Search tips
Search criteria

Results 1-13 (13)

Clipboard (0)

Select a Filter Below

Year of Publication
Document Types
1.  Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes 
Nature genetics  2012;44(3):297-301.
Genome-wide association studies revealed that common non-coding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT2) increase type 2 diabetes (T2D) risk1,2. Although the strongest association signal was highly significant (P<10−20), its contribution to T2D risk was modest (odds ratio, OR~1.10-1.15)1-3. We performed large-scale exon resequencing in 7,632 Europeans including 2,186 T2D patients and identified 40 non-synonymous variants, including 36 very rare variants (minor allele frequency, MAF<0.1%) associated with T2D (OR=3.31[1.78;6.18]95%); P=1.64×10−4. A four-tier functional investigation of all 40 mutants revealed that 14 were non-functional and rare (MAF<1%); four were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial or total loss-of-function variants, but not the neutral ones, contributed to T2D (OR=5.67[2.17;14.82]95%; P=4.09×10−4). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (Ncases=8,153/Ncontrols=10,100; OR=3.88[1.49;10.07]95%; P=5.37×10−3). This study establishes a firm functional link between MTNR1B and T2D risk.
PMCID: PMC3773908  PMID: 22286214
2.  Mosaic Overgrowth with Fibroadipose Hyperplasia is Caused by Somatic Activating Mutations in PIK3CA 
Nature genetics  2012;44(8):928-933.
The phosphatidylinositol-3-kinase (PI3K)/AKT signaling pathway is critical for cellular growth and metabolism. Correspondingly, loss of function of PTEN, a negative regulator of PI3K, or activating mutations in AKT1, AKT2, or AKT3 have been found in distinct disorders featuring overgrowth or hypoglycemia. We performed exome sequencing of DNA from unaffected and affected cells of a patient with an unclassified syndrome of congenital, progressive segmental overgrowth of fibrous and adipose tissue and bone and identified the cancer-associated p.His1047Leu mutation in PIK3CA, which encodes the p110α catalytic subunit of PI3K, only in affected cells. Sequencing of PIK3CA in 10 further patients with overlapping syndromes identified either p.His1047Leu or a second cancer-associated mutation, p.His1047Arg, in 9 cases. Affected dermal fibroblasts showed enhanced basal and EGF-stimulated phosphatidylinositol-3,4,5-trisphosphate (PIP3) generation and concomitant activation of downstream signaling. Our findings characterize a distinct overgrowth syndrome, biochemically demonstrate activation of PI3K signaling and thereby identify a rational therapeutic target.
PMCID: PMC3461408  PMID: 22729222
3.  Underlying Genetic Models of Inheritance in Established Type 2 Diabetes Associations 
American Journal of Epidemiology  2009;170(5):537-545.
For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.
PMCID: PMC2732984  PMID: 19602701
Bayes theorem; diabetes mellitus, type 2; meta-analysis; models, genetic; polymorphism, genetic; population characteristics
4.  Underlying genetic models of inheritance in established type 2 diabetes associations 
American journal of epidemiology  2009;170(5):537-545.
For most associations of common polymorphisms with common diseases, the genetic model of inheritance is unknown. We extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations for type 2 diabetes. For 13 polymorphisms, the data fit very well to an additive model, for 4 polymorphisms the data were consistent with either an additive or dominant model, and for 2 polymorphisms with an additive or recessive model of inheritance for the diabetes risk allele. Results were robust to using different priors and after excluding data where index polymorphisms had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that are very similar to those previously reported based on fixed or random effects models, but uncertainty about several of the effects was substantially larger. We also examined the extent of between-study heterogeneity in the genetic model and found generally small values of the between-study deviation for the genetic model parameter. Heterosis could not be excluded in 4 SNPs. Information on the genetic model of robustly replicated GWA-derived association signals may be useful for predictive modeling, and for designing biological and functional experiments.
PMCID: PMC2732984  PMID: 19602701
5.  Genetic evidence that raised sex hormone binding globulin (SHBG) levels reduce the risk of type 2 diabetes 
Human Molecular Genetics  2009;19(3):535-544.
Epidemiological studies consistently show that circulating sex hormone binding globulin (SHBG) levels are lower in type 2 diabetes patients than non-diabetic individuals, but the causal nature of this association is controversial. Genetic studies can help dissect causal directions of epidemiological associations because genotypes are much less likely to be confounded, biased or influenced by disease processes. Using this Mendelian randomization principle, we selected a common single nucleotide polymorphism (SNP) near the SHBG gene, rs1799941, that is strongly associated with SHBG levels. We used data from this SNP, or closely correlated SNPs, in 27 657 type 2 diabetes patients and 58 481 controls from 15 studies. We then used data from additional studies to estimate the difference in SHBG levels between type 2 diabetes patients and controls. The SHBG SNP rs1799941 was associated with type 2 diabetes [odds ratio (OR) 0.94, 95% CI: 0.91, 0.97; P = 2 × 10−5], with the SHBG raising allele associated with reduced risk of type 2 diabetes. This effect was very similar to that expected (OR 0.92, 95% CI: 0.88, 0.96), given the SHBG-SNP versus SHBG levels association (SHBG levels are 0.2 standard deviations higher per copy of the A allele) and the SHBG levels versus type 2 diabetes association (SHBG levels are 0.23 standard deviations lower in type 2 diabetic patients compared to controls). Results were very similar in men and women. There was no evidence that this variant is associated with diabetes-related intermediate traits, including several measures of insulin secretion and resistance. Our results, together with those from another recent genetic study, strengthen evidence that SHBG and sex hormones are involved in the aetiology of type 2 diabetes.
PMCID: PMC2798726  PMID: 19933169
6.  Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden 
Human Molecular Genetics  2009;18(8):1489-1496.
Recent genome-wide association studies (GWAS) have identified multiple risk loci for common obesity (FTO, MC4R, TMEM18, GNPDA2, SH2B1, KCTD15, MTCH2, NEGR1 and PCSK1). Here we extend those studies by examining associations with adiposity and type 2 diabetes in Swedish adults. The nine single nucleotide polymorphisms (SNPs) were genotyped in 3885 non-diabetic and 1038 diabetic individuals with available measures of height, weight and body mass index (BMI). Adipose mass and distribution were objectively assessed using dual-energy X-ray absorptiometry in a sub-group of non-diabetics (n = 2206). In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP was FTO rs1121980 (P < 0.001). Five other SNPs (SH2B1 rs7498665, MTCH2 rs4752856, MC4R rs17782313, NEGR1 rs2815752 and GNPDA2 rs10938397) were significantly associated with obesity. To summarize the overall genetic burden, a weighted risk score comprising a subset of SNPs was constructed; those in the top quintile of the score were heavier (+2.6 kg) and had more total (+2.4 kg), gynoid (+191 g) and abdominal (+136 g) adipose tissue than those in the lowest quintile (all P < 0.001). The genetic burden score significantly increased diabetes risk, with those in the highest quintile (n = 193/594 cases/controls) being at 1.55-fold (95% CI 1.21–1.99; P < 0.0001) greater risk of type 2 diabetes than those in the lowest quintile (n = 130/655 cases/controls). In summary, we have statistically replicated six of the previously associated obese-risk loci and our results suggest that the weight-inducing effects of these variants are explained largely by increased adipose accumulation.
PMCID: PMC2664142  PMID: 19164386
7.  Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes 
Nature genetics  2007;39(7):857-864.
The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan1 on seven diseases, including the multifactorial, autoimmune disease, type 1 diabetes (T1D), shows significant association (P < 5 × 10−7 between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios that were independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (Pfollow-up ≤ 1.35 × 10−9; Poverall ≤ 1.15 × 10−14), leaving eight regions with small effects or false-positive associations with T1D. We also obtained evidence for chromosome 18q22 (Poverall = 1.38 × 10−8) from a genome-wide association study of nonsynonymous SNPs. Several regions, including 18q22 and 18p11, showed association with autoimmune thyroid disease. This study increases the number of T1D loci with compelling evidence from six to at least ten.
PMCID: PMC2492393  PMID: 17554260
8.  The candidate genes TAF5L, TCF7, PDCD1, IL6 and ICAM1 cannot be excluded from having effects in type 1 diabetes 
BMC Medical Genetics  2007;8:71.
As genes associated with immune-mediated diseases have an increased prior probability of being associated with other immune-mediated diseases, we tested three such genes, IL23R, IRF5 and CD40, for an association with type 1 diabetes. In addition, we tested seven genes, TAF5L, PDCD1, TCF7, IL12B, IL6, ICAM1 and TBX21, with published marginal or inconsistent evidence of an association with type 1 diabetes.
We genotyped reported polymorphisms of the ten genes, nonsynonymous SNPs (nsSNPs) and, for the IL12B and IL6 regions, tag SNPs in up to 7,888 case, 8,858 control and 3,142 parent-child trio samples. In addition, we analysed data from the Wellcome Trust Case Control Consortium genome-wide association study to determine whether there was any further evidence of an association in each gene region.
We found some evidence of associations between type 1 diabetes and TAF5L, PDCD1, TCF7 and IL6 (ORs = 1.05 – 1.13; P = 0.0291 – 4.16 × 10-4). No evidence of an association was obtained for IL12B, IRF5, IL23R, ICAM1, TBX21 and CD40, although there was some evidence of an association (OR = 1.10; P = 0.0257) from the genome-wide association study for the ICAM1 region.
We failed to exclude the possibility of some effect in type 1 diabetes for TAF5L, PDCD1, TCF7, IL6 and ICAM1. Additional studies, of these and other candidate genes, employing much larger sample sizes and analysis of additional polymorphisms in each gene and its flanking region will be required to ascertain their contributions to type 1 diabetes susceptibility.
PMCID: PMC2217539  PMID: 18045485
9.  Sequencing and association analysis of the type 1 diabetes – linked region on chromosome 10p12-q11 
BMC Genetics  2007;8:24.
In an effort to locate susceptibility genes for type 1 diabetes (T1D) several genome-wide linkage scans have been undertaken. A chromosomal region designated IDDM10 retained genome-wide significance in a combined analysis of the main linkage scans. Here, we studied sequence polymorphisms in 23 Mb on chromosome 10p12-q11, including the putative IDDM10 region, to identify genes associated with T1D.
Initially, we resequenced the functional candidate genes, CREM and SDF1, located in this region, genotyped 13 tag single nucleotide polymorphisms (SNPs) and found no association with T1D. We then undertook analysis of the whole 23 Mb region. We constructed and sequenced a contig tile path from two bacterial artificial clone libraries. By comparison with a clone library from an unrelated person used in the Human Genome Project, we identified 12,058 SNPs. We genotyped 303 SNPs and 25 polymorphic microsatellite markers in 765 multiplex T1D families and followed up 22 associated polymorphisms in up to 2,857 families. We found nominal evidence of association in six loci (P = 0.05 – 0.0026), located near the PAPD1 gene. Therefore, we resequenced 38.8 kb in this region, found 147 SNPs and genotyped 84 of them in the T1D families. We also tested 13 polymorphisms in the PAPD1 gene and in five other loci in 1,612 T1D patients and 1,828 controls from the UK. Overall, only the D10S193 microsatellite marker located 28 kb downstream of PAPD1 showed nominal evidence of association in both T1D families and in the case-control sample (P = 0.037 and 0.03, respectively).
We conclude that polymorphisms in the CREM and SDF1 genes have no major effect on T1D. The weak T1D association that we detected in the association scan near the PAPD1 gene may be either false or due to a small genuine effect, and cannot explain linkage at the IDDM10 region.
PMCID: PMC1885446  PMID: 17509149
10.  Analysis of polymorphisms in 16 genes in type 1 diabetes that have been associated with other immune-mediated diseases 
BMC Medical Genetics  2006;7:20.
The identification of the HLA class II, insulin (INS), CTLA-4 and PTPN22 genes as determinants of type 1 diabetes (T1D) susceptibility indicates that fine tuning of the immune system is centrally involved in disease development. Some genes have been shown to affect several immune-mediated diseases. Therefore, we tested the hypothesis that alleles of susceptibility genes previously associated with other immune-mediated diseases might perturb immune homeostasis, and hence also associate with predisposition to T1D.
We resequenced and genotyped tag single nucleotide polymorphisms (SNPs) from two genes, CRP and FCER1B, and genotyped 27 disease-associated polymorphisms from thirteen gene regions, namely FCRL3, CFH, SLC9A3R1, PADI4, RUNX1, SPINK5, IL1RN, IL1RA, CARD15, IBD5-locus (including SLC22A4), LAG3, ADAM33 and NFKB1. These genes have been associated previously with susceptibility to a range of immune-mediated diseases including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Graves' disease (GD), psoriasis, psoriatic arthritis (PA), atopy, asthma, Crohn disease and multiple sclerosis (MS). Our T1D collections are divided into three sample subsets, consisting of set 1 families (up to 754 families), set 2 families (up to 743 families), and a case-control collection (ranging from 1,500 to 4,400 cases and 1,500 to 4,600 controls). Each SNP was genotyped in one or more of these subsets. Our study typically had approximately 80% statistical power for a minor allele frequency (MAF) >5% and odds ratios (OR) of 1.5 with the type 1 error rate, α = 0.05.
We found no evidence of association with T1D at most of the loci studied 0.02

Polymorphisms in a variety of genes previously associated with immune-mediated disease susceptibility and/or having effects on gene function and the immune system, are unlikely to be affecting T1D susceptibility in a major way, even though some of the genes tested encode proteins of immune pathways that are believed to be central to the development of T1D. We cannot, however, rule out effect sizes smaller than OR 1.5.
PMCID: PMC1420277  PMID: 16519819
BMC Genetics  2006;7:12.
The aetiology of the autoimmune disease type 1 diabetes (T1D) involves many genetic and environmental factors. Evidence suggests that innate immune responses, including the action of interferons, may also play a role in the initiation and/or pathogenic process of autoimmunity. In the present report, we have adopted a linkage disequilibrium (LD) mapping approach to test for an association between T1D and three regions encompassing 13 interferon alpha (IFNA) genes, interferon omega-1 (IFNW1), interferon beta-1 (IFNB1), interferon gamma (IFNG) and the interferon consensus-sequence binding protein 1 (ICSBP1).
We identified 238 variants, most, single nucleotide polymorphisms (SNPs), by sequencing IFNA, IFNB1, IFNW1 and ICSBP1, 98 of which where novel when compared to dbSNP build 124. We used polymorphisms identified in the SeattleSNP database for INFG. A set of tag SNPs was selected for each of the interferon and interferon-related genes to test for an association between T1D and this complex gene family. A total of 45 tag SNPs were selected and genotyped in a collection of 472 multiplex families.
We have developed informative sets of SNPs for the interferon and interferon related genes. No statistical evidence of a major association between T1D and any of the interferon and interferon related genes tested was found.
PMCID: PMC1402321  PMID: 16504056
BMC Genetics  2005;6:9.
One strategy to help identify susceptibility genes for complex, multifactorial diseases is to map disease loci in a representative animal model of the disorder. The nonobese diabetic (NOD) mouse is a model for human type 1 diabetes. Linkage and congenic strain analyses have identified several NOD mouse Idd (insulin dependent diabetes) loci, which have been mapped to small chromosome intervals, for which the orthologous regions in the human genome can be identified. Here, we have conducted re-sequencing and association analysis of six orthologous genes identified in NOD Idd loci: NRAMP1/SLC11A1 (orthologous to Nramp1/Slc11a1 in Idd5.2), FRAP1 (orthologous to Frap1 in Idd9.2), 4-1BB/CD137/TNFRSF9 (orthologous to 4-1bb/Cd137/Tnrfrsf9 in Idd9.3), CD101/IGSF2 (orthologous to Cd101/Igsf2 in Idd10), B2M (orthologous to B2m in Idd13) and VAV3 (orthologous to Vav3 in Idd18).
Re-sequencing of a total of 110 kb of DNA from 32 or 96 type 1 diabetes cases yielded 220 single nucleotide polymorphisms (SNPs). Sixty-five SNPs, including 54 informative tag SNPs, and a microsatellite were selected and genotyped in up to 1,632 type 1 diabetes families and 1,709 cases and 1,829 controls.
None of the candidate regions showed evidence of association with type 1 diabetes (P values > 0.2), indicating that common variation in these key candidate genes does not play a major role in type 1 diabetes susceptibility in the European ancestry populations studied.
PMCID: PMC551616  PMID: 15720714
Human Genomics  2004;1(2):98-109.
The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.
PMCID: PMC3525068  PMID: 15601538
type 1 diabetes; complex disease; genome informatics; data management; genetics

Results 1-13 (13)