Local genetic covariance between serum urate and kidney function estimated with Bayesian multitrait models

Author:

Lupi Alexa S12ORCID,Sumpter Nicholas A3ORCID,Leask Megan P34ORCID,O’Sullivan Justin5ORCID,Fadason Tayaza5ORCID,de los Campos Gustavo126ORCID,Merriman Tony R3ORCID,Reynolds Richard J3ORCID,Vazquez Ana I12ORCID

Affiliation:

1. Department of Epidemiology and Biostatistics, Michigan State University , East Lansing, MI 48824, USA

2. Institute for Quantitative Health Science and Engineering, Systems Biology, Michigan State University , East Lansing, MI 48824, USA

3. Department of Medicine, The University of Alabama at Birmingham , Birmingham, AL 35294, USA

4. Department of Biochemistry, University of Otago , Dunedin 9016, New Zealand

5. Liggins Institute, The University of Auckland , Auckland 1142, New Zealand

6. Department of Statistics and Probability, Michigan State University , East Lansing, MI 48824, USA

Abstract

Abstract Hyperuricemia (serum urate >6.8 mg/dl) is associated with several cardiometabolic and renal diseases, such as gout and chronic kidney disease. Previous studies have examined the shared genetic basis of chronic kidney disease and hyperuricemia in humans either using single-variant tests or estimating whole-genome genetic correlations between the traits. Individual variants typically explain a small fraction of the genetic correlation between traits, thus the ability to map pleiotropic loci is lacking power for available sample sizes. Alternatively, whole-genome estimates of genetic correlation indicate a moderate correlation between these traits. While useful to explain the comorbidity of these traits, whole-genome genetic correlation estimates do not shed light on what regions may be implicated in the shared genetic basis of traits. Therefore, to fill the gap between these two approaches, we used local Bayesian multitrait models to estimate the genetic covariance between a marker for chronic kidney disease (estimated glomerular filtration rate) and serum urate in specific genomic regions. We identified 134 overlapping linkage disequilibrium windows with statistically significant covariance estimates, 49 of which had positive directionalities, and 85 negative directionalities, the latter being consistent with that of the overall genetic covariance. The 134 significant windows condensed to 64 genetically distinct shared loci which validate 17 previously identified shared loci with consistent directionality and revealed 22 novel pleiotropic genes. Finally, to examine potential biological mechanisms for these shared loci, we have identified a subset of the genomic windows that are associated with gene expression using colocalization analyses. The regions identified by our local Bayesian multitrait model approach may help explain the association between chronic kidney disease and hyperuricemia.

Funder

National Institute of Arthritis and Musculoskeletal and Skin Diseases

Michigan State University

Dines Family Charitable Trust and a Health Research Council Explorer Grant

Publisher

Oxford University Press (OUP)

Subject

Genetics (clinical),Genetics,Molecular Biology

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