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.

糖尿病的外显子组测序对诊断提出了挑战，因为根据频率、功能影响以及基因组和环境背景，HNF1A变异可能导致青年人发病的成熟期糖尿病 (MODY)、增加 2 型糖尿病的风险，或者是良性的。正确的诊断很重要，因为它可以告知治疗、进展和家庭风险。我们描述了在 12,940 个个体的外显子组中识别出的 73 个HNF1A错义变异的多维功能数据集。我们的目标是开发一个分析框架，用于沿HNF1A表型连续体对变异进行分层，以促进诊断解释。氢氟酸1A变体功能由四种不同的分子测定法确定。使用主成分分析、k 均值和层次聚类探索多维数据集的结构。整合了组织特异性亚型表达和功能域的权重。功能注释的变异亚组被用于重新评估国家 MODY 诊断登记处的基因诊断。HNF1A变体在整个检测中展示了一系列行为。多参数数据的结构主要由反式激活形成。使用无监督学习方法，我们获得了变异的高分辨率功能簇，这些变异将已知的 MODY 致病变异与良性和 2 型糖尿病风险变异分开，并导致对 4% 和 9% 的HNF1A进行了重新分类分别在英国和挪威 MODY 诊断登记处确定的变体。我们的原理验证分析促进了HNF1A变体沿连续体的信息分层，从而改进了对糖尿病诊所的临床意义、管理和精准医学的评估。转录活性似乎是一种出色的读数，支持追求以反式激活为中心的高通量功能筛选实验设计。