Heritability estimation of dichotomous phenotypes using a liability threshold model on ascertained family‐based samples

Abstract

Numerous methods for estimating heritability have been proposed; however, unlike quantitative phenotypes, heritability estimation for dichotomous phenotypes is computationally and statistically complex, and the use of heritability is infrequent. In this study, we developed a statistical method to estimate heritability of dichotomous phenotypes using a liability threshold model in the context of ascertained family‐based samples. This model assumes that dichotomous phenotypes are determined by unobserved latent variables that are normally distributed and can be applied to general pedigree data. The proposed methods were applied to simulated data and Korean type‐2 diabetes family‐based samples, and the accuracy of the estimates provided by the experimental methods was compared with that of the established methods.

Multiple Sclerosis Heritability Estimation on Sardinian Ascertained Extended Families Using Bayesian Liability Threshold Model

“…To estimate MS h 2 making use of our ascertained families from a proband, we relied on the LTMH method [ 29 ]. Given N individuals clustered in F families, the N observed binary phenotypes (Y) are determined by unobserved continuous liability scores (L) and a fixed threshold (c), which depends on the trait’s prevalence in the population [ 41 ].…”

“…Given the limitations of the EM algorithm implemented in [ 29 ], this approach was inefficient to estimate MS h 2 using our sample since we were dealing with extended families with the aim of including other variance components in the model, e.g., shared environment effects. Therefore, we implemented a Bayesian framework using simulated-based methods as MCMC techniques as it represents an alternative and faster process, compared to maximum likelihood estimation, in the case of complicated statistical models with many unobserved variables [ 53 , 54 , 55 , 56 , 57 ].…”

“…In this context, different methodologies have been developed to produce unbiased h 2 estimates for binary traits [ 26 ]. However, ascertainment bias arises when using families ascertained from a sampled proband [ 27 , 28 , 29 ]. To overcome this problem, Kim et al [ 29 ] developed a liability threshold model for binary traits (LTMH) allowing to estimate h 2 , on a liability scale, adjusted from ascertainment bias.…”

“…However, ascertainment bias arises when using families ascertained from a sampled proband [ 27 , 28 , 29 ]. To overcome this problem, Kim et al [ 29 ] developed a liability threshold model for binary traits (LTMH) allowing to estimate h 2 , on a liability scale, adjusted from ascertainment bias. However, their expectation–maximization (EM)-based approach presented some limitations, such as lacking a precision measure for h 2 (i.e., standard error) [ 30 , 31 ], consequent difficulty in calculating confidence intervals [ 32 ], computational inefficiency when handling extended families, and convergence issues when including additional variance components in the model, e.g., to adjust for shared environment effects, a feature particularly important when dealing with families and complex diseases.…”

A pedigree-based cohort to study the genetic risk factors for cardiometabolic diseases: study design, baseline characteristics and preliminary results

“…The maximum likelihood-based variance components decomposition and liability-threshold model were used to estimate the heritability of continuous and categorical variables, respectively (14,15). The estimation of heritability was conducted in Solar version 8.1.1 after adjusting for age, sex, age 2 , age by sex, and age 2 by sex.…”