How to estimate heritability, a guide for epidemiologists

TL;DR

This paper provides a comprehensive guide for epidemiologists on estimating heritability using various methods, highlighting their assumptions, benefits, and limitations in different sample contexts.

Contribution

It offers an overview of both traditional and modern heritability estimation methods, clarifying their applications and underlying assumptions for genetic epidemiology research.

Findings

Comparison of heritability estimation methods

Discussion of assumptions and limitations

Guidance on method selection based on sample type

Abstract

Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of alternative methods that utilise large samples of unrelated or related individuals. Yet, specific challenges persist in the estimation of heritability such as epistasis, assortative mating and indirect genetic effects. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability i.e., the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals (LD score regression, GREML), and family-based genomic designs (Sibling regression, GREML-KIN, Trio-GCTA, MGCTA, RDR). For each method, we describe how heritability is estimated, the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared to samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.