Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population

• Published in: Diabetes (New York, N.Y.) Vol. 66; no. 2; pp. 335 - 346
• Main Authors: Najmi, Laeya Abdoli, Aukrust, Ingvild, Flannick, Jason, Molnes, Janne, Burtt, Noel, Molven, Anders, Groop, Leif, Altshuler, David, Johansson, Stefan, Bjørkhaug, Lise, Njølstad, Pål Rasmus
• Format: Journal Article
• Language: English
• Published: United States American Diabetes Association 01-02-2017
• Subjects: Bioinformatics; Clinical Medicine; Cohort Studies; Diabetes; Diabetes Mellitus, Type 2 - genetics; Endocrinology and Diabetes; Endokrinologi och diabetes; Female; Gene Frequency; Genetic Predisposition to Disease; Genetic Variation; Genotype; Genotype & phenotype; HeLa Cells; Hepatocyte Nuclear Factor 1-alpha - genetics; Humans; Klinisk medicin; Male; Medical and Health Sciences; Medicin och hälsovetenskap; Metabolism; Odds Ratio; Risk assessment; Transcriptional Activation
• ISSN: 0012-1797; 1939-327X; 1939-327X
• DOI: 10.2337/db16-0460

Variants in HNF1A encoding hepatocyte nuclear factor 1α (HNF-1A) are associated with maturity-onset diabetes of the young form 3 (MODY 3) and type 2 diabetes. We investigated whether functional classification of HNF1A rare coding variants can inform models of diabetes risk prediction in the general population by analyzing the effect of 27 HNF1A variants identified in well-phenotyped populations (n = 4,115). Bioinformatics tools classified 11 variants as likely pathogenic and showed no association with diabetes risk (combined minor allele frequency [MAF] 0.22%; odds ratio [OR] 2.02; 95% CI 0.73-5.60; P = 0.18). However, a different set of 11 variants that reduced HNF-1A transcriptional activity to <60% of normal (wild-type) activity was strongly associated with diabetes in the general population (combined MAF 0.22%; OR 5.04; 95% CI 1.99-12.80; P = 0.0007). Our functional investigations indicate that 0.44% of the population carry HNF1A variants that result in a substantially increased risk for developing diabetes. These results suggest that functional characterization of variants within MODY genes may overcome the limitations of bioinformatics tools for the purposes of presymptomatic diabetes risk prediction in the general population.