The study has two aims: 1. To (1a) determine the frequency of monogenic diabetes misdiagnosed as type 1 diabetes (T1D) and (2) to define an algorithm for case selection. 2. To discover novel genes whose mutations cause monogenic diabetes misdiagnosed as T1D.
Aim 1. The investigators will recruit 5,000 cases diagnosed as T1D under the age of 25, from 17 participating clinics across Canada. All cases will be tested for four antibodies (against proinsulin, GAD65, islet antigen 2 (IA-2), and ZnT8). Cases negative for all four will be exome-sequenced. 1. Variant annotation will be focused on known monogenic diabetes genes. Variants rated as pathogenic, likely pathogenic or of unknown significance whose zygosity fits the genetic model, will be confirmed in a clinically certified laboratory and communicated to the treating health care team. End-point is the frequency of such variants compared to their frequency in control, non-T1D exomes. 2. The following variables will be examined for the ability to predict monogenic diabetes: Negativity for all autoantibodies tested, family history, polygenic T1D risk score, age of onset, sex, glycosylated hemoglobin (HbA1c), insulin dose, and presence of syndromic features. Predictors will be analyzed by multiple regression and results subjected to jackknife (leave-one-out) validation. Machine-learning techniques may be used. Aim 2. Variants outside known genes in non-diagnostic exomes will be annotated and examined under autosomal dominant, recessive, X-linked and mitochondrial inheritance models. Corresponding frequency cutoffs will be 0.0005, 0.01, 0.001 and 0.0005 (if heteroplasmy \>70%). Formal mutation-burden analysis will be based on depth-adjusted data from the Genome Aggregation Database (gnomAD). Genes mutated in more than one unrelated proband will be examined by a statistical approach taking into account the presence of a large number of phenocopies (Akawi et al., Nat Genet. 2015;47:1363-1369). Genes that achieve statistical significance will be tested in additional cohorts with international collaborations.
Study Type
OBSERVATIONAL
Enrollment
5,000
No further intervention planned for either group as part of the current study.
The Montreal Children's Hospital
Montreal, Quebec, Canada
RECRUITINGProportion of monogenic diabetes among patients diagnosed as type 1 diabetes.
The exomes of all patients negative for four T1D autoantibodies will be sequenced and pathogenic variants in genes known to cause monogenic diabetes will be called and annotated. The frequency of genes carrying such variants among these patients will be compared to control exomes from public databases.
Time frame: 6 years
Proportion of patients carrying mutations in previously unstudied genes that meet statistical criteria of pathogenicity for monogenic diabetes.
Exomes not found to carry a mutation (per outcome 1) will be analyzed to discover pathogenic variants in novel genes. Genes mutated in more than one unrelated probands will be statistically evaluated to see if variants in these gene occur more frequently than in control exomes. The number of probands that is needed to fulfill this criterion will depend on the gene's tolerance to protein-altering mutations.
Time frame: 7 years
Risk-prediction score for monogenic diabetes mutation in antibody negative T1D patients
Composit score with a statistically significant ROC curve for predicting monogenic diabetes in individuals previously diagnosed as T1D. It will be based on age of onset, T1D polygenic risk score. The risk score will aim to predict monogenic diabetes in cases with clinical T1D diagnosis and known to be antibody negative. The scale will be calculated as follows: From the exome sequencing, the investigators will be able to determine genotype at the three most important loci determining risk for autoimmune T1D (HLA, INS and PTPN22).The composite risk score, along with family history, age of onset, HbA1c+4\*insulin dose/kg (as proxy for residual beta cell function) will be subjected to logistic regression for an overall risk. The ROC curve will be used to select a point likely to capture most cases unlikely to have autoimmune T1D, sacrificing specificity to maximize sensitivity. Data will be validated with jackknife cross-validation.
Time frame: 5 years
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