  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.
