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| Home > Publications Database > A18_ Heritability Estimation from Summary StatisticsUsing Generalized Estimating Equations |
| Home > Publications Database > A18_ Heritability Estimation from Summary StatisticsUsing Generalized Estimating Equations |
| Abstract/Journal Article | DZNE-2020-00973 |
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2015
Abstract: Under the assumption of a polygenic architecture, Yangshowed for complex traits that the test statistics in genome-wideassociation studies are expected to be inflated, even in the absenceof confounding biases like cryptic relatedness or population stratification (Yang et al., 2011).In a recently published work, Bulik-Sullivan and Finucane (Bulik-Sullivan et al., 2015) provide a methodological approach to differentiate between an inflation resulting from a polygenic architecture and from cryptic relatedness by considering all test statisticssimultaneously. This makes it also possible to estimate the narrowsense heritability from summary statistics without requiring individual-level genotype data.The approach of Bulik-Sullivan and Finucane (Bulik-Sullivanet al., 2015) estimates so-called LD Scores from a reference paneland utilizes these quantities as covariates in a weighted linear regression of the squared test statistics. Since the test statistics are notindependent of each other, a bootstrap estimator is applied to obtain robust standard errors.Building on the same mean model, our objective is to incorporate more useful extern information into the estimation in orderto improve the efficiency of the estimation. In particular, we dividethe genomic region into blocks of moderate size. For these blocks,the correlation structure between squared test statistics can be wellapproximated by LD information from a reference panel. Our estimation procedure is based on generalized estimating equations(GEE). We use the LD information to set up the working-correlation matrices for each block, whereas we do not require that nearby blocks are independent. We show that the GEE-related asymptotic results are still valid under reasonable assumptions. It is important to note that the working-correlation matrices are notrequired to be exactly the true correlation matrices in order to obtain consistent estimates and correct standard errors.In conclusion, these results imply that our approach improvesthe heritability estimation framework.
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