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Chunk #0 — Introduction

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GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.
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Hundreds of genome-wide association studies (GWAS) have been conducted to study the genetic bases of complex human traits. As of January, 2014, more than 12,000 single-nucleotide polymorphisms (SNPs) have been reported to be significantly associated with at least one complex trait (see the web resource of GWAS catalog [1] http://www.genome.gov/gwastudies/). Despite of these successes, these significantly associated SNPs can only explain a small portion of genetic contributions to complex traits/diseases [2]. For example, human height is a highly heritable trait whose heritability is estimated to be around 80%, i.e., 80% of variation in height within the same population can be attributed to genetic effects [3]. Based on large-scale GWAS, about 180 SNPs have been reported to be significantly associated with human height [4]. However, these loci together only explain about 5-10% of variation in height [2], [4], [5]. This phenomenon is referred to as the “missing heritability” [2], [6], [7].