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

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

Research output: Contribution to journalArticle

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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 = 2x10-5, FDR p = 0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p = 4610-6, 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.

Original languageEnglish (US)
Article numbere81503
JournalPLoS One
Volume8
Issue number12
DOIs
StatePublished - Dec 12 2013

Fingerprint

Genome-Wide Association Study
Phenotype
Electronic medical equipment
phenotype
Single Nucleotide Polymorphism
Ontology
Reverse Genetics
Electronic Health Records
Genetic Models
thrombosis
Coagulation
Thrombosis
electronics
Repair
Blood
Genes
Chromatids
blood coagulation
Blood Coagulation
neoplasms

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Mosley, J. D., Van Driest, S. L., Larkin, E. K., Weeke, P. E., Witte, J. S., Wells, Q. S., ... Roden, D. M. (2013). Mechanistic phenotypes: An aggregative phenotyping strategy to identify disease mechanisms using GWAS data. PLoS One, 8(12), [e81503]. https://doi.org/10.1371/journal.pone.0081503

Mechanistic phenotypes : An aggregative phenotyping strategy to identify disease mechanisms using GWAS data. / Mosley, Jonathan D.; Van Driest, Sara L.; Larkin, Emma K.; Weeke, Peter E.; Witte, John S.; Wells, Quinn S.; Karnes, Jason H.; Guo, Yan; Bastarache, Lisa; Olson, Lana M.; McCarty, Catherine A.; Pacheco, Jennifer A.; Jarvik, Gail P.; Carrell, David S.; Larson, Eric B.; Crosslin, David R.; Kullo, Iftikhar Jan; Tromp, Gerard; Kuivaniemi, Helena; Carey, David J.; Ritchie, Marylyn D.; Denny, Josh C.; Roden, Dan M.

In: PLoS One, Vol. 8, No. 12, e81503, 12.12.2013.

Research output: Contribution to journalArticle

Mosley, JD, Van Driest, SL, Larkin, EK, Weeke, PE, Witte, JS, Wells, QS, Karnes, JH, Guo, Y, Bastarache, L, Olson, LM, McCarty, CA, Pacheco, JA, Jarvik, GP, Carrell, DS, Larson, EB, Crosslin, DR, Kullo, IJ, Tromp, G, Kuivaniemi, H, Carey, DJ, Ritchie, MD, Denny, JC & Roden, DM 2013, 'Mechanistic phenotypes: An aggregative phenotyping strategy to identify disease mechanisms using GWAS data', PLoS One, vol. 8, no. 12, e81503. https://doi.org/10.1371/journal.pone.0081503
Mosley, Jonathan D. ; Van Driest, Sara L. ; Larkin, Emma K. ; Weeke, Peter E. ; Witte, John S. ; Wells, Quinn S. ; Karnes, Jason H. ; Guo, Yan ; Bastarache, Lisa ; Olson, Lana M. ; McCarty, Catherine A. ; Pacheco, Jennifer A. ; Jarvik, Gail P. ; Carrell, David S. ; Larson, Eric B. ; Crosslin, David R. ; Kullo, Iftikhar Jan ; Tromp, Gerard ; Kuivaniemi, Helena ; Carey, David J. ; Ritchie, Marylyn D. ; Denny, Josh C. ; Roden, Dan M. / Mechanistic phenotypes : An aggregative phenotyping strategy to identify disease mechanisms using GWAS data. In: PLoS One. 2013 ; Vol. 8, No. 12.
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