Kyle Jeffrie Gaulton, Ph.D.
Post-doctoral research fellow
I am working in the McCarthy/Donnelly groups as part of a collaborate study (GoT2D) with the Broad Institute and University of Michigan to identify risk factors for type 2 diabetes using a combination of whole-genome sequencing, exome sequencing and dense array genotyping. Within this project I am interested generally in using and developing novel methodology to identify low allele frequency risk factors for type 2-diabetes from multiple data sources. Related to these efforts, I am also interested in developing approaches for functional annotation and visualization of whole-genome data and using high-throughput sequencing as a platform for medical sequencing of risk factors and genes of interest in large numbers of phenotyped samples.
My specific research interests involve identifying non-coding regulatory variants influencing type 2 diabetes (and related traits) and the biological mechanisms that underlie them. Positional identification of functional elements in non-coding regions of the genome, their specific activities, and the biological context of this activity is critical to a broad understanding of molecular and physiological processes that contribute to human phenotype and disease. Variation that alters the activity of non-coding elements similarly plays a crucial role in differences in phenotype and disease susceptibility between individuals. I plan to use computational and statistical approaches to identify regulatory variants influencing disease susceptibility down to low allele frequencies, and eventually, using both high-throughput and targeted functional assays to identify the mechanistic contexts of the differential activity of these variants as a route towards understanding both the genetic and biological etiology of type 2 diabetes.
Keywords / Research Area(s)
Genetics of type 2 diabetes, transcriptional regulatory genomics, pancreatic islet biology, statistical genetics, high-throughput sequence bioinformatics
CHAOS - Annotation, analysis and visualization of variants from high-throughput sequencing experiments
AURIGA - Perl-based software for processing of sequence data, including identity checking, DNA contamination checking, variant-based quality control and a full pipeline for generating high-quality annotated variant sets
LLAMA - Computational tools to use transcription factor binding motif predictions to identify regulatory variants
CAESAR/STIG- Computational tools for relating genes to Mendelian and complex traits using semantic indexing of biological data
Publication List (plus PubMed IDs)
Gaulton KJ et al. A map of open chromatin in human pancreatic islets. Nat Genet. Jan 2010. 20118932
Gaulton KJ et al. Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes. Diabetes. Nov 2008. 18678618
Gaulton KJ et al. A computational system to select candidate genes for complex human traits. Bioinformatics. May 2007. 17237041
Lange LA et al. Genome-wide association study of homocysteine levels in Filipinos provides evidence for CPS1 in women and a stronger MTHFR effect in young adults. Hum Mol Genet. 2010. 20154341
The most recent copies of my curriculum vitae and research statement can be obtained by emailing firstname.lastname@example.org
Wellcome Trust Centre for Human Genetics
University of Oxford
Oxford; OX3 7BN; UK
Mark McCarthy, Peter Donnelly