Exploring a Region on Chromosome 8p23.1 Displaying Positive Selection Signals in Brazilian Admixed Populations: Additional Insights Into Predisposition to Obesity and Related Disorders

Abstract

We recently reported a deviation of local ancestry on the chromosome (ch) 8p23.1, which led to positive selection signals in a Brazilian population sample. The deviation suggested that the genetic variability of candidate genes located on ch 8p23.1 may have been evolutionarily advantageous in the early stages of the admixture process. In the present work, we aim to extend the previous work by studying additional Brazilian admixed individuals and examining DNA sequencing data from the ch 8p23.1 candidate region. Thus, we inferred the local ancestry of 125 exomes from individuals born in five towns within the Southeast region of Brazil (São Paulo, Campinas, Barretos, and Ribeirão Preto located in the state of São Paulo and Belo Horizonte, the capital of the state of Minas Gerais), and compared to data from two public Brazilian reference genomic databases, BIPMed and ABraOM, and with information from the 1000 Genomes Project phase 3 and gnomAD databases. Our results revealed that ancestry is similar among individuals born in the five Brazilian towns assessed; however, an increased proportion of sub-Saharan African ancestry was observed in individuals from Belo Horizonte. In addition, individuals from the five towns considered, as well as those from the ABRAOM dataset, had the same overrepresentation of Native-American ancestry on the ch 8p23.1 locus that was previously reported for the BIPMed reference sample. Sequencing analysis of ch 8p23.1 revealed the presence of 442 non-synonymous variants, including frameshift, inframe deletion, start loss, stop gain, stop loss, and splicing site variants, which occurred in 24 genes. Among these genes, 13 were associated with obesity, type II diabetes, lipid levels, and waist circumference (PRAG1, MFHAS1, PPP1R3B, TNKS, MSRA, PRSS55, RP1L1, PINX1, MTMR9, FAM167A, BLK, GATA4, and CTSB). These results strengthen the hypothesis that a set of variants located on ch 8p23.1 that result from positive selection during early admixture events may influence obesity-related disease predisposition in admixed individuals of the Brazilian population. Furthermore, we present evidence that the exploration of local ancestry deviation in admixed individuals may provide information with the potential to be translated into health care improvement.

Genomic epidemiology of SARS-CoV-2 in Esteio, Rio Grande do Sul, Brazil

“…The broad spectrum of symptoms, days to symptoms onset, and the unpredictability of outcome in COVID-19 patients can also be linked with the vastly admixed Brazilian inhabitants. Moreover, recent reports identified HLA alleles previously associated with SARS-CoV-2 counteraction [38], and showed positive selection in genes associated with obesity, type II diabetes, lipid levels, and waist circumference [44]. Regarding COVID-19, epigenetics, specific variants, ACE2 and TMPRSS2 polymorphisms, ethnicity, as well as inborn immunity errors, have been reported worldwide.…”

“…A new mutation in the Receptor Binding Domain (RBD) of the spike protein (G23012A: E484) was found in two genomes (9.5%) (GISAID IDs: EPI_ISL_832010 and EPI_ISL_ 832013) from mid-October 2020. Since the municipality of Esteio has a higher CFR (3.26) than the national CFR (as the Brazilian states of São Paulo, Amazonas, Pernambuco, and Rio de Janeiro that were highly affected by the pandemic) (Supplementary Table 2), it is possible that the emergence of new viral mutations and lineages combined with genetic factors in these populations [38,44] are partially associated with differential COVID-19 severity.…”

A review of ancestrality and admixture in Latin America and the caribbean focusing on native American and African descendant populations

“…Furthermore, a close look at the LAC countries also reveals the different distribution of ancestry proportions in subpopulations and regions, as observed in Cuba ( Fortes-Lima et al, 2018 ), Garifuna population in Honduras ( Herrera-Paz et al, 2010 ), Mexico ( Rubi-Castellanos et al, 2009 ; Silva-Zolezzi et al, 2009 ), Venezuela ( Moura et al, 2015 ), Colombia ( Godinho et al, 2008 ; Ibarra et al, 2014 ; Mogollón Olivares et al, 2020 ; Chande et al, 2021 ), Bolivia ( Heinz et al, 2013 ; Taboada-Echalar et al, 2013 ), Paraguay ( Simão et al, 2021 ), Argentina ( Godinho et al, 2008 ), Chile ( Eyheramendy et al, 2015 ) and Brazil ( Kehdy et al, 2015 ; Moura et al, 2015 ; Mychaleckyj et al, 2017 ; Secolin et al, 2019 ; 2021 ). Also, as observed in several studies, the admixture events were sex biased and occurred between European males and Native American and/or sub-Saharan African females in Cuba ( Mendizabal et al, 2008 ), Jamaica ( Deason et al, 2012 ), Dominican Republic ( D’Atanasio et al, 2020 ), Nicaragua ( Nuñez et al, 2010 ), Belize ( Monsalve and Hagelberg, 1997 ), El Salvador ( Lovo-Gómez et al, 2007 ; Casals et al, 2022 ), Panama ( Migliore et al, 2021 ), Costa Rica ( Carvajal-Carmona et al, 2003 ), Mexico ( Green et al, 2000 ), Ecuador ( González-Andrade et al, 2007 ), Colombia ( Carvajal-Carmona et al, 2003 ), Brazil ( Pena et al, 2011 ), Uruguay ( Sans et al, 2002 ), and Argentina ( Dipierri et al, 1998 ).…”

“…Similarly, to other admixed American populations, the Brazilian population is derived from sub-Saharan Africa, Europe, and Native American populations ( Kehdy et al, 2015 ; Moura et al, 2015 ; Mychaleckyj et al, 2017 ; Secolin et al, 2019 ; 2021 ). Although it is underrepresented in the global context of genomic studies, in the last 10 years projects such as 1) DNA do Brasil ( Patrinos et al, 2020 ), 2) the Brazilian Initiative on Precision Medicine (BIPMed) ( Rocha et al, 2020 ), 3) the Health, Welfare, and Aging Project (SABE) ( Naslavsky et al, 2022 ), and 4) EPIGEN-Brasil ( Lima-Costa et al, 2015 ) have attempted to understand and map the genomes of the Brazilian population.…”

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

“…Relevant findings of mosaic detection include their contribution to Autism Spectrum Disorder risk [ 242 ], predisposition to infections [ 243 ] and clonal hematopoiesis [ 143 ]. Recent applications on the detection of inversions, included the genotyping of 20 inversions with scoreInvHap to study their role in obesity-related diseases [ 244 ], while a similar study in admixed population in Brazil used invClust [ 245 ].…”