Our group is entirely computational. We collaborate with experimental groups to generate and analyze genetic variation data. Additionally, we make extensive use of existing publicly-available datasets.

Our work consists of both developing new methods and well as applying existing methods to new datasets. Though our work is computational, our focus lies in addressing interesting biological questions where new, careful analyses can yield novel and interesting insights.

While much of Kirk’s previous work has involved studying genetic variation in humans, we are also currently studying additional organisms such as dogs and chimpanzees. Future work will involve applying genomic approaches to non-models systems.

Current work in the lab is focused in four areas:

  Inference of demographic history from genetic variation

 We develop and implement statistical approaches to estimate demographic parameters from genetic variation data.  Previous work has resulted in an approximate likelihood approach that uses haplotype patterns to estimate demographic parameters.  We have also studied the history of European, African, and Australian populations, as well as the domestication of dogs.

  Understanding negative natural selection

  Negative natural selection is the process where deleterious mutations are removed from the population.  We use population genetic models and empirical data to study how this process works.  We are specifically interested in how population history influences negative selection and how negatively selected sites affect linked neutral variation (background selection). Current work also involves learning about the distribution of fitness effects for deleterious mutations.

 Unraveling the genetic basis of complex traits

  Common diseases are caused, in part, but many genetic factors.  Efforts are underway to find the genes responsible.  Our work takes advantage of population genetics to better inform such mapping studies. In particular recent empirical and theoretical projects involve using exome sequencing data to understand the genetic architecture of type 2 diabetes, as well as examining the effect of recent population growth on the architecture of complex traits.

 Interpreting forensic DNA evidence

 DNA evidence is a powerful investigative tool.  However, interpreting low-level DNA profiles or DNA mixtures is a challenging process.  Our work in this area, funded by the National Institute of Justice, seeks to evaluate and improve statistical methods to interpret low-level DNA evidence.