Nature CommunicationsGeneticsPeer-reviewed journal article
Regionally enriched rare deleterious exonic variants in the UK and Ireland
Mihail R. HalacheviD, Viktoria‐Eleni GountounaiD, Alison M. MeynertiD, Gannie TzonevaiD, Alan R. ShuldineriD, Colin A M SempleiD, James F. WilsoniD
Abstract
Geographic clustering of haplotypes appears to have emerged in the UK as a result of differing patterns of immigration and drift in regions that have been relatively isolated from each other. However, until recently it has been unclear how such patterns of regional genetic differentiation might impact the protein-coding fraction of the genome. Here, we exploit UK Biobank (UKB) and Viking Genes whole exome sequencing data to study regional genetic differentiation across the UK and Ireland in protein coding genes, encompassing 20 regions of origin and 44,696 unrelated individuals. We rediscover the strong influence of genetic drift in shaping variation in the Northern Isles of Scotland and among those with full or partial Ashkenazi Jewish (AJ) ancestry. For full AJ, almost half the known rare exonic variants (45%) are at least two-fold more or less frequent than in a Europe-wide reference sample, while the degree of variant frequency differences in Shetland and Orkney are comparable to part AJ (19%, 17%, 16%, respectively). We also demonstrate substantial genetic differentiation among several mainland regions of origin, particularly north and south Wales, SE Scotland and Ireland. With stringent filtering criteria we found 67 variants likely to have adverse biomedical consequences, enriched by at least five-fold in frequency in one or more British or Irish regions relative to a European reference group, and we calculate that this may lead to tens or hundreds of affected individuals. We conclude that regional genetic variation across the UK and Ireland should be considered in the design of genetic studies, and may inform effective genetic screening and counselling.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 7
- FWCI
- 1.85
- Field percentile
- top 14%
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