Titre
Liability-scale heritability estimation for biobank studies of low-prevalence disease.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Ojavee, S.E.
Auteure/Auteur
Kutalik, Z.
Auteure/Auteur
Robinson, M.R.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1537-6605
Statut éditorial
Publié
Date de publication
2022-11-03
Volume
109
Numéro
11
Première page
2009
Dernière page/numéro d’article
2017
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Résumé
Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare, and understand results generated from biological studies. In particular, through estimation of the liability-scale heritability (LSH), liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes. Increasingly, large-scale biobank studies that link genetic information to electronic health records, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample, are becoming available. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower mean square error (MSE) and is less sensitive to prevalence misspecification as compared to previous transformations for diseases with ≤2% population prevalence and LSH of ≤0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of ≤3% prevalence in the UK Biobank study revealed important differences in LSH obtained from the different theoretical expressions that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low-prevalence disease outcomes and facilitates improved inference of the underlying genetic basis of ≤2% population prevalence diseases, especially where biobank sample ascertainment results in a healthier sample population.
PID Serval
serval:BIB_9B059FA24681
PMID
Open Access
Oui
Date de création
2022-10-27T12:06:41.929Z
Date de création dans IRIS
2025-05-21T03:21:19Z
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Nom
36265482_BIB_9B059FA24681.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by-nc-nd/4.0
Taille
688.67 KB
Format
Adobe PDF
PID Serval
serval:BIB_9B059FA24681.P001
URN
urn:nbn:ch:serval-BIB_9B059FA246817
Somme de contrôle
(MD5):b3511ca43064f51c3ab6959082cf0fd2