| Title: | A Guide for Selection of Genetic Instruments in Mendelian randomisation (MR) studies of Type-2 diabetes and HbA1c: towards an integrated approach |
| Journal: | Diabetes |
| Published: | 20 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36669000/ |
| DOI: | https://doi.org/10.2337/db22-0110 |
| URL: | https://diabetesjournals.org/diabetes/article-pdf/72/2/175/696171/db220110.pdf |
| Citations: | 8 (8 in last 2 years) as of 8 Aug 2024 |
Publication 7980
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Abstract
In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.4 Authors
- Victoria Garfield
- Antoine Salzmann
- Stephen Burgess
- Nish Chaturvedi
1 Application
| Application ID | Title |
|---|---|
| 7661 | Chronic disease outcomes across the glycaemic spectrum: harnessing UK Biobank and linked eHealth data to quantify risks, explore mechanisms and determine treatment impacts. |
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