Evolution in the Anthropocene: landscape genomics of NYC Peromyscus

2013-08-20T20:34:01Z (GMT) by Jason Munshi-South
<p>Slides from presentation at American Society of Mammalogists' 2013 conference in Philadelphia, PA on 15 June 2013: Thematic Session on Recent Advances in Mammalogy</p> <p>ABSTRACT</p> <p>Over 50% of humans now live in cities, and urbanization is 1 of the most important drivers of land transformation<br>around the world. Increasingly, humans are also a selective force driving rapid evolutionary change in other species. This presentation describes ongoing efforts to develop Peromyscus (white-footed mice) in New York City as a model for examining the evolutionary implications of urbanization. Our lab integrates complementary approaches from<br>landscape ecology, urban ecology, and population genomics. Most recently, we have been using a landscape genomics approach to examine how urbanization structures both neutral and adaptive genetic variation. We have generated high-density, genome-wide SNP (single nucleotide polymorphism) genotypes using RAD-Seq from over 200 mice sampled from 25 populations along an urban-to-rural gradient spanning New York City to rural Connecticut.<br>We are using outlier analyses to identify candidate genomic regions experiencing selection, and spatial approaches to identify SNPs exhibiting a strong frequency threshold along an urban-to-rural gradient. We are also examining environmental correlations between SNP frequencies and variables related to urbanization. Preliminary results indicate that spatial models based on relatively few high-contrast landscape variables (e.g., vegetation versus impervious urban surfaces) explain connectivity between urban-suburban-rural populations. Genomic regions containing coding sequences involved in metabolism, immunity, and reproduction exhibit statistical signatures of<br>selection in isolated urban populations of white-footed mice. Ongoing work examines variation in gene expression and environment-genotype correlations in urban versus rural populations.</p> <p> </p>