Orienting Future Trends in Local Ancestry Deconvolution Models to Optimally Decipher Admixed Individual Genome Variations

Abstract

Rapid advances in sequencing and genotyping technologies have significantly contributed to shaping the area of medical and population genetics. Several thousand genomes are completed with millions of variants identified in the human deoxyribonucleic acid (DNA) sequences. These genomic variations highly influence changes in phenotypic manifestations and physiological functions of different individuals or population groups. Of particular importance are variations introduced by admixture event, contributing significantly to a remarkable phenotypic variability with medical and/or evolutionary implications. In this case, knowledge of local ancestry estimates and date of admixture is of utmost importance for a better understanding of genomic variation patterns throughout modern human evolution and adaptive processes. In this chapter, we survey existing local ancestry deconvolution and dating admixture event models to identify possible gaps that still need to be filled and orient future trends in designing more effective models, which account for current challenges and produce more accurate and biological relevant estimates.

Clotting factor genes are associated with preeclampsia in high-altitude pregnant women in the Peruvian Andes

“…57,58 Phasing and local ancestry estimation We used RFMix v1.5.4 59 to infer genome wide local ancestry calls for the Puno cohort founders, assuming a model of K=3 ancestral populations. Given that local ancestry methods have decreased accuracy when attempting to distinguish between closely related ancestries, 59,60 in this analysis we focused on the three major continental ancestries identified in the Puno cohort through the ADMIXTURE analysis. The reference panel included 108 YRI and 94 CEU individuals from 1KG, and 94 native individuals from Mexico (30 Mixe, 15 Zapotec, 49 Nahua) genotyped as part of the GALA II study.…”

Prospective avenues for human population genomics and disease mapping in southern Africa

“…Genetic regions associated with multifactorial diseases could be identified by investigating the allelic architecture of highly complex admixed individuals, since they received haplotypes from diverse continental populations previously exposed to various environments and pathogens (Dias-Alves et al 2018;Mazandu et al 2019). If such gene regions could be successfully identified, it will aid in the advancement of drug therapies, implementation of personalized medicine and vaccine development in underdeveloped countries such as South Africa.…”

“…These disparities are exploited to map disease-causing variants of multifactorial diseases in admixed genomes, better known as admixture mapping (Shriner 2013). However, additional modifications are required to conduct admixture mapping studies for individuals from southern Africa, since most computational tools are designed to infer local ancestry for two-or threeway admixed populations only (Chimusa et al 2018;Schurz et al 2019;Mazandu et al 2019). In addition, statistical methods assume homogeneity and may not be applicable for Africans with more complex haplotype structures and mosaic patterns present on chromosomes generated by recent admixture events across the African continent (Fan et al 2019).…”

Improving the personalized prediction of complex traits and diseases: application to type 2 diabetes

“…Besides, current GWASs effect sizes might be population dependent due to the differences in linkage disequilibrium patterns, allele frequencies, rare variants and environmental effects 29,57,58 , which makes studying admixed individuals' genomes with several ancestral backgrounds especially complicated. However, since admixture is one of the fastest evolutionary processes, it is a great mechanism to reveal differences in ancestral genetic variation related to disease 59 . For example, leveraging ancestral inference, a method to detect the genetic ancestry of a locus (local ancestry inference) or relative proportions of ancestry in a genome (global ancestry inference), may help overcoming confounding effects introduced by population specific LD patterns, hence pinpointing the true causative variants.…”

“…For example, leveraging ancestral inference, a method to detect the genetic ancestry of a locus (local ancestry inference) or relative proportions of ancestry in a genome (global ancestry inference), may help overcoming confounding effects introduced by population specific LD patterns, hence pinpointing the true causative variants. Furthermore, such an ancestry informed approach may improve prevention and treatment especially for complex traits, where incorporating local ancestry inference has already shown promising results in increasing detection of more genetic associations and in improving the genetic prediction for admixed individuals 59,60 .…”