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4 recordsLinked to original sources
PubMed2026

Genetic Ancestry and Carrier Variant Frequency Enrichment in a Colombian Andean Population: Insights From the Eje Cafetero.

Colombia is one of the most genetically diverse populations in Latin America, and its demographic process has promoted the persistence and local enrichment of deleterious alleles, increasing the frequency of autosomal recessive disorders, particularly in semi-isolated Andean populations such as the Eje Cafetero. However, exome-based reference data from this region remain scarce, limiting ancestry-aware variant interpretation and carrier screening strategies. We aimed to characterize the ancestry proportions of this population using exome data, and to estimate the carrier frequency and distribution of pathogenic and likely pathogenic (P/LP) variants in clinically relevant recessive genes. We conducted a cross-sectional study with whole-exome sequencing (WES) in 316 unrelated individuals from the Colombian Eje Cafetero. P/LP variants were evaluated in 454 genes associated with autosomal recessive disorders. The global ancestry proportions were estimated using a validated panel of 250 exome-compatible ancestry-informative markers. Carrier frequencies were compared against Non-Finnish Europeans (NFE) and Admixed Americans (AMX) from gnomAD v4. The cohort showed predominant European ancestry (mean 51%), followed by Native American (36%) and African (13%) components. We identified 151 carriers of 89 distinct pathogenic variants across autosomal recessive genes. The most frequent variants were SERPINA1 c.863A>T (5.5%), CFTR c.1210-11T>G (3.5%), and PYGM c.1094C>T (1.5%). Also, recurrent variants were significantly enriched compared with both NFE and AMX populations, supporting regional founder effects. This study represents one of the most comprehensive exome-based genetic characterizations of the Colombian Eje Cafetero, revealing ancestry-specific enrichment of clinically relevant autosomal recessive variants driven by founder effects.

Female↗
PubMed2026

Inference of elevated mutation rates and variant effects using 700k exomes.

Genomic sequencing is now widely accessible for genetic diagnostics and is emerging as a component of newborn screening. This technological development generates the need to characterize incoming mutations, create comprehensive datasets of genes causing rare Mendelian disorders, and identify pathogenic variants. Large-scale exome sequencing datasets such as Genome Aggregation Database (gnomAD) have been assembled to help address these challenges. The recent release of gnomAD (v4; n = 730,947) uncovers millions of rare coding variants, many of which have arisen more than once by independent recurrent mutations in the rapidly growing recent human population. Here, we use newly developed theoretical understanding of sampling properties of rare variants to estimate key population genetics parameters of practical importance to human genetics such as demography history, mutation rate, and selection. Solely relying on population data, our method Population Inferred Estimates of Selection (PIES) identifies novel genes with loss-of-function mutational hotspots likely due to selection in spermatogonia. PIES efficiently estimates selection coefficients for heterozygous loss-of-function variants. Combining population genetics inference with variant effect predictors, PIES predicts pathogenic missense mutations and improves variant prioritization for genetic diagnostics and newborn screening.

Journal Article↗
PubMed2024

Estimation of carrier frequencies of autosomal and X-linked recessive genetic conditions based on gnomAD v4.0 data in different ancestries.

PURPOSE: Monogenic rare diseases contribute significantly to infant deaths and pediatric hospitalizations and cause burden to the patients and their families. The American College of Medical Genetics and Genomics recommended in 2021 that carrier screening of autosomal recessive and X-linked conditions with a carrier frequency of ≥1/200 and a severe or moderate phenotype should be offered when planning or during pregnancy. In November 2023 gnomAD v4.0 was released. It contains in total 807,162 individuals, being nearly 5× larger than previous versions, which have been used to estimate gene carrier frequencies (GCF). METHODS: We utilized gnomAD v4.0 (GRCh38) to calculate the GCFs for available genetic ancestry groups for variants having pathogenic or likely pathogenic classification (>80% of submissions) in ClinVar. We calculated GCF separately for exomes and genomes, combined data, and at-risk couple frequencies (ACF) per genetic ancestry group. RESULTS: In total, 324 genes had a GCF ≥1/200 in at least 1 ancestry subgroup. The number of genes with GCF ≥1/200 varied greatly between subgroups. ACFs were more similar, Ashkenazi Jewish having the highest ACF of 6.11%. CONCLUSION: Improved understanding of carrier risks and updated carrier screening content would allow patients to make more informed reproductive decisions.

Humans↗
PubMed2026

WxS-QC-a quality control pipeline for human germline short-variant Whole-Genome and Whole-Exome cohorts for population-scale analyses.

SUMMARY: Whole-exome (WES) and whole-genome (WGS) sequencing are rapidly becoming preferred methods for population-scale analysis of the human genetic landscape. However, there are currently no standardized quality control (QC) pipelines for human WES and WGS datasets. In this paper, we present WxS-QC, a powerful, scalable, and convenient pipeline for the QC of human germline short-variant WGS and WES cohorts for population-scale analyses. Our pipeline is suitable for both rare-variant discovery and common-variant association studies. It is based on deeply refactored gnomAD v3 and v4 quality control pipelines, contains several methods we have developed de novo, and is aligned with current best practices in WGS/WES germline cohort QC. We provide all methods in a single codebase, aligned to work together and controlled via a single YAML config, with automatic export of resulting graphs and summary tables, excellent performance and scalability, and comprehensive documentation. The pipeline can run in any UNIX-like environment and can efficiently process cohorts of up to 200 000 whole-exome samples, with the potential to handle bigger datasets. AVAILABILITY AND IMPLEMENTATION: The pipeline code is written in Python using the Hail library and is freely available under the BSD-3 license here: https://github.com/wtsi-hgi/wxs-qc. The detailed description of the pipeline is available in the pipeline documentation: https://github.com/wtsi-hgi/wxs-qc/blob/main/README.md. We also provide an open dataset with all required metadata, which is available at https://wxs-qc-data.cog.sanger.ac.uk/wxs-qc_public_dataset_v3.tar. An example of test dataset analysis is available in the supplementary materials.

Humans↗