A genealogy-based framework to infer the demographic history, genetic structure, and phenotype association
- 日時
- 2025年11月11日(火)14:00 - 15:00 (JST)
- 講演者
-
- Charleston Chiang (Associate Professor, University of Southern California, USA)
- 会場
- セミナー室 (359号室) (メイン会場)
- via Zoom
- 言語
- 英語
- ホスト
- Leo Speidel
We propose a conceptual analogy in population genetics to the central dogma of molecular biology. While the central dogma describes the flow of information from DNA to RNA to protein, we posit that under neutrality, a population's demography shapes its underlying genealogy, which in turn determines patterns of genetic variation that give rise to phenotypic variation. At the center of this analogous dogma is the genetic genealogies. Recent advances in inferring the Ancestral Recombination Graph (ARG), a complete record of a population's genealogies, have enabled us to develop a suite of methods that interrogates each stage these fundamental and connected components:
- Genealogy → Demography: We developed gLike, a method that uses a graph-based summary of the ARG to accurately infer a population's demographic history.
- Genealogy → Genetic Variation: We created eGRM, which computes the expected genetic relatedness between individuals directly from the ARG, providing a precise characterization of genetic variation patterns, even in recently admixed populations.
- Genealogy → Genetic Variation → Phenotype: We devised sycamore, a framework that extends the eGRM to map quantitative trait loci, particularly where multiple alleles contribute to a phenotype.
We have benchmarked each method in simulations and validated them using empirical human datasets. While the performance of these tools relies on the accuracy and scalability of ARG inference, which is continuously improving, we demonstrate that our genealogy-based approach already enhances the analysis of demography, relatedness, and trait architecture in diverse human populations.
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