PhD, North Carolina State University
Postdoctoral, University of North Carolina at Chapel Hill
Research Areas: Behavioral / Biomedical | Computation / Bioinformatics | Population / Quantitative
My research focus is the integration of genomic and genetic data to understand the biological mechanisms underlying complex traits. We are specifically interested in the molecular basis of gene-by-gene (epistasis) and gene-by-environment interactions (G×E). Epistasis is the phenomenon by which genetic variants interact to produce distinct effects. G×E refers to the highly variable responses that genetically distinct individuals have to their environments. Complex traits result from a combination of genetic and environmental factors, and understanding epistasis and G×E will advance our understanding of fundamental biological processes such as speciation, adaptation, development, and disease. We use both experimental and purely analytical approaches to understand these factors, with a focus on reproduction in the laboratory mouse.
Mice exist in the wild as several distinct subspecies that have limited gene flow between them. Gene flow is restricted because hybrids of two subspecies are often infertile – a classic and dramatic example of epistasis. These reproductive barriers are the mechanisms of incipient speciation. We and others have identified several combinations of laboratory mouse strains that display such hybrid incompatibilities, but the genes driving them are largely unknown. Our goal is to identify the genes and genetic interactions underlying infertility, which will increase our knowledge of both gametogenesis and mouse speciation.
We have begun to study GxE in the context of endocrine disrupting chemicals (EDCs) that alter genome function and affect the reproductive system. In our current project, we expose mice from the Collaborative Cross (CC) reference population to a synthetic estrogen called diethylstilbestrol (DES) during their development. We will measure how this exposure alters DNA methylation, gene expression, testis development, and ultimately reproductive function across these diverse genomes. Genetic reference populations like the CC are powerful new tools that enable us to replicate entire mouse populations and place them in a range of environments. The genetic and functional diversity of the CC make it the most appropriate mouse model for understanding differential susceptibility to exposure in humans, which is an important health concern.
My analytical research integrates heterogeneous genomic data into cohesive biological models. I am broadly interested in causal inference, network modeling, and data mining focused toward the goal of explaining the molecular mechanisms underlying phenotypic variation. I am also actively involved in new experimental designs that leverage genetic reference populations like the CC.
Xiao H, Ciavatta D, Aylor DL, Hu P, Pardo-Manuel de Villena F, Falk RJ, Jennette JC. Genetic basis for variable severity of glomerulonephritis caused by myeloperoxidase specific anti-neutrophil autoantibodies among inbred mouse strains. In press at American Journal of Pathology. 2013.
Ferris MT, Aylor DL, Bottomly D, Whitmore AC, Aicher LD, Bell TA, Bradel-Tretheway B, Bryan JT, Buus RJ, Gralinski LE, Haagmans BL, McMillan L, Miller DR, Rosenzweig E, Valdar W, Wang J, Churchill GA, Threadgill DW, McWeeney SK, Katze MG, Pardo-Manuel de Villena F, Baric RS, Heise MT. Modeling host genetic regulation of Influenza pathogenesis in the Collaborative Cross. In press at PLoS Pathogens. 2013.
Fangrui M, Aylor DL, Foulds-Mathes W, Legge R, Kim J, Walter J, Bell TA, Pardo-Manuel de Villena F, Threadgill DW, Pomp D, and Benson AK. Phenotyping and QTL analysis in the emerging Collaborative Cross mouse resource implicates the immunoglobulin heavy chain locus in host genetic control of gut microbiota composition. Submitted.
Bottomly D, Ferris MT, Aicher LD, Rosenzweig E, Whitmore A, Aylor DL, Haagmans BL, Gralinski LE, Bradel-Tretheway BG, Bryan JT, Threadgill DW, Pardo-Manuel de Villena F, Baric RS, Katze MG, Heise M, McWeeney SK. Expression Quantitative Trait Loci for Extreme Host Response to Influenza A in pre-Collaborative Cross mice. G3: Genes, Genomes, Genetics 2012, 2:213-221.
Kelada SNP*, Aylor DL* , Peck BCE, Tavarez U, Buus RJ, Miller DR, Chesler EJ, Threadgill DW, Churchill GA, Pardo-Manuel de Villena F, Collins FS. Genetic Analysis of Hematological Parameters in Incipient Lines of the Collaborative Cross. G3: Genes, Genomes, Genetics 2012, 2:157-165.
Collaborative Cross Consortium. The Genome Architecture of the Collaborative Cross Mouse Genetic Reference Population. Genetics 2012, 190:389-401.
Foulds-Mathes W, Aylor DL, Miller DR, Churchill GA, Chesler E, Pardo-Manuel de Villena F, Threadgill DW, Pomp D. Architecture of energy balance traits in emerging lines of the Collaborative Cross. American Journal of Physiology-Endocrinology and Metabolism 2011 June, 300(6): E1124-34.
Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA, Baric RS, Ferris MT, Frelinger JA, Heise M, Frieman MB, Gralinski LE, Bell TA, Didion JD, Hua K, Nehrenberg DL, Powell CL, Steigerwalt J, Xie Y, Kelada SN, Collins FS, Yang IV, Schwartz DA, Branstetter LA, Chesler EJ, Miller DR, Spence J, Liu EY, McMillan L, Sarkar A, Wang J, Wang W, Zhang Q, Broman KW, Korstanje R, Durrant C, Mott R, Iraqi FA, Pomp D, Threadgill D, Pardo-Manuel de Villena F, Churchill GA. Genetic analysis of complex traits in the emerging Collaborative Cross. Genome Research 2011, 21: 1213-1222.
Munger SC, Aylor DL, Syed HA, Magwene PM, Threadgill DW, Capel B. Elucidation of the transcription network governing mammalian sex determination by exploiting strain-specific susceptibility to sex reversal. Genes & Development 2009, 23: 2521-2536.
Aylor DL and ZB Zeng. From classical genetics to quantitative genetics to systems biology: Modeling epistasis. PLoS Genetics 2008, 4(3): e1000029.
Zou W, Aylor DL, and ZB Zeng. eQTL Viewer: visualizing how sequence variation affects genome-wide transcription. BMC Bioinformatics 2007, 8(7).
Aylor DL, Price EW, and I Carbone. SNAP: Combine and Map modules for multilocus population genetic analysis. Bioinformatics 2006, 22(11): 1399-1401.