Tade Souaiaia: the edge of statistical genetics, race and sports
Feb 20, 2025
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Tade Souaiaia, a statistical geneticist at SUNY Downstate, dives into the complex world of genetic architecture and its implications for traits like height and athletic ability. He discusses new insights from his research on polygenic traits and how data influx is reshaping our understanding. The conversation takes a turn to race and sports, where he argues against firm conclusions about group differences in athletic performance due to variable historical factors, challenging simplifications in genetic ancestry narratives.
Polygenic architecture explains how complex traits like height involve many genes with small effects, complicating the identification of causal variants.
Polygenic risk scores, while useful for general predictions, often struggle with extreme values, indicating a need for more nuanced approaches.
The interplay between genetics, environment, and culture in athletic performance highlights the complexities of race and sports beyond strict classifications.
Deep dives
Understanding Polygenic Architecture
Polygenic architecture refers to the concept that complex traits, such as height or athletic ability, are influenced by many genes, each contributing a small effect. This idea traces back to R.A. Fisher's early 20th-century insights, which dismissed the notion that single genes could solely determine these traits. Instead, Fisher proposed an infinitesimal model suggesting an almost infinite number of genetic variants could collectively influence a trait. Such a framework allows for greater variability among individuals, exemplified in diverse family structures where siblings can inherit different combinations of traits from their parents.
Challenges in Genetic Mapping
The expectations from the Human Genome Project highlighted a significant surprise regarding the genetic underpinnings of complex traits; rather than discovering a multitude of genes responsible for specific characteristics, researchers found many traits to be polygenic. For instance, traits like height are determined not by a single gene, but by numerous genetic variants, complicating the task of identifying causal genes through genome-wide association studies (GWAS). This polygenic nature also means that the predictive power of these studies is limited, as the small effect sizes of individual variants make it challenging to draw direct conclusions about causation. Consequently, researchers have shifted focus to understanding the overall architecture of genes associated with traits rather than seeking one definitive causative variant.
Insights from Family Studies
Family studies have been essential in revealing the heritability of traits by examining sibling similarity that often deviates in both extremes of a population distribution. When exploring traits like height, it becomes evident that extreme outliers may not inherit the expected traits from their siblings due to a mix of shared genetic variance and individual luck contingent on parental combinations. The concept of regression to the mean is critical, indicating that unusually tall or short siblings will likely have relatives whose traits are closer to the average, reinforcing the understanding of genetic influences on traits. Thus, this perspective emphasizes the complexity of inheritance patterns while providing insights into sibling relationships regarding polygenic traits.
Polygenic Risk Scores and Their Limitations
Polygenic risk scores (PRS) have emerged as a crucial tool for predicting traits based on genetic data, yet they encounter limitations, especially among individuals exhibiting extreme values in specific traits. For example, when analyzing individuals with an incredibly high or low height, the PRS may not accurately reflect their genetic predisposition regarding their exceptional stature. This discrepancy suggests that these extreme cases may involve rare genetic variants not captured by broader population studies, leading to underprediction of PRS in those tails of the distribution. Therefore, PRS are most effective as generalized predictors within the population's average range, highlighting a need for targeted approaches to understand these outlier effects better.
Natural Selection and Athletic Performance
The influence of natural selection on genetic traits poses intriguing questions, particularly regarding athletic performance and population structures. Although certain characteristics may provide advantages in specific sports, the overall genetic landscape suggests polygenic traits influenced by diverse selection pressures rather than strict racial categorizations. For instance, while East African runners excel in long-distance events due to specific physiological adaptations, it does not imply that only individuals from that region can succeed. Emphasizing the complexities of sports performance genetics illustrates that environmental, cultural, and training factors interplay with genetic predispositions, making accurate predictions about who might excel in a sport exceedingly nuanced.
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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