Genome wide signatures of positive selection: the comparison of independent samples and the identification of regions associated to traits.

Genome wide signatures of positive selection: the comparison of independent samples and the identification of regions associated to traits.
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DOI:
10.1186/1471-2164-10-178
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发表时间:
2009-04-24
期刊:
影响因子:
4.4
通讯作者:
Turner LB
Turner LB
中科院分区:
生物学2区
文献类型:
--
作者:
Barendse W;Harrison BE;Bunch RJ;Thomas MB;Turner LB

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全基因组多态分析的目标是更好地理解基因和表型之间的联系。这一目标的一部分是理解对人口产生作用的选择性力量。在这项研究中,我们将牛HapMap项目中通过种群差异识别的选择信号与澳大利亚独立的牛样本中发现的信号进行了比较。根据FST的测量,跨基因组的种群分化的证据在两个数据集中高度相关。然而,两个研究之间FST差异的40%归因于品种组成的差异。当比较相同品种时,76%的FST变异可归因于SNP组成和密度的差异。随着SNP间距离的增加,相邻座位间的FST差值迅速增大,在20kb后达到一渐近线。使用129个在两个数据集中具有高度差异的FST值的SNP,我们确定了12个区域,这些区域对澳大利亚样本中测量的剩余饲料摄入量、牛肉产量或肌肉内脂肪含量具有加性效应。这些区域中有四个对不止一个性状有影响。其中一个区域包括R3HDM1基因,该基因在欧洲人类中处于选择状态。首先,完整描述整个基因组中的选择性签名需要许多不同的种群,而不仅仅是一小部分高度分化的种群。其次,在比较一个研究和另一个研究的选择签名时,有必要使用相同的SNP。第三,在许多群体只有很小的遗传差异并且在主成分分析中可能没有明显分离的情况下,可以获得有用的选择特征。第四,结合全基因组选择标记和全基因组与性状的关联分析,有助于确定被选择的性状或QTL可能分离的群体群。最后,相邻基因座之间的FST差异表明,需要150,000个均匀分布的SNP来研究牛基因组所有部分的选择性签名。
The goal of genome wide analyses of polymorphisms is to achieve a better understanding of the link between genotype and phenotype. Part of that goal is to understand the selective forces that have operated on a population. In this study we compared the signals of selection, identified through population divergence in the Bovine HapMap project, to those found in an independent sample of cattle from Australia. Evidence for population differentiation across the genome, as measured by FST, was highly correlated in the two data sets. Nevertheless, 40% of the variance in FST between the two studies was attributed to the differences in breed composition. Seventy six percent of the variance in FST was attributed to differences in SNP composition and density when the same breeds were compared. The difference between FST of adjacent loci increased rapidly with the increase in distance between SNP, reaching an asymptote after 20 kb. Using 129 SNP that have highly divergent FST values in both data sets, we identified 12 regions that had additive effects on the traits residual feed intake, beef yield or intramuscular fatness measured in the Australian sample. Four of these regions had effects on more than one trait. One of these regions includes the R3HDM1 gene, which is under selection in European humans. Firstly, many different populations will be necessary for a full description of selective signatures across the genome, not just a small set of highly divergent populations. Secondly, it is necessary to use the same SNP when comparing the signatures of selection from one study to another. Thirdly, useful signatures of selection can be obtained where many of the groups have only minor genetic differences and may not be clearly separated in a principal component analysis. Fourthly, combining analyses of genome wide selection signatures and genome wide associations to traits helps to define the trait under selection or the population group in which the QTL is likely to be segregating. Finally, the FST difference between adjacent loci suggests that 150,000 evenly spaced SNP will be required to study selective signatures in all parts of the bovine genome.
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