Trait association mapping in the genomic era
Identifying genes underlying human traits has long been a goal for human geneticists. Over the past 20 years, the most successful approach has been applying genetic linkage analysis in families with Mendelian diseases. Attempts to use the complementary approach of candidate gene studies have been less consistently successful. With the emergence of the tools and information from the human genome project, there has been a dramatic shift toward trying to identify the genetic variations underlying far more complex traits. While linkage analysis continues to be valuable, much effort has shifted toward using population genetic techniques to identify common variation associated with traits. An explosion of interest and applications has occurred over the past five years with the introduction of large scale SNP genotyping and large scale information about the linkage disequilibrium relationships of those SNPs. Sorting the true signals from the noise in these very large scale datasets has become a significant challenge.
n this presentation I will discuss various study designs and strategies toward trait association mapping. Classical case-control datasets have some advantages relative to ease of collection and theoretical power, but may not be robust to some underlying assumptions. The complementary designs using family-based datasets have advantages in using existing datasets and are more robust to some underlying assumptions, but often at the expense of power. Many statistical analytical methods are being proposed for these study designs including single locus, haplotype, and epistatic effects. Additionally, genomic convergence approaches, which integrate disparate data types (e.g. linkage, association, expression, function), are also being explored.