Michael Boehnke, PhD
- Richard G. Cornell Distinguished University Professor of Biostatistics
Dr. Boehnke is Director of the University of Michigan Center for Statistical Genetics and Genome Science Training Program, a member of the National Academy of Medicine, and a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. He did his undergraduate degree in Mathematics at the University of Oregon and his PhD in Biomathematics at UCLA. He has been on the faculty at Michigan since 1984. Dr. Boehnke's research addresses problems of study design and statistical analysis of human genetic data with a particular emphasis on statistical methods for human gene mapping. His current focus is on disease and trait association studies based on genome sequence and genotype-array data. He is a principal investigator of the Finland-United States Investigation of NIDDM (FUSION) study of the genetics of type 2 diabetes. He is a founder and steering committee member of the DIAGRAM (type 2 diabetes), DIAMANTE (type 2 diabetes), MAGIC (glucose and insulin traits), GIANT (anthropometric traits), and Global Lipids genome-wide association meta-analysis consortia. Dr. Boehnke has >400 refereed publications and has chaired or co-chaired 24 doctoral committees and supervised 12 post-doctoral fellows; 28 of these 36 trainees went directly to faculty positions at major research universities.
- PhD, University of California Los Angeles 1983
- BA Honors College, University of Oregon 1977
Statistical and computational human genetics, type 2 diabetes and related traits
Boehnke and his colleagues and trainees develop statistical methods and computational tools to analyze human genetic data, with a particular focus on human gene mapping (PI, R01 HG009976)
Boehnke and his colleagues and trainees seek to better understand the genetic basis of type 2 diabetes and related traits through their own studies and by assembling large genetic consortia that analyze data on millions of study participants and have identified thousands of disease and trait loci (PI, U01 DK062370)
Boehnke and his colleagues seek to bring together and make accessible and usable genetic, genomic, and phenotypic data relevant to type 2 diabetes and related traits through the Accelerating Medicines Partnership Common Metabolic Diseases Knowledge Portal (Multi PI, UM1 DK105554)
Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, Fuchsberger C, Stringham HM, Welch R, Yu K, Silva LF, Service SK, Zhang D, Hector EC, Young E, Ganel L, Das I, Abel H, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Wagner GR, FinnGen, Kang J, Morrison J, Burant CF, Collins FS, Ripatti S, Palotie A, Freimer NB, Mohlke KL, Scott LJ, Wen X, Fauman EB, Laakso M, and Boehnke M (2022) Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nature Communications 13:1644. https://doi.org/10.1038/s41467-022-2914.
Boughton AP, Welch RP, Flickinger M, VandeHaar P, Taliun D, Abecasis GR, and Boehnke M (2021) LocusZoom.js: interactive and embeddable visualization of genetic association study results. Bioinformatics 37:3017-3018. PMCID: PMC8479674.
Kwong AM, Blackwell TW, LeFaive J, de Andrade M, Barnard J, Barnes KC, Blangero J, Boerwinkle E, Burchard EG, Cade BE, Chasman DL, Chen H, Conomos MP, Cupples LA, Ellinor PT, Eng C, Gao Y, Guo X, Irvin MR, Kelly TN, Kim W, Kooperberg C, Lubitz SA, Mak ACY, Manichaikul AW, Mathias RA, Montasser ME, Montgomery CG, Musani S, Palmer ND, Peloso GM, Qiao D, Reiner AP, Roden DM, Shoemaker MB, Smith JA, Smith NL, Su JL, Tiwari HK, Weeks DE, Weiss ST, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group, Scott LJ, Smith AV, Abecasis GR, Boehnke M, and Kang HM (2021) Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries. Genetics 218: iyab044. PMCID: PMC8128395.
Gagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, Taliun D, Schmidt EM, Zhou W, Nielsen JB, Willer CJ, Lee S, Fritsche L, Boehnke M, and Abecasis GR (2020) Exploring and visualizing large-scale genetic associations by using PheWeb. Nature Genetics 52:550-552. PMCID: PMC7754083.
Quick C, Anugu P, Musana S, Weiss ST, Burchard EG, White MJ, Keys KL, Cucca F, Sidore C, Boehnke M, and Fuchsberger C (2020) Sequencing and imputation in GWAS: cost-effective strategies to increase power and genomic coverage across diverse populations. Genetic Epidemiology 44:537-549. PMCID: PMC7449570.
Locke AE, Steinberg KM, Chiang CWK, Service SK, Havulinna AS, Stell L, Pirinen M, Abel HJ, Chiang CC, Fulton RS, Jackson AU, Kang CJ, Kanchi KL, Koboldt DC, Larson DE, Nelson J, Nicholas TJ, Pietila A, Ramensky V, Ray D, Scott LJ, Stringham HM, Vangipurapu J, Welch R, Yajnik P, Yin X, Eriksson JG, Ala-Korpela M, Jarvelin MR, Mannikko M, Laivuori H; FinnGen Project, Dutcher SK, Stitziel NO, Wilson RK, Hall IM, Sabatti C, Palotie A, Salomaa V, Laakso M, Ripatti S, Boehnke M, and Freimer NB (2019) Exome sequencing of Finnish isolates enhances rare-variant association power. Nature 572:323-328. PMCID: PMC6697530.
View full list of publications at www.ncbi.nlm.nih.gov/myncbi/browse/collection/44261299/?sort=dateanddirection=ascending