Mechanistic Phenotypes: An Aggregative Phenotyping Strategy to Identify Disease Mechanisms Using GWAS Data

Jonathan D Mosley,Lisa Bastarache,Gerard Tromp,Lana M Olson,Yan Guo,Iftikhar J Kullo,David R Crosslin,Emma K Larkin,David J Carey,Helena Kuivaniemi,Sara L Van Driest,Jennifer A Pacheco,Dan M Roden,Eric B Larson,Marylyn D Ritchie,John S Witte,Quinn S Wells,Gail P Jarvik,Catherine A Mccarty,Jason H Karnes,Joshua C Denny ,David Carrell ,Peter Weeke

doi:10.1371/journal.pone.0081503

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 with ontologies related to blood coagulation (Fisher's p = 0.0001, FDR p = 0.03), driven by the F5, P2RY12 and F2RL2 genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10−5, FDR p = 0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p = 4×10−6, FDR p = 0.005) (KIF25, PINX1). We were able to replicate nsSNP associations for POLG/FANCI, BRCA1, FANCA and CHD1L in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.

Highlights

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases, as seen in coagulopathies and cancer syndromes [1,2,3]
We identified associations between low minor allele frequency (MAF) non-synonymous single nucleotide polymorphism (SNP) variants and mechanistic phenotypes derived from diagnoses extracted from electronic medical records (EMRs) data
Simulation Studies We tested two reverse genetics models that computed association p-values for a mechanistic phenotype based on features of the constituent diagnoses comprising the phenotype

Summary

Introduction

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases, as seen in coagulopathies and cancer syndromes [1,2,3] This genetic pleiotropy is not captured by current forward genetic approaches, such as genome wide association studies (GWAS) for specific diseases, which typically narrow case definitions to reduce genetic heterogeneity and improve the signal-to-noise ratio [4]. Quantitative approaches to address genetic pleiotropy typically utilize either data reduction methods [5,6,7] or post-hoc evaluation of association statistics derived from individual phenotypes [8] These approaches, either require correlated traits or are limited to a relatively few traits [9]. Current implementations such as Phenome-Wide Association Study (PheWAS), which serially tests for associations between common polymorphisms and hundreds of clinical disease entities [11], rely on discrete, pre-specified phenotypes

Methods

Results

Conclusion