eLifeGeneticsPeer-reviewed journal article
Recent fluctuations in Mexican American genomes have altered the genetic architecture of biomedical traits
Melissa L. SpeariD, Alex Diaz-PapkovichiD, Elad ZiviD, Joseph YrachetaiD, Simon GraveliD, Dara G. Torgerson, Ryan D. HernandeziD
Abstract
People in the Americas represent a diverse group of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, many populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genome-wide genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry has increased over time across Hispanic/Latino populations, particularly in Mexican Americans where Amerindigenous ancestry increased by an average of [~]20% over the 50-year period spanning 1940s-1990s. We find similar patterns across American cities, and replicate our observations in an independent sample of Mexican Americans. These dynamic ancestry patterns are a result of a complex interaction of several population and cultural factors, including strong ancestry-related assortative mating and subtle shifts in migration with differences in subcontinental Amerindigenous ancestry over time. These factors have shaped patterns of genetic variation, including an increase in runs of homozygosity in Amerindigenous ancestral tracts, and also influenced the genetic architecture of complex traits within the Mexican American population. We show for height, a trait correlated with ancestry, polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a crucial need to improve our understanding of the genetic diversity of admixed populations.
- organism: human
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 30
- FWCI
- 2.34
- Field percentile
- top 11%
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