Abstract
Introduction
A number of genes have been identified in which rare variants can cause obesity. Here we analyse a sample of exome sequenced subjects from UK Biobank using BMI as a phenotype with the aims of identifying genes in which rare, functional variants influence BMI and characterising the effects of different categories of variant.
Methods
There were 199,807 exome sequenced subjects for whom BMI was recorded. Weighted burden analysis of rare, functional variants was carried out, incorporating population principal components and sex as covariates. For selected genes, additional analyses were carried out to clarify the contribution of different categories of variant. Statistical significance was summarised as the signed log 10 of the p value (SLP), given a positive sign if the weighted burden score was positively correlated with BMI.
Results
Two genes were exome-wide significant, MC4R (SLP = 15.79) and PCSK1 (SLP = 6.61). In MC4R, disruptive variants were associated with an increase in BMI of 2.72 units and probably damaging nonsynonymous variants with an increase of 2.02 units. In PCSK1, disruptive variants were associated with a BMI increase of 2.29 and protein-altering variants with an increase of 0.34. Results for other genes were not formally significant after correction for multiple testing, although SIRT1, ZBED6 and NPC2 were noted to be of potential interest.
Conclusion
Because the UK Biobank consists of a self-selected sample of relatively healthy volunteers, the effect sizes noted may be underestimates. The results demonstrate the effects of very rare variants on BMI and suggest that other genes and variants will be definitively implicated when the sequence data for additional subjects becomes available.
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Data availability
The raw data is available on application to UK Biobank. Detailed results with variant counts cannot be made available because they might be used for subject identification. Scripts and relevant derived variables will be deposited in UK Biobank. Software and scripts used to carry out the analyses are available at https://github.com/davenomiddlenamecurtis.
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Acknowledgements
This research has been conducted using the UK Biobank Resource. The author wishes to acknowledge the staff supporting the High Performance Computing Cluster, Computer Science Department, University College London. This work was carried out in part using resources provided by BBSRC equipment grant BB/R01356X/1. The author wishes to thank the participants who volunteered for the UK Biobank project.
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Curtis, D. Weighted burden analysis in 200,000 exome-sequenced subjects characterises rare variant effects on BMI. Int J Obes 46, 782–792 (2022). https://doi.org/10.1038/s41366-021-01053-4
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DOI: https://doi.org/10.1038/s41366-021-01053-4
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