Razib Khan's Unsupervised Learning
Razib Khan's Unsupervised Learning
Alex S. Young and James J. Lee: quantitative genetics in 2023
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Alex S. Young and James J. Lee: quantitative genetics in 2023

Two statistical geneticists discuss how genomics is making complex traits simpler
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On this episode of Unsupervised Learning Razib talks with Alex Young of UCLA and James Lee of the University of Minnesota about quantitative genetics and its relationship to complex traits and the genomic revolution. Young, trained as a mathematician, and Lee, trained as a psychologist, have both converged upon research programs exploring the role of genetics in generating variation in human behavior and disease. First, the trio reviews quantitative genetics’ modern basis in R. A. Fisher’s 1918 paper The Correlation between Relatives on the Supposition of Mendelian Inheritance, and how the field emerged from the same intellectual root as population genetics in the first decades of the 20th century. They then discuss phenomena closely associated with quantitative characteristics: polygenicity, heritability and the central limit theorem. Razib also outlines the role of population genetic parameters like mutation, selection and drift in shaping the distribution of any given trait, particularly the characteristic’s variation and median values.

After a deep dive into major concerns like the difference between heritability in the “broad sense” and “narrow sense,” what additive genetic variance is and why it’s so important to evolution and applied breeding and contemporary heritability estimates of traits like height and intelligence using twin studies and family-based genomic analyses, the conversation concludes with a discussion of Gregory Clark’s new PNAS paper, The inheritance of social status: England, 1600 to 2022. What are its implications? Why did it ignite a firestorm on social media? Lee in fact contributed a comment on the paper to PNAS, while Young has tackled its methods and conclusions on social media.

In a conversation that stretches on for over two hours, Razib, Lee and Young touch upon many aspects of a discipline that combines the statistical insights of the 20th century with the genomic technologies of the 21st. Lee also expounds on a result from one of his papers that didn’t make it into the final publication due to reviewer skepticism: what he calls a “beer-chugging phenotype” reported from the study of twins.

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Razib Khan's Unsupervised Learning
Razib Khan's Unsupervised Learning
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