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

08:00 EDT 12th July 2019 | BioPortfolio

Summary of "Heritability estimation of dichotomous phenotypes using a liability threshold model on ascertained family-based samples."

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.

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Name: Genetic epidemiology
ISSN: 1098-2272
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Medical and Biotech [MESH] Definitions

A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.

Insurance against loss resulting from liability for injury or damage to the persons or property of others.

The attempt to improve the PHENOTYPES of future generations of the human population by fostering the reproduction of those with favorable phenotypes and GENOTYPES and hampering or preventing BREEDING by those with "undesirable" phenotypes and genotypes. The concept is largely discredited. (McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

The smallest difference which can be discriminated between two stimuli or one which is barely above the threshold.

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