| Title: | Systematic Heritability and Heritability Enrichment Analysis for Diabetes Complications in UK Biobank and ACCORD Studies. |
| Journal: | Diabetes |
| Published: | 8 Feb 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35133398/ |
| DOI: | https://doi.org/10.2337/db21-0839 |
| URL: | https://diabetesjournals.org/diabetes/article-pdf/71/5/1137/673156/db210839.pdf |
| Citations: | 11 (9 in last 2 years) as of 8 Aug 2024 |
Publication 7196
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Abstract
Diabetes-related complications reflect longstanding damage to small and large vessels throughout the body. In addition to the duration of diabetes and poor glycemic control, genetic factors are important contributors to the variability in the development of vascular complications. Early heritability studies found strong familial clustering of both macrovascular and microvascular complications. However, they were limited by small sample sizes and large phenotypic heterogeneity, leading to less accurate estimates. We take advantage of two independent studies-UK Biobank and the Action to Control Cardiovascular Risk in Diabetes trial-to survey the single nucleotide polymorphism heritability for diabetes microvascular (diabetic kidney disease and diabetic retinopathy) and macrovascular (cardiovascular events) complications. Heritability for diabetic kidney disease was estimated at 29%. The heritability estimate for microalbuminuria ranged from 24 to 60% and was 41% for macroalbuminuria. Heritability estimates of diabetic retinopathy ranged from 6 to 33%, depending on the phenotype definition. More severe diabetes retinopathy possessed higher genetic contributions. We show, for the first time, that rare variants account for much of the heritability of diabetic retinopathy. This study suggests that a large portion of the genetic risk of diabetes complications is yet to be discovered and emphasizes the need for additional genetic studies of diabetes complications.10 Authors
- Juhyun Kim
- Aubrey Jensen
- Seyoon Ko
- Sridharan Raghavan
- Lawrence S Phillips
- Adriana Hung
- Yan Sun
- Hua Zhou
- Peter Reaven
- Jin J Zhou
1 Application
| Application ID | Title |
|---|---|
| 48152 | Developing statistical methods and computational algorithms for identifying biomarkers at Biobank-data scale for cardio-metabolic traits |
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