Our R01 proposal “Joint inferences of natural selection between sites and populations” has been funded by NIH for 5 years. Briefly, our goals are to 1) infer quantitative models of linked natural selection and 2) infer joint distributions of fitness effects between populations. To accomplish these goals, we will develop new inference methods and apply them to data from humans, Drosphila, and other species. (For slightly more detail, see our Specific Aims.) To carry out this project, we will be hiring at least one postdoc and grad student. Interested applicants are encouraged to contact Ryan.
We are excited to welcome Paul Blischak to the group as a postdoc. Paul is supported by a National Plant Genome Initiative (NPGI) Postdoctoral Research Fellowship from the NSF, and he is being co-mentored by Prof. Mike Barker. During his fellowship, Paul will be working with Ryan on adapting dadi and other population genetics inference tools to polyploid species and with Mike on applying them to various crop species.
Our paper showing that recent GWAS prompt substantially less follow-up research into associated genes than older GWAS has been published in Human Genomics. It has been a long road to final publication, but we hope our work motivates new efforts to encourage GWAS follow-up.
Alyssa Fortier has been awarded an NSF Graduate Research Fellowship. The prestigious award recognizes not only her academic accomplishments, but also her leadership as president of MathCats. There she set up a tutoring program that connects UA students with refugee children. She was also named Outstanding Senior for the department of Mathematics.
After graduation, Alyssa will pursue her Ph.D. in Biology at Stanford University. There she’ll be among one of the greatest groups of population genetics researchers in the world.
Also, see the nice writeup in the MCB newsletter.
We’ve posted an updated version of our bioRxiv preprint on the ability of genome-wide association studies to spur subsequent research into newly associated genes. Most importantly, we found that the effect of GWAS has declined dramatically, suggesting that researchers are following-up on GWAS much less than they used to. Although our work does not show why recent GWAS have much less impact than earlier GWAS, we hope it motivates efforts to encourage GWAS follow-up.