Biogeographical ancestry is associated with socioenvironmental conditions and infections in a Latin American urban population

Abstract

Racial inequalities are observed for different diseases and are mainly caused by differences in socioeconomic status between ethnoracial groups. Genetic factors have also been implicated, and recently, several studies have investigated the association between biogeographical ancestry (BGA) and complex diseases. However, the role of BGA as a proxy for non-genetic health determinants has been little investigated. Similarly, studies comparing the association of BGA and self-reported skin colour with these determinants are scarce. Here, we report the association of BGA and self-reported skin colour with socioenvironmental conditions and infections. We studied 1246 children living in a Brazilian urban poor area. The BGA was estimated using 370,539 genome-wide autosomal markers. Standardised questionnaires were administered to the children’s guardians to evaluate socioenvironmental conditions. Infection (or pathogen exposure) was defined by the presence of positive serologic test results for IgG to seven pathogens (Toxocara spp, Toxoplasma gondii, Helicobacter pylori, and hepatitis A, herpes simplex, herpes zoster and Epstein-Barr viruses) and the presence of intestinal helminth eggs in stool samples (Ascaris lumbricoides and Trichiuris trichiura). African ancestry was negatively associated with maternal education and household income and positively associated with infections and variables, indicating poorer housing and living conditions. The self-reported skin colour was associated with infections only. In stratified analyses, the proportion of African ancestry was associated with most of the outcomes investigated, particularly among admixed individuals. In conclusion, BGA was associated with socioenvironmental conditions and infections even in a low-income and highly admixed population, capturing differences that self-reported skin colour miss. Importantly, our findings suggest caution in interpreting significant associations between BGA and diseases as indicative of the genetic factors involved. Keywords: Biogeographical ancestry, Racial inequalities, Socioeconomic status, Infections