The Trans-Ancestral Genomic Architecture of Glycaemic Traits

Abstract

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P<5×10-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

Telomere length and metabolic syndrome traits: A Mendelian randomisation study

“…Secondly, our findings are based on data from GWAS conducted in subjects of European ancestry. Hence, our results and conclusions might not extend to other ethnic populations although, evidence from a recent, large, ancestrally diverse GWAS meta‐analysis of glycaemic traits suggests that similar results might also be expected (Chen et al, 2021). Thirdly, two‐sample MR studies assume that the SNP‐exposure (in this case LTL) associations are also present in the outcome dataset(s).…”

Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis

“…We selected 17 cardiometabolic traits and diseases that cluster clinically with metabolic syndrome, obesity, diabetes, and their complications: type 1 diabetes [ 34 ], type 2 diabetes [ 35 ], hemoglobin A1c [ 36 ], fasting glucose adjusted for body mass index (BMI) [ 36 ], fasting insulin adjusted for BMI [ 36 ], BMI [ 37 ], waist–hip ratio adjusted for BMI [ 38 ], low-density lipoprotein cholesterol [ 39 ], high-density lipoprotein cholesterol [ 39 ], triglycerides [ 39 ], systolic blood pressure [ 40 ], diastolic blood pressure [ 40 ], creatinine-based estimated glomerular filtration rate (eGFR) [ 41 ], chronic kidney disease [ 41 ], coronary artery disease [ 42 ], any stroke [ 43 ], and C-reactive protein (CRP) [ 44 ], a nonspecific biomarker of inflammation that can be elevated in people with high cardiometabolic risk. As our study was conducted to narrowly test an a priori hypothesis, we did not have a prespecified analysis plan.…”

Cardiometabolic Risk Factors for COVID-19 Susceptibility and Severity: A Mendelian Randomization Analysis

“…We extracted association summary statistics from published large-scale GWAS meta-analysis to generate sets of genetic instruments for 17 cardiometabolic diseases and traits, type 1 diabetes 34 , type 2 diabetes 35 , hemoglobin A1c 36 , fasting glucose adjusted for body mass index (BMI) 36 , fasting insulin adjusted for BMI 36 , BMI 37 , waist-hip ratio 38 , low-density lipoprotein cholesterol 39 , high-density lipoprotein cholesterol 39 , triglycerides 39 , systolic blood pressure 40 , diastolic blood pressure 40 , creatinine-based estimated glomerular filtration rate (eGFR) 41 , chronic kidney disease 41 coronary artery disease 42 , any stroke 43 , and c-reactive protein 44 (CRP), a non-specific biomarker of inflammation that can be elevated in people with high cardiometabolic risk. We used genetic variants associated with these exposures at genome-wide significance ( p <5×10 -8 ) and excluded those that were not represented in the COVID-19 outcome GWAS datasets.…”