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1.  Yeast one-hybrid screen of a thymus epithelial library identifies ZBTB7A as a regulator of thymic insulin expression 
Molecular immunology  2013;56(4):637-642.
Insulin self-tolerance is, to a large extent, assured by the expression of small quantities of insulin by medullary thymic epithelial cells (mTECs). Regulation of thymic insulin expression differs from that in pancreas and its therapeutic manipulation could play an important role in the prevention of type 1 diabetes (T1D). Knowledge of the transcriptional regulators involved in the mTEC nuclear environment is essential for the development of such therapeutics. The yeast one-hybrid (Y1H) approach was used in order to identify such mTEC-specific nuclear proteins. We used a target composed of the human insulin gene promoter joined to the upstream class III VNTR allele, which is associated with both protection from T1D and higher thymic insulin expression, and a cDNA library from our insulin-producing mouse mTEC line. The Y1H screening allowed the identification of eleven proteins. An in vitro assay was used to confirm and quantify protein-DNA binding to the human insulin gene promoter alone or joined to a class I or class III VNTR allele, and identified the transcription factors ZBTB7A, JUN and EWSR1 as strong interacting partners. All three proteins could induce insulin expression in transfected HEK-293 cells, but ZBTB7A provided the most robust results especially in the presence of AIRE, with an additional 11-fold increase of the insulin mRNA levels from a co-transfected reporter driven by the class III VNTR allele. Thus, ZBTB7A is identified as a strong candidate for regulation of thymic insulin expression.
PMCID: PMC3783542  PMID: 23911422
insulin expression; thymus; transcription factors; type 1 diabetes; yeast one-hybrid
2.  Expression Profile of a Clonal Insulin-Expressing Epithelial Cell in the Thymus 
Molecular immunology  2013;56(4):804-810.
Type 1 Diabetes is an autoimmune disease resulting from the destruction of pancreatic beta-cells. One of the main antigens targeted in this auto reactive response is insulin. It has been shown that insulin is expressed in small amounts in the thymus, and more specifically in the medullary thymic epithelial cells (mTECs), which also express a variety of other tissue-specific antigens. This thymic expression enables the maintenance of self-tolerance, and is essential in preventing auto-immune disease. Our laboratory has created a mouse mTEC clonal cell line specifically expressing insulin in order to better understand the regulatory mechanisms of this ectopic expression of insulin. In this study, we compared the insulin expressing cell line to an insulin non-expressing mTEC line by genome-wide expression profiling.
The most important difference was overexpression of CD34 in the insulin expressing clone, confirmed by Real-time Rt-PCR and flow cytometry. Cells in the thymus expressing higher levels of CD34 were found to contain higher levels of insulin and, to a lesser extent, Aire, a master regulator of self-antigen expression in the thymus. The cells expressing CD34 were not enriched in CD80, a known mTEC maturity marker.
CD34 may be a specific marker for functionality, with some specificity for insulin.
PMCID: PMC3792572  PMID: 23973805
Autoimmunity; Ifn-γ; Insulin; mTEC; Self-antigens; Thymus; Tnf-α
3.  One year remission of type 1 diabetes mellitus in a patient treated with sitagliptin 
Type 1 diabetes mellitus (T1DM) is a chronic disease characterized by the autoimmune destruction of pancreatic β-cells. This paper describes the case of a 19-year-old male patient who presented with glutamic acid decarboxylase (GAD) antibody positive and diabetic ketoacidosis, which mandated intensive insulin treatment. Once the ketoacidosis was controlled, an oral dose of 100 mg of sitagliptin was administered once a day. Ketoacidosis was managed by insulin and insulin daily requirement began to dwindle after one month, until its complete withdrawal at 8 weeks, when partial remission was reached. The patient has now remained on sitagliptin treatment alone for a year, without requiring insulin. The benefit observed with this medication is possibly associated with its immunological effects. Inhibition of dipeptidyl peptidase 4 in animal models deregulates the Th1 immune response, increases secretion of Th2 cytokines, activates CD4+CD25+FoxP3+ regulatory T-cells, and prevents IL17 production.
Learning points
The use of insulin-dose-adjusted HbA1c constitutes the best way to define partial remission in T1DM patients.The use of sitagliptin in T1DM patients could help to decrease daily requirement of insulin by delaying β-cell loss and improving endogenous insulin production.The determination of antibodies against insulin, islet cells, and GAD permits differentiation of T1DM patients from those with atypical or ketosis-prone diabetes.
PMCID: PMC4190822  PMID: 25332771
4.  Gene-Specific Function Prediction for Non-Synonymous Mutations in Monogenic Diabetes Genes 
PLoS ONE  2014;9(8):e104452.
The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY) molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.
PMCID: PMC4138110  PMID: 25136813
5.  Genome-wide search for exonic variants affecting translational efficiency 
Nature communications  2013;4:2260.
The search for expression quantitative trait loci (eQTL) has traditionally centered entirely on the process of transcription, whereas variants with effects on mRNA translation have not been systematically studied. Here we present a high throughput approach for measuring translational cis-regulation in the human genome. Using ribosomal association as proxy for translational efficiency of polymorphic mRNAs, we test the ratio of polysomal/nonpolysomal mRNA level as a quantitative trait for association with single-nucleotide polymorphisms on the same mRNA transcript. We identify one important ribosomal-distribution effect, from rs1131017 in the 5’UTR of RPS26 , that is in high linkage disequilibrium (LD) with the 12q13 locus for susceptibility to type 1 diabetes. The effect on translation is confirmed at the protein level by quantitative Western blots, both ex vivo and after in vitro translation. Our results are a proof-of-principle that allelic effects on translation can be detected at a transcriptome-wide scale.
PMCID: PMC3749366  PMID: 23900168
6.  Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease 
Human Genetics  2013;132(10):1077-1130.
Some individuals with a particular disease-causing mutation or genotype fail to express most if not all features of the disease in question, a phenomenon that is known as ‘reduced (or incomplete) penetrance’. Reduced penetrance is not uncommon; indeed, there are many known examples of ‘disease-causing mutations’ that fail to cause disease in at least a proportion of the individuals who carry them. Reduced penetrance may therefore explain not only why genetic diseases are occasionally transmitted through unaffected parents, but also why healthy individuals can harbour quite large numbers of potentially disadvantageous variants in their genomes without suffering any obvious ill effects. Reduced penetrance can be a function of the specific mutation(s) involved or of allele dosage. It may also result from differential allelic expression, copy number variation or the modulating influence of additional genetic variants in cis or in trans. The penetrance of some pathogenic genotypes is known to be age- and/or sex-dependent. Variable penetrance may also reflect the action of unlinked modifier genes, epigenetic changes or environmental factors. At least in some cases, complete penetrance appears to require the presence of one or more genetic variants at other loci. In this review, we summarize the evidence for reduced penetrance being a widespread phenomenon in human genetics and explore some of the molecular mechanisms that may help to explain this enigmatic characteristic of human inherited disease.
PMCID: PMC3778950  PMID: 23820649
8.  Association of RASGRP1 with type 1 diabetes is revealed by combined follow-up of two genome-wide studies 
Journal of Medical Genetics  2009;46(8):553-554.
The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.
We analyzed data from two sources: 1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1,046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); 2) The RR2 project of the T1DGC, which genotyped 4,417 individuals from 1,062 non-overlapping families, including 2,059 affected individuals (mostly sibling pairs) for the 1,536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.
One locus, mapping to an LD block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect linkage disequilibrium (LD) with each other (r2=1). We obtained a joint p value of 1.3 ×10−6, which exceeds by an order of magnitude the conservative threshold of 3.26×10−5 obtained by correcting for the 1,536 SNPs tested in our study. Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83×10−9.
A novel type 1 diabetes locus was discovered. It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and TCR signaling by activating the Ras signaling pathway.
PMCID: PMC3272492  PMID: 19465406
Etiology; Genetic susceptibility; Type 1 diabetes; RASGRP1
9.  The effect of the MHC locus on autoantibodies in type 1 diabetes 
Journal of Medical Genetics  2009;46(7):469-471.
This study aimed to investigate whether the presence of autoantibodies specific for type 1 diabetes (T1D) is determined by the major genetic susceptibility locus for the disease at the HLA genes, using the T1D Genetics Consortium data.
We analyzed anti-IA-2 and anti-GAD 65 autoantibody data from 2,282 T1D patients from 1117 multiplex families. HLA genotyping was available for all cases and their parents and association with autoantibody positivity was tested by the transmission disequilibrium test.
Association of anti-IA-2 with the HLA genes was detected at high statistical signficance. HLA-DRB1*0401 confers both the strongest type 1 diabetes risk, and positive association of anti-IA-2, whereas the DRB1*03- DQA1*0501-DQB1*0201 haplotype, associated less strongly with T1D, showed a significant negative association with anti-IA-2 positivity. Interestingly, HLA-A*24 is also negatively associated with anti-IA-2, independently of the DRB1*03- DQA1*0501-DQB1*0201 haplotype. No statistically significant association was identified between anti-GAD65 and HLA.
This study highlights that IA-2 as an autoantigen depends on HLA genotype and suggests new insights into the mechanism of loss of immune tolerance.
PMCID: PMC3270821  PMID: 19429597
autoantibody; GAD65; HLA; IA-2; Type 1 diabetes
11.  A Genome-Wide Meta-Analysis of Six Type 1 Diabetes Cohorts Identifies Multiple Associated Loci 
PLoS Genetics  2011;7(9):e1002293.
Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10−11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10−9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10−9) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.
Author Summary
Despite the fact that there is clearly a large genetic component to type 1 diabetes (T1D), uncovering the genes contributing to this disease has proven challenging. However, in the past three years there has been relatively major progress in this regard, with advances in genetic screening technologies allowing investigators to scan the genome for variants conferring risk for disease without prior hypotheses. Such genome-wide association studies have revealed multiple regions of the genome to be robustly and consistently associated with T1D. More recent findings have been a consequence of combining of multiple datasets from independent investigators in meta-analyses, which have more power to pick up additional variants contributing to the trait. In the current study, we describe the largest meta-analysis of T1D genome-wide genotyped datasets to date, which combines six large studies. As a consequence, we have uncovered three new signals residing at the chromosomal locations 13q22, 2p23, and 6q27, which went on to be replicated in independent sample sets. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.
PMCID: PMC3183083  PMID: 21980299
12.  Comparative genetic analysis of inflammatory bowel disease and type 1 diabetes implicates multiple loci with opposite effects 
Human Molecular Genetics  2010;19(10):2059-2067.
Inflammatory bowel disease, including Crohn's disease (CD) and ulcerative colitis (UC), and type 1 diabetes (T1D) are autoimmune diseases that may share common susceptibility pathways. We examined known susceptibility loci for these diseases in a cohort of 1689 CD cases, 777 UC cases, 989 T1D cases and 6197 shared control subjects of European ancestry, who were genotyped by the Illumina HumanHap550 SNP arrays. We identified multiple previously unreported or unconfirmed disease associations, including known CD loci (ICOSLG and TNFSF15) and T1D loci (TNFAIP3) that confer UC risk, known UC loci (HERC2 and IL26) that confer T1D risk and known UC loci (IL10 and CCNY) that confer CD risk. Additionally, we show that T1D risk alleles residing at the PTPN22, IL27, IL18RAP and IL10 loci protect against CD. Furthermore, the strongest risk alleles for T1D within the major histocompatibility complex (MHC) confer strong protection against CD and UC; however, given the multi-allelic nature of the MHC haplotypes, sequencing of the MHC locus will be required to interpret this observation. These results extend our current knowledge on genetic variants that predispose to autoimmunity, and suggest that many loci involved in autoimmunity may be under a balancing selection due to antagonistic pleiotropic effect. Our analysis implies that variants with opposite effects on different diseases may facilitate the maintenance of common susceptibility alleles in human populations, making autoimmune diseases especially amenable to genetic dissection by genome-wide association studies.
PMCID: PMC2860894  PMID: 20176734
13.  Genome-wide profiling using single-nucleotide polymorphism arrays identifies novel chromosomal imbalances in pediatric glioblastomas 
Neuro-Oncology  2010;12(2):153-163.
Available data on genetic events in pediatric grade IV astrocytomas (glioblastoma [pGBM]) are scarce. This has traditionally been a major impediment in understanding the pathogenesis of this tumor and in developing ways for more effective management. Our aim is to chart DNA copy number aberrations (CNAs) and get insight into genetic pathways involved in pGBM. Using the Illumina Infinium Human-1 bead-chip-array (100K single-nucleotide polymorphisms [SNPs]), we genotyped 18 pediatric and 6 adult GBMs. Results were compared to BAC-array profiles harvested on 16 of the same pGBM, to an independent data set of 9 pediatric high-grade astrocytomas (HGAs) analyzed on Affymetrix 250K-SNP arrays, and to existing data sets on HGAs. CNAs were additionally validated by real-time qPCR in a set of genes in pGBM. Our results identify with nonrandom clustering of CNAs in several novel, previously not reported, genomic regions, suggesting that alterations in tumor suppressors and genes involved in the regulation of RNA processing and the cell cycle are major events in the pathogenesis of pGBM. Most regions were distinct from CNAs in aGBMs and show an unexpectedly low frequency of genetic amplification and homozygous deletions and a high frequency of loss of heterozygosity for a high-grade I rapidly dividing tumor. This first, complete, high-resolution profiling of the tumor cell genome fills an important gap in studies on pGBM. It ultimately guides the mapping of oncogenic networks unique to pGBM, identification of the related therapeutic predictors and targets, and development of more effective therapies. It further shows that, despite commonalities in a few CNAs, pGBM and aGBMs are two different diseases.
PMCID: PMC2940568  PMID: 20150382
pediatric high-grade astrocytomas; brain tumors; SNP arrays; LOH
14.  The IRF5 polymorphism in type 1 diabetes 
Journal of Medical Genetics  2007;44(10):670-672.
The interferon regulatory factor 5 gene (IRF5) has been shown to play a crucial role in harmful immune responses by induction of proinflammatory cytokines. Functional genetic variants associated with increasd IRF5 expression of specific isoforms are associated with systemic lupus erythematosus (SLE) and it is possible that they may also predispose to other autoimmune disorders. We tested the association of two IRF5 SNPs, correlated with IRF5 expression and SLE risk, in 947 nuclear family trios type 1 diabetes (T1D) using the transmission disequilibrium test. Our results suggest that the functional IRF5 variations do not confer an obvious risk for T1D.
PMCID: PMC2597969  PMID: 17557928
15.  Rfx6 Directs Islet Formation and Insulin Production in Mice and Humans 
Nature  2010;463(7282):775-780.
Insulin from the β-cells of the pancreatic islets of Langerhans controls energy homeostasis in vertebrates, and its deficiency causes diabetes mellitus. During embryonic development, the transcription factor Neurogenin3 initiates the differentiation of the β-cells and other islet cell types from pancreatic endoderm, but the genetic program that subsequently completes this differentiation remains incompletely understood. Here we show that the transcription factor Rfx6 directs islet cell differentiation downstream of Neurogenin3. Mice lacking Rfx6 failed to generate any of the normal islet cell types except for pancreatic-polypeptide-producing cells. In human infants with a similar autosomal recessive syndrome of neonatal diabetes, genetic mapping and subsequent sequencing identified mutations in the human RFX6 gene. These studies demonstrate a unique position for Rfx6 in the hierarchy of factors that coordinate pancreatic islet development in both mice and humans. Rfx6 could prove useful in efforts to generate β-cells for patients with diabetes.
PMCID: PMC2896718  PMID: 20148032
16.  Study of Transcriptional Effects in Cis at the IFIH1 Locus 
PLoS ONE  2010;5(7):e11564.
The Thr allele at the non-synonymous single-nucleotide polymorphism (nsSNP) Thr946Ala in the IFIH1 gene confers risk for Type 1 diabetes (T1D). The SNP is embedded in a 236 kb linkage disequilibrium (LD) block that includes four genes: IFIH1, GCA, FAP and KCNH7. The absence of common nsSNPs in the other genes makes the IFIH1 SNP the strongest functional candidate, but it could be merely a marker of association, due to LD with a variant regulating expression levels of IFIH1 or neighboring genes.
Methodology/Principal Findings
We investigated the effect of the T1D-associated variation on mRNA transcript expression of these genes. Heterozygous mRNA from lymphoblastoid cell lines (LCLs), pancreas and thymus was examined by allelic expression imbalance, to detect effects in cis on mRNA expression. Using single-nucleotide primer extension, we found no difference between mRNA transcripts in 9 LCLs, 6 pancreas and 13 thymus samples, suggesting that GCA and FAP are not involved. On the other hand, KCNH7 was not expressed at a detectable level in all tissues examined. Moreover, the association of the Thr946Ala SNP with T1D is not due to modulation of IFIH1 expression in organs involved in the disease, pointing to the IFIH1 nsSNP as the causal variant.
The mechanism of the association of the nsSNP with T1D remains to be determined, but does not involve mRNA modulation. It becomes necessary to study differential function of the IFIH1 protein alleles at Thr946Ala to confirm that it is responsible for the disease association.
PMCID: PMC2903489  PMID: 20644636
17.  Follow-Up Analysis of Genome-Wide Association Data Identifies Novel Loci for Type 1 Diabetes 
Diabetes  2009;58(1):290-295.
OBJECTIVE—Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component. To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject–parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals.
RESEARCH DESIGN AND METHODS—From our existing datasets, we selected 982 markers with P < 0.05 in both GWA cohorts. Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P < 0.05 in this third cohort. Among these, six single nucleotide polymorphisms in five novel loci also had P < 0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects.
RESULTS—Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P = 2.33 × 10−8) and BACH2 (1.13; combined P = 1.25 × 10−6).
CONCLUSIONS—Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes. The two genes at these respective loci, UBASH3A and BACH2, are both biologically relevant to autoimmunity.
PMCID: PMC2606889  PMID: 18840781
18.  From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes 
PLoS Genetics  2009;5(10):e1000678.
Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.
Author Summary
An often touted utility of genome-wide association studies (GWAS) is that the resulting discoveries can facilitate implementation of personalized medicine, in which preventive and therapeutic interventions for complex diseases can be tailored to individual genetic profiles. However, recent studies using whole-genome SNP genotype data for disease risk assessment have generally failed to achieve satisfactory results, leading to a pessimistic view of the utility of genotype data for such purposes. Here we propose that sophisticated machine-learning approaches on a large ensemble of markers, which contain both confirmed and as yet unconfirmed disease susceptibility variants, may improve the performance of disease risk assessment. We tested an algorithm called Support Vector Machine (SVM) on three large-scale datasets for type 1 diabetes and demonstrated that risk assessment can be highly accurate for the disease. Our results suggest that individualized disease risk assessment using whole-genome data may be more successful for some diseases (such as T1D) than other diseases. However, the predictive accuracy will be dependent on the heritability of the disease under study, the proportion of the genetic risk that is known, and that the right set of markers and right algorithms are being used.
PMCID: PMC2748686  PMID: 19816555
19.  Association Analysis of Type 2 Diabetes Loci in Type 1 Diabetes 
Diabetes  2008;57(7):1983-1986.
OBJECTIVE—To search for a possible association of type 1 diabetes with 10 validated type 2 diabetes loci, i.e., PPARG, KCNJ11, WFS1, HNF1B, IDE/HHEX, SLC30A8, CDKAL1, CDKN2A/B, IGF2BP2, and FTO/RPGRIP1L.
RESEARCH DESIGN AND METHODS—Two European population samples were studied: 1) one case-control cohort of 514 type 1 diabetic subjects and 2,027 control subjects and 2) one family cohort of 483 complete type 1 diabetic case-parent trios (total 997 affected). A total of 13 tag single nucleotide polymorphisms (SNPs) from the 10 type 2 diabetes loci were analyzed for type 1 diabetes association.
RESULTS—No association of type 1 diabetes was found with any of the 10 type 2 diabetes loci, and no age-at-onset effect was detected. By combined analysis using the Wellcome Trust Case-Control Consortium type 1 diabetes data, SNP rs1412829 in the CDKN2A/B locus bordered on significance (P = 0.039) (odds ratio 0.929 [95% CI 0.867–0.995]), which did not reach the statistical significance threshold adjusted for 13 tests (α = 0.00385).
CONCLUSIONS—This study suggests that the type 2 diabetes loci do not play any obvious role in type 1 diabetes genetic susceptibility. The distinct molecular mechanisms of the two diseases highlighted the importance of differentiation diagnosis and different treatment principles.
PMCID: PMC2453613  PMID: 18426861
20.  The TCF7L2 locus and type 1 diabetes 
BMC Medical Genetics  2007;8:51.
TCF7L2 belongs to a subfamily of TCF7-like HMG box-containing transcription factors, and maps to human chromosome 10q25.3. A recent study identified genetic association of type 2 diabetes (T2D) with this gene, correlated with diminished insulin secretion. This study aimed to investigate the possibility of genetic association between TCF7L2 and type 1 diabetes (T1D).
The SNP most significantly associated with T2D, rs7903146, was genotyped in 886 T1D nuclear family trios with ethnic backgrounds of mixed European descent.
This study found no T1D association with, and no age-of-onset effect from rs7903146.
This study suggests that a T2D mechanism mediated by TCF7L2 does not participate in the etiology of T1D.
PMCID: PMC1978206  PMID: 17683561
21.  Strand bias in complementary single-nucleotide polymorphisms of transcribed human sequences: evidence for functional effects of synonymous polymorphisms 
BMC Genomics  2006;7:213.
Complementary single-nucleotide polymorphisms (SNPs) may not be distributed equally between two DNA strands if the strands are functionally distinct, such as in transcribed genes. In introns, an excess of A↔G over the complementary C↔T substitutions had previously been found and attributed to transcription-coupled repair (TCR), demonstrating the valuable functional clues that can be obtained by studying such asymmetry. Here we studied asymmetry of human synonymous SNPs (sSNPs) in the fourfold degenerate (FFD) sites as compared to intronic SNPs (iSNPs).
The identities of the ancestral bases and the direction of mutations were inferred from human-chimpanzee genomic alignment. After correction for background nucleotide composition, excess of A→G over the complementary T→C polymorphisms, which was observed previously and can be explained by TCR, was confirmed in FFD SNPs and iSNPs. However, when SNPs were separately examined according to whether they mapped to a CpG dinucleotide or not, an excess of C→T over G→A polymorphisms was found in non-CpG site FFD SNPs but was absent from iSNPs and CpG site FFD SNPs.
The genome-wide discrepancy of human FFD SNPs provides novel evidence for widespread selective pressure due to functional effects of sSNPs. The similar asymmetry pattern of FFD SNPs and iSNPs that map to a CpG can be explained by transcription-coupled mechanisms, including TCR and transcription-coupled mutation. Because of the hypermutability of CpG sites, more CpG site FFD SNPs are relatively younger and have confronted less selection effect than non-CpG FFD SNPs, which can explain the asymmetric discrepancy of CpG site FFD SNPs vs. non-CpG site FFD SNPs.
PMCID: PMC1559705  PMID: 16916449

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