TY - JOUR
T1 - Fast and Accurate Construction of Confidence Intervals for Heritability
AU - Schweiger, Regev
AU - Kaufman, Shachar
AU - Laaksonen, Reijo
AU - Kleber, Marcus E.
AU - März, Winfried
AU - Eskin, Eleazar
AU - Rosset, Saharon
AU - Halperin, Eran
N1 - Publisher Copyright:
© 2016 American Society of Human Genetics.
PY - 2016/6/2
Y1 - 2016/6/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84971525481&partnerID=8YFLogxK
U2 - 10.1016/j.ajhg.2016.04.016
DO - 10.1016/j.ajhg.2016.04.016
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AN - SCOPUS:84971525481
SN - 0002-9297
VL - 98
SP - 1181
EP - 1192
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
IS - 6
ER -