RESUMO
Nonrandom mating in human populations has important implications for genetics and medicine as well as for economics and sociology. In this study, we performed an integrative analysis of a large cohort of Mexican and Puerto Rican couples using detailed socioeconomic attributes and genotypes. We found that in ethnically homogeneous Latino communities, partners are significantly more similar in their genomic ancestries than expected by chance. Consistent with this, we also found that partners are more closely related--equivalent to between third and fourth cousins in Mexicans and Puerto Ricans--than matched random male-female pairs. Our analysis showed that this genomic ancestry similarity cannot be explained by the standard socioeconomic measurables alone. Strikingly, the assortment of genomic ancestry in couples was consistently stronger than even the assortment of education. We found enriched correlation of partners' genotypes at genes known to be involved in facial development. We replicated our results across multiple geographic locations. We discuss the implications of assortment and assortment-specific loci on disease dynamics and disease mapping methods in Latinos.
Assuntos
Genética Médica , Hispânico ou Latino , Relações Interpessoais , Fatores Socioeconômicos , Estudos de Coortes , Feminino , Heterozigoto , Humanos , Masculino , México/etnologia , Porto Rico/etnologiaRESUMO
MOTIVATION: A basic problem of broad public and scientific interest is to use the DNA of an individual to infer the genomic ancestries of the parents. In particular, we are often interested in the fraction of each parent's genome that comes from specific ancestries (e.g. European, African, Native American, etc). This has many applications ranging from understanding the inheritance of ancestry-related risks and traits to quantifying human assortative mating patterns. RESULTS: We model the problem of parental genomic ancestry inference as a pooled semi-Markov process. We develop a general mathematical framework for pooled semi-Markov processes and construct efficient inference algorithms for these models. Applying our inference algorithm to genotype data from 231 Mexican trios and 258 Puerto Rican trios where we have the true genomic ancestry of each parent, we demonstrate that our method accurately infers parameters of the semi-Markov processes and parents' genomic ancestries. We additionally validated the method on simulations. Our model of pooled semi-Markov process and inference algorithms may be of independent interest in other settings in genomics and machine learning.