Fast and accurate construction of confidence intervals for heritability

• Verfasst von: Schweiger, Regev [VerfasserIn]
• Verfasst von: Kleber, Marcus E. [VerfasserIn]
• Verfasst von: März, Winfried [VerfasserIn]
• Titel: Fast and accurate construction of confidence intervals for heritability
• Verf.angabe: Regev Schweiger, Shachar Kaufman, Reijo Laaksonen, Marcus E. Kleber, Winfried März, Eleazar Eskin, Saharon Rosset, and Eran Halperin
• E-Jahr: 2016
• Jahr: June 2, 2016
• Umfang: 12 S.
• Fussnoten: Gesehen am 27.06.2019
• Titel Quelle: Enthalten in: The American journal of human genetics
• Ort Quelle: New York, NY [u.a.] : Cell Press, 1949
• Jahr Quelle: 2016
• Band/Heft Quelle: 98(2016), 6, Seite 1181-1192
• ISSN Quelle: 1537-6605
• DOI: doi:10.1016/j.ajhg.2016.04.016
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1668069679

Abstract

Estimation of heritability is fundamental in genetic studies. Recently, heritability estimation using linear mixed models (LMMs) has gained popularity because these estimates can be obtained from unrelated individuals collected in genome-wide association studies. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. Existing methods for the construction of confidence intervals and estimators of SEs for REML rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals. Here, we show that the estimation of confidence intervals by state-of-the-art methods is inaccurate, especially when the true heritability is relatively low or relatively high. We further show that these inaccuracies occur in datasets including thousands of individuals. Such biases are present, for example, in estimates of heritability of gene expression in the Genotype-Tissue Expression project and of lipid profiles in the Ludwigshafen Risk and Cardiovascular Health study. We also show that often the probability that the genetic component is estimated as 0 is high even when the true heritability is bounded away from 0, emphasizing the need for accurate confidence intervals. We propose a computationally efficient method, ALBI (accurate LMM-based heritability bootstrap confidence intervals), for estimating the distribution of the heritability estimator and for constructing accurate confidence intervals. Our method can be used as an add-on to existing methods for estimating heritability and variance components, such as GCTA, FaST-LMM, GEMMA, or EMMAX.