The genotype-phenotype interface underlies a broad range of critical questions in basic and applied biology. My lab uses diverse genomic approaches to illuminate the genetic properties of adaptive trait evolution, and in this seminar I will focus on the statistical methods and computational resources that we've developed to advance this research:
1. A simulation-based analysis pipeline for bulk QTL mapping. This method allows us to separate QTLs with overlapping statistical signals. Applying it to adaptive trait differences between natural fly populations, we've found that adaptation often yields large effect QTLs, but that these often vary within and between populations.
2. New population genetic statistics to detect adaptive differences between populations. These include a method to quantify allele frequency change unique to a focal population, and a comparison of identical haplotype lengths between populations. Our application of these approaches has underscored the importance of standing genetic variation as raw material for adaptive evolution.
3. A database with over 1000 sequenced Drosophila genomes. In our Drosophila Genome Nexus, my lab has combined hundreds of our own fly genomes with those published elsewhere, using a consistent and improved reference alignment pipeline to minimize bias from comparisons across data sets.
I will also discuss ongoing and upcoming projects, including the population genetic analysis of RNAseq data and the modeling of epistatic fitness interactions in admixed populations.