Areas of Interest
With the goal of improving treatment and prevention of cardiovascular disease, the Willer lab uses state-of-the-art approaches to generate and analyze genetic and bioinformatics data. We study individuals with abnormal cholesterol, heart attack, congenital heart defects, aortic aneurysm, and atrial fibrillation. We are primarily a computational lab but we also collaborate with experimental laboratories to perform exciting follow-up experiments in animal models and human induced pluripotent stem cells. Students in my lab develop broad training in developing scientific questions, asking them through experimentation and genetic studies, and interpreting and presenting results. Students should have at least a little programming experience, or at least a very strong interest in learning programming. A statistics background is a plus.
Honors & Awards
- 2011 Biological Sciences Scholar, University of Michigan
- 2011 nominated for Presidential Early Career Award in Science and Engineering (PECASE)
- 2014 Schulak Family Emerging Science Fund for innovative research
- 2014 Thomson-Reuters Highly Cited Researcher, top 1% of field (between 2002-2012)
- 2014 University of Michigan Medical School’s League of Research Excellence
- 2015 Thomson-Reuters Highly Cited Researcher, top 1% of field (2014)
- 2015 University of Michigan Medical School Dean’s Basic Science Research Award
- 2016 Frankel Cardiovascular Center Research Team Award (awarded to CHIP Biobank team)
- 2004-2010 Department of Biostatistics, University of Michigan, Postdoctoral Research
- 2003 University of Oxford, Oxford, UK, Ph.D.
- 1998 McMaster University, Hamilton, Canada, B. Sc.
McCarthy S, Das S, Kretzschmar W, Delaneau O, et al. ; Haplotype Reference Consortium. (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet doi: 10.1038/ng.3643.
Zanoni P, Khetarpal SA, Larach DB, Hancock-Cerutti WF, et al. (2016). A Rare Loss-of-Function Variant in Scavenger Receptor Class B Type I (SCARB1) Raises HDL Cholesterol and Increases Risk of Coronary Disease. Science 351(6278):1166-71
Stitziel NO, Stirrups KE, Masca NGD, Erdmann J, et al. (2016). Coding variants in ANGPTL4, LPL, and SVEP1 and risk of coronary disease. NEJM 374(12):1134-44
Schmidt EM, Zhang J, Zhou W, Chen J, Mohlke KL, Chen E, Willer CJ (2015). GREGOR: Evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31(16):2601-6 PMID: 25886982
Guo Y, Fan Y, Zhang J, Lomberk GA, Zhou Z, Sun L, Mathison AJ, Garcia-Barrio MT, Zhang J, Zeng L, Li L, Pennathur S, Willer CJ, Rader DJ, Urrutia R, Chen YE (2015). Perhexiline activates KLF14 and reduces atherosclerosis by modulating ApoA-I production. J Clin Invest 125(10):3819-30
Schmidt EM, Willer CJ (2015). Insights into blood lipids from rare variant discovery. Curr Opin Genet Dev 33:25-31.
Tang CS, Zhang H, Cheung CYY, Xu M, et al. (2015). Exome-wide Association Analysis Reveals Novel Coding Sequence Variants Associated with Lipid Traits in Chinese. Nature Comm 6:10206
Lange LA, Willer CJ, Rich SS. (2015) Recent developments in genome- and exome-wide analyses of plasma lipids. (Invited Review) Current Opin Lipid 26(2):96-102
Locke AE, Kahali B, Berndt SI, Justice AE, et al (2015) Large-scale genetic studies of body mass index provide insight into the biological basis of obesity. Nature 518(7538):197-206
Lange L*, Hu Y*, Zhang H*, Xue C, Tang Z, Bizon C, … [many additional authors], Cupples LA, Kooperberg C, Wilson JG, Nickerson DA, Abecasis GR, Rich SS, Tracy RP, Willer CJ, NHLBI Exome Sequencing Project. (2014) Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet 94:233-45.
Holmen OL*, Zhang H*, Schmidt EM, Fan Y, Hovelson D, Schmidt EM, Zhou W, Gou Y, Zhang J, Langhammer A, Lochen M-L, Ganesh SK, Vatten L, Skorpen F, Dalen H, Zhang J, Pennathur S, Chen J, Platou C, Mathiesen EB, Wilsgaard T, Njølstad I, Boehnke M, Chen YE, Abecasis GR, Hveem K, Willer CJ. (2014) Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol. Nat Genet 46:345-351.
Holmen OL*, Zhang H*, Zhou W*, Schmidt EM, Hovelson DH, Langhammer A, Lochen M-L, Ganesh SK, Mathiesen EB, Vatten L, Platou C, Wilsgaard T, Chen J, Skorpen F, Dalen H, Boehnke M, Abecasis GR, Njølstad I, Hveem K, Willer CJ. (2014) No large-effect low frequency coding variation found for myocardial infarction. Hum Mol Genet 23:4721-8.