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PLoS One. 2013 Dec 12;8(12):e81503. doi: 10.1371/journal.pone.0081503. eCollection 2013.

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

Author information

1
Department of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America.
2
Department of Pediatrics, Vanderbilt University, Nashville, Tennessee, United States of America.
3
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
4
Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee, United States of America.
5
Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America.
6
Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.
7
Essentia Institute of Rural Health, Duluth, Minnesota, United States of America.
8
Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
9
Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, Washington, United States of America.
10
Group Health Research Institute, Seattle, Washington, United States of America.
11
Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, United States of America.
12
The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, Pennsylvania, United States of America.
13
Department of Biochemistry and Molecular Biology, Penn State University, University Park, Pennsylvania, United States of America.
14
Department of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America ; Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America.

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.

PMID:
24349080
PMCID:
PMC3861317
DOI:
10.1371/journal.pone.0081503
[Indexed for MEDLINE]
Free PMC Article

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