Michael A. Province, Ph.D.
Human and Statistical Genetics Program
Computational and Systems Biology Program
Suite 6318, 4444 Forest Park Building
biostatistics, genetic epidemiology, human genetics, mathematical modeling, statistical genetics, systems biology
Mathematical development of new statistical genetics methodology for complex traits
My research focuses on the derivation and application of new computational statistical genetics models to dissect the genetic nature of complex traits, through various paradigms: association, gene expression, linkage. Right now, a key issue in complex trait genetics is how to efficiently find the genetic causes of human traits. We can measure the genome with greater precision and depth every day on larger numbers of subjects. Sequencing costs continue to plummet and the $1000 genome is right around the corner, but what will all of that massive data mean? For any given trait, by far, most of the genome will be noise and should be ignored. Genome-Wide Association Scan (GWAS) results are showing us that there are few common variants with large effects for complex traits, which means that the "missing heritability" for most traits is diffused among large numbers of small effect common variants and/or rare ones. This presents a statistical challenge to find many small population level signals in a vast sea of noise. My lab has been working on computational methods to deal with this general systems biology problem, by leveraging multiple sources of information, combining statistical (association, linkage) as well as biological (various bioinformatic databases) to improve our ability to identify and replicate signal variants. We are currently focusing our efforts on heart disease and healthy aging genetics by serving as the Data Coordinating Center for several large, NIH sponsored multicenter family and genetics studies of complex traits, the NHLBI Family Heart Study (heart disease genetics), the Genetics Of Lipid Lower Drugs and Diet Network (GOLDN) study (lipid pharmacogenetics), and the Long Life Family Study (longevity genetics).
Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, Mardis ER, Wilson RK, Borecki IB, Province MA. CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics 2010 Feb 15;26(4):464-9. Epub 2009 Dec 23. PubMed PMID: 20031968; PubMed Central PMCID: PMC2852218.
Johnson AD, Yanek LR, Chen MH, Faraday N, Larson MG, Tofler G, Lin SJ, Kraja AT, Province MA, Yang Q, Becker DM, O'Donnell CJ, Becker LC. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat Genet. 2010 Jul;42(7):608-13. Epub 2010 Jun 6. PubMed PMID: 20526338.
Province MA, Borecki IB. Gathering the gold dust: methods for assessing the aggregate impact of small effect genes in genomic scans. Pac Symp Biocomput. 2008:190-200. PubMed PMID: 18229686
Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol. 2010 Jan;34(1):100-5. PubMed PMID: 19434714; PubMed Central PMCID: PMC2796708.
Watters JW, Kraja A, Meucci MA, Province MA, McLeod HL. Genome-wide discovery of loci influencing chemotherapy cytotoxicity. Proc Natl Acad Sci U S A. 2004 Aug 10;101(32):11809-14. Epub 2004 Jul 28. PubMed PMID: 15282376; PubMed Central PMCID: PMC511056.
Last Updated: 8/4/2011 11:45:58 AM