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A map of the human genome in linkage disequilibrium units

机译:连锁不平衡单位中的人类基因组图

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摘要

Two genetic maps with additive distances contribute information about recombination patterns, recombinogenic sequences, and discovery of genes affecting a particular phenotype. Recombination is measured in morgans (w) over a single generation in a linkage map but may cover thousands of generations in a linkage disequilibrium (LD) map measured in LID units (LDU). We used a subset of single nucleotide polymorphisms from the HapMap Project to create a genome-wide map in LDU. Recombination accounts for 96.8% of the LDU variance in chromosome arms and 92.4% in their deciles. However, deeper analysis shows that LDU/w, an estimate of the effective bottleneck time (t), is significantly variable among chromosome arms because (i) the linkage map is approximated from the Haldane function, then adjusted toward the Kosambi function that is more accurate but still exaggerates w for all chromosomes, especially shorter ones; (ii) the norpseudoautosomal region of the X chromosome is subject to hemizygous selection; and (iii) at resolution less than approximate to 40,000 markers per w, there are indeterminacies (holes) in the LID map reflecting intervals of very high recombination. Selection and stochastic variation in small regions must have effects, which remain to be investigated by comparisons among populations. These considerations suggest an optimal strategy to eliminate holes quickly, greatly enhance the resolution of sex-specific linkage maps, and maximize the gain in association mapping by using LD maps.
机译:两个具有加和距离的遗传图谱提供有关重组模式,重组序列和发现影响特定表型的基因的信息。重组是在连锁图中以单代(m)来衡量的,但在以LID单位(LDU)为单位的连锁不平衡(LD)图中可能涵盖了数千代。我们使用了HapMap项目的单核苷酸多态性子集在LDU中创建了全基因组图谱。重组占染色体臂的LDU变异的96.8%,占其臂架的92.4%。但是,更深入的分析表明,LDU / w是有效瓶颈时间(t)的估计值,在染色体臂之间存在显着差异,因为(i)连锁图谱从Haldane函数近似,然后朝Kosambi函数进行调整准确,但仍夸大了所有染色体的w,尤其是较短的染色体; (ii)X染色体的正伪常染色体区域是半合子选择的; (iii)在分辨率低于每w约40,000个标记的情况下,LID图中存在不确定性(空洞),反映出非常高的重组间隔。小区域的选择和随机变异必须产生影响,仍有待通过人口比较进行研究。这些考虑提出了一种最佳策略,该策略可以快速消除漏洞,大大提高按性别划分的连锁图的分辨率,并通过使用LD图最大化关联映射的收益。

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