Statistical and population genetics have always been rich in mathematical challenges. The recent availability of sequence from 1000s of human genomes, only increases these challenges. These data will provide unprecedented opportunity to analyze the structure of the human population and understand how genetic variation affects traits. Increasingly, recent studies show that the genomes of modern individuals are shaped by many forces including complex patterns of ancestry from multiple ancestral populations, ancient migration patterns and spatial structure of human population. This sequence data provides opportunities to study, model, and analyze the complex genetic structure of the human population and the processes of mutation and selection that have established and maintain human variation. Increasingly, inherited variants of interest are not simply DNA base changes, but include copy-number variants (small duplications or deletions), inversions, and the products of gene conversion.
Over the last few years, Genome Wide Association Studies (GWAS) have been a central tool for elucidating the connections between genetic variation and traits. While in the past the genetic variation has consisted of Single Nucleotide Polymorphism arrays (which collect only the common variants), in the coming years this will likely switch to whole genome sequencing which will provide the opportunity to understand how rare variation affects traits.
This workshop focuses on mathematical challenges in analyzing human variation data and its effect on traits both from sequences and SNP arrays. Topics include mathematical, statistical and computational challenges in modeling human populations and admixture, spatial population structure, as well as development of techniques for analysis of genome-wide association studies, gene-gene interactions, gene-environment interactions, and rare variation effects on traits.
Eleazar Eskin, Chair (University of California, Los Angeles (UCLA), Computer Science)
Steven Evans (University of California, Berkeley (UC Berkeley), Statistics)
Phil Green (University of Washington)
Elizabeth Thompson (University of Washington)