Detection and interpretation of shared genetic influences on 42 human traits

Abstract

We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10(-12)) and Parkinson disease (log OR = -0.15, P = 1.6 × 10(-7)), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.

Figure 1. Schematic of the different models considered for a given genomic region and two GWAS

We divide the genome into approximately independent blocks (see Methods), and estimate the proportion of blocks that fit into the shown patterns. The null model with no associations is not shown. Each point represents a single genetic variant.

Figure 2. Heatmap showing patterns of overlap between traits

Each square [i,j] shows the maximum a posteriori estimate of the proportion of genetic variants that influence trait i that also influence trait j, where i indexes rows and j indexes columns. Note that this is not symmetric. Darker colors represent larger proportions. Colors are shown for all pairs of traits that have at least one region in the set of 341 identified loci; all other pairs are set to white. Phenotypes were clustered by hierarchical clustering in R .

Figure 3. Multiple associations near the ABO gene. A. Association signals for coronary artery disease and tonsillectomy

In the top panel, we show the P-values for association with coronary artery disease for variants in the window around the ABO gene. In the bottom panel are the P-values for association with tonsillectomy. In both panels, SNPs that tag functionally-important alleles at ABO are in color. In the middle are the gene models in the region–exons are denoted by blue boxes, and introns with red lines. Note that the ABO gene is transcribed on the negative strand. B. Association effect sizes for rs635634 on all tested traits. Shown are the effect size estimates for rs635634 for all traits. The lines represent 95% confidence intervals. Traits are grouped according to whether they are quantitative traits (in which case the x-axis is in units of standard deviations) or case/control traits (in which case the x-axis is in units of log-odds).

Figure 4. Heatmap showing patterns of correlated effect sizes of variants across pairs of traits

For each pair of traits [i,j], we extracted the set of variants that influence trait i and their effect sizes on both i and j. We then calculated Spearman's rank correlation between the effect sizes on i and the effect sizes on j, and tested whether this correlation was significantly different from zero. Shown in color are all pairs where this test had a P-value less than 0.01. Darker colors correspond to smaller P-values, and the color corresponds to the direction of the correlation (in red are positive correlations and in blue are negative correlations). The phenotypes are in the same order as in Figure 2. For a comparison to genome-wide genetic correlations, see Supplementary Figure 13.

Figure 5. Putative causal relationships between pairs of traits

For each pair of traits identified as candidates to be related in a causal manner (see Methods), we show the effect sizes of genetic variants on the two traits (at genetic variants successfully genotyped or imputed in both studies). Lines represent one standard error. A. and B. BMI and triglycerides. The effect sizes of genetic variants on BMI and triglyceride levels for variants identified in the GWAS for BMI (A.) or triglycerides (B.). C. and D. LDL and coronary artery disease. The effect sizes of genetic variants on LDL levels and coronary artery disease for variants identified in the GWAS for LDL (C.) or coronary artery disease (D.). E. and F. BMI and type 2 diabetes. The effect sizes of genetic variants on BMI and type 2 diabetes for variants identified in the GWAS for BMI (E.) or type 2 diabetes (F.). G. and H. Hypothyroidism and height. The effect sizes of genetic variants on hypothyroidism and height for variants identified in the GWAS for hypothyroidism (G.) or height (H.).