Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

By Joseph K. Pickrell. bioRxiv, 2013

573 citations recorded by the source corpus.

Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWAS). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. We describe a statistical model that uses association statistics computed across the genome to identify classes of genomic element that are enriched or depleted for loci that influence a trait. The model naturally incorporates multiple types of annotations. We applied the model to GWAS of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, BMI, and Crohn’s disease. For each trait, we evaluated the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over a hundred tissues and cell lines. We show that the fraction of phenotype-associated SNPs that influence protein sequence ranges from around 2% (for platelet volume) up to around 20% (for LDL cholesterol); that repressed chromatin is significantly depleted for SNPs associated with several traits; and that cell type-specific DNase-I hypersensitive sites are enriched for SNPs associated with several traits (for example, the spleen in platelet volume). Finally, by re-weighting each GWAS using information from functional genomics, we increase the number of loci with high-confidence associations by around 5%.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/aebf1d0dce345b39b78cbdef663922a2a7546567

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Citations in the corpus

Polygenic scores via penalized regression on summary statistics (2016, background): “Likewise additional information gained in the consideration of functional annotations of the genome (Schork et al., 2013; Pickrell, 2014; Kichaev et al., 2014) can be incorporated similarly.”
A SPECTRAL APPROACH INTEGRATING FUNCTIONAL GENOMIC ANNOTATIONS FOR CODING AND NONCODING VARIANTS (2016, background): “The shift from supervised (CADD, GWAVA) to unsupervised algorithms as discussed here reduces the dependence on existing databases of observed variants, previously characterized elements and existing models of mutation, and allows extensions to cell type/tissue specific scores [16, 17, 37, 40, 41, 42”
Partitioning heritability by functional annotation using genome-wide association summary statistics (2015, methodology): “However, many current approaches that leverage functional information [3, 4] and GWAS data to inform disease biology use only SNPs in genome-wide significant loci [5–8], assume only one causal SNP per locus [9], or do not account for linkage disequilibrium (LD) [10].”
Detection and interpretation of shared genetic influences on 42 human traits (2016, unknown): “%) in each study using fgwas v.0.3.6 37 .”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, unknown): “Consequently, much work has gone into intersecting GWAS signals with bulk DNase hypersensitivity data (and other epigenetic features), with the goal of systematically linking particular diseases to the dysfunction of specific tissues (Finucane et al., 2015; Maurano et al., 2012; Pickrell, 2014).”
An expanded view of complex traits: from polygenic to omnigenic (2017, background): “There has recently been considerable interest in identifying particular variants with pleiotropic effects on different traits (Cotsapas et al., 2011; Pickrell et al., 2016) as well as in identifying pairs of traits with correlated genetic effects (Bulik-Sullivan et al., 2015a).”
RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data (2016, methodology): “Summary association statistics generated by RVTESTS can be used by tools such as fGWAS (Pickrell, 2014) to prioritize causal variants.”