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Chunk #16 — Materials and Methods — Power of a GWAS meta-analysis under heterogeneity

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Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies.
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Notably, our expression for E [Zk2] bears a great resemblance to expressions for the expected value of the squared Z statistic when accounting for LD, population stratification, and polygenicity [38, 43, 44]. Consider the scenario where the CGR between two samples of equal size is positive one. Based of Eq 1, we then have that E [Zk2]≈1+hSNP2MNT for a trait-affecting haplotype block, where NT = 2N denotes the total sample size. This expression is equivalent to the expected squared Z statistic from the linear regression analysis for a trait-affecting variant reported in Section 4.2 of the Supplementary Note to [44] as well as the first equation in [38] when assuming that confounding biases and LD are absent.