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1000 human genomes carry widespread signatures of GC biased gene conversion.


ABSTRACT: GC-Biased Gene Conversion (gBGC) is one of the important theories put forward to explain profound long-range non-randomness in nucleotide compositions along mammalian chromosomes. Nucleotide changes due to gBGC are hard to distinguish from regular mutations. Here, we present an algorithm for analysis of millions of known SNPs that detects a subset of so-called "SNP flip-over" events representing recent gBGC nucleotide changes, which occurred in previous generations via non-crossover meiotic recombination.This algorithm has been applied in a large-scale analysis of 1092 sequenced human genomes. Altogether, 56,328 regions on all autosomes have been examined, which revealed 223,955 putative gBGC cases leading to SNP flip-overs. We detected a strong bias (11.7%?±?0.2% excess) in AT-?>?GC over GC-?>?AT base pair changes within the entire set of putative gBGC cases.On average, a human gamete acquires 7 SNP flip-over events, in which one allele is replaced by its complementary allele during the process of meiotic non-crossover recombination. In each meiosis event, on average, gBGC results in replacement of 7 AT base pairs by GC base pairs, while only 6 GC pairs are replaced by AT pairs. Therefore, every human gamete is enriched by one GC pair. Happening over millions of years of evolution, this bias may be a noticeable force in changing the nucleotide composition landscape along chromosomes.

SUBMITTER: Dutta R 

PROVIDER: S-EPMC5902838 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

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1000 human genomes carry widespread signatures of GC biased gene conversion.

Dutta Rajib R   Saha-Mandal Arnab A   Cheng Xi X   Qiu Shuhao S   Serpen Jasmine J   Fedorova Larisa L   Fedorov Alexei A  

BMC genomics 20180416 1


<h4>Background</h4>GC-Biased Gene Conversion (gBGC) is one of the important theories put forward to explain profound long-range non-randomness in nucleotide compositions along mammalian chromosomes. Nucleotide changes due to gBGC are hard to distinguish from regular mutations. Here, we present an algorithm for analysis of millions of known SNPs that detects a subset of so-called "SNP flip-over" events representing recent gBGC nucleotide changes, which occurred in previous generations via non-cro  ...[more]

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