"It was very interesting to see how much these pairwise relations explained of the trait," Eskin said. "So what we did in this paper is we proposed a statistical method that also allowed us to correct for a wide range of sample structure by explicitly accounting for pairwise relatedness between individuals using high-density markers in modeling the distribution of observable traits."

This variance component in the new strategy, called EMMAX (Efficient Mixed Model Association Expedited), would capture the complex mixture of both population structure and hidden relatedness, direct byproducts of genealogy, and correct for these relationships when performing genetic mapping.

"Capitalizing on the characteristics of complex traits in humans, we made a few simplifying assumptions that allowed us to dramatically increase the speed of computations, making our approach readily applicable to genome-wide association studies with tens of thousands of samples," Eskin said.

"Our variance component model is actually a widely known classical model for genetic mapping," Kang said. "However it was too computationally costly to be applied to the current scale of GWAS involving thousands of individuals with hundreds of thousands of genetic variants because even the fastest method - which we previously developed - took years of computational time to analyze the data once. We further expedited the method by capitalizing the characteristics of most human association studies, reducing the computational time from years to hours."

According to Eskin, their method will also have a large impact on admixed populations, which are basically samples of individuals who have ancestry from multiple regions around the world. Studies on Los Angeles, for example, would benefit from this method greatly, as people in the city are very ethnically diverse and it's difficult to obtain very accurate estimates of people's ancestry.

Source: University of California - Los Angeles