Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program

Abstract

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) ¼ 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF ¼ 12% in African-Americans, MAF ¼ 2% in Hispanics) lowered HbA1c (À0.88% in hemizygous males, À0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF ¼ 0.5%; À0.98% in hemizygous males, À0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

HbA1cPerformance in African Descent Populations in the United States With Normal Glucose Tolerance, Prediabetes, or Diabetes: A Scoping Review

“…However, genetics do not fully explain HbA 1c differences among African American people ( 44 ), because increases in HbA 1c may be mediated by social determinants of health (eg, chronic financial strain as seen in study 3) or chronic inflammation (sIL-6R) ( 28 , 45 ). Additionally, G6PD variant or deficiency is often correlated with lower HbA 1c values in various populations ( 40 ), especially in African American people and African people because of its higher prevalence in these groups ( 14 , 46 , 47 ). Similarly, the sickle cell trait is associated with lower HbA 1c values in African descent populations ( 21 , 25 ).…”

The Trans-Ancestral Genomic Architecture of Glycaemic Traits

“…We, and others, have previously suggested that HbA1c-associated variants appear to exert their effects on HbA1c levels through both glycaemic and non-glycaemic pathways 7,45 Classification of loci into these pathways can have important implications for T2D diagnostic accuracy 7,46 . To further elucidate the biology of HbA1c-associated variants, we took advantage of prior association results for other glycaemic, RBC, and iron traits, and used a fuzzy clustering approach to classify variants into their most likely mode of action ( Methods, Supplementary note ).…”

My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

“…Future updates for MPM will be focused on providing more complete, high-quality human interactome (including protein-DNA/RNA interactions as well), functional sites, and proteogenomics data from CPTAC. We will integrate more human genome sequencing data, including Trans-Omics for Precision Medicine (TOPMed) Program [ 63 ], Alzheimer’s Disease Sequencing Project (ADSP) [ 64 ], and International Cancer Genome Consortium (ICGC) [ 65 ], to improve utilities of MPM by adding more personalized genome analyses.…”