bioRxivGenomicsPreprint
Detection and interpretation of shared genetic influences on 40 human traits
Joseph K. Pickrell, Tomaz Berisa, Laure Segurel, Joyce Y. Tung, David Hinds
Abstract
We performed a genome-wide scan for genetic variants that influence multiple human phenotypes by comparing large genome-wide association studies (GWAS) of 40 traits or diseases, including anthropometric traits (e.g. nose size and male pattern baldness), immune traits (e.g. susceptibility to childhood ear infections and Crohns disease), metabolic phenotypes (e.g. type 2 diabetes and lipid levels), and psychiatric diseases (e.g. schizophrenia and Parkinsons disease). First, we identified 307 loci (at a false discovery rate of 10%) that influence multiple traits (excluding \"trivial\" phenotype pairs like type 2 diabetes and fasting glucose). Several loci influence a large number of phenotypes; for example, variants near the blood group gene ABO influence eleven of these traits, including risk of childhood ear infections (rs635634: log-odds ratio = 0.06, P = 1.4 x 10-8) and allergies (log-odds ratio = 0.05, P = 2.5 x 10-8), among others. Similarly, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of these traits, including risk of schizophrenia (rs13107325: log-odds ratio = 0.15, P = 2 x 10-12) and Parkinsons disease (log-odds ratio = -0.15, P = 1.6 x 10-7), among others. Second, we used these loci to identify traits that share multiple genetic causes in common. For example, genetic variants that delay age of menarche in women also, on average, delay age of voice drop in men, decrease body mass index (BMI), increase adult height, and decrease risk of male pattern baldness. Finally, we identified four pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased BMI causally increases triglyceride levels, and that increased liability to hypothyroidism causally decreases adult height.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- research focus: polygenic risk
- approach: statistical model
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