Mechanistic phenotypes: an aggregative phenotyping strategy to identify disease mechanisms using GWAS data

PloS One
Jonathan D MosleyDan M Roden

Abstract

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with "mechanistic phenotypes", comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated ...Continue Reading

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Citations

May 17, 2014·Scientifica·Helena KuivaniemiGerard Tromp
Oct 17, 2014·Nature Protocols·Yan GuoJirong Long
Sep 6, 2015·Journal of the American Medical Informatics Association : JAMIA·Huan MoPaul A Harris
Apr 26, 2016·Nature Communications·Jonathan D MosleyJoshua C Denny
Mar 19, 2019·Frontiers in Medicine·Tim HulsenEoin F McKinney

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Methods Mentioned

BETA
genotyping

Software Mentioned

Genome
pheWAS
DAVID
SAS
Phenome Association Study ( PheWAS )
Studio
STRUCTURE
Database for Annotation , Visualization and Integrated Discove...
PLINK

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