Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

High-resolution HLA class II sequencing of Swedish multiple sclerosis patients.

Multiple sclerosis (MS) is a chronic neurological disease believed to be caused by autoimmune pathogenesis. The aetiology is likely explained by a complex interplay between inherited and environmental factors. Genetic investigations into MS have been conducted for over 50 years, yielding >100 associations to date. Globally, the strongest linkage is with the human leukocyte antigen (HLA) HLA-DRB5*01:01:01-DRB1*15:01:01-DQA1*01:02:01-DQB1*06:02:01 haplotype. Here, high-resolution sequencing of HLA was used to determine the alleles of DRB3, DRB4, DRB5, DRB1, DQA1, DQB1, DPA1 and DPB1 as well as their extended haplotypes and genotypes in 100 Swedish MS patients. Results were compared to 636 population controls. The heterogeneity in HLA associations with MS was demonstrated; among 100 patients, 69 extended HLA-DR-DQ genotypes were found. Three extended HLA-DR-DQ genotypes were found to be correlated to MS; HLA-DRB5*01:01:01-DRB1*15:01:01-DQA1*01:02:01-DQB1*06:02:01 haplotype together with (A) HLA-DRB4*01:01:01//DRB4*01:01:01:01-DRB1*07:01:01-DQA1*02:01//02:01:01-DQB1*02:02:01, (B) HLA-DRBX*null-DRB1*08:01:01-DQA1*04:01:01-DQB1*04:02:01, and (C) HLA-DRB3*01:01:02-DRB1*03:01:01-DQA1*05:01:01-DQB1*02:01:01. At the allelic level, HLA-DRB3*01:01:02 was considered protective against MS. However, when combined with HLA-DRB3*01:01:02-DRB1*03:01:01-DQA1*05:01:01-DQB1*02:01:01, this extended haplotype was considered a predisposing risk factor. This highlights the limitations as included with investigations of single alleles relative to those of extended haplotypes/genotypes. In conclusion, with 69 genotypes presented among 100 patients, high-resolution sequencing was conducted to underscore the wide polymorphisms present among MS patients. Additional studies in larger cohorts will be of importance to define MS among the patient group not associated with HLA-DRB5*01:01:01-DRB1*15:01:01-DQA1*01:02:01-DQB1*06:02:01.

Value of a genetics clinic evaluation in identifying women at risk for hereditary breast-ovarian cancer syndrome.

Our work evaluates the contributions of a genetics clinic visit in assessing patients' risk of hereditary cancers and in meeting National Cancer Comprehensive Network (NCCN) criteria for genetic testing. We reviewed the electronic health records (EHR) of 56 women seen for medical care in our healthcare system who were subsequently seen in the Adult Genetics Clinic. We searched for all personal or family cancer history available in either free-text or structured form within the EHR prior to the genetics visit. For each patient, we then compared the aggregate data with the pedigree information obtained at the Genetics Clinic visit for first-, second-, and third-degree relatives. During the genetics clinic visit, the number of relatives with cancer diagnoses doubled from 121 to 235, and for 17 of 56 (30%) of patients, family histories changed one or more NCCN criteria. For 39/56 (70%) of patients, the family history in the EHR was not changed during the genetics clinic visit. Of 56 women referred to the genetics clinic, 45 (80%) met NCCN guidelines for testing, 40 women underwent genetic testing, and 9 of 40 (23%) tested were positive for a Likely Pathogenic or Pathogenic (LP/P) variant. This study of 56 women quantitatively demonstrates the value of a genetics clinic visit by improved identification of key family history components.

Big data visualization identifies the multidimensional molecular landscape of human gliomas.

We show that visualizing large molecular and clinical datasets enables discovery of molecularly defined categories of highly similar patients. We generated a series of linked 2D sample similarity plots using genome-wide single nucleotide alterations (SNAs), copy number alterations (CNAs), DNA methylation, and RNA expression data. Applying this approach to the combined glioblastoma (GBM) and lower grade glioma (LGG) The Cancer Genome Atlas datasets, we find that combined CNA/SNA data divide gliomas into three highly distinct molecular groups. The mutations commonly used in clinical evaluation of these tumors are regionally distributed in these plots. One of the three groups is a mixture of GBM and LGG that shows similar methylation and survival characteristics to GBM. Altogether, our approach identifies eight molecularly defined glioma groups with distinct sequence/expression/methylation profiles. Importantly, we show that regionally clustered samples are enriched for specific drug targets.

Genetic risk factors for sclerotic graft-versus-host disease.

Sclerotic graft-versus-host disease (GVHD) is a distinctive phenotype of chronic GVHD after allogeneic hematopoietic cell transplantation, characterized by fibrosis of skin or fascia. Sclerotic GVHD has clinical and histopathological similarities with systemic sclerosis, an autoimmune disease whose risk is influenced by genetic polymorphisms. We examined 13 candidate single-nucleotide polymorphisms (SNPs) that have a well-documented association with systemic sclerosis to determine whether these SNPs are also associated with the risk of sclerotic GVHD. The study cohort included 847 consecutive patients who were diagnosed with chronic GVHD. Genotyping was performed using microarrays, followed by imputation of unobserved SNPs. The donor rs10516487 (BANK1: B-cell scaffold protein with ankyrin repeats 1) TT genotype was associated with lower risk of sclerotic GVHD (hazard ratio [HR], 0.43; 95% confidence interval [CI], 0.21-0.87; P = .02). Donor and recipient rs2056626 (CD247: T-cell receptor ζ subunit) GG or GT genotypes were associated with higher risk of sclerotic GVHD (HR, 1.57; 95% CI, 1.13-2.18; P = .007 and HR, 1.66; 95% CI, 1.19-2.32; P = .003, respectively). Donor and recipient rs987870 (5'-flanking region of HLA-DPA1) CC genotypes were associated with higher risk of sclerotic GVHD (HR, 2.50; 95% CI, 1.22-5.11; P = .01 and HR, 2.13; 95% CI, 1.00-4.54; P = .05, respectively). In further analyses, the recipient DPA1*01:03∼DPB1*04:01 haplotype and certain amino acid substitutions in the recipient P1 peptide-binding pocket of the HLA-DP heterodimer were associated with risk of sclerotic GVHD. Genetic components associated with systemic sclerosis are also associated with sclerotic GVHD. HLA-DP-mediated antigen presentation, T-cell response, and B-cell activation have important roles in the pathogenic mechanisms of both diseases.

Replication of associations between genetic polymorphisms and chronic graft-versus-host disease.

Previous studies have identified single-nucleotide polymorphisms (SNPs) associated with the risk of chronic graft-versus-host disease (GVHD) after allogeneic hematopoietic cell transplantation. The current study determined whether these associations could be replicated in large cohorts of donors and recipients. Each SNP was tested with cohorts of patients having the same donor type (HLA-matched related, unrelated, or both) reported in the original publication, and testing was limited to the same genome (recipient or donor) and genetic model (dominant, recessive, or allelic) reported in the original study. The 21 SNPs reported in this study represent 19 genes, and the analysis encompassed 22 SNP association tests. The hazard ratio (HR) point estimates and risk ratio point estimates corresponding to odds ratios in previous studies consistently fall outside the 95% confidence intervals of HR estimates in the current study. Despite the large size of the cohorts available for the current study, the 95% confidence intervals for most HRs did not exclude 1.0. Three SNPs representing CTLA4, HPSE, and IL1R1 showed evidence of association with the risk of chronic GVHD in unrelated donor-recipient pairs from 1 cohort, but none of these associations was replicated when tested in unrelated donor-recipient pairs from an independent cohort. Two SNPs representing CCR6 and FGFR1OP showed possible associations with the risk of chronic GVHD in related donor-recipient pairs but not in unrelated donor-recipient pairs. These results remain to be tested for replication in other cohorts of related donor-recipient pairs.

An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes.

Recent genome-wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA-based disease predictive model. However, conventional model-building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as "objects" or "exemplars," one focuses on systemic associations of disease phenotype with "objects" via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease-associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity-based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease-associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes.

Costs, Trends, and Related Factors in Treating Lung Cancer Patients in 67 Hospitals in Guangxi, China.

Lung cancer is a common disease with high mortality in China. Recent economic advances have led to improved medical capabilities, while costs associated with treating this disease have increased. Such change contributes to a commonly held belief that healthcare costs are out of control. However, few studies have examined this issue. Here, we use 34,678 hospitalization summary reports from 67 Guangxi hospitals (period 2013-2016) to document costs, temporal trends, and associated factors. Findings from this study are surprising in that they debunk the myth of uncontrolled healthcare costs. In addition, results and experiences from Guangxi are informative for other comparable regions.

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

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.

Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only.

The recent successes of genome-wide association studies (GWAS) have renewed interest in genome environment wide interaction studies (GEWIS) to discover genetic factors that modulate penetrance of environmental exposures to human diseases. Indeed, gene-environment interactions (G × E), which have not been emphasized in the GWAS era, could be a source contributing to the missing heritability, a major bottleneck limiting continuing GWAS successes. In this manuscript, we describe a design and analytic strategy to focus on G × E using only exposed subjects, dubbed as e-GEWIS. Operationally, an e-GEWIS analysis is equivalent to a GWAS analysis on exposed subjects only, and it has actually been used in some earlier GWAS without being explicitly identified as such. Through both analytics and simulations, e-GEWIS has been shown better efficiency than the usual cross-product-based analysis of G × E interaction with both cases and controls (cc-GEWIS), and they have comparable efficiency to case-only analysis of G × E (c-GEWIS), with potentially smaller sample sizes. The formalization of e-GEWIS here provides a theoretical basis to legitimize this framework for routine investigation of G × E, for more efficient G × E study designs, and for improvement of reproducibility in replicating GEWIS findings. As an illustration, we apply e-GEWIS to a lung cancer GWAS data set to perform a GEWIS, focusing on gene and smoking interaction. The e-GEWIS analysis successfully uncovered positive genetic associations on chromosome 15 among current smokers, suggesting a gene-smoking interaction. Although this signal was detected earlier, the current finding here serves as a positive control in support of this e-GEWIS strategy.

Clinical and Genetic Determinants of Cardiomyopathy Risk among Hematopoietic Cell Transplantation Survivors.

Cardiomyopathy has been recognized as a complication after hematopoietic cell transplantation (HCT). Using a nested case-cohort design, we examined the relationships between demographic, therapeutic, and selected cardiovascular disease risk factors among ≥1-year HCT survivors who developed cardiomyopathy before (n = 43) or after (n = 89) 1 year from HCT as compared to a randomly selected subcohort of survivors without cardiomyopathy (n = 444). Genomic data were available for 79 cases and 267 noncases. Clinical and genetic covariates were examined for association with the risk of early or late cardiomyopathy. Clinical risk factors associated with both early- and late-onset cardiomyopathy included anthracycline exposure ≥250 mg/m(2) and pre-existing hypertension. Among late-onset cardiomyopathy cases, the development of diabetes and ischemic heart disease further increased risk. We replicated several previously reported genetic associations among early-onset cardiomyopathy cases, including rs1786814 in CELF4, rs2232228 in HAS3, and rs17863783 in UGT1A6. None of these markers were associated with risk of late-onset cardiomyopathy. A combination of demographic, treatment, and clinical covariates predicted early-onset cardiomyopathy with reasonable accuracy (area under the curve [AUC], .76; 95% confidence interval [CI], .68 to .83), but prediction of late cardiomyopathy was poor (AUC, .59; 95% CI .53 to .67). The addition of genetic polymorphisms with marginal associations (odds ratios ≥1.3) did not enhance prediction for either early- or late-onset cardiomyopathy. Conventional cardiovascular risk factors influence the risk of both early- and late-onset cardiomyopathy in HCT survivors. Although certain genetic markers may influence the risk of early-onset disease, further work is required to validate previously reported findings and to determine how genetic information should be incorporated into clinically useful risk prediction models.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies.

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015).

Building a Patient-Specific Risk Score with a Large Database of Discharge Summary Reports.

BACKGROUND There is increasing interest in clinical research with electronic medical data, but it often faces the challenges of heterogeneity between hospitals. Our objective was to develop a single numerical score for characterizing such heterogeneity via computing inpatient mortality in treating acute myocardial infarction (AMI) patients based on diagnostic information recorded in the database of Discharge Summary Reports (DSR). MATERIAL AND METHODS Using 4 216 135 DSRs of 49 tertiary hospitals from 2006 to 2010 in Beijing, more than 200 secondary diagnoses were identified to develop a risk score for AMI (n=50 531). This risk score was independently validated with 21 571 DSRs from 65 tertiary hospitals in 2012. The c-statistics of new risk score was computed as a measure of discrimination and was compared with the Charlson comorbidity index (CCI) and its adaptions for further validation. RESULTS We finally identified and weighted 22 secondary diagnoses using a logistic regression model. In the external validation, the novel risk score performed better than the widely used CCI in predicting in-hospital mortality of AMI patients (c-statistics: 0.829, 0.832, 0.824 vs. 0.775, 0.773, and 0.710 in training, testing, and validating dataset, respectively). CONCLUSIONS The new risk score developed from DSRs outperform the existing administrative data when applied to healthcare data from China. This risk score can be used for adjusting heterogeneity between hospitals when clinical data from multiple hospitals are included.

Integrative genomics in combination with RNA interference identifies prognostic and functionally relevant gene targets for oral squamous cell carcinoma.

In oral squamous cell carcinoma (OSCC), metastasis to lymph nodes is associated with a 50% reduction in 5-year survival. To identify a metastatic gene set based on DNA copy number abnormalities (CNAs) of differentially expressed genes, we compared DNA and RNA of OSCC cells laser-microdissected from non-metastatic primary tumors (n = 17) with those from lymph node metastases (n = 20), using Affymetrix 250K Nsp single-nucleotide polymorphism (SNP) arrays and U133 Plus 2.0 arrays, respectively. With a false discovery rate (FDR)<5%, 1988 transcripts were found to be differentially expressed between primary and metastatic OSCC. Of these, 114 were found to have a significant correlation between DNA copy number and gene expression (FDR<0.01). Among these 114 correlated transcripts, the corresponding genomic regions of each of 95 transcripts had CNAs differences between primary and metastatic OSCC (FDR<0.01). Using an independent dataset of 133 patients, multivariable analysis showed that the OSCC-specific and overall mortality hazards ratio (HR) for patients carrying the 95-transcript signature were 4.75 (95% CI: 2.03-11.11) and 3.45 (95% CI: 1.84-6.50), respectively. To determine the degree by which these genes impact cell survival, we compared the growth of five OSCC cell lines before and after knockdown of over-amplified transcripts via a high-throughput siRNA-mediated screen. The expression-knockdown of 18 of the 26 genes tested showed a growth suppression ≥ 30% in at least one cell line (P<0.01). In particular, cell lines derived from late-stage OSCC were more sensitive to the knockdown of G3BP1 than cell lines derived from early-stage OSCC, and the growth suppression was likely caused by increase in apoptosis. Further investigation is warranted to examine the biological role of these genes in OSCC progression and their therapeutic potentials.

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system.

BACKGROUND: Healthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically. METHODS: Utilizing the modern cloud-based technologies, we have developed a comprehensive system that includes multiple subsystems, from capturing MRs in clinics, to securely transferring MRs, storing and managing cloud-based MRs, to facilitating both machine learning and manual reading, and to performing iterative quality control before committing the semi-structured data into the desired database. To evaluate the reproducibility of extracted medical data elements by DRESS, we conduct a blinded reproducibility study, with 100 MRs from patients who have undergone surgical treatment of lung cancer in China. The study uses Kappa statistic to measure concordance of discrete variables, and uses correlation coefficient to measure reproducibility of continuous variables. RESULTS: Using the DRESS, we have demonstrated the feasibility of extracting clinical data from unstructured MRs to create semi-structured and patient-centered electronic health record database. The reproducibility study with 100 patient's MRs has shown an overall high reproducibility of 98 %, and varies across six modules (pathology, Radio/chemo therapy, clinical examination, surgery information, medical image and general patient information). CONCLUSIONS: DRESS uses a double-reading, double-entry, and an independent adjudication, to manually curate structured data elements from unstructured clinical data. Further, through distributed computing strategies, DRESS protects data privacy by dividing MR data into de-identified modules. Finally, through internet-based computing cloud, DRESS enables many data specialists to work in a virtual environment to achieve the necessary scale of processing thousands MRs within days. This hybrid system represents probably a workable solution to solve the big medical data challenge.

Evaluation of published single nucleotide polymorphisms associated with acute GVHD.

Candidate genetic associations with acute GVHD (aGVHD) were evaluated with the use of genotyped and imputed single-nucleotide polymorphism data from genome-wide scans of 1298 allogeneic hematopoietic cell transplantation (HCT) donors and recipients. Of 40 previously reported candidate SNPs, 6 were successfully genotyped, and 10 were imputed and passed criteria for analysis. Patient and donor genotypes were assessed for association with grades IIb-IV and III-IV aGVHD, stratified by donor type, in univariate and multivariate allelic, recessive and dominant models. Use of imputed genotypes to replicate previous IL10 associations was validated. Similar to previous publications, the IL6 donor genotype for rs1800795 was associated with a 20%-50% increased risk for grade IIb-IV aGVHD after unrelated HCT in the allelic (adjusted P = .011) and recessive (adjusted P = .0013) models. The donor genotype was associated with a 60% increase in risk for grade III-IV aGVHD after related HCT (adjusted P = .028). Other associations were found for IL2, CTLA4, HPSE, and MTHFR but were inconsistent with original publications. These results illustrate the advantages of using imputed single-nucleotide polymorphism data in genetic analyses and demonstrate the importance of validation in genetic association studies.

Effect of MHC and non-MHC donor/recipient genetic disparity on the outcome of allogeneic HCT.

The outcome of allogeneic hematopoietic cell transplantation is influenced by donor/recipient genetic disparity at loci both inside and outside the MHC on chromosome 6p. Although disparity at loci within the MHC is the most important risk factor for the development of severe GVHD, disparity at loci outside the MHC that encode minor histocompatibility (H) antigens can elicit GVHD and GVL activity in donor/recipient pairs who are otherwise genetically identical across the MHC. Minor H antigens are created by sequence and structural variations within the genome. The enormous variation that characterizes the human genome suggests that the total number of minor H loci is probably large and ensures that all donor/recipient pairs, despite selection for identity at the MHC, will be mismatched for many minor H antigens. In addition to mismatch at minor H loci, unrelated donor/recipient pairs exhibit genetic disparity at numerous loci within the MHC, particularly HLA-DP, despite selection for identity at HLA-A, -B, -C, and -DRB1. Disparity at HLA-DP exists in 80% of unrelated pairs and clearly influences the outcome of unrelated hematopoietic cell transplantation; the magnitude of this effect probably exceeds that associated with disparity at any locus outside the MHC.

Oral Insulin Delay of Stage 3 Type 1 Diabetes Revisited in HLA DR4-DQ8 Participants in the TrialNet Oral Insulin Prevention Trial (TN07).

OBJECTIVE: To explore if oral insulin could delay onset of stage 3 type 1 diabetes (T1D) among patients with stage 1/2 who carry HLA DR4-DQ8 and/or have elevated levels of IA-2 autoantibodies (IA-2As). RESEARCH AND METHODS: Next-generation targeted sequencing technology was used to genotype eight HLA class II genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, and DPB1) in 546 participants in the TrialNet oral insulin preventative trial (TN07). Baseline levels of autoantibodies against insulin (IAA), GAD65 (GADA), and IA-2A were determined prior to treatment assignment. Available clinical and demographic covariables from TN07 were used in this post hoc analysis with the Cox regression model to quantify the preventive efficacy of oral insulin. RESULTS: Oral insulin reduced the frequency of T1D onset among participants with elevated IA-2A levels (HR 0.62; P = 0.012) but had no preventive effect among those with low IA-2A levels (HR 1.03; P = 0.91). High IA-2A levels were positively associated with the HLA DR4-DQ8 haplotype (OR 1.63; P = 6.37 × 10-6) and negatively associated with the HLA DR7-containing DRB1*07:01-DRB4*01:01-DQA1*02:01-DQB1*02:02 extended haplotype (OR 0.49; P = 0.037). Among DR4-DQ8 carriers, oral insulin delayed the progression toward stage 3 T1D onset (HR 0.59; P = 0.027), especially if participants also had high IA-2A level (HR 0.50; P = 0.028). CONCLUSIONS: These results suggest the presence of a T1D endotype characterized by HLA DR4-DQ8 and/or elevated IA-2A levels; for those patients with stage 1/2 disease with such an endotype, oral insulin delays the clinical T1D onset.

HLA Class II (DR, DQ, DP) Genes Were Separately Associated With the Progression From Seroconversion to Onset of Type 1 Diabetes Among Participants in Two Diabetes Prevention Trials (DPT-1 and TN07).

OBJECTIVE: To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression. RESULTS: First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression. CONCLUSIONS: These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.

Recursive organizer (ROR): an analytic framework for sequence-based association analysis.

The advent of next-generation sequencing technologies affords the ability to sequence thousands of subjects cost-effectively, and is revolutionizing the landscape of genetic research. With the evolving genotyping/sequencing technologies, it is not unrealistic to expect that we will soon obtain a pair of diploidic fully phased genome sequences from each subject in the near future. Here, in light of this potential, we propose an analytic framework called, recursive organizer (ROR), which recursively groups sequence variants based upon sequence similarities and their empirical disease associations, into fewer and potentially more interpretable super sequence variants (SSV). As an illustration, we applied ROR to assess an association between HLA-DRB1 and type 1 diabetes (T1D), discovering SSVs of HLA-DRB1 with sequence data from the Wellcome Trust Case Control Consortium. Specifically, ROR reduces 36 observed unique HLA-DRB1 sequences into 8 SSVs that empirically associate with T1D, a fourfold reduction of sequence complexity. Using HLA-DRB1 data from Type 1 Diabetes Genetics Consortium as cases and data from Fred Hutchinson Cancer Research Center as controls, we are able to validate associations of these SSVs with T1D. Further, SSVs consist of nine nucleotides, and each associates with its corresponding amino acids. Detailed examination of these selected amino acids reveals their potential functional roles in protein structures and possible implication to the mechanism of T1D.