Biological interpretation of genome-wide association studies using predicted gene functions

By T. Pers, J. Karjalainen, Yingleong Chan, H. Westra, A. Wood, Jian Yang, Julian C. Lui, S. Vedantam, S. Gustafsson, T. Esko and 7 more. Nature Communications, 2015

821 citations recorded by the source corpus.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. Identifying which genes and pathways explain genetic associations is challenging. Here, the authors present DEPICT, a tool for gene prioritization, pathway analysis and tissue/cell-type enrichment analysis that can be used to generate testable hypotheses from genetic association studies.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3388f6923348fa24ba7f11a79b6aa4fdbd3b4392

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Citations in the corpus

A catalog of genetic loci associated with kidney function from analyses of a million individuals (2019, methodology): “First, 18 we used eGFR-associated SNPs (P 5×10-8) to explore enriched pathways, tissues and 19 cell types based on gene expression data using DEPICT.29 We identified 16 significantly 20 enriched physiological systems, cell types and tissues highlighting several aspects of 21 kidney function, physiol”
Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms (2019, methodology): “We used MAGENTA 37 , DEPICT 38 , PASCAL 36 and MAGMA 34 to perform pathway and tissue enrichment.”
Genome-wide analysis identifies 12 loci influencing human reproductive behavior (2016, positive): “DEPICT—a fourth comprehensive and unbiased recently described gene prioritization tool 33 —identified three genes in GWAS significant loci as likely being causal for AFB ( MON1A , RBM6 and U73166.”
Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics (2016, background): “Genes are commonly assigned to pathways using wellestablished databases (such as Gene Ontology[6], KEGG[7], PANTHER[8], REACTOME[9], BIOCARTA[10]) or in-house annotation (based on co-expression[4], for example).”
Genome-wide enhancer maps link risk variants to disease genes (2021, methodology): “We next evaluated other approaches to connect regulatory variants to disease-associated genes, including predictions based on signals from expression quantitative trait loci (eQTLs) 18–21 , three-dimensional contacts 22 , gene set enrichment 23 or other enhancer–gene maps 24–31 (Methods).”
Functional mapping and annotation of genetic associations with FUMA (2017, methodology): “Additionally, there are several tools that perform not only gene-based test but also gene-set analyses using full distribution of genetic associations (e.g., MAGMA 35 , MAGENTA 37 , INRICH 38 , and DEPICT 39 ).”
An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans (2017, positive): “Using DEPICT (32), 29 genes were prioritised as driving observed associations (FDR<0.”