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Review
. 2011 Feb;187(2):367-83.
doi: 10.1534/genetics.110.120907. Epub 2010 Nov 29.

Progress and promise of genome-wide association studies for human complex trait genetics

Barbara E Stranger  1 , Eli A Stahl, Towfique Raj
Affiliations
Review

Progress and promise of genome-wide association studies for human complex trait genetics

Barbara E Stranger et al. Genetics. 2011 Feb.

Abstract

Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene-gene and gene-environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fundamental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics.

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Figures

F<sc>igure</sc> 1.—
Figure 1.—
Q-Q plot of the RA GWAS meta-analysis (Stahl et al. 2010). Results for all SNPs excluding the strongly associated PTPN22 (chr1, 113.5–114.5 Mb) and MHC (chr6, 26–34 Mb) regions (which would otherwise dominate the tail of the distribution) are plotted in black. Results excluding SNPs in LD (r2 > 0.1) with previously known RA risk associations are plotted in red, showing that substantial association signal remains in the data. And results excluding SNPs in LD with validated autoimmune disease associations are plotted in blue, showing a degree of overlap between RA and related complex diseases. Genomic control λGC (scaled for 1000 cases and 1000 controls) for the data excluding PTPN22 and MHC (black) is shown in the inset. Image adapted from Stahl et al. 2010.
F<sc>igure</sc> 2.—
Figure 2.—
Manhattan plot for RA GWAS meta-analysis. Statistical strength of association (-Log10P) is plotted against genomic position with the 22 autosomal chromosomes in different colors. The blue horizontal line indicates the genome-wide significance threshold of P = 5 × 10−8; the red line is a threshold for “suggestive” association (P = 10−5). SNPs at 5 of 29 loci known from previous studies (gene symbols shown), and one of the 10 new loci identified in this study (marked by red triangles), achieved genome-wide significance in this meta-analysis (prior to the replication phase of the study). Over 200 SNPs representing 35 loci achieved P <10−5, versus roughly 10 expected by chance.
F<sc>igure</sc> 3.—
Figure 3.—
Validated RA risk alleles, their effect sizes (odds ratios, OR), and their percent variance explained. Modern understanding of the genetic etiology of RA has progressed quickly in the age of the GWAS, with scores of recently discovered risk alleles. As resources and technology have improved, our ability to discover alleles with more modest effect sizes has accelerated our ability to discover new risk alleles. On the other hand, with each discovered risk locus, the heritability (percent variance) explained is increasingly modest because new risk alleles' odds ratios are smaller while their frequencies in the population are comparable. Image courtesy of S. Raychaudhuri.
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