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Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. Although classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations, we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ∼4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico, and Peru. Our approach replicates previous reports of selection in the human leukocyte antigen region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite notable anthropometric variability with ancestry proportions, and a high burden of growth stunting and overweight/obesity in Hispanic/Latino populations. This address this knowledge gap, we analyzed densely-imputed genetic data in a sample of Hispanic/Latino adults, to identify and fine-map common genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (Stage 1, n=59,769) and validated our findings in 9 additional studies (HISLA Stage 2, n=9,336). We conducted a trans-ethnic GWAS with summary statistics from HISLA Stage 1 and existing consortia of European and African ancestries. In our HISLA Stage 1+2 analyses, we discovered one novel BMI locus, as well two novel BMI signals and another novel height signal, each within established anthropometric loci. In our trans-ethnic meta- analysis, we identified three additional novel BMI loci, one novel height locus, and one novel WHRadjBMI locus. We also identified three secondary signals for BMI, 28 for height, and two for WHRadjBMI. We replicated >60 established anthropometric loci in Hispanic/Latino populations at genome-wide significance—representing up to 30% of previously-reported index SNP anthropometric associations. Trans-ethnic meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our novel findings demonstrate that future studies may also benefit from leveraging differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

We report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

Low HDL-C is the most frequent dyslipidemia in Mexicans, but few studies have examined the underlying genetic basis. Moreover, few lipid-associated variants have been tested for coronary artery disease (CAD) in Hispanic populations. Here, we performed a GWAS for HDL-C levels in 2,183 Mexican individuals, identifying 7 loci, including three with genome-wide significance and containing the candidate genes CETP, ABCA1 and SIDT2. The SIDT2 missense Val636Ile variant was associated with HDL-C levels for the first time, and this association was replicated in 3 independent cohorts (P=5.5×10−21 in the conjoint analysis). The SIDT2/Val636Ile variant is more frequent in Native American and derived populations than in other ethnic groups. This variant was also associated with increased ApoA1 and glycerophospholipid serum levels, decreased LDL-C and ApoB levels and a lower risk of premature CAD. Because SIDT2 was previously identified as a protein involved in sterol transport, we tested whether the SIDT2/Ile636 protein affected this function using an in vitro site-directed mutagenesis approach. The SIDT2/Ile636 protein showed increased uptake of the cholesterol analog dehydroergosterol, suggesting this variant is functional. Finally, liver transcriptome data from humans and the Hybrid Mouse Diversity Panel (HMDP) are consistent with the involvement of SIDT2 in lipid and lipoprotein metabolism. In conclusion, this is the first study assessing genetic variants contributing to HDL-C levels and coronary artery disease in the Mexican population. Our findings provide new insight into the genetic architecture of HDL-C and highlight SIDT2 as a new player in cholesterol and lipoprotein metabolism in humans.

Changes in the composition of the human gut microbiome are recognized to have a significant association with obesity and metabolic syndrome. Mexico leads worldwide childhood-obesity rankings representing an epidemic problem for public health. To this date, it is still unclear how the gut phageome, the bacteriophage component of the virome, influences childhood obesity and obesity with metabolic syndrome. We characterized the gut phageome of 28 school-age children with healthy normal-weight (NW), obese (O), and obese with metabolic syndrome (OMS) profiles, using metagenomic sequencing of virus-like particles (VLPs) from fecal samples. Viromes derived from VLPs were mainly dominated by Caudovirales, and only Inoviridae family was significantly increased in obesity. The three groups showed a similar number of VLPs, while a significant increase in phage richness and diversity was found in obesity groups compared NW. Few phage contigs dominated the phageome composition in NW, being increased in obesity groups. Interestingly, the majority of the phageome was shared among all individuals, establishing a core and common phageome, which abundances correlated with anthropometric and biochemical traits and bacteria previously associated with obesity and metabolic syndrome. We also established a healthy core phageome shared in >80% of NW samples, with a decreased prevalence in the O and OMS groups. Our data support that changes in the gut phageome may contribute to obesity and metabolic syndrome development via bacterial dysbiosis. We consider the phageome characterization provides the basis for novel diagnostic and therapeutic strategies for managing obesity and preventing metabolic syndrome development in obese children through potential phage manipulation. To the best of our knowledge, this study represents the most in-depth sequenced dataset of human bacteriophages, demonstrating for the first time that alterations of the gut phageome characterize obesity.