Tade Souaiaia: the edge of statistical genetics, race and sports

Razib Khan's Unsupervised Learning

On this episode of Unsupervised Learning Razib talks to Tade Souaiaia, a statistical geneticist at SUNY Downstate about his new preprint, Striking Departures from Polygenic Architecture in the Tails of Complex Traits. Souaiaia trained as a computational biologist at USC, but also has a background as a division I track and field athlete.

Razib and Souaiaia discuss what “genetic architecture” means, and consider what we're finding when we look at extreme trait values in characteristics along a normal distribution. Though traits like height or risk for type II diabetes can be thought of as represented by an idealized Gaussian distribution, real molecular and cellular processes still underlie their phenotypic expression. Souaiaia talks about how genomics has resulted in an influx of data and allowed statistical geneticists with a theoretical bent to actually test some of the models that underpin our understanding of traits and examine how models like mutation-selection balance might differ from what we’ve long expected. After wading through the depths of genetic abstraction and how it intersects with the new age of big data, Razib and Souaiaia talk about race and sports, and whether there might be differences between groups in athletic ability. Souaiaia argues that the underlying historical track record is too variable to draw firm conclusions, while Razib argues that there are theoretical reasons that one should expect differences between groups at the tails and even around the memes.

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