4.8 Article

Signals of Variation in Human Mutation Rate at Multiple Levels of Sequence Context

Journal

MOLECULAR BIOLOGY AND EVOLUTION
Volume 36, Issue 5, Pages 955-965

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/molbev/msz023

Keywords

mutation rate; statistical genetics; sequence context models

Funding

  1. US National Institutes of Health [R01 DK101478, T32 LM012409, T32 GM008216]
  2. Linda Pechenik Montague Investigator Award

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Our understanding of the human mutation rate helps us build evolutionary models and interpret patterns of genetic variation observed in human populations. Recent work indicates that the frequencies of specific polymorphism types have been elevated in Europe, and that many more, subtler signatures of global polymorphism variation may yet remain unidentified. Here, we present an analysis of the 1000 Genomes Project supported by analysis in the Simons Genome Diversity Panel, suggesting additional putative signatures of mutation rate variation across populations and the extent to which they are shaped by local sequence context. First, we compiled a list of themost significantly variable polymorphism types in a cross-continental statistical test. Clustering polymorphisms together, we observe three sets that showed distinct shared patterns of relative enrichment among ancestral populations, and we characterize each one of these putative signatures of polymorphism variation. For three of these signatures, we found that a single flanking base pair of sequence context was sufficient to determine the majority of enrichment or depletion of a polymorphism type. However, local genetic context up to 2-3 bp away contributes additional variability and may help to interpret a previously noted enrichment of certain polymorphism types in some East Asian groups. Moreover, considering broader local genetic context highlights patterns of polymorphism variation, which were not captured by previous approaches. Building our understanding of mutation rate in this way can help us to constructmore accurate evolutionarymodels and better understand the mechanisms that underlie genetic change.

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