Karen Conneely, Ph.D.
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My research focuses on the application and development of statistical methods for genetic and epigenetic association studies, with a particular interest in the epigenetics of aging. Through the development of novel techniques and adaptation of existing ones, my work seeks to identify biomarkers and variants involved in disease, and to explore the role of DNA methylation as mediator between environment and phenotype. Ongoing work in the Conneely lab uses computational approaches to understand the relationship between multiple epigenetic mechanisms, gene expression, and human aging, with particular interest in the evolutionary origins of this relationship and its contribution to risk for age-related disease.
- PhD, Biostatistics, University of Michigan, 2008
- MA, Economics, Princeton University, 1997
- BS, Statistics, University of Illinois, 1994
- View publications on PubMed
Robins C, McRae AF, Powell JE, Wiener HW, Aslibekyan S, Kennedy EM, Absher DM, Arnett DK, Montgomery GW, Visscher PM, Cutler DJ, Conneely KN. Testing Two Evolutionary Theories of Human Aging with DNA Methylation Data. Genetics, 2017, 207(4):1547-1560.
Grant, CD, Jafari N, Hou L, Li Y, Stewart JD, Zhang G, Lamichhane A, Manson JE, Baccarelli AA, Whitsel EA, Conneely KN. A Longitudinal Study of DNA Methylation as a Potential Mediator of Age-Related Diabetes Risk, Geroscience, 2017, 39(5-6):475-489.
Jiang Y, Conneely KN*, Epstein MP*, Robust Rare-Variant Association Tests For Quantitative Traits in General Pedigrees, Statistics in Biosciences, 2017, 1-15.
Johnson ND, Wiener HW, Smith AK, Absher DM, Arnett DK, Aslibekyan S, Conneely KN, Non-linear patterns in age-related DNA methylation may reflect CD4+ T cell differentiation. Epigenetics, 2017, 12(6):492-403.
Knight AK, Craig JM, Theda C, Bækvad-Hansen M, Bybjerg-Grauholm J, Hansen CS, Hollegaard MV, Hougaard MD, Mortensen, PB, Weinsheimer SM, Werge TM, Brennan PA, Cubells JF, Newport DJ, Stowe ZA, Cheong J, Dalach P, Doyle LW, Loke YJ, Baccarelli A, Just A, Wright R, Tellez-Rojo M, Svensson K, Trevisi L, Kennedy EM, Binder EB, Iurato S, Czamara D, Räikkönen K, Lahti J, Pesonen AK, Kajantie E, Villa P, Laivuori H, Hämäläinen E, Parets SE, Menon R, Horvath S, Bush NR, LeWinn K, Tylavsky FA, Conneely KN*, Smith AK*, An epigenetic clock for gestational age at birth based on blood methylation data, Genome Biology, 2016 17(1):206. PMCID: PMC5054584
Wu H, Xu T, Feng H, Chen L, Li B, Yao B, Qin Z, Jin P, Conneely KN. Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic Acids Research, 2015 43(21):e141.
Peters MJ*, Joehanes R*, Pilling LC*, Schurmann C*, Conneely KN*, Powell J*, Reinmaa E*, Sutphin GL*, Zhernakova A*, Schramm K*, Wilson YA*, et al. (115 authors + consortium). The transcriptional landscape of age in human peripheral blood. Nature Communications 2015 6:8570.
Smith AK, Kilaru V, Kocak M, Almli LM, Mercer KB, Ressler KJ, Tylavsky FA, Conneely KN (2014) Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics, in press.
Feng H, Conneely KN*, Wu H* (2014) A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Research, in press. (* indicates shared authorship)
Barfield RT, Almli LM, Kilaru V, Smith AK, Mercer KB, Duncan R, Klengel T, Mehta D, Binder EB, Epstein MP, Ressler KJ, Conneely KN (2014) Accounting for population stratification in DNA methylation studies. Genetic Epidemiology, Epub ahead of print.
Jiang Y, Epstein MP, Conneely KN (2013) Assessing the impact of population stratification on association studies of rare variation. Human Heredity, 76:28-35.Barfield RT, Kilaru V, Smith AK, Conneely KN (2012) CpGassoc: An R function for analysis of DNA methylation microarray data, Bioinformatics, 28:1280-1.
Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST (2012) Age-associated DNA methylation in pediatric populations. Genome Research, 22:623-632.
Kilaru V, Barfield RT, Schroeder JW, Smith AK, Conneely KN (2012) MethLAB: A graphical user interface package for the analysis of array-based DNA methylation data. Epigenetics 7(3):225-9.
Schroeder JW, Conneely KN, Cubells JC, Kilaru V, Newport JD, Knight BT, Stowe ZN, Brennan PA, Krushkal J, Tylavsky FA, Taylor RN, Adkins RM, Smith AK (2011) Neonatal DNA methylation patterns associate with gestational age. Epigenetics, 6(12):1498-504.
Smith AK, Conneely KN, Kilaru V, Mercer KB, Weiss TE, Bradley-Davino B, Tang Y, Gillespie CF, Cubells JF, Ressler KJ (2011) Differential immune system DNA methylation and cytokine regulation in Posttraumatic Stress Disorder. American Journal of Medical Genetics Part B:Neuropsychiatric Genetics 156(6):700-8.
Conneely KN, Boehnke M (2010) Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits. Genetic Epidemiology 34(7):739-746.
Conneely KN, Boehnke M (2007) So many correlated tests, so little time! Rapid adjustment of p-values for multiple correlated tests. American Journal of Human Genetics 81:1158-1168
Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316:1341-1345
Conneely KN, Silander K, Scott LJ, Mohlke KL, Lazaridis KN, Valle TT, Tuomilehto J, Bergman RN, Watanabe RM, Buchanan TA, Collins FS, Boehnke M (2004) Variation in the resistin gene is associated with obesity and insulin-related phenotypes in Finnish subjects. Diabetologia47:1782-1788