Detection and interpretation of shared genetic influences on 42 human traits

By Joseph K. Pickrell, Tomaz Berisa, Jimmy Zhenli Liu, Laure Ségurel, J. Tung, David A. Hinds. Nature Genetics, 2016

1,123 citations recorded by the source corpus.

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.

Subfields: Genetics, Nutrition and Dietetics, Rheumatology

View Paper: https://www.semanticscholar.org/paper/ebbc08edf7b3950f78aae259cf9e3ae8ac59295f

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Citations in the corpus

Interpreting findings from Mendelian randomization using the MR-Egger method (2017, background): “with different measured variables tend to be uncorrelated with each other, as demonstrated in empirical studies [33].”
Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes (2018, methodology): “We expanded our efforts to identify disorder-specific genomic regions by jointly analyzing independent GWAS results from BD and SCZ (Pickrell et al., 2016).”
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells (2016, background): “…model test to estimate the posterior probability (PP) of each genomic locus containing a single variant affecting both disease and molecular trait (‘‘colocalization’’) against other possible models (single trait or two independent associations) (Giambartolomei et al., 2014; Pickrell et al., 2016).”
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses (2019, methodology): “For example, gwas-pw [50] extends the original coloc by using empirical Bayes to estimate per-hypothesis priors via joint analysis of all regions genomewide.”
Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians (2018, methodology): “50 MR Egger regression yields less precise estimates than other methods, owing to a power penalty.”
The genomics of local adaptation in trees: are we out of the woods yet? (2017, background): “It seems that if model organisms (e.g., Pickrell et al. 2016, Smith 2016) are taken as a bellwether for expectations in trees, pleiotropy is likely a contributing factor for many quantitative traits.”
Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, methodology): “1–5 and Supplementary Tables 4–6).”
The MR-Base platform supports systematic causal inference across the human phenome (2018, background): “As the availability of published GWAS summary data continues to grow, MR offers an attractive approach for exploring the aetiology of disease ( Pickrell et al., 2016 ).”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, background): “In another context, the eﬀect sizes of genetic variants across many diﬀerent traits can be systematically correlated to one another to suggest potential relationships between the traits [23].”