Liability-scale heritability estimation for biobank studies of low-prevalence disease.

Details

Serval ID
serval:BIB_9B059FA24681
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Liability-scale heritability estimation for biobank studies of low-prevalence disease.
Journal
American journal of human genetics
Author(s)
Ojavee S.E., Kutalik Z., Robinson M.R.
ISSN
1537-6605 (Electronic)
ISSN-L
0002-9297
Publication state
Published
Issued date
03/11/2022
Peer-reviewed
Oui
Volume
109
Number
11
Pages
2009-2017
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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.
Keywords
Humans, Biological Specimen Banks, Prevalence, Causality, Computer Simulation, Genome-Wide Association Study, GWAS, biobanks, liability-scale heritability
Pubmed
Web of science
Open Access
Yes
Create date
27/10/2022 13:06
Last modification date
21/01/2023 6:46
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