Proceedings of the National Academy of SciencesEvolutionary BiologyPeer-reviewed journal article
De novo rates of a Trypanosoma-resistant mutation in two human populations
Daniel Melamed, Revital ShemeriD, Evgeni BolotiniD, Michael Bright YakassiD, Dorit Fink-Barkai, Edem Kojo Hiadzi, Karl L. SkoreckiiD, Adi LivnatiD
Abstract
Until recently, mutation rates have only been measured as averages across many genomic positions. Recently, a method to measure mutation rates at the single-mutation resolution was applied to a narrow region in the human hemoglobin subunit beta (HBB) gene containing the site of the hemoglobin S (HbS) mutation as well as to a paralogous hemoglobin subunit delta (HBD) region, in sperm samples from sub-Saharan African and northern European donors. The HbS mutation, which protects against malaria while causing sickle-cell anemia in homozygotes originated de novo significantly more frequently in the HBB gene in Africans compared to the other three test cases combined (the European HBB gene and the European and African HBD gene). Here, we apply this approach to the human apolipopro-tein L1 (APOL1) gene containing the site of the G1 1024A[->]G mutation, which protects against African sleeping sickness caused by Trypanosoma brucei gambiense while causing a substantially increased risk of chronic kidney disease (CKD) in homozygotes. We find that the 1024A[->]G mutation is the mutation of highest de novo origination rate and deviates most from the genome-wide average rate for its type (A[->]G) compared to all other observable mutations in the region, and that it originates de novo significantly more frequently in Africans than in Europeans--i.e., in the population where it is of adaptive significance. The results are inconsistent with the notion that the probability of a specific mutational event is independent of its value to the organism and underscore the importance of studying mutation rates at the single-mutation resolution.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 5
- FWCI
- 2.61
- Field percentile
- top 10%
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