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病例对照研究中基因-环境交互作用的基因型推断:有效性和功效。

Genotype imputation in case-only studies of gene-environment interaction: validity and power.

机构信息

Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.

Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.

出版信息

Hum Genet. 2021 Aug;140(8):1217-1228. doi: 10.1007/s00439-021-02294-z. Epub 2021 May 26.

Abstract

Case-only (CO) studies are a powerful means to uncover gene-environment (G × E) interactions for complex human diseases. Moreover, such studies may in principle also draw upon genotype imputation to increase statistical power even further. However, genotype imputation usually employs healthy controls such as the Haplotype Reference Consortium (HRC) data as an imputation base, which may systematically perturb CO studies in genomic regions with main effects upon disease risk. Using genotype data from 719 German Crohn Disease (CD) patients, we investigated the level of imputation accuracy achievable for single nucleotide polymorphisms (SNPs) with or without a genetic main effect, and with varying minor allele frequency (MAF). Genotypes were imputed from neighbouring SNPs at different levels of linkage disequilibrium (LD) to the target SNP using the HRC data as an imputation base. Comparison of the true and imputed genotypes revealed lower imputation accuracy for SNPs with strong main effects. We also simulated different levels of G × E interaction to evaluate the potential loss of statistical validity and power incurred by the use of imputed genotypes. Simulations under the null hypothesis revealed that genotype imputation does not inflate the type I error rate of CO studies of G × E. However, the statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased. Our study thus highlights that genotype imputation should be employed with great care in CO studies of G × E interaction.

摘要

仅病例(CO)研究是发现复杂人类疾病的基因-环境(G×E)相互作用的有力手段。此外,此类研究原则上还可以利用基因型推断来进一步提高统计效力。然而,基因型推断通常采用健康对照(如 Haplotype Reference Consortium [HRC] 数据)作为推断基础,这可能会在对疾病风险有主要影响的基因组区域中系统地干扰 CO 研究。我们使用来自 719 名德国克罗恩病(CD)患者的基因型数据,研究了具有或不具有遗传主要效应且具有不同次要等位基因频率(MAF)的单核苷酸多态性(SNP)可实现的推断准确性水平。使用 HRC 数据作为推断基础,根据目标 SNP 与相邻 SNP 之间不同程度的连锁不平衡(LD),对 SNP 进行推断。比较真实基因型和推断基因型,发现具有强主要效应的 SNP 的推断准确性较低。我们还模拟了不同程度的 G×E 相互作用,以评估使用推断基因型可能导致的统计有效性和效力损失。在零假设下的模拟显示,基因型推断不会增加 G×E 的 CO 研究的 I 型错误率。然而,推断发现会降低统计效力,特别是对于 MAF 较低的 SNP,并且当驱动推断的 SNP 的 LD 水平降低时,统计效力逐渐降低。因此,我们的研究强调,在 G×E 相互作用的 CO 研究中,应该谨慎使用基因型推断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d8c/8263402/cea56a2845d6/439_2021_2294_Fig1_HTML.jpg

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