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HLA-DQ molecules account over 50% genetic risk of type 1 diabetes (T1D), but little is known about associated residues. Through next generation targeted sequencing technology and deep learning of DQ residue sequences, the aim was to uncover critical residues and their motifs associated with T1D. Our analysis uncovered (αa1, α44, α157, α196) and (β9, β30, β57, β70, β135) on the HLA-DQ molecule. Their motifs captured all known susceptibility and resistant T1D associations. Three motifs, "DCAA-YSARD" (OR = 2.10, p = 1.96*10−20), "DQAA-YYARD" (OR = 3.34, 2.69*10−72) and "DQDA-YYARD" (OR = 3.71, 1.53*10−6) corresponding to DQ2.5 and DQ8.1 (the latter two motifs) associated with susceptibility. Ten motifs were significantly associated with resistance to T1D. Collectively, homozygous DQ risk motifs accounted for 43% of DQ-T1D risk, while homozygous DQ resistant motifs accounted for 25% protection to DQ-T1D risk. Of the identified nine residues five were within or near anchoring pockets of the antigenic peptide (α44, β9, β30, β57 and β70), one was the N-terminal of the alpha chain (αa1), one in the CD4-binding region (β135), one in the putative cognate TCR-induced αβ homodimerization process (α157), and one in the intra-membrane domain of the alpha chain (α196). Finding these critical residues should allow investigations of fundamental properties of host immunity that underlie tolerance to self and organ-specific autoimmunity. [ABSTRACT FROM AUTHOR]

Next-generation targeted sequencing of HLA-DRB1 and HLA-DRB3, -DRB4, and -DRB5 (abbreviated as DRB345) provides high resolution of functional variant positions to investigate their associations with type 1 diabetes risk and with autoantibodies against insulin (IAA), GAD65 (GADA), IA-2 (IA-2A), and ZnT8 (ZnT8A). To overcome exceptional DR sequence complexity as a result of high polymorphisms and extended linkage disequilibrium among the DR loci, we applied a novel recursive organizer (ROR) to discover disease-associated amino acid residues. ROR distills disease-associated DR sequences and identifies 11 residues of DRB1, sequences of which retain all significant associations observed by DR genes. Furthermore, all 11 residues locate under/adjoining the peptide-binding groove of DRB1, suggesting a plausible functional mechanism through peptide binding. The 15 residues of DRB345, located respectively in the β49-55 homodimerization patch and on the face of the molecule shown to interact with and bind to the accessory molecule CD4, retain their significant disease associations. Further ROR analysis of DR associations with autoantibodies finds that DRB1 residues significantly associated with ZnT8A and DRB345 residues with GADA. The strongest association is between four residues (β14, β25, β71, and β73) and IA-2A, in which the sequence ERKA confers a risk association (odds ratio 2.15, P = 10 -18 ), and another sequence, ERKG, confers a protective association (odds ratio 0.59, P = 10 -11 ), despite a difference of only one amino acid. Because motifs of identified residues capture potentially causal DR associations with type 1 diabetes, this list of residuals is expected to include corresponding causal residues in this study population., (© 2019 by the American Diabetes Association.)

Aim: It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies., Methods: Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D., Results: In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime., Conclusion: Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations., (Copyright © 2017 John Wiley & Sons, Ltd.)