Family-based association tests for genomewide association scans
Family-based association tests for genomewide association scans
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DOI：
10.1086/521580
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发表时间：
2007-11-01
影响因子：
9.8
通讯作者：
Abecasis, Goncalo R.
中科院分区：
文献类型：
--
作者：
Chen, Wei-Min;Abecasis, Goncalo R.
随着数百万单核苷酸多态性（SNP）被识别和鉴定，全基因组关联研究已开始确定复杂性状和疾病的易感基因。这些研究涉及对数百或数千个体的超高分辨率SNP基因型数据进行鉴定和分析。我们描述了一种计算高效的方法来检测SNP与数量性状之间的关联，该方法可应用于全基因组关联扫描。除了观察到的基因型外，我们的方法还允许对缺失的基因型进行估计，当基因分型资源有限时，这会大大提高功效。我们使用兰德 - 格林（Lander - Green）或埃尔斯顿 - 斯图尔特（Elston - Stewart）算法概率性地估计缺失的基因型，并将每个家系中一部分个体的高分辨率SNP基因型与其余个体较稀疏的标记数据相结合。我们表明，只要将未进行基因分型个体的表型信息纳入分析，功效就会提高，并且在一个核心家庭中仅对三个精心挑选的个体进行高密度基因分型，如果对每个个体都进行基因分型，就可以恢复>90%的可用信息，而成本和实验工作量只是一小部分。为了辅助研究设计，我们评估了对每个家系中不同个体子集进行基因分型策略的功效，并就哪些个体应该进行高密度基因分型提出了建议。为了说明我们的方法，我们对三代家庭（人类多态性研究中心家系）中的27种基因表达表型进行了全基因组关联分析，其中90位祖父母和父母中约860,000个SNP的基因型与总共168个个体中约6,700个SNP的基因型相互补充。除了增加15个先前确定的顺式作用相关等位基因的关联证据外，我们的基因型推断算法使我们能够识别4个顺式作用位点的相关等位基因，当分析仅限于具有高密度SNP数据的个体时，这些等位基因被遗漏了。我们的基因型推断算法和提出的关联检验在可免费获得的软件中得以实现。
With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for similar to 860,000 SNPs in 90 grandparents and parents are complemented by genotypes for similar to 6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.