A new multipoint method for genome-wide association studies by imputation of genotypes
A new multipoint method for genome-wide association studies by imputation of genotypes
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DOI：
10.1038/ng2088
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发表时间：
2007-07-01
期刊：
影响因子：
30.8
通讯作者：
Donnelly, Peter
中科院分区：
文献类型：
--
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
基因组 - 广泛的关联研究将成为发现人类疾病遗传基础的首选方法。在该领域的一个核心挑战是开发强大的多点方法，该方法可以检测尚未直接基因分型的因果变异。我们提出了一个连贯的分析框架，将问题视为涉及缺失或不确定基因型的问题。我们方法的核心是一种基于模型的插补方法，用于在观察到的或未观察到的SNP上推断基因型，从而提高了对现有方法的多点关联映射的功率。使用实际的基因组研究数据，我们表明我们的方法（i）是准确且校准的，（ii）提供了相关区域的详细视图，这些区域可促进随访研究，并且（iii）可用于验证和纠正数据在基因分型标记中。我们方法的一个值得注意的使用将是通过结合使用不同SNP集的基因组扫描的数据来提高功率。
Genome- wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model- based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome- wide association study data, we show that our approach ( i) is accurate and well calibrated, ( ii) provides detailed views of associated regions that facilitate follow- up studies and ( iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome- wide scans that use different SNP sets.