Impact of cross-ancestry genetic architecture on GWASs in admixed populations.

Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.

Opportunities and constraints in women's resource security amid climate change: A case study of arid-living Namibian agro-pastoralists.

OBJECTIVE: We describe the composition and variation of women's resource strategies in an arid-living Southern African agro-pastoralist society to gain insights into adaptation to climate-change-induced increased aridity. METHODS: Using cross-sectional data from 210 women collected in 2009 across 28 agro-pastoralist villages in Kaokoveld Namibia, we conducted principal-component (PC) analysis of resource variables and constructed profiles of resource strategies from the major PCs. Next, we explored associations between key resource strategies and demographic measures and fitness proxies. RESULTS: The first two PCs accounted for 43% of women's overall resource variation. PC1 reflects women's ability to access market resources via livestock trading, while PC2 captured women's direct food access. We found that market strategies were more common among married women and less common among women who have experienced child mortality. Women with higher subsistence security were more likely to be from the OvaHimba tribe and had a higher risk of gonorrhea exposure. We also qualitatively explored drought-induced pressure on women's livestock. Finally, we show that sexual networks were attenuated during drought, indicating strain on social support. CONCLUSIONS: Our results highlight how agro-pastoralist women manage critical resources in unpredictable environments, and how resource strategies distribute among the women in our study. Goats as a commodity to obtain critical resources suggests that some women have flexibility during drought when gardens fail and cattle die. However, increased aridity and drought may eventually overwhelm husbandry practices in this region.

Impact of cross-ancestry genetic architecture on GWAS in admixed populations.

Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.