Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation
Year: 2020
Type: article
Abstract: Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes risk, or be benign. A correct diagnosis matters as it informs on treatment, progression, and family risk. We describe a multi-dimensional functional dataset of 73 HNF1A missense variants identified in exomes of 12,940 individuals. Our aim was to develop an analytical framework for stratifying variants along the HNF1A phenotypic continuum to facilitate diagnostic interpretation. HNF1A variant function was determined by four different molecular assays. Structure of the multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical clustering. Weights for tissue-specific isoform expression and functional domain were integrated. Functionally annotated variant subgroups were used to re-evaluate genetic diagnoses in national MODY diagnostic registries. HNF1A variants demonstrated a range of behaviors across the assays. The structure of the multi-parametric data was shaped primarily by transactivation. Using unsupervised learning methods, we obtained high-resolution functional clusters of the variants that separated known causal MODY variants from benign and type 2 diabetes risk variants and led to reclassification of 4% and 9% of HNF1A variants identified in the UK and Norway MODY diagnostic registries, respectively. Our proof-of-principle analyses facilitated informative stratification of HNF1A variants along the continuum, allowing improved evaluation of clinical significance, management, and precision medicine in diabetes clinics. Transcriptional activity appears a superior readout supporting pursuit of transactivation-centric experimental designs for high-throughput functional screens.
Institutions: University of Oxford, Oxford Centre for Diabetes, Endocrinology and Metabolism, Haukeland University Hospital, Stanford Medicine, University of Bergen, Stanford University, University of Exeter, Royal Devon & Exeter NHS Foundation Trust, Royal Devon University Healthcare NHS Foundation Trust, Centre for Human Genetics, Churchill Hospital, NIHR Oxford Biomedical Research Centre, Western Norway University of Applied Sciences
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $5,000
Funders: Stiftelsen Kristian Gerhard Jebsen, Wellcome Trust, National Institute for Health and Care Research, Universitetet i Bergen, Norges Forskningsråd, Novo Nordisk Fonden, Norges Idrettshøgskole, National Institutes of Health, Horizon 2020 Framework Programme, Medical Research Council, European Research Council, Helse Vest Regionalt Helseføretak, Horizon 2020, National Institute of Diabetes and Digestive and Kidney Diseases
Awards: G125904209, G1872206146, Characterising causal alleles for common disease., Bridging the gap from type 2 diabetes genetics to functional mechanisms of disease., Identifying variants causal for Type 2 Diabetes in Major human populations, G458152742, G4985203053, G5298326573, Integrating genome-scale data to reveal causal mechanisms in type 2 diabetes, Defining the molecular and physiological mechanisms of pancreatic islet dysfunction which lead to type 2 diabetes, G7227324620, Human Genetics and Disease Biology: Core Renewal for the Wellcome Trust Centre for Human Genetics, Defining mechanisms for pancreatic beta-cell dysfunction in type 2 diabetes, G7649336331, A systematic approach to understanding the biology underpinning GWAS hits., NIHR Senior Investigator Award, G9000206245
OpenAlex ID: W3083789238