directly imputes the association statistics at the untyped SNPs given the association statistics at the typed SNPs. As shown in previous work [17,18], the joint distribution of marginal statistics at the typed SNPs and untyped SNPs follow a multivariate normal distribution (MVN) [17][18][19][20][21]. This class of methods utilizes the correlation between the association statistics induced by their dependence on the underlying genotypes [22,23].…”

Dissecting the genetics of complex traits using summary association statistics

“…Imputation is traditionally performed using individual-level data, which requires substantial computational resources and can be logistically cumbersome when new reference panels become available, particularly for large consortia combining data from multiple studies. As an alternative to imputation using individual-level data, approaches have been developed to perform imputation directly at the level of summary statistics [12][13][14][15][16][17][18] . The key insight of these approaches is that LD induces correlations between z-scores, which can be modeled using a multivariate normal (MVN) distribution with variance equal to the LD correlation matrix 19 .…”