AD-LIBS: inferring ancestry across hybrid genomes using low-coverage sequence data

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.

Distribution of HLA-B Alleles and Haplotypes in Qatari: Recommendation for Establishing Pharmacogenomic Markers Screening for Drug Hypersensitivity

Human leukocyte antigen (HLA) proteins are present at the cellular surface of antigen-presenting cells and play a crucial role in the adaptive immune response. Class I genes, specifically certain HLA-B alleles, are associated with adverse drug reactions (ADRs) and are used as pharmacogenetic markers. Although ADRs are a common causes of hospitalization and mortality, the data on the prevalence of HLA-B pharmacogenetics markers in Arab countries are scarce. In this study, we investigated the frequencies of major HLA-B pharmacogenomics markers in the Qatari population. Next-generation sequencing data from 1,098 Qatari individuals were employed for HLA-B typing using HLA-HD version 1.4.0 and IPD-IMGT/HLA database. In addition, HLA-B pharmacogenetics markers were obtained from the HLA Adverse Drug Reaction Database. In total, 469 major HLA-B pharmacogenetic markers were identified, with HLA-B*51:01 being the most frequent pharmacogenetic marker (26.67%) in the Qatari population. Moreover, HLA-B*51:01 is associated with phenytoin- and clindamycin-induced ADRs. The second most frequent pharmacogenetic marker was the HLA-B*58:01 allele (6.56%), which is associated with allopurinol-induced ADRs. The third most frequent pharmacogenetic marker was the HLA-B*44:03 allele, which is associated with phenytoin-induced ADRs. The establishment of a pharmacogenetics screening program in Qatar for cost effective interventions aimed at preventing drug-induced hypersensitivity can be aided by the highly prevalent HLA-B pharmacogenetic markers detected here.

Selection against admixture and gene regulatory divergence in a long-term primate field study

Genetic admixture is central to primate evolution. We combined 50 years of field observations of immigration and group demography with genomic data from ~9 generations of hybrid baboons to investigate the consequences of admixture in the wild. Despite no obvious fitness costs to hybrids, we found signatures of selection against admixture similar to those described for archaic hominins. These patterns were concentrated near genes where ancestry is strongly associated with gene expression. Our analyses also show that introgression is partially predictable across the genome. This study demonstrates the value of integrating genomic and field data for revealing how "genomic signatures of selection" (e.g., reduced introgression in low-recombination regions) manifest in nature; moreover, it underscores the importance of other primates as living models for human evolution.

Genomic divergence landscape in recurrently hybridizing Chironomus sister taxa suggests stable steady state between mutual gene flow and isolation

Divergence is mostly viewed as a progressive process often initiated by selection targeting individual loci, ultimately resulting in ever increasing genomic isolation due to linkage. However, recent studies show that this process may stall at intermediate stable equilibrium states without achieving complete genomic isolation. We tested the extent of genomic isolation between two recurrently hybridizing nonbiting midge sister taxa, Chironomus riparius and Chironomus piger, by analyzing the divergence landscape. Using a principal component-based method, we estimated that only about 28.44% of the genomes were mutually isolated, whereas the rest was still exchanged. The divergence landscape was fragmented into isolated regions of on average 30 kb, distributed throughout the genome. Selection and divergence time strongly influenced lengths of isolated regions, whereas local recombination rate only had minor impact. Comparison of divergence time distributions obtained from several coalescence-simulated divergence scenarios with the observed divergence time estimates in an approximate Bayesian computation framework favored a short and concluded divergence event in the past. Most divergence happened during a short time span about 4.5 million generations ago, followed by a stable equilibrium between mutual gene flow through ongoing hybridization for the larger part of the genome and isolation in some regions due to rapid purifying selection of introgression, supported by high effective population sizes and recombination rates.

Conservation of biodiversity in the genomics era

"Conservation genomics" encompasses the idea that genome-scale data will improve the capacity of resource managers to protect species. Although genetic approaches have long been used in conservation research, it has only recently become tractable to generate genome-wide data at a scale that is useful for conservation. In this Review, we discuss how genome-scale data can inform species delineation in the face of admixture, facilitate evolution through the identification of adaptive alleles, and enhance evolutionary rescue based on genomic patterns of inbreeding. As genomic approaches become more widely adopted in conservation, we expect that they will have a positive impact on management and policy decisions.