Admixture with indigenous people helps local adaptation: admixture-enabled selection in Polynesians

Anthropometric and metabolic differences and distribution of ABCG2 rs2231142 variant between lowland and highland Papuans in West Papua, Indonesia

BACKGROUND

Papuan people inhabiting the island of New Guinea are the most ancient population living outside Africa, having resided in the region for at least 50,000 years. The arrival of Austronesian speakers and other group from mainland Asia around 3000 years or so created a peculiar genetic mixture, particularly in lowland/coastal areas. We investigated the anthropometric and blood chemical differences alongside the population structure of the ABCG2 rs2231142 genetic variant of West Papuans from lowland/coastal and highland areas to understand metabolic risk differences between these two populations.

RESULTS

We studied West Papuan students from lowland/coastal areas (n = 78, 45 males, 33 females) and from highland areas (n = 65, 40 males, 25 females). We found the following: (1) The lowland/coastal Papuans were taller, with lower BMI, central obesity, and triceps. Contrarily, highland Papuans have a more gynoid body shape, with higher WC, HC, WHR, and WHtR. The skinfolds were significantly thicker in women from the highlands. (2) There was actually a negative correlation between BMI and central adiposity with UA and FBG to those from the highlands. The lowland/coastal Papuans indicated an Asian-type metabolic traits, with higher fasting glucose levels at lower BMI and lower central adiposity. (3) UA concentration and DBP were strongly correlated with obesity of the Papuans from lowlands/coasts and not in the Papuans from highlands. (4) There was a striking difference in the ABCG2 rs2231142 > T allele frequency in those from the lowlands/coasts (22%) compared to those from the highlands of West Papua (7%). The T variant in the latter is all heterozygous.

CONCLUSIONS

The higher adiposity and thicker skinfolds observed in highland Papuans are thought to be adaptive responses to the high-altitude environment, enabling greater adipose tissue expandability and energy storage capacity while maintaining metabolic homeostasis. In contrast, the lowland/coastal Papuans exhibit an Asian metabolic phenotype, which is more prone to metabolic derangements at lower adiposity. Our findings on the population distribution of the ABCG2 rs2231142 > T variant support the idea that its presence in the Papuan highlands is through demic diffusion of the variant from ISEA, indicating that the two populations are separate entities displaying differences in metabolic risks.

Opportunities and challenges of local ancestry in genetic association analyses

Recently, admixed populations make up an increasing percentage of the US and global populations, and the admixture is not uniform over space or time or across genomes. Therefore, it becomes indispensable to evaluate local ancestry in addition to global ancestry to improve genetic epidemiological studies. Recent advances in representing human genome diversity, coupled with large-scale whole-genome sequencing initiatives and improved tools for local ancestry inference, have enabled studies to demonstrate that incorporating local ancestry information enhances both genetic association analyses and polygenic risk predictions. Along with the opportunities that local ancestry provides, there exist challenges preventing its full usage in genetic analyses. In this review, we first summarize methods for local ancestry inference and illustrate how local ancestry can be utilized in various analyses, including admixture mapping, association testing, and polygenic risk score construction. In addition, we discuss current challenges in research involving local ancestry, both in terms of the inference itself and its role in genetic association studies. We further pinpoint some future study directions and methodology development opportunities to help more effectively incorporate local ancestry in genetic analyses. It is worth the effort to pursue those future directions and address these analytical challenges because the appropriate use of local ancestry estimates could help mitigate inequality in genomic medicine and improve our understanding of health and disease outcomes.

Interplay of human ABCC11 transporter gene variants with axillary skin microbiome functional genomics

The human armpit microbiome is metabolically entangled with skin cell physiology. This "meta-organism" symbiotic mutualism results in sweat either with or without odor (osmidrosis), depending on host ABCC11 gene haplotypes. Apocrine metabolism produces odorless S-glutathione conjugate that is transferred by ABCC11 transporters into secretory vesicles, deglutamylated to S-Cys-Gly-3M3SH thiol, and exuded to skin surface. An anthropogenic clade of skin bacteria then takes up the thiol and bioconverts it to malodorous 3-methyl-3-sulfanylhexan-1-ol (3M3SH). We hypothesized a familial meta-organism association of human ABCC11 gene non-synonymous SNP rs17822931 interplaying with skin microbiome 3M3SH biosynthesis. Subjects were genotyped for ABCC11 SNPs, and their haplotypes were correlated with axilla microbiome DNA sequencing profiles and predicted metagenome functions. A multigeneration family pedigree revealed a Mendelian autosomal recessive pattern: the C allele of ABCC11 correlated with bacterial Cys-S-conjugate β-lyase (PatB) gene known for Staphylococcus hominis biosynthesis of 3M3SH from human precursor; PatB was rescinded in hosts with homozygous TT alleles encoding ABCC11 loss-of-function mutation. We posit that a C allele encoding functional ABCC11 is key to delivering host conjugate precursors that shape heritable skin niche conditions favorable to harboring Staphylococcus having genomics of odor thiol production. This provides existential insights into human evolution and global regional population ancestries.

Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes

Gene flow between previously differentiated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry "outliers" compared with the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from Cabo Verde, we localize a known adaptive locus to a single narrow region compared with multiple or long windows obtained using two other ancestry-based methods.