Dmitri Zaykin, Ph.D.
Biostatistics & Computational Biology Branch
Building 101, Room B356B
111 T.W. Alexander Drive
Research Triangle Park, NC 27709
We study relationships between genetic variation and phenotypic traits using statistical methodology. A broad class of such problems is related to association mapping of genetic polymorphisms that are involved in disease susceptibility, response to medicine, and differential reactions to environmental exposures. Development of novel statistical methods to accompany technological advances is essential in order to further efforts to improve health-related quality of life and treat and prevent disease. Identification of genetic pathways whose dysfunction leads to disease can help to accelerate understanding of environmentally induced disruptions of those same pathways. Human genetic data is currently being produced at an increasing rate. The sheer volume of data and its complexity create new challenges for methodological research. How not to drown in the data while extracting useful signals? Our research focuses on developing efficient methods for detecting and characterizing genetic associations with discrete and quantitative traits, while taking into account the large and expanding scale of genomic data.
Dmitri Zaykin joined the Biostatistics Branch at NIEHS in October 2004. His previous positions in statistical and population genetics were at the Institute of Marine Biology in Vladivostok, Russia, North Carolina State University's Statistics Department and at GlaxoSmithKline Inc.
Kuo CL, Zaykin DV. Novel rank-based approaches for discovery and replication in genome-wide association studies. Genetics. 2011;189(1):329-40.
Zaykin DV, Kozbur DO. P-value based analysis for shared controls design in genome-wide association studies. Genet Epidemiol. 2010;34(7):725-38.
Shibata K, Diatchenko L, Zaykin DV. Haplotype associations with quantitative traits in the presence of complex multilocus and heterogeneous effects. Genet Epidemiol. 2009;33(1):63-78.
Zaykin DV, Pudovkin A, Weir BS. Correlation-based inference for linkage disequilibrium with multiple alleles. Genetics. 2008;180(1):533-45.
Zaykin DV, Zhivotovsky LA, Czika W, Shao S, Wolfinger RD. Combining p-values in large-scale genomics experiments. Pharm Stat. 2007;6(3):217-26.