Leveraging Genetic Variability across Populations for the Identification of Causal Variants
Leveraging Genetic Variability across Populations for the Identification of Causal Variants
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DOI：
10.1016/j.ajhg.2009.11.016
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发表时间：
2010-01-08
影响因子：
9.8
通讯作者：
Halperin, Eran
中科院分区：
文献类型：
--
作者：
Zaitlen, Noah;Pasaniuc, Bogdan;Halperin, Eran
全基因组关联研究在过去几年中得到了广泛的开展，导致许多与复杂性状相关的基因组区域的新发现。通常情况下，发现与该病症相关的SNP并不是致病SNP，而是连锁不平衡的结果。为了确定实际的致病SNP，通过使用密集基因分型或对该区域进行测序，进行了精细定位随访。在任何一种情况下，如果因果SNP与其他SNP处于高度连锁不平衡状态，则精细映射程序将需要非常大的样本量来识别因果SNP。在这里，我们表明，通过利用群体间的遗传变异性，我们在涉及多个群体的后续研究中显著提高了因果SNP的定位成功率（LSR），而不是只涉及一个群体的研究。因此，在联合分析中发现因果变异的平均能力比在一次只分析一个群体的精细映射研究中要高。在此观察的基础上，我们开发了一个框架来有效地搜索后续研究设计：我们的框架从可用人群池中搜索最佳人群组合，以最大化检测因果变异的LSR。该框架及其配套软件可用于大大增强精细映射研究的能力。
Genome-wide association studies have been performed extensively in the last few years, resulting in many new discoveries of genomic regions that are associated with complex traits. It is often the case that a SNP found to be associated with the condition is not the causal SNP, but a proxy to it as a result of linkage disequilibrium. For the identification of the actual causal SNP, fine-mapping follow-up is performed, either with the use of dense genotyping or by sequencing of the region. In either case, if the causal SNP is in high linkage disequilibrium with other SNPs, the fine-mapping procedure win require a very large sample size for the identification of the causal SNP. Here, we show that by leveraging genetic variability across populations, We Significantly increase the localization success rate (LSR) for a causal SNP in a follow-up study that involves Multiple populations as compared to a study that involves only one population. Thus, the average power for detection of the causal variant win be higher in a joint analysis than than in Studies fine-mapping which only One population is analyzed at a time. Oil the basis of this observation, we developed a framework to efficiently search for a follow-up Study design: Our framework searches for the best combination of populations from a pool Of available populations to maximize the LSR for detection of a causal variant. This framework and its accompanying software can be used to considerably enhance the power of fine-mapping Studies.