Computational Biology Branch, NCBI, NLM, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
Genome Biol. 2010;11(10):R103. doi: 10.1186/gb-2010-11-10-r103. Epub 2010 Oct 20.
Meiotic recombination events tend to cluster into narrow spans of a few kilobases long, called recombination hotspots. Such hotspots are not conserved between human and chimpanzee and vary between different human ethnic groups. At the same time, recombination hotspots are heritable. Previous studies showed instances where differences in recombination rate could be associated with sequence polymorphisms.
In this work we developed a novel computational approach, LDsplit, to perform a large-scale association study of recombination hotspots with genetic polymorphisms. LDsplit was able to correctly predict the association between the FG11 SNP and the DNA2 hotspot observed by sperm typing. Extensive simulation demonstrated the accuracy of LDsplit under various conditions. Applying LDsplit to human chromosome 6, we found that for a significant fraction of hotspots, there is an association between variations in intensity of historical recombination and sequence polymorphisms. From flanking regions of the SNPs output by LDsplit we identified a conserved 11-mer motif GGNGGNAGGGG, whose complement partially matches 13-mer CCNCCNTNNCCNC, a critical motif for the regulation of recombination hotspots.
Our result suggests that computational approaches based on historical recombination events are likely to be more powerful than previously anticipated. The putative associations we identified may be a promising step toward uncovering the mechanisms of recombination hotspots.
减数分裂重组事件往往聚集在几个千碱基长的狭窄区域,称为重组热点。这些热点在人类和黑猩猩之间没有保守性,并且在不同的人类族群之间存在差异。同时,重组热点是可遗传的。先前的研究表明,重组率的差异可能与序列多态性有关。
在这项工作中,我们开发了一种新的计算方法 LDsplit,用于对重组热点与遗传多态性进行大规模关联研究。LDsplit 能够正确预测 FG11 SNP 与精子分型观察到的 DNA2 热点之间的关联。广泛的模拟证明了 LDsplit 在各种条件下的准确性。将 LDsplit 应用于人类染色体 6,我们发现对于很大一部分热点,历史重组强度的变化与序列多态性之间存在关联。从 LDsplit 输出的 SNP 的侧翼区域,我们鉴定出一个保守的 11 -mer 基序 GGNGGNAGGGG,其互补部分与 13-mer CCNCCNTNNCCNC 匹配,后者是调控重组热点的关键基序。
我们的结果表明,基于历史重组事件的计算方法可能比预期的更有效。我们鉴定出的假定关联可能是揭示重组热点机制的一个有前途的步骤。