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Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation

DNA甲基化 表观遗传学 样本量测定 生物 表观基因组 统计能力 甲基化 遗传学 遗传关联 全基因组关联研究 计算生物学 DNA 统计 数学 单核苷酸多态性 基因型 基因 基因表达
作者
Pei-Chien Tsai,Jordana T. Bell
出处
期刊:International Journal of Epidemiology [Oxford University Press]
卷期号:44 (4): 1429-1441 被引量:204
标识
DOI:10.1093/ije/dyv041
摘要

Epigenome-wide association scans (EWAS) are under way for many complex human traits, but EWAS power has not been fully assessed. We investigate power of EWAS to detect differential methylation using case-control and disease-discordant monozygotic (MZ) twin designs with genome-wide DNA methylation arrays.We performed simulations to estimate power under the case-control and discordant MZ twin EWAS study designs, under a range of epigenetic risk effect sizes and conditions. For example, to detect a 10% mean methylation difference between affected and unaffected subjects at a genome-wide significance threshold of P = 1 × 10-6, 98 MZ twin pairs were required to reach 80% EWAS power, and 112 cases and 112 controls pairs were needed in the case-control design. We also estimated the minimum sample size required to reach 80% EWAS power under both study designs. Our analyses highlighted several factors that significantly influenced EWAS power, including sample size, epigenetic risk effect size, the variance of DNA methylation at the locus of interest and the correlation in DNA methylation patterns within the twin sample.We provide power estimates for array-based DNA methylation EWAS under case-control and disease-discordant MZ twin designs, and explore multiple factors that impact on EWAS power. Our results can help guide EWAS experimental design and interpretation for future epigenetic studies.
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