Estimation of Genetic Relationships Between Individuals Across Cohorts and Platforms: Application to Childhood Height.
Estimation of Genetic Relationships Between Individuals Across Cohorts and Platforms: Application to Childhood Height.
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DOI：
10.1007/s10519-015-9725-7
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发表时间：
2015-09
影响因子：
2.6
通讯作者：
Boomsma DI
中科院分区：
文献类型：
--
作者：
Fedko IO;Hottenga JJ;Medina-Gomez C;Pappa I;van Beijsterveldt CE;Ehli EA;Davies GE;Rivadeneira F;Tiemeier H;Swertz MA;Middeldorp CM;Bartels M;Boomsma DI
在分析遗传关系矩阵(GRM)的基础上，结合队列中的基因数据增加了估计常见单核苷酸多态(SNPs)引起的遗传力的能力。然而，例如，当使用不同的基因分型平台时，跨多个队列的SNP数据的组合可能导致分层。在目前的研究中，我们解决了结合来自不同队列、荷兰双胞胎登记册(NTR)和R世代(GENR)研究的SNP数据的问题。这两个队列都包括在不同平台上进行基因分型的北欧荷兰背景的儿童(分别为N=0.93102+0.2826)。我们探索了补偿和阶段化作为工具，并比较了三种构建GRM的策略，其中来自两个队列的数据是(1)只合并，(2)预先合并和跨平台估计，(3)跨平台估计和后合并。我们用无关个体(N=3.124，平均年龄6.7岁)的童年身高数据测试这三种策略，以探索它们对SNP遗传力估计的影响，并将结果与独立研究获得的结果进行比较。所有组合策略得到的SNP遗传力估计的标准误差都小于独立研究的标准误差。我们没有观察到基于不同跨平台归因于GRM的SNP遗传力估计的显著差异。儿童身高的SNP遗传力平均估计为0.50(SE=0.10)。引入队列作为一个协变量，导致≈下降2%。主成分(PC)调整导致SNP遗传力估计约为0.39(SE=0.11)。值得注意的是，我们没有发现跨平台归因式和组合式GRM之间的显著差异。不管使用PCS调整，所有的估计都是显着的。在这些分析的基础上，我们得出结论，通过结合不同平台上基因分型的同一种族的队列，使用参考集合有助于增加估计SNP遗传度的能力。然而，应考虑重要的因素，如归罪后剩余的队列分层和/或队列之间和队列内的表型异质性。是否应该使用推算，或者只是结合基因数据，取决于重叠的SNPs的数量相对于两个队列的基因分型SNPs的总数，以及他们标记与感兴趣的特定性状相关的所有遗传变异的能力。
Combining genotype data across cohorts increases power to estimate the heritability due to common single nucleotide polymorphisms (SNPs), based on analyzing a Genetic Relationship Matrix (GRM). However, the combination of SNP data across multiple cohorts may lead to stratification, when for example, different genotyping platforms are used. In the current study, we address issues of combining SNP data from different cohorts, the Netherlands Twin Register (NTR) and the Generation R (GENR) study. Both cohorts include children of Northern European Dutch background (N = 3102 + 2826, respectively) who were genotyped on different platforms. We explore imputation and phasing as a tool and compare three GRM-building strategies, when data from two cohorts are (1) just combined, (2) pre-combined and cross-platform imputed and (3) cross-platform imputed and post-combined. We test these three strategies with data on childhood height for unrelated individuals (N = 3124, average age 6.7 years) to explore their effect on SNP-heritability estimates and compare results to those obtained from the independent studies. All combination strategies result in SNP-heritability estimates with a standard error smaller than those of the independent studies. We did not observe significant difference in estimates of SNP-heritability based on various cross-platform imputed GRMs. SNP-heritability of childhood height was on average estimated as 0.50 (SE = 0.10). Introducing cohort as a covariate resulted in ≈2 % drop. Principal components (PCs) adjustment resulted in SNP-heritability estimates of about 0.39 (SE = 0.11). Strikingly, we did not find significant difference between cross-platform imputed and combined GRMs. All estimates were significant regardless the use of PCs adjustment. Based on these analyses we conclude that imputation with a reference set helps to increase power to estimate SNP-heritability by combining cohorts of the same ethnicity genotyped on different platforms. However, important factors should be taken into account such as remaining cohort stratification after imputation and/or phenotypic heterogeneity between and within cohorts. Whether one should use imputation, or just combine the genotype data, depends on the number of overlapping SNPs in relation to the total number of genotyped SNPs for both cohorts, and their ability to tag all the genetic variance related to the specific trait of interest.