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Pulmonary tuberculosis (TB), caused by , is a complex disease. The risk of developing active TB is in part determined by host genetic factors. Most genetic studies investigating TB susceptibility fail to replicate association signals particularly across diverse populations. South African populations arose because of multi-wave genetic admixture from the indigenous KhoeSan, Bantu-speaking Africans, Europeans, Southeast Asian-and East Asian populations. This has led to complex genetic admixture with heterogenous patterns of linkage disequilibrium and associated traits. As a result, precise estimation of both global and local ancestry is required to prevent both false positive and false-negative associations. Here, 820 individuals from South Africa were genotyped on the SNP-dense Illumina Multi-Ethnic Genotyping Array (∼1.7M SNPs) followed by local and global ancestry inference using RFMix. Local ancestry adjusted allelic association (LAAA) models were utilized owing to the extensive genetic heterogeneity present in this population. Hence, an interaction term, comprising the identification of the minor allele that corresponds to the ancestry present at the specific locus under investigation, was included as a covariate. One SNP (rs28647531) located on chromosome 4q22 was significantly associated with TB susceptibility and displayed a SNP minor allelic effect (G allele, frequency = 0.204) whilst correcting for local ancestry for Bantu-speaking African ancestry (-value = 5.518 × 10; OR = 3.065; SE = 0.224). Although no other variants passed the significant threshold, clear differences were observed between the lead variants identified for each ancestry. Furthermore, the LAAA model robustly captured the source of association signals in multi-way admixed individuals from South Africa and allowed the identification of ancestry-specific disease risk alleles associated with TB susceptibility that have previously been missed.
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http://dx.doi.org/10.3389/fgene.2021.716558 | DOI Listing |
Syst Biol
September 2025
Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA.
Genomes are composed of a mosaic of segments inherited from different ancestors, each separated by past recombination events. Consequently, genealogical relationships among multiple genomes vary spatially across different genomic regions. Genealogical variation among unlinked (uncorrelated) genomic regions is well described for either a single population (coalescent) or multiple structured populations (multispecies coalescent).
View Article and Find Full Text PDFNAR Genom Bioinform
September 2025
Centre for Integrative Biology and Systems Medicine (IBSE), Wadhwani School of Data Science and AI, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Genome graphs provide a powerful reference structure for representing genetic diversity. Their structure emphasizes the polymorphic regions in a collection of genomes, enabling network-based comparisons of population-level variation. However, current tools are limited in their ability to quantify and compare structural features across large genome graphs.
View Article and Find Full Text PDFGenetics
September 2025
Institute of Ecology and Evolution, School of Biological Sciences, The University of Edinburgh, Edinburgh, EH9 3FL, United Kingdom.
Recent advances in methods to infer and analyse ancestral recombination graphs (ARGs) are providing powerful new insights in evolutionary biology and beyond. Existing inference approaches tend to be designed for use with fully-phased datasets, and some rely on model assumptions about demography and recombination rate. Here I describe a simple model-free approach for genealogical inference along the genome from unphased genotype data called Sequential Tree Inference by Collecting Compatible Sites (sticcs).
View Article and Find Full Text PDFPsychoneuroendocrinology
August 2025
Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas, Universidade de Sao Paulo, São Paulo, Brazil. Electronic address:
Altered cortisol regulation is implicated in Attention-Deficit/Hyperactivity Disorder (ADHD), but causality remains debated. While meta-analyses suggest that lower basal cortisol levels, especially in the morning, correlate with ADHD, study heterogeneity prompts further inquiry. Leveraging post-genome-wide association approaches, we examined morning cortisol levels (n = 25,314) and ADHD (n = 225,543).
View Article and Find Full Text PDFEvolutionary biology has long recognized the tendency for populations to be locally adapted to their ancestral habitat, resulting in higher resident fitness. However, immigrants can also introduce beneficial alleles. The resulting adaptive introgression is usually inferred retrospectively, rather than as a contemporary process.
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