A robust and unified framework for estimating heritability in twin studies using generalized estimating equations

Jaron Arbet, Matt McGue, Saonli Basu

Research output: Contribution to journalArticle

Abstract

The “heritability” of a phenotype measures the proportion of trait variance due to genetic factors in a population. In the past 50 years, studies with monozygotic and dizygotic twins have estimated heritability for 17 804 traits; thus twin studies are popular for estimating heritability. However, overestimation of heritability in twin studies is one suggested cause of the “missing-heritability phenomena” (estimates from empirical genetic studies are often smaller than twin studies). Developing more accurate methods for estimating heritability may be warranted. Therefore, we propose a robust framework for estimating heritability in twin studies using generalized estimating equations (GEE2). Two popular methods for estimating heritability, the normal ACE model (NACE) and Falconer's method, are derived within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot adjust for covariates, the corresponding “GEE2-Falconer” can incorporate mean and variance-level covariate effects (eg, let heritability vary by sex or age). Given nonnormal data, the GEE2 models are shown to attain better coverage of the true heritability compared to traditional methods. Finally, a scenario is demonstrated where NACE produces biased estimates of heritability while Falconer remains unbiased. Therefore, we recommend the more robust GEE2-Falconer method for estimating heritability in twin studies.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • generalized estimating equations
  • heritability
  • twin studies

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