How to Interpret a Genome-wide Association Study

Abstract

Genome-wide association (GWA) studies use high-throughput genotyping technologies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) and relate them to clinical conditions and measurable traits. Since 2005, nearly 100 loci for as many as 40 common diseases and traits have been identified and replicated in GWA studies, many in genes not previously suspected of having a role in the disease under study, and some in genomic regions containing no known genes. GWA studies are an important advance in discovering genetic variants influencing disease but also have important limitations, including their potential for false-positive and false-negative results and for biases related to selection of study participants and genotyping errors. Although these studies are clearly many steps removed from actual clinical use, and specific applications of GWA findings in prevention and treatment are actively being pursued, at present these studies mainly represent a valuable discovery tool for examining genomic function and clarifying pathophysiologic mechanisms. This article describes the design, interpretation, application, and limitations of GWA studies for clinicians and scientists for whom this evolving science may have great relevance.

A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6

“…Hardy-Weinberg equilibrium (HWE) was not considered relevant as a quality control tool as HWE is underpowered to detect genotyping errors [35] and only extreme sib pairs have been genotyped. GWA studies are particularly prone to spurious associations because ten thousands of associations are tested inflating the rate of false positives [36,37]. In this study, FDR was used to control for false-positive associations due to multiple testing.…”

Shared susceptibility variations in autoimmune diseases: a brief perspective on common issues

“…This problem is further compounded by studies performed by multiple different laboratories and the bias towards only reporting positive results, which particularly affects smaller studies. 34 Applying a false-discovery paradigm is an alternative approach, which seems appealing, although this approach has not yet been widely adopted 35,36 and studies should report false discovery rates along with significance levels. Another alternative approach would be to weight the previous information and obtain a posterior probability of association allowing for the cost of false-negative and false-positive discoveries, using a Bayesian approach.…”

Translating Lung Function Genome-Wide Association Study (GWAS) Findings

“…In GWAS, hundreds of thousands of SNPs in large populations are assayed to determine the co-occurrence of these variants with disease symptoms or with certain trait distribution (Pearson & Manolio, 2008). Importantly these SNPs are selected to capture the maximum information on the human genome by using optimised panels that tag haplotype blocks which is made possible by our improved understanding of the human genome, thanks to the initiatives such as HapMap (International HapMap, 2005).…”