Fast and Accurate Construction of Confidence Intervals for Heritability

Regev Schweiger, Shachar Kaufman, Reijo Laaksonen, Marcus E. Kleber, Winfried März, Eleazar Eskin, Saharon Rosset, Eran Halperin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1181-1192
Number of pages12
JournalAmerican Journal of Human Genetics
Volume98
Issue number6
DOIs
StatePublished - 2 Jun 2016

Funding

FundersFunder number
Israeli Science Foundation1487/12, 1425/13
United States – Israel Binational Science Foundation2012304
National Science Foundation1331176, III-1217615
National Institutes of HealthR01-ES022282
National Institute of Mental HealthR01MH101782
Edmond J. Safra Center for Ethics, Harvard University
Tel Aviv University
Seventh Framework Programme1302448, 1065276, 201668, 1320589
Colton Foundation

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