A Guide for Selection of Genetic Instruments in Mendelian randomisation (MR) studies of Type-2 diabetes and HbA1c: towards an integrated approach

This study examines the instrument selection strategies currently employed throughout the type-2 diabetes and HbA1c 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 Mendelian randomisation studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (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-SNP instrument for diabetes and a 51-SNP instrument (as well as partitioned into glycaemic and erythrocytic) 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 was partitioned 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 and total strength, but these were both substantially smaller 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. MR studies of glycaemia should take a more integrated approach when selecting genetic instruments and we give specific guidance on how to do this. </p

Additional file 1 of Adiposity and grip strength: a Mendelian randomisation study in UK Biobank

Additional file 1: Tables S1-S8. Table S1. R-squared and F-statistic of adiposity genetic instruments. Table S2. Summary statistics describing SNP associations with (i) adiposity exposures (BMI, BF%, WC and WHR) and (ii) grip strength. Table S3. Correlation coefficients between adiposity measures for males and females: (a) phenotypic correlations, (b) genetic correlations. Table S4. Mean difference (95% CI) in grip strength (kg) by markers of total and central adiposity. Table S5. Mean difference (95% CI) in grip strength (kg) by markers of total and central adiposity, stratified by age. Table S6. Mean difference (95% CI) in grip strength (kg) by markers of total and central adiposity: exploring pleiotropy. Table S7. Estimation of bias due to sample overlap. Table S8. MR PRESSO Outlier correction

Additional file 2 of Adiposity and grip strength: a Mendelian randomisation study in UK Biobank

Additional file 2: Figure S1. Genetic instrument selection process

Investigating the relationship between IGF-I, IGF-II, and IGFBP-3 concentrations and later-life cognition and brain volume

Background The insulin/insulin-like signaling (IIS) pathways, including insulin-like growth factors (IGFs), vary with age. However, their association with late-life cognition and neuroimaging parameters is not well characterized. Methods Using data from the British 1946 birth cohort, we investigated associations of IGF-I, IGF-II and IGF binding protein 3 (IGFBP-3; measured at 53 and 60-64 years of age) with cognitive performance [word-learning test (WLT) and visual letter search (VLS) at 60-64 years and 69 years of age] and cognitive state [Addenbrooke’s Cognitive Exam III (ACE-III) at 69-71 years of age], and in a proportion, quantified neuroimaging measures [whole brain volume (WBV), white matter hyperintensity volume (WMHV), hippocampal volume (HV)]. Regression models included adjustments for demographic, lifestyle, and health factors. Results Higher IGF-I and IGF-II at 53 years of age was associated with higher ACE-III scores [ß 0.07 95% confidence interval (CI) (0.02, 0.12); scoreACE-III 89.48 (88.86, 90.1), respectively). IGF-II at 53 years of age was additionally associated with higher WLT scores [scoreWLT 20 (19.35, 20.65)]. IGFBP-3 at 60 to 64 years of age was associated with favorable VLS score at 60 to 64 and 69 years of age [ß 0.07 (0.01, 0.12); ß 0.07 (0.02, 0.12), respectively], higher memory and cognitive state at 69 years of age [ß 0.07 (0.01, 0.12); ß 0.07 (0.01, 0.13), respectively], and reduced WMHV [ß -0.1 (-0.21, -0.00)]. IGF-I/IGFBP-3 at 60 to 64 years of was associated with lower VLS scores at 69 years of age [ß -0.08 (-0.15, -0.02)]. Conclusions Increased measure in IIS parameters (IGF-I, IGF-II, and IGFBP-3) relate to better cognitive state in later life. There were apparent associations with specific cognitive domains (IGF-II relating to memory; IGFBP-3 relating to memory, processing speed, and WMHV; and IGF-I/IGFBP-3 molar ratio related to slower processing speed). IGFs and IGFBP-3 are associated with favorable cognitive function outcomes