Latin Americans show wide-spread Converso ancestry and imprint of local Native ancestry on physical appearance

Abstract

Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the intermixing (admixture) of Native Americans, Europeans and Sub-Saharan Africans. Using novel haplotype-based methods, here we infer sub-continental ancestry in over 6,500 Latin Americans and evaluate the impact of regional ancestry variation on physical appearance. We find that Native American ancestry components in Latin Americans correspond geographically to the present-day genetic structure of Native groups, and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming mostly from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that ancestry related to highland (Central Andean) versus lowland (Mapuche) Natives is associated with variation in facial features, particularly nose morphology, and detect significant differences in allele frequencies between these groups at loci previously associated with nose morphology in this sample.

The Genetic Population Structure of Robinson Crusoe Island, Chile

“…Approximately 50% of mainland Chileans perceive themselves as predominantly of European ancestry ( Adhikari et al, 2017 ), and similarly, the Robinson Crusoe Islanders self-identify as European ( Villanueva et al, 2014 ). Our results, similar to studies on mainland Chile ( Adhikari et al, 2017 ; Chacon-Duque et al, 2018 ), indicate the islanders have a substantial genetic contribution from indigenous admixture.…”

“…Our findings estimate the genetic contribution from indigenous South Americans to the RC Island population at 46.9%, similar to both Chilean controls (43.4%) and Chile_GRU non-indigenous controls (49.1%). Previous research in outbred Chilean populations identified 40–45% indigenous admixture with European and African estimated at 49–52 and 3% respectively ( Eyheramendy et al, 2015 ; Adhikari et al, 2017 ; Lorenzo Bermejo et al, 2017 ; Chacon-Duque et al, 2018 ). At the autosomal level, we were unable to clearly distinguish between Iberian (IBS) and European (CEU) ancestry at this level of resolution in the RCI population.…”

“…Most recently, the regional indigenous contribution to Chilean ancestry has become better understood. The genetic contribution of indigenous groups (Aymara in the north, and Mapuche in the south) was found to be relative to longitude, geographically correlating with the regions inhabited by these indigenous groups ( Chacon-Duque et al, 2018 ). Similarly, the proportion of indigenous ancestry shows a correlation with socioeconomic status, where people from a lower socioeconomic background are more likely to have a higher proportion or indigenous ancestry, whereas those from a higher socioeconomic background are likely to have a larger European contribution ( Lorenzo Bermejo et al, 2017 ).…”

Ancestry effects on type 2 diabetes genetic risk inference in Hispanic/Latino populations

“…Rather, HL groups encompass an extraordinarily diverse set of populations, which are characterized by distinct combinations of ancestry from Africa, Europe, and the Americas [23][24][25][26][27]. Additionally, the Native American component of HL ancestry varies substantially according to the regional origins of the populations [28,49,50]. With this in mind, we have been investigating the contributions of ancestry to genetic risk and T2D health disparities in diverse HL populations.…”

Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond

“…These methods describe kinship or haplotype sharing with the reference. This, in turn, can be used to learn an individual's ancestry mixture, which is routinely done for example via Non-Negative Least Squares (NNLS) (Hellenthal et al, 2014;Pagani et al, 2016) or SOURCEFIND (Chacón-Duque et al, 2018). Because the computational cost of these approaches is linear in the size of the target dataset, they can be used at the biobank scale.…”