We often work on problems where we are tackling new kinds of genomic data or new questions. Thus, a central part of our work involves developing appropriate statistical and computational approaches that can yield new insights into modern genome-scale data sets. Some of our main research interests are described below, with representative references.
One primary focus in our lab is to understand the primary mechanisms by which variation impacts expression, and to be able to predict which variants have regulatory activity. Our work, in collaboration with Yoav Gilad, applies a combination of computational and experimental approaches. We are using QTL mapping for a broad range of cellular phenotypes - ranging from chromatin measurements to mRNA to protein levels - to measure the regulatory effects of genetic variants.
WASP: allele-specific software for robust molecular quantitative trait locus discovery. van de Geijn et al 2015. Nature Methods. 12:1061-3. [PDF]
Impact of regulatory variation from RNA to protein. Battle et al 2015. Science 347:664-7. [PDF]
Identification of Genetic Variants That Affect Histone Modifications in Human Cells. McVicker et al 2013. Science 342:747-9. [PDF]
Primate Transcript and Protein Expression Levels Evolve under Compensatory Selection Pressures. Khan et al 2013. Science 342:1100-4. [PDF]
DNaseI sensitivity QTLs are a major determinant of human expression variation. Degner et al 2012. Nature 482:390-4. [PDF]
Dissecting the regulatory architecture of gene expression QTLs. Gaffney et al 2012. Genome Biology 13(1):R7. [PDF]
Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Pique-Regi et al 2011. Genome Research 21:447-455. [PDF]
Understanding mechanisms underlying human gene expression variation with RNA sequencing. Pickrell et al 2010. Nature 464:768-72. [PDF]
One class of methods that we have developed makes use of multilocus data from SNPs or other markers, most notably as implemented in our Structure algorithm (in collaboration with Matthew Stephens, Peter Donnelly, Daniel Falush and others). Structure views a sample of individuals as (potentially) representing a mixture from different genetic populations. It uses the marker data to infer both the overall genetic structure and the ancestry of individuals. This type of approach has become widely used in many applications of population genetics. Closely related models - developed independently by David Blei and colleagues - have been very influential in the topic modeling literature.
In other early work, we built on key papers from Simon Tavare and Gunther Weiss to develop the first application of Approximate Bayesian Computation, in this case to estimate human demography from Y chromosome data [PDF].
fastSTRUCTURE: variational inference of population structure in large SNP data sets. Raj et al 2014. Genetics 197:573-89. [PDF]
Sequencing and Analysis of Neanderthal Genomic DNA. Noonan et al 2006. Science 314:1113-1118. [PDF]
The genetic structure of human populations. Rosenberg et al 2002. Science 298: 2381-2385. [PDF]
In our early work on this problem our goal was to identify the strongest signals of selective sweeps in the genome (Voight et al 2006).
Instead we have proposed that most adaptation likely occurs through a process of "polygenic adapation" in which small allele frequencies at large numbers of quantitative trait loci allow very rapid phenotypic adaptation but are difficult to detect by standard tests. We are now working actively on new approaches for studying soft sweeps and polygenic adaptation.
The deleterious mutation load is insensitive to recent population history. Simons et al 2014. Nature Genetics 46:220-4. [PDF]
The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Pritchard et al 2010 Current Biology. 20:R208-15. [PDF]
How we are evolving. Pritchard 2010 Scientific American. 301(10):41-47. [link]
The role of geography in human adaptation. Coop et al 2009 PLoS Genetics 5:e1000500. [PDF]
One area of interest has been in understanding linkage disequilibrium and recombination (much of this in collaboration with Molly Przeworski and Graham Coop), including providing evidence for variation in hotspot usage across individuals (now known to be due to variation at PRDM9).
When Don Conrad was in the lab he provided one of the early genome-wide surveys of deletion polymormisms, when it was first becoming clear that copy number variation is an important aspect of genome variation (see figure at right).
We also helped to introduce the idea of using genotype data to detect and controlling for the confounding effects of population structure in association mapping. More broadly we have been interested in population genetic models of complex traits, including work on the role of rare variants in disease.
High-Resolution Mapping of Crossovers Reveals Extensive Variation in Fine-Scale Recombination Patterns Among Humans. Coop et al 2008. Science 319: 1395-1398. [PDF]
A high-resolution survey of deletion polymorphism in the human genome. Conrad et al 2006. Nature Genetics 38:75-81. [PDF]
Clonal origin and evolution of a transmissible cancer. Murgia, et al 2006. Cell 126:477-87. [PDF]
Linkage disequilibrium in humans: models and data. Pritchard and Przeworski 2001. Am. J. Hum. Genet. 69:1-14 [PDF]
Use of unlinked genetic markers to detect population stratification in association studies. Pritchard and Rosenberg 1999. Am. J. of Hum. Gen. 65: 220-228. [PDF]