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Confidence intervals for heritability via Haseman-Elston regression

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  • Sofer Tamar

    (University of Washington – Biostatistics UW Tower, 15th Floor 4333 Brooklyn Ave. NE, Seattle, WA 98105, USA)

Abstract

Heritability is the proportion of phenotypic variance in a population that is attributable to individual genotypes. Heritability is considered an important measure in both evolutionary biology and in medicine, and is routinely estimated and reported in genetic epidemiology studies. In population-based genome-wide association studies (GWAS), mixed models are used to estimate variance components, from which a heritability estimate is obtained. The estimated heritability is the proportion of the model’s total variance that is due to the genetic relatedness matrix (kinship measured from genotypes). Current practice is to use bootstrapping, which is slow, or normal asymptotic approximation to estimate the precision of the heritability estimate; however, this approximation fails to hold near the boundaries of the parameter space or when the sample size is small. In this paper we propose to estimate variance components via a Haseman-Elston regression, find the asymptotic distribution of the variance components and proportions of variance, and use them to construct confidence intervals (CIs). Our method is further developed to obtain unbiased variance components estimators and construct CIs by meta-analyzing information from multiple studies. We demonstrate our approach on data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Suggested Citation

  • Sofer Tamar, 2017. "Confidence intervals for heritability via Haseman-Elston regression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(4), pages 259-273, September.
    • Handle: RePEc:bpj:sagmbi:v:16:y:2017:i:4:p:259-273:n:3
    DOI: 10.1515/sagmb-2016-0076
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    References listed on IDEAS

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    1. Duchesne, Pierre & Lafaye De Micheaux, Pierre, 2010. "Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 858-862, April.
    2. Burch, Brent D., 2011. "Assessing the performance of normal-based and REML-based confidence intervals for the intraclass correlation coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1018-1028, February.
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