Dataset Information

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

ABSTRACT: 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 the most 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 construct more accurate evolutionary models and better understand the mechanisms that underlie genetic change.

SUBMITTER: Aikens RC

PROVIDER: S-EPMC6501879 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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

Aikens Rachael C RC Johnson Kelsey E KE Voight Benjamin F BF

Molecular biology and evolution 20190501 5

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 p ...[more]

PMID: 30753705

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Project description:Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.Two predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.Predicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

Dataset Information

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

Publications

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

Similar Datasets

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