Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

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.

Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus.

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.

Insights into the pathogenicity of rare missense GCK variants from the identification and functional characterization of compound heterozygous and double mutations inherited in cis.

OBJECTIVE: To demonstrate the importance of using a combined genetic and functional approach to correctly interpret a genetic test for monogenic diabetes. RESEARCH DESIGN AND METHODS: We identified three probands with a phenotype consistent with maturity-onset diabetes of the young (MODY) subtype GCK-MODY, in whom two potential pathogenic mutations were identified: [R43H/G68D], [E248 K/I225M], or [G261R/D217N]. Allele-specific PCR and cosegregation were used to determine phase. Single and double mutations were kinetically characterized. RESULTS: The mutations occurred in cis (double mutants) in two probands and in trans in one proband. Functional studies of all double mutants revealed inactivating kinetics. The previously reported GCK-MODY mutations R43H and G68D were inherited from an affected father and unaffected mother, respectively. Both our functional and genetic studies support R43H as the cause of GCK-MODY and G68D as a neutral rare variant. CONCLUSIONS: These data highlight the need for family/functional studies, even for previously reported pathogenic mutations.

Indirect macrophage responses to ionizing radiation: implications for genotype-dependent bystander signaling.

In addition to the directly mutagenic effects of energy deposition in DNA, ionizing radiation is associated with a variety of untargeted and delayed effects that result in ongoing bone marrow damage. Delayed effects are genotype dependent with CBA/Ca mice, but not C57BL/6 mice, susceptible to the induction of damage and also radiation-induced acute myeloid leukemia. Because macrophages are a potential source of ongoing damaging signals, we have determined their gene expression profiles and we show that bone marrow-derived macrophages show widely different intrinsic expression patterns. The profiles classify macrophages derived from CBA/Ca mice as M1-like (pro-inflammatory) and those from C57BL/6 mice as M2-like (anti-inflammatory); measurements of NOS2 and arginase activity in normal bone marrow macrophages confirm these findings. After irradiation in vivo, but not in vitro, C57BL/6 macrophages show a reduction in NOS2 and an increase in arginase activities, indicating a further M2 response, whereas CBA/Ca macrophages retain an M1 phenotype. Activation of specific signal transducer and activator of transcription signaling pathways in irradiated hemopoietic tissues supports these observations. The data indicate that macrophage activation is not a direct effect of radiation but a tissue response, secondary to the initial radiation exposure, and have important implications for understanding genotype-dependent responses and the mechanisms of the hemotoxic and leukemogenic consequences of radiation exposure.