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Journal Article

Genome-wide linkage-disequilibrium profiles from single individuals

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Haubold,  Bernhard
Research Group Bioinformatics, Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Citation

Lynch, M., Xu, S., Maruki, T., Jiang, X., Pfaffelhuber, P., & Haubold, B. (2014). Genome-wide linkage-disequilibrium profiles from single individuals. Genetics, 198(1), 269-281. doi:10.1534/genetics.114.166843.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0023-EC70-F
Abstract
Although the analysis of linkage disequilibrium (LD) plays a central role in many areas of population genetics, the sampling variance of LD is known to be very large with high sensitivity to numbers of nucleotide sites and individuals sampled. Here we show that a genome-wide analysis of the distribution of heterozygous sites within a single diploid genome can yield highly informative patterns of LD as a function of physical distance. The proposed statistic, the correlation of zygosity, is closely related to the conventional population-level measure of LD, but is agnostic with respect to allele frequencies and hence likely less prone to outlier artifacts. Application of the method to several vertebrate species leads to the conclusion that >80% of recombination events are typically resolved by gene-conversion-like processes unaccompanied by crossovers, with the average lengths of conversion patches being on the order of one to several kilobases in length. Thus, contrary to common assumptions, the recombination rate between sites does not scale linearly with distance, often even up to distances of 100 kb. In addition, the amount of LD between sites separated by <200 bp is uniformly much greater than can be explained by the conventional neutral model, possibly because of the nonindependent origin of mutations within this spatial scale. These results raise questions about the application of conventional population-genetic interpretations to LD on short spatial scales and also about the use of spatial patterns of LD to infer demographic histories.