Lehrstuhl für Tierzucht, Technische Universität München, Hochfeldweg 1, 85376 Freising-Weihenstephan, Germany.
BMC Genomics. 2012 Jan 30;13:48. doi: 10.1186/1471-2164-13-48.
Hitchhiking mapping and association studies are two popular approaches to map genotypes to phenotypes. In this study we combine both approaches to complement their specific strengths and weaknesses, resulting in a method with higher statistical power and fewer false positive signals. We applied our approach to dairy cattle as they underwent extremely successful selection for milk production traits and since an excellent phenotypic record is available. We performed whole genome association tests with a new mixed model approach to account for stratification, which we validated via Monte Carlo simulations. Selection signatures were inferred with the integrated haplotype score and a locus specific permutation based integrated haplotype score that works with a folded frequency spectrum and provides a formal test of signifance to identify selection signatures.
About 1,600 out of 34,851 SNPs showed signatures of selection and the locus specific permutation based integrated haplotype score showed overall good accordance with the whole genome association study. Each approach provides distinct information about the genomic regions that influence complex traits. Combining whole genome association with hitchhiking mapping yielded two significant loci for the trait protein yield. These regions agree well with previous results from other selection signature scans and whole genome association studies in cattle.
We show that the combination of whole genome association and selection signature mapping based on the same SNPs increases the power to detect loci influencing complex traits. The locus specific permutation based integrated haplotype score provides a formal test of significance in selection signature mapping. Importantly it does not rely on knowledge of ancestral and derived allele states.
搭便车映射和关联研究是两种将基因型映射到表型的流行方法。在这项研究中，我们结合了这两种方法，以补充它们的特定优势和劣势，从而得到一种具有更高统计能力和更少假阳性信号的方法。我们将这种方法应用于奶牛，因为它们在牛奶生产性状方面经历了极其成功的选择，并且有极好的表型记录。我们使用新的混合模型方法进行全基因组关联测试，以考虑分层，我们通过蒙特卡罗模拟验证了该方法。选择信号是通过整合单倍型得分和基于局部的置换整合单倍型得分推断出来的，后者适用于折叠频率谱，并提供了一种正式的检验方法来识别选择信号。
在 34851 个 SNP 中，约有 1600 个显示出选择的特征，基于局部的置换整合单倍型得分与全基因组关联研究总体上具有很好的一致性。每种方法都提供了关于影响复杂性状的基因组区域的独特信息。将全基因组关联与搭便车映射相结合，为蛋白质产量性状检测到两个显著的位点。这些区域与牛的其他选择信号扫描和全基因组关联研究的先前结果非常吻合。
我们表明，基于相同 SNP 的全基因组关联与选择信号映射的结合增加了检测影响复杂性状的基因座的能力。基于局部的置换整合单倍型得分在选择信号映射中提供了一种正式的检验方法。重要的是，它不依赖于祖先和衍生等位基因状态的知识。
