A meta-analysis of four European genome screens (GIFT Consortium) shows evidence for a novel region on chromosome 17p11.2-q22 linked to type 2 diabetes

Truncated variant rs373056577 confers increased risk of type 2 diabetes and missense variant rs121912717 is associated with hypertriglyceridemia in Bangladeshi population

This study investigates the association of allelic and genotypic variations of rs121912717 and rs373056577 within APOA1 and APOA2 genes, respectively with the risk of type 2 diabetes (T2D). In this cross-sectional study, real-time quantitative PCR with specific Taqman probes was used to determine the genotypic and allelic frequencies of rs121912717 and rs373056577 in 300 unrelated Bangladeshi individuals (Healthy = 144, T2D patients = 156). Logistic regression analysis was performed to investigate the association of genotypic and allelic frequencies of these SNPs with respect to T2D under different inheritance models. Neither allelic nor genotypic frequencies of rs121912717 within APOA1 showed any significant association with T2D. Genotypes with respect to rs373056577 within APOA2 showed significant association with the risk of T2D under co-dominant heterozygous model (GG vs GA) [OR (95 %CI): 2.64 (1.32-5.59), p = 0.008], dominant [OR (95 %CI): 2.31 (1.24-4.49), p = 0.01] and over-dominant [OR (95 %CI): 2.62 (1.31-5.53), p = 0.008] models without adjusting for age, gender and BMI. After adjusting for age, gender and BMI, the A allele of rs373056577 showed significant association with T2D only in the dominant model [OR (95 %CI): 3.20 (1.12-10.51), p = 0.04]. Also, A allele of rs373056577 demonstrated significant association with the risk of T2D compared to allele G with [OR (95 %CI): 2.90 (1.15-8.14), p = 0.03] and without adjusting for confounders [OR (95 %CI): 1.97 (1.14-3.52), p = 0.02]. The genotypic frequency was significantly associated with T2D in codominant, dominant, and overdominant models in male participants when a gender-stratified analysis was conducted for rs373056577. However, when the logistic regression analysis was adjusted for age and BMI, the association was not significant in any of the models with respect to rs373056577 for male participants. On the other hand, gender-stratified regression analyses revealed no significant association with T2D before and after adjusting for age and BMI with respect to both allelic and genotypic frequencies of rs121912717. Individuals with CT genotype of rs121912717 had significantly higher triglyceride levels (322.2 mg/dL) compared to those harboring CC genotype (202.8 mg/dL) with or without adjusting for age, gender, BMI and disease status of the study participants. In conclusion, this study revealed that individuals harboring the allele A of rs373056577 possessed an increased risk of developing T2D and individuals having CT genotype of rs121912717 had increased triglyceride levels. The result of this study needs to be validated in a larger cohort for a more robust assessment.

miR-539-5p regulates Srebf1 transcription in the skeletal muscle of diabetic mice by targeting DNA methyltransferase 3b

Aberrant DNA methylation is associated with diabetes, but the precise regulatory events that control the levels and activity of DNA methyltransferases (DNMTs) is not well understood. Here we show that miR-539-5p targets Dnmt3b and regulates its cellular levels. miR-539-5p and Dnmt3b show inverse patterns of expression in skeletal muscle of diabetic mice. By binding to the 3′ UTR of Dnmt3b, miR-539-5p downregulates its levels in C2C12 cells and in human primary skeletal muscle cells. miR-539-5p-Dnmt3b interaction regulates Srebf1 transcription by altering methylation at CpG islands within Srebf1 in C2C12 cells. Dnmt3b inhibition alone was sufficient to upregulate Srebf1 transcription. In vivo antagonism of miR-539-5p in normal mice induced hyperglycemia and hyperinsulinemia and impaired oral glucose tolerance. These mice had elevated Dnmt3b and decreased Srebf1 levels in skeletal muscle. db/db mice injected with miR-539-5p mimics showed improved circulatory glucose and cholesterol levels. Oral glucose tolerance improved together with normalization of Dnmt3b and Srebf1 levels in skeletal muscle. Our results support a critical role of miR-539-5p and Dnmt3b in aberrant skeletal muscle metabolism during diabetes by regulating Srebf1 transcription; modulating the miR-539-5p-Dnmt3b axis might have therapeutic potential for addressing altered skeletal muscle physiology during insulin resistance and type 2 diabetes.

Identification of therapeutic targets from genetic association studies using hierarchical component analysis

Background

Mapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues.

Results

We present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets.

Conclusions

We tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn’s disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.

Schizophrenia, antipsychotics and diabetes: Genetic aspects

The relatively high comorbidity of type 2 diabetes and schizophrenia may suggest a shared biological susceptibility to these twoconditions. Family studies have demonstrated an increased risk of diabetes in unaffected relatives of patients with schizophrenia, consistent with a heritable susceptibility trait. Linkage analyses have identified several loci that are associated with schizophrenia and some of these, notably those on chromosomes 2p22.1-p13.2 and 6g21-824.1 have also been observed in linkage studies in type 2 diabetes. In addition, the dopamine D5 receptor on chromosome 5 and the tyrosine hydroxylase gene on chromosome 11 have both been suggested as candidate genes in schizophrenia and may also be implicated in susceptibility to poor glycaemic control. In addition, an increased rate of type II diabetes has been observed in some patients treated with antipsychotics. Potential neurochemical substrates of this effect include the histamine H1 receptor, the 5-HT2C serotonin receptor or the β3 adrenoreceptor. However, the search for a genetic basis to the association between diabetes and schizophrenia is still in its infancy, and much further work needs to be performed, including the systematic screening of all confirmed susceptibility loci and quantitative trait locus mapping of glycaemic control.

Obesity genetics in mouse and human: back and forth, and back again

Obesity is a major public health concern. This condition results from a constant and complex interplay between predisposing genes and environmental stimuli. Current attempts to manage obesity have been moderately effective and a better understanding of the etiology of obesity is required for the development of more successful and personalized prevention and treatment options. To that effect, mouse models have been an essential tool in expanding our understanding of obesity, due to the availability of their complete genome sequence, genetically identified and defined strains, various tools for genetic manipulation and the accessibility of target tissues for obesity that are not easily attainable from humans. Our knowledge of monogenic obesity in humans greatly benefited from the mouse obesity genetics field. Genes underlying highly penetrant forms of monogenic obesity are part of the leptin-melanocortin pathway in the hypothalamus. Recently, hypothesis-generating genome-wide association studies for polygenic obesity traits in humans have led to the identification of 119 common gene variants with modest effect, most of them having an unknown function. These discoveries have led to novel animal models and have illuminated new biologic pathways. Integrated mouse-human genetic approaches have firmly established new obesity candidate genes. Innovative strategies recently developed by scientists are described in this review to accelerate the identification of causal genes and deepen our understanding of obesity etiology. An exhaustive dissection of the molecular roots of obesity may ultimately help to tackle the growing obesity epidemic worldwide.

SREBF1 gene variations modulate insulin sensitivity in response to a fish oil supplementation

BACKGROUND

An important inter-individual variability in the response of insulin sensitivity following a fish oil supplementation has been observed. The objective was to examine the associations between single nucleotide polymorphisms (SNPs) within sterol regulatory element binding transcription factor 1 (SREBF1) gene and the response of insulin sensitivity to a fish oil supplementation.

METHODS

Participants (n = 210) were recruited in the greater Quebec City area and followed a 6-week fish oil supplementation protocol (5 g/day: 1.9-2.2 g EPA; 1.1 g DHA). Insulin sensitivity was assessed by the quantitative insulin sensitivity check index (QUICKI). Three tag SNPs (tSNPs) within SREBF1 gene were genotyped according to TAQMAN methodology.

RESULTS

Three tSNPs (rs12953299, rs4925118 and rs4925115) covered 100% of the known genetic variability within SREBF1 gene. None of the three tSNPs was associated with either baseline fasting insulin concentrations (rs12953299, rs4925118 and rs4925115) (p = 0.29, p = 0.20 and p = 0.70, respectively) or QUICKI (p = 0.20, p = 0.18 and p = 0.76, respectively). The three tSNPs (rs12953299, rs4925118 and rs4925115) were associated with differences in the response of plasma insulin levels (p = 0.01, p = 0.005 and p = 0.004, respectively) and rs12953299 as well as rs4925115 were associated with the insulin sensitivity response (p = 0.009 and p = 0.01, respectively) to the fish oil supplementation, independently of the effects of age, sex and BMI.

CONCLUSIONS

The genetic variability within SREBF1 gene has an impact on the insulin sensitivity in response to a fish oil supplementation.

TRIAL REGISTRATION

clinicaltrials.gov: NCT01343342.

Overweight condition and waist circumference and a candidate gene within the 12q24 locus

Aims

Obesity and obesity-associated phenotypes are linked to the chromosome12q24 locus, the non-insulin-dependent-diabetes 2 (NIDDM2) locus. The gene of proteasome modulator 9 (PSMD9) lies in the NIDDM2 region and is linked to type 2 diabetes (T2D), microvascular and macrovascular complications of T2D.

We aimed at studying whether the PSMD9 T2D risk single nucleotide polymorphisms (SNPs) IVS3+nt460, IVS3+nt437, and 197G are linked to obesity, overweight status and waist circumference in Italian T2D families.

Methods and results

We screened 200 Italians T2D siblings/families for PSMD9 variants. Using Merlin software, we performed non-parametric linkage analysis to test for linkage with obesity and overweight condition and variance component analysis to test for linkage with waist circumference in our T2D siblings/families dataset.

Our study shows that the PSMD9 SNPs IVS3+nt460, IVS3+nt437, and 197G are in linkage with overweight condition and waist circumference in Italians. The statistical power tests performed via simulations on real data confirm that the results are not due to random chance.

Conclusions

In summary, the linkage strategy using a homogeneous family/subject dataset can identify a gene contributing to a complex trait. PMSD9 may be at least one of the genes responsible for the linkage to obesity and obesity-associated phenotypes at the locus 12q24 in other populations.

Proteasome modulator 9 is linked to microvascular pathology of T2D

The locus 12q24 is linked to type 2 diabetes (T2D) and to changes in retinal vascular caliber in Caucasians. Proteasome Modulator 9 gene (PSMD9) lies in the 12q24 locus and is implicated in diabetes onset and in degradation of intracellular proteins in antigenic peptides in the immune response to antigen presentation by MHC class I cells. Within PSMD9, we reported a linkage to T2D and to MODY3 in Italian families. We recently demonstrated a linkage of the PSMD9 T2D risk SNPs with T2D-nephropathy, T2D-neuropathy, retinopathy, hypercholesterolemia, and macrovascular pathology. We aimed at studying the presence of the linkage signal of the PSMD9 T2D risk SNPs IVS3 + nt460, IVS3 + nt437, E197G to microvascular pathology associated to T2D in Italian siblings/families. We screened 200 T2D siblings/families for the PSMD9 above-mentioned variants and performed a parametric and non-parametric linkage study by Merlin software. Our results show significant LOD score in linkage with microvascular pathology for the PSMD9 SNPs studied using the non-parametric and parametric linkage analysis. The strongest signal is present under the recessive model. Our statistical power relies on the presence of T2D affected siblings, which represent an ideal dataset to identify linkage with a recessive disease model. Our simulation analysis confirms that the results are not due to random chance. In summary, the PSMD9 IVS3 + nt460, IVS3 + nt437, E197G SNPs are linked via the recessive model to microvascular pathology of T2D in Italians. A possible role of PSMD9 in microvascular pathology may be related to a causative pathogenetic role in inflammation as part of an autoimmune process.

Direct evidence for susceptibility genes for type 2 diabetes on mouse chromosomes 11 and 14

AIMS/HYPOTHESIS

Diabetogenic loci for type 2 diabetes have been mapped to mouse chromosome (Chr) 11 and 14 in the Nagoya-Shibata-Yasuda (NSY) mouse, an animal model of type 2 diabetes. We aimed to obtain direct evidence of these genes on each chromosome and to clarify their function and interaction in conferring susceptibility to type 2 diabetes.

METHODS

We established three consomic strains homozygous for diabetogenic NSY-Chr11, NSY-Chr14 or both on the control C3H background (C3H-11(NSY), C3H-14(NSY) and C3H-11(NSY)14(NSY), respectively), and monitored diabetes-related phenotypes longitudinally. The glucokinase gene was sequenced as a positional candidate gene on Chr11.

RESULTS

C3H-11(NSY) mice showed hyperglycaemia associated with impaired insulin secretion and age-dependent insulin resistance without obesity. C3H-14(NSY) mice exhibited hyperglycaemia mainly due to insulin resistance, with a slight increase in percentage body fat. C3H-11(NSY)14(NSY) double consomic mice showed marked hyperglycaemia and obesity, which was not observed in single consomic strains. Sequences of the glucokinase gene were allelically variant between NSY and C3H mice.

CONCLUSIONS/INTERPRETATION

These data provide direct evidence that Chr11 and Chr14 harbour major susceptibility genes for type 2 diabetes. These two chromosomes interact to cause more severe hyperglycaemia and obesity, which was not observed with the presence of either single chromosome, indicating different modes of gene-gene interaction depending on the phenotype. Marked changes in the phenotypes retained in the consomic strains will facilitate fine mapping and the identification of the responsible genes and their interaction with each other, other genes and environmental factors.

PSMD9 is linked to MODY3

Type 2 diabetes has a replicated linkage on chromosome12q24.2 (NIDDM2 locus/non-insulin-dependent diabetes mellitus 2 locus), near the HNF-1alpha/MODY3 gene. The MODY3 gene is not responsible for this linkage. PSMD9--contributing to T2D in Italians by rare unique mutations and by the common haplotype A/T/G--lies in the NIDDM2 region. By genotyping the two markers D12S1721/D12S2073 nearby the MODY3 gene in our unrelated T2D cases, we previously excluded that the PSMD9 SNPs are in linkage disequilibrium (LD) with the MODY3 gene. In the present study, we aimed at identifying whether the PSMD9 A/T/G haplotype is present in the Italy-1 and Italy-3 MODY3 families and whether it cosegregates with diabetes/MODY3. We raised the question whether there is a digenic additive model within the MODY3 families to which the PSMD9 A/T/G haplotype contributes. We demonstrated that the PSMD9 A/T/G haplotype is linked to the MODY3 established mutations in the Italy-1 and Italy-3 families. By non-parametric and parametric linkage analyses, and LD modeling, in the Italy-1 and Italy-3 families we hereby show that the MODY3 mutation and the PSMD9 IVS3 + nt460A/IVS3 + nt437T/G197 SNPs act in an additional model to cause diabetes. Since in the two MODY3 Italian families the PSMD9 A/T/G haplotype is linked to MODY3, it contributes to MODY3/diabetes via an additional model. All MODY3 families should be tested for the PSMD9 A/T/G haplotype. The potential clinical impact of our study is of relevance.

Genome-wide linkage scan for factors of metabolic syndrome in a Chinese population

BACKGROUND

Shared genetic factors may contribute to the phenotypic clustering of different components of the metabolic syndrome (MES). This study aims to identify genetic loci that contribute to individual or multiple factors related to MES.

RESULTS

We studied 478 normoglycemic subjects ascertained through 163 families participating in the Hong Kong Family Diabetes Study. Factor analysis on 15 MES-related traits yielded 6 factors including adiposity factor (body mass index, waist and hip circumferences), insulin factor (fasting insulin and insulin AUC during OGTT), glucose factor (fasting glucose and glucose AUC during OGTT), TC-LDLC factor (total cholesterol and LDL-cholesterol), blood pressure factor (systolic and diastolic blood pressure) and TG-HDLC factor (triglycerides and HDL-cholesterol). Genome-wide linkage analyses were performed on these factors using variance component approach. Suggestive evidence for linkage (LOD = 1.24 - 2.46) were observed for adiposity factor (chromosome 1 at 187 cM, chromosome 9 at 34 cM and chromosome 17 at 10 cM), insulin factor (chromosome 2 at 128 cM, chromosome 5 at 21 cM and chromosome 12 at 7 cM), glucose factor (chromosome 7 at 155 cM), TC-LDLC factor (chromosome 7 at 151 cM and chromosome 13 at 15 cM) and TG-HDLC factor (chromosome 7 at 155 cM).

CONCLUSIONS

In summary, our findings suggest the presence of susceptibility loci that influence either single (chromosomes 1, 2, 5, 9, 12, 13 and 17) or multiple factors (chromosome 7) for MES in Hong Kong Chinese without diabetes.

PSMD9 gene in the NIDDM2 locus is linked to type 2 diabetes in Italians

Type 2 diabetes (T2D) has a replicated linkage on chromosome12q24.2, the non-insulin-dependent-diabetes 2 (NIDDM2) locus. PSMD9 (which rarely causes T2D in Italians) lies in the NIDDM2 region and is implicated in beta cell insulin transcription and diabetes onset in mice. Thus, PSMD9 is a candidate T2D gene for the NIDDM2 locus. We aimed at identifying any linkage of the PSMD9 A/T/G haplotype, or of any of its single variants, to Italian T2D siblings/families. We screened 201 T2D siblings/families for PSMD9 variants and performed a parametric and non-parametric linkage study, including linkage disequilibrium (LD) modeling and simulation analyses. Our results show a consistent significant LOD score in linkage with T2D for each single PSMD9 SNP variant (IVS3 + nt460A, P = 3.546E - 007, IVS3 + nt437T, P = 7.723E - 008, and 197G, P = 4.921E - 007) and for the haplotype (A/T/G for the above-cited variants, P = 3.078E - 015) using the non-parametric analysis, as well as the LD modeling test (P = 4.178E - 009) and the parametric linkage analysis. The strongest signal is present under the recessive model (P = 4.905E - 011). Our statistical power in the present study relies on the presence of T2D affected siblings, which represent an ideal dataset to identify linkage with a recessive disease model. Our 1,000 simulation analyses, performed for each single test, confirm that the results are not due to random chance. In summary, the A/T/G haplotype in PSMD9 is linked via the recessive allelic model to T2D in Italians. By our observation and testing, the linkage strategy can identify a gene contributing to T2D in a homogeneous subject dataset.

Association of sterol regulatory element-binding protein-1c gene polymorphism with type 2 diabetes mellitus, insulin resistance and blood lipid levels in Chinese population

AIMS

The sterol regulatory element-binding protein (SREBP)-1c gene has been identified as a susceptibility gene in metabolic diseases such as type 2 diabetes mellitus (T2DM), obesity, dyslipidemia and insulin resistance. Previous studies suggest that SNP17 (rs2297508, exon18c and G952G) of SREBP-1c gene and a common SREBP-1c SNP6 (rs11868035) are associated with an increased risk of T2DM. The present study aimed to confirm the previously reported association in a Chinese population and to examine the two SREBP-1c SNPs for their associations with insulin resistance and blood lipid.

METHODS

We genotyped two SREBP-1c SNPs in a case-control study (n=327) from Chinese, including 156 patients with T2DM and 171 healthy controls, using polymerase chain reaction-denaturing high-performance liquid chromatography (PCR-DHPLC) and tested for association with type 2 diabetes, insulin resistance and blood lipid, respectively. Genotype and allele distributions and haplotype construction were analysed.

RESULTS

The genotype and allele distributions of rs2297508 and rs11868035 polymorphisms were significantly different in type 2 diabetic patients compared to controls (P=0.002 and P=0.013; 0.00 and 0.001, respectively). Haplotype analyses showed significant association with diabetes risk and confirmed the results of the single SNP analyses. The plasma levels of LDL-c of the minor allele-C carriers of the two SNPs were both significantly higher than the noncarriers in the control group (P<0.05). Furthermore, insulin resistance index (HOMA-IRI) of the rare homozygotes C/C of rs11868035 was significantly lower than that of T/T in the T2DM group (P<0.05).

CONCLUSIONS

These findings indicate that the SREBP-1c SNPs rs2297508 and rs11868035 are associated with a significantly increased risk of T2DM and dyslipidemia in the Chinese population. Moreover, the SNP (rs11868035) is closely related to insulin resistance (IR) in diabetic patients.

Common variants in maturity-onset diabetes of the young genes and future risk of type 2 diabetes

OBJECTIVE

Mutations in the hepatocyte nuclear factor (HNF)-1alpha, HNF-4alpha, glucokinase (GCK), and HNF-1beta genes cause maturity-onset diabetes of the young (MODY), but it is not known whether common variants in these genes predict future type 2 diabetes.

RESEARCH DESIGN AND METHODS

We tested 14 previously associated polymorphisms in HNF-1alpha, HNF-4alpha, GCK, and HNF-1beta for association with type 2 diabetes-related traits and future risk of type 2 diabetes in 2,293 individuals from the Botnia study (Finland) and in 15,538 individuals from the Malmö Preventive Project (Sweden) with a total follow-up >360,000 years.

RESULTS

The polymorphism rs1169288 in HNF-1alpha strongly predicted future type 2 diabetes (hazard ratio [HR] 1.2, P = 0.0002). Also, SNPs rs4810424 and rs3212198 in HNF-4alpha nominally predicted future type 2 diabetes (HR 1.3 [95% CI 1.0-1.6], P = 0.03; and 1.1 [1.0-1.2], P = 0.04). The rs2144908 polymorphism in HNF-4alpha was associated with elevated rate of hepatic glucose production during a hyperinsulinemic-euglycemic clamp (P = 0.03) but not with deterioration of insulin secretion over time. The SNP rs1799884 in the GCK promoter was associated with elevated fasting plasma glucose (fPG) concentrations that remained unchanged during the follow-up period (P = 0.4; SE 0.004 [-0.003-0.007]) but did not predict future type 2 diabetes (HR 0.9 [0.8-1.0], P = 0.1). Polymorphisms in HNF-1beta (transcription factor 2 [TCF2]) did not significantly influence insulin or glucose values nor did they predict future type 2 diabetes.

CONCLUSIONS

In conclusion, genetic variation in both HNF-1alpha and HNF-4alpha predict future type 2 diabetes, whereas variation in the GCK promoter results in a sustained but subtle elevation of fPG that is not sufficient to increase risk for future type 2 diabetes.

Association of variants in the sterol regulatory element-binding factor 1 (SREBF1) gene with type 2 diabetes, glycemia, and insulin resistance: a study of 15,734 Danish subjects

OBJECTIVE

We evaluated the association of variants in the sterol regulatory element-binding factor 1 gene (SREBF1) with type 2 diabetes. Due to the previous inconclusive quantitative trait associations, we also did studies of intermediate quantitative phenotypes.

RESEARCH DESIGN AND METHODS

We genotyped four variants in SREBF1 in the population-based Inter99 cohort (n = 6,070), the Danish ADDITION study (n = 8,662), and in additional type 2 diabetic patients (n = 1,002). The case-control studies involved 2,980 type 2 diabetic patients and 4,522 glucose-tolerant subjects.

RESULTS

The minor alleles of rs2297508, rs11868035, and rs1889018 (linkage disequilibrium R(2) = 0.6-0.8) associated with a modestly increased risk of type 2 diabetes (rs2297508: OR 1.17 [95% CI 1.05-1.30], P = 0.003), which was confirmed in meta-analyses of all published studies (rs2297508 G-allele: 1.08 [1.03-1.14] per allele, P = 0.001). The diabetes-associated alleles also associated strongly with a higher plasma glucose at 30 and 120 min and serum insulin at 120 min during an oral glucose tolerance test (all P < 0.006) and the minor allele of rs1889018 with a surrogate measure of insulin sensitivity (P = 0.03). Furthermore, the diabetes-associated alleles associated with a modestly increased A1C level in the population-based Inter99 of middle-aged subjects and in the ADDITION study of high-risk individuals (P = 0.006 and P = 0.008, respectively).

CONCLUSIONS

We associate sequence variation in SREBF1 with a modestly increased predisposition to type 2 diabetes. In the general population, the diabetes-associated alleles are discreetly associated with hyperglycemia presumably due to decreased insulin sensitivity. Because sterol regulatory element-binding protein-1c is a mediator of insulin action, the findings are consistent with the presence of a yet undefined subtle loss-of-function SREBF1 variant.

The role of membrane glycoprotein plasma cell antigen 1/ectonucleotide pyrophosphatase phosphodiesterase 1 in the pathogenesis of insulin resistance and related abnormalities

Insulin resistance is a major feature of most patients with type 2 diabetes mellitus (T2D). A number of laboratories have observed that PC-1 (membrane [corrected] glycoprotein plasma cell antigen 1; also termed [corrected] ectonucleotide pyrophosphatase phosphodiesterase 1 or ENPP1) [corrected] is either overexpressed or overactive in muscle, adipose tissue, fibroblasts, and other tissues of insulin-resistant individuals, both nondiabetic and diabetic. Moreover, PC-1 (ENPP1) overexpression [corrected] in cultured cells in vitro and in transgenic mice in vivo, [corrected] impairs insulin stimulation of insulin receptor (IR) activation and downstream signaling. PC-1 binds to the connecting domain of the IR alpha-subunit that is located in residues 485-599. The connecting domain transmits insulin binding in the alpha-subunit to activation of tyrosine kinase activation in the beta-subunit. When PC-1 is overexpressed, it inhibits insulin [corrected]induced IR beta-subunit tyrosine kinase activity. In addition, a polymorphism of PC-1 (K121Q) in various ethnic populations is closely associated with insulin resistance, T2D, and cardio [corrected] and nephrovascular diseases. The product of this polymorphism has a 2- to 3-fold increased binding affinity for the IR and is more potent than the wild-type PC-1 protein (K121K) in inhibiting the IR. These data suggest therefore that PC-1 is a candidate protein that may play a role in human insulin resistance and T2D by its overexpression, its overactivity, or both.

ENPP1 gene, insulin resistance and related clinical outcomes

PURPOSE OF REVIEW

Insulin resistance plays a significant role in both morbidity and mortality of the general population. Understanding the molecular mechanisms of insulin resistance would help the identification of at-risk individuals in the presymptomatic stage, and the discovery of novel and more effective treatments. The transmembrane glycoprotein ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) inhibits insulin receptor signalling and has recently emerged as a key player in the development of insulin resistance. This review will summarize data available on the relationship between ENPP1 and insulin resistance.

RECENT FINDINGS

Overexpression of ENPP1 in insulin target tissues is an early, intrinsic defect observed in human insulin resistance. A missense ENPP1 single nucleotide polymorphism, K121Q, has been recently described with the Q121 variant being a stronger inhibitor than K121 of insulin receptor function. In addition, the Q121 variant has been repeatedly associated with insulin resistance and related abnormalities including body weight changes, type 2 diabetes and macrovascular complications, thus suggesting a pleiotropic role of the ENPP1 gene on several metabolic abnormalities.

SUMMARY

A deep understanding of ENPP1 mode of action and the mechanisms regulating its expression and function are likely to provide new tools for early identification and treatments of patients at risk for the devastating clinical outcomes related to insulin resistance.

A German genome-wide linkage scan for type 2 diabetes supports the existence of a metabolic syndrome locus on chromosome 1p36.13 and a type 2 diabetes locus on chromosome 16p12.2

AIMS/HYPOTHESIS

The aim was to identify type 2 diabetes susceptibility regions in 250 German families.

SUBJECTS AND METHODS

We conducted a genome-wide linkage scan using 439 short tandem repeat polymorphisms at an average resolution of 7.76 +/- 3.80 cM (Marshfield). In an affected-only-design (affected sib pairs), we performed nonparametric multipoint linkage analyses. Conditional analyses were applied where linkage signals were found in the baseline analyses.

RESULTS

We identified two loci with nominal evidence for linkage on chromosomes 1p36.13 and 16p12.2 (D1S3669, 37.05 cM, logarithmic odds ratio [LOD] = 1.49, p = 0.004; D16S403, 43.89 cM, LOD = 1.85, p = 0.002). D16S403 crossed the empirically obtained threshold of genome-wide suggestive significance of LOD = 1.51. Positive findings in those regions have been reported by the following other linkage studies on: (1) symptomatic/clinical gall bladder disease with type 2 diabetes in Mexican Americans from the San Antonio Family Diabetes/Gallbladder Study (LOD = 3.7, D1S1597-D1S407, 29.93-33.75 cM); (2) body size-adiposity in another Mexican American population (D1S1597, LOD = 2.53, 29.93 cM); (3) lipid abnormalities (LOD = 3.1, D1S2826-D1S513, 41.92-60.01 cM); and (4) hypertension in Australian sib pairs (LOD = 3.1, D1S2834-D1S2728, 31.02-33.75 cM); as well as (5) a meta-analysis of four European type 2 diabetes-related genome scans (LOD = 1.09, D16S412, 42.81 cM). In linkage analyses conditional on evidence for linkage at D16S403 we identified a LOD increase (Delta LOD) of 1.55 (p = 0.0075) at D17S2180. Similar conditioning on D17S2180 revealed evidence for interaction with D1S3669 (Delta LOD = 1.67, p = 0.0055), D16S403 (Delta LOD = 1.48, p = 0.0091) and another locus on chromosome 1 where several genome scans have reported evidence for linkage ( approximately 200 cM, Delta LOD = 1.60, p = 0.0066).

CONCLUSIONS/INTERPRETATION

Our results and the findings of other studies are consistent with the presence of a locus for a complex metabolic syndrome on chromosome 1p36.13.

The genetic landscape of type 2 diabetes in mice

Inbred mouse strains provide genetic diversity comparable to that of the human population. Like humans, mice have a wide range of diabetes-related phenotypes. The inbred mouse strains differ in the response of their critical physiological functions, such as insulin sensitivity, insulin secretion, beta-cell proliferation and survival, and fuel partitioning, to diet and obesity. Most of the critical genes underlying these differences have not been identified, although many loci have been mapped. The dramatic improvements in genomic and bioinformatics resources are accelerating the pace of gene discovery. This review describes how mouse genetics can be used to discover diabetes-related genes, summarizes how the mouse strains differ in their diabetes-related phenotypes, and describes several examples of how loci identified in the mouse may directly relate to human diabetes.

Meta-analysis of 23 type 2 diabetes linkage studies from the International Type 2 Diabetes Linkage Analysis Consortium

BACKGROUND

The International Type 2 Diabetes Linkage Analysis Consortium was formed to localize type 2 diabetes predisposing variants based on 23 autosomal linkage scans.

METHODS

We carried out meta-analysis using the genome scan meta-analysis (GSMA) method which divides the genome into bins of approximately 30 cM, ranks the best linkage results in each bin for each sample, and then sums the ranks across samples. We repeated the meta-analysis using 2 cM bins, and/or replacing bin ranks with measures of linkage evidence: bin maximum LOD score or bin minimum p value for bins with p value <0.05 (truncated p value). We also carried out computer simulations to assess the empirical type I error rates of these meta-analysis methods.

RESULTS

Our analyses provided modest evidence for type 2 diabetes-predisposing variants on chromosomes 4, 10, and 14 (using LOD scores or truncated p values), or chromosome 10 and 16 (using ranks). Our simulation results suggested that uneven marker density across studies results in substantial variation in empirical type I error rates for all meta-analysis methods, but that 2 cM bins and scores that make more explicit use of linkage evidence, especially the truncated p values, reduce this problem.

CONCLUSION

We identified regions modestly linked with type 2 diabetes by summarizing results from 23 autosomal genome scans.

The SREBF-1 locus is associated with type 2 diabetes and plasma adiponectin levels in a middle-aged Austrian population

CONTEXT

The sterol regulatory element-binding protein-1c (SREBP-1c) is a transcription factor involved in the regulation of lipid and glucose metabolism and has been implicated in the pathophysiology of type 2 diabetes mellitus (T2DM).

OBJECTIVE

We aimed to confirm associations of the SREBF-1 gene with T2DM in an Austrian population and to study possible associations with diabetes-related quantitative traits.

DESIGN, SETTINGS AND PARTICIPANTS

We genotyped a diabetic cohort (n=446) along with a control group (n=1524) for a common C/G variation that is located in exon 18c (rs2297508) and has been associated with obesity and T2DM in French populations.

MAIN OUTCOME MEASURES

Body mass index (BMI), indices of insulin sensitivity and beta-cell function, plasma adiponectin, T2DM and single-nucleotide polymorphism rs2297508.

RESULTS

Genotype distributions associated with rs2297508 differed by T2DM status (P=0.0045), but not by BMI. The variant G allele was associated with a modest, but significant, increase in the prevalence of T2DM after adjustment for age, sex and BMI (G/G: odds ratios (OR) (95% confidence intervals)=1.45 (0.99-2.11) and G/C: OR=1.37 (1.04-1.81)). In a cross-sectional population of non-diabetic subjects, associations of rs2297508 genotypes with plasma adiponectin levels adjusted for age, sex and BMI (P=0.0017) were observed in that the risk G/G genotype displayed the lowest adiponectin levels.

CONCLUSIONS

We observed associations of rs2297508 with T2DM prevalence and plasma adiponectin. SREBP-1c has been implicated in the regulation of adiponectin gene expression. Our results therefore raise the possibility that sequence variations at the SREBF-1 gene locus might contribute to T2DM risk, at least in part, by altering circulating adiponectin levels.

Mechanisms of Disease: ectonucleotide pyrophosphatase phosphodiesterase 1 as a 'gatekeeper' of insulin receptors

Insulin resistance is pathogenic for type 2 diabetes and cardiovascular disease. Several inhibitors of insulin signaling have a role in human insulin resistance. The transmembrane glycoprotein ectonucleotide pyrophosphatase phosphodiesterase 1 (E-NPP1; also known as plasma cell membrane glycoprotein PC-1) interacts with the insulin receptor and inhibits subsequent signaling by decreasing its beta-subunit autophosphorylation. E-NPP1 is overexpressed in skeletal muscle, adipose tissue and cultured skin fibroblasts of insulin-resistant individuals who are not yet obese or diabetic, which indicates that excessive E-NPP1 expression is an early, intrinsic defect in human insulin resistance. Genetic studies also support a primary role of E-NPP1 in insulin resistance. Among other variants, a missense polymorphism, Lys121Gln, has been described. The Gln121 variant is a stronger inhibitor than Lys121 of insulin receptor function, and is associated with insulin resistance, type 2 diabetes and both cardiovascular and nephrovascular complications in diabetic patients. E-NPP1 is measurable in human serum, where it might represent a valuable biomarker of insulin resistance, but its relationship to tissue and systemic insulin resistance remains to be thoroughly elucidated. Understanding the mechanisms that regulate E-NPP1 expression and/or function might render this protein a new target for strategies to treat and prevent type 2 diabetes and cardiovascular disease.

Genotype-by-sex interaction in the aetiology of type 2 diabetes mellitus: support for sex-specific quantitative trait loci in Hypertension Genetic Epidemiology Network participants

AIMS/HYPOTHESIS

While there are sex-related differences in both the prevalence of type 2 diabetes mellitus and disease risk factors, there is only limited research on sex-specific influences on type 2 diabetes aetiology within the same study population. Thus, we assessed genotype-by-sex interaction using a liability threshold model in an attempt to localise sex-specific type 2 diabetes quantitative trait loci (QTLs).

SUBJECTS, MATERIALS AND METHODS

Hypertensive siblings and their offspring and/or parents in the Hypertension Genetic Epidemiology Network of the Family Blood Pressure Program were recruited from five field centres. The diabetic phenotype was adjusted for race, study centre, age and non-linear age effects. In total, 567 diabetic individuals were identified in 385 families. Variance component linkage analyses in the combined sample and stratified by sex and race were performed (SOLAR program) using race-specific marker allele frequencies derived from a random sample of participants at each centre.

RESULTS

We observed a QTL-specific genotype-by-sex interaction (p=0.009) on chromosome 17 at 31 cM, with females displaying a robust adjusted logarithm of odds (LOD) of 3.0 compared with 0.2 in males and 1.3 in the combined sample. Three additional regions demonstrating suggestive evidence for linkage were detected: chromosomes 2 and 5 in the female sample and chromosome 22 (adjusted LOD=1.9) in the combined sample.

CONCLUSIONS/INTERPRETATION

These findings suggest that multiple genes may regulate susceptibility to type 2 diabetes, demonstrating the importance of considering the interaction of genes and environment in the aetiology of common complex traits.

Heterozygosity for a POMC-null mutation and increased obesity risk in humans

Congenital deficiency of proopiomelanocortin (POMC) results in a syndrome of hypoadrenalism, severe obesity, and altered skin and hair pigmentation. The concept that subtle variation in POMC expression and/or function might contribute to common obesity is suggested by studies reporting linkage of obesity-related traits to a locus on chromosome 2p22 encompassing the POMC gene. We identified a novel homozygous frameshift (C6906del) mutation in POMC in a child of Turkish origin with severe obesity and hypoadrenalism. This mutation would be predicted to lead to the loss of all POMC-derived peptides. The availability of a large extended pedigree provided the opportunity to address whether loss of one copy of the POMC gene was sufficient to alter obesity risk. Twelve relatives were heterozygous for the mutation and 7 were wild type. Of the heterozygotes, 11 of 12 heterozygotes were obese or overweight compared with only 1 of 7 of the wild-type relatives. The mean BMI SD score was 1.7 +/- 0.5 in heterozygotes and 0.4 +/- 0.4 in the wild-type relatives. Parametric linkage analysis of the trait "overweight" provided statistically significant evidence of linkage with this locus, with a maximum "location score" (comparable with multipoint logarithm of odds scores) of 3.191. We conclude that loss of one copy of the POMC gene predisposes to obesity in humans. Thus, genetic variants having relatively subtle effects on POMC expression and function could influence susceptibility to obesity.

Studies of the relationship between the ENPP1 K121Q polymorphism and type 2 diabetes, insulin resistance and obesity in 7,333 Danish white subjects

AIMS/HYPOTHESIS

Plasma cell membrane glycoprotein 1 (PC-1) inhibits insulin signalling by direct interaction with the insulin receptor alpha subunit. This inhibition is enhanced by the minor Q allele of the K121Q polymorphism (rs1044498) in the gene (ENPP1) encoding PC-1. This polymorphism has been studied in relation to insulin resistance, type 2 diabetes and obesity in several populations with conflicting results. We assessed the impact of the ENPP1 K121Q polymorphism on type 2 diabetes, obesity and quantitative metabolic traits in 7,333 Danes.

SUBJECTS AND METHODS

The K121Q polymorphism was genotyped in the population-based Inter99 study cohort (5,961 subjects) and in a group of 1,386 patients with type 2 diabetes. All subjects were Danish whites.

RESULTS

No significant associations with type 2 diabetes or related quantitative metabolic traits, including measures of insulin resistance, were detected. However, a meta-analysis of the present and published studies revealed an association with type 2 diabetes (odds ratio per Q allele, 1.17 [95% CI 1.10-1.25], p=1x10(-6)). In case-control studies comparing subjects of different BMI strata, we observed a putative association of the codon 121 QQ genotype with being overweight (BMI>25 kg/m(2); odds ratio 1.63 [95% CI 1.09-2.46], p=0.015), an association not observed when comparing other levels of BMI or when analysing BMI as a quantitative trait.

CONCLUSIONS/INTERPRETATION

In a meta-analysis, the ENPP1 codon 121 Q allele associates with type 2 diabetes. However, a similar association was not found in the present study of Danish white subjects. The effect of this variant on obesity in Danish subjects is contentious and further study is needed.

Heterogeneity-based genome search meta-analysis for preeclampsia

Preeclampsia is a pregnancy-related disorder that causes maternal and fetal morbidity and mortality. Its exact inheritance pattern is still unknown, and genome searches for identifying susceptibility loci for preeclampsia have thus far produced inconclusive or inconsistent results. We performed a heterogeneity-based genome search meta-analysis (HEGESMA) that synthesized the available genome scan data on preeclampsia. HEGESMA identifies genetic regions (bins) that rank highly on average in terms of linkage statistics across genome scans (searches). The significance of each bin's average rank and heterogeneity across scans was calculated using Monte Carlo tests. The meta-analysis involved four genome-scans on general preeclampsia and five scans on severe preeclampsia. In general preeclampsia, 13 bins had significantly high average rank (Prank< 0.05) by either unweighted or weighted analyses, while four of them (2p11.2-2q21.1, 9q21.32-9q31.2, 2p15-2p11.2, 2q32.1-2q35) were formally significant by both analyses. Heterogeneity of bin 2.8 (2q32.1-2q35) was significantly low in both unweighted and weighted analysis (PQ< 0.01). In severe preeclampsia, 10 bins had significantly high average rank by either unweighted or weighted analyses and five of them (3q11.1-3q21.2, 2q37.1-2q37.3, 18p11.32-18p11.22, 2p15-2p11.2, 7q34-7q36.3) were significant by both analyses. Bin 2q37.1-2q37.3 showed marginal low heterogeneity in unweighted and weighted analysis (PQ= 0.06). Results should be interpreted with caution as the p values were modest. Further investigation of these regions by genotyping with additional markers and families may help to direct the identification of candidate genes for preeclampsia.

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

Common variations in the ALMS1 gene do not contribute to susceptibility to type 2 diabetes in a large white UK population

AIMS/HYPOTHESIS

Alström syndrome is a rare monogenic disorder characterised by retinal dystrophy, deafness and obesity. Patients also have insulin resistance, central obesity and dyslipidaemia, thus showing similarities with type 2 diabetes. Rare mutations in the ALMS1 gene cause severe gene disruption in Alström patients; however, ALMS1 gene polymorphisms are common in the general population. The aim of our study was to determine whether common variants in ALMS1 contribute to susceptibility to type 2 diabetes in the UK population.

METHODS

Direct sequencing was performed on coding regions and intron/exon boundaries of the ALMS1 gene in 30 unrelated probands with type 2 diabetes. The linkage disequilibrium (LD; D' and r2) and haplotype structure were examined for the identified variants. The common (minor allele frequency [MAF] >5%) single-nucleotide polymorphisms tagging the common haplotypes (tagged SNPs [tSNPs]) were identified and genotyped in 1985 subjects with type 2 diabetes, 2,047 control subjects and 521 families.

RESULTS

We identified 18 variants with MAF between 6 and 38%. Three SNPs efficiently tagged three common haplotypes (rs1881245, rs3820700 and rs1320374). There was no association (all p > 0.05) between the tSNPs and type 2 diabetes in the case-control study and minor alleles of the tSNPs were not overtransmitted to probands with type 2 diabetes in the family study.

CONCLUSIONS/INTERPRETATION

Common variations in the ALMS1 gene were not associated with type 2 diabetes in a large study of a white UK population.

Linkage and potential association of obesity-related phenotypes with two genes on chromosome 12q24 in a female dizygous twin cohort

Obesity is a multifactorial disorder with a complex phenotype. It is a significant risk factor for diabetes and hypertension. We assessed obesity-related traits in a large cohort of twins and performed a genome-wide linkage scan and positional candidate analysis to identify genes that play a role in regulating fat mass and distribution in women. Dizygous female twin pairs from 1,094 pedigrees were studied (mean age 47.0+/-11.5 years (range 18-79 years)). Nonparametric multipoint linkage analyses showed linkage for central fat mass to 12q24 (141 cM) with LOD 2.2 and body mass index to 8q11 (67 cM) with LOD 1.3, supporting previously established linkage data. Novel areas of suggestive linkage were for total fat percentage at 6q12 (LOD 2.4) and for total lean mass at 2q37 (LOD 2.4). Data from follow-up fine mapping in an expanded cohort of 1243 twin pairs reinforced the linkage for central fat mass to 12q24 (LOD 2.6; 143 cM) and narrowed the -1 LOD support interval to 22 cM. In all, 45 single-nucleotide polymorphisms (SNPs) from 26 positional candidate genes within the 12q24 interval were then tested for association in a cohort of 1102 twins. Single-point Monks-Kaplan analysis provided evidence of association between central fat mass and SNPs in two genes - PLA2G1B (P = 0.0067) and P2RX4 (P = 0.017). These data provide replication and refinement of the 12q24 obesity locus and suggest that genes involved in phospholipase and purinoreceptor pathways may regulate fat accumulation and distribution.

Testing for genetic heterogeneity in the genome search meta-analysis method

The Genome Search Meta-Analysis (GSMA) method is widely used to detect linkage by pooling results of previously published genome-wide linkage studies. The GSMA uses a non-parametric summed rank statistic in 30 cM bins of the genome. Zintzaras and Ioannidis ([2005] Genet. Epidemiol. 28:123-137) developed a method of testing for heterogeneity of evidence for linkage in the GSMA, with three heterogeneity statistics (Q, Ha, B). They implement two testing procedures, restricted versus unrestricted for the summed rank within the bin. We show here that the rank-unrestricted test provides a conservative test for high heterogeneity and liberal test for low heterogeneity in linked regions. The rank-restricted test should therefore be used, despite the extensive simulations needed. In a simulation study, we show that the power to detect heterogeneity is low. For 20 studies of affected sib pairs, simulated assuming linkage in all studies to a gene with sibling relative risk of 1.3, the power to detect low heterogeneity using the Q statistic was 14%. With linkage present in 50% of the studies (to a gene with sibling relative risk of 1.4), the Q heterogeneity statistic had power of 29% to detect high heterogeneity. The power to detect linkage using the summed rank was high in both of these situations, at 98% and 79%, respectively. Although testing for heterogeneity in the GSMA is of interest, the currently available method provides little additional information to that provided by the summed rank statistic.

Search for autism loci by combined analysis of Autism Genetic Resource Exchange and Finnish families

OBJECTIVE

Several genome-wide screens have been performed in autism spectrum disorders resulting in the identification of numerous putative susceptibility loci. Analyses of pooled primary data should result in an increased sample size and the different study samples have a potential to strengthen the evidence for some earlier identified loci, reveal novel loci, and even to provide information of the general significance of the locus. The objective of this study was to search for potential susceptibility loci for autism, which are supported by two independent samples.

METHODS

We performed a combined analysis of the primary genome scan data of the Autism Genetic Resource Exchange (AGRE) and Finnish autism samples to reveal susceptibility loci potentially shared by these study samples.

RESULTS

In the initial combined data analysis, the best loci (p < 0.05) were observed at 1p12-q25, 3p24-26, 4q21-31, 5p15-q12, 6q14-21, 7q33-36, 8q22-24, 17p12-q21, and 19p13-q13. The combined analysis of Finnish and AGRE families showed the most promising shared locus on 3p24-26 with nonparametric logarithm of odds (NPL) score of 2.20 (p = 0.011). The combined data analysis did not provide increased linkage evidence for the earlier identified loci on 3q25-27 or 17p12-q21. However, the 17p12-q21 locus remained promising also in the combined sample (NPL(all) =2.38, p = 0.0076).

INTERPRETATION

Our study of 314 autism families highlights the importance of further analyses on 3p24-26 locus involving comprehensive molecular genetic analyses of oxytocin receptor gene (OXTR), a positional and functional candidate gene for autism.

[ENPP1, the first example of common genetic link between childhood and adult obesity and type 2 diabetes]

Clinical studies have established the strong link between obesity and type 2 diabetes, especially in children, where the rising prevalence of childhood severe obesity has preceded the recent emergence of early-onset forms of "diabesity". These data suggested a common genetic background shared by both conditions, which was also supported by the identification by genome scans of several diabesity chromosomal regions of linkage. The genetic investigation of early-onset form of familial obesity linkage to chromosome 6q led to the identification of ENPP1, an inhibitor of the insulin receptor, as a possible molecular mechanism behind both obesity and type 2 diabetes. Analysis of the DNA variations of ENPP1 in 6,147 subjects showed association between a combination of variants and both childhood obesity, morbid or moderate obesity in adults and also with type 2 diabetes. This study provides a first molecular basis for the physiopathologic association between severe insulin resistance and obesity, and further type 2 diabetes, and offers a new perspective for prevention and treatment of these conditions.

GSMA: software implementation of the genome search meta-analysis method

Meta-analysis can be used to pool results of genome-wide linkage scans. This is of great value in complex diseases, where replication of linked regions occurs infrequently. The genome search meta-analysis (GSMA) method is widely used for this analysis, and a computer program is now available to implement the GSMA.

Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis

To date, several susceptibility loci for systemic lupus erythematosus (SLE) have been identified by individual genome-wide scans, but many of these loci have shown inconsistent results across studies. Additionally, many individual studies are at the lower limit of acceptable power recommended for declaring significant linkage. The genome search meta-analysis (GSMA) has been proposed as a valid and robust method for combining several genome scan results. The aim of this study is to investigate whether there is any consistent evidence of linkage across multiple studies, and to identify novel SLE susceptibility loci by using GSMA method. Twelve genome scan results generated from nine independent studies have been used for the present GSMA. All together, the data consists of 605 families with 1,355 SLE affected individuals from three self-reported ethnicities; Caucasian, African-American, and Hispanic. For each study, the genome was divided into 120 bins (30 cM) and ranked according to the maximum evidence of linkage within each bin. The ranks were summed and averaged across studies following which the significance was assessed by the permutation tests. The present study identified two genomic locations at 6p22.3-6p21.1 and 16p12.3-16q12.2 that met genome-wide significance (p<0.000417). The identified region at 6p22.3-6p21.1 contains the HLA region. The combined p-values using Fisher's method also supported the significance in these regions. Clustering of significant adjacent bins was observed for chromosomes 6 and 16. Additionally, there are 12 other bins with two point-wise p-values (Psumrnk and Pord) <0.05, suggesting that these bin regions are highly likely to contain SLE susceptibility loci. Among them, present GSMA also identified two novel regions at 4q32.1-4q34.3 and 13q13.2-13q22.2. However, separate analysis using only Caucasian populations identified the strongest evidence for linkage at chromosome 6p21.1-6q15 (Psumrnk=0.00021). One interesting novel region suggests that 3q22.1-3q25.33 (Psumrnk=0.01376) may be an ethnicity-specific SLE linkage. In summary, the present GSMA have identified two statistically significant genomic regions that reconfirmed the SLE linkage at chromosomes 6 and 16.

Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes

We identified a locus on chromosome 6q16.3-q24.2 (ref. 1) associated with childhood obesity that includes 2.4 Mb common to eight genome scans for type 2 diabetes (T2D) or obesity. Analysis of the gene ENPP1 (also called PC-1), a candidate for insulin resistance, in 6,147 subjects showed association between a three-allele risk haplotype (K121Q, IVS20delT-11 and A-->G+1044TGA; QdelTG) and childhood obesity (odds ratio (OR) = 1.69, P = 0.0006), morbid or moderate obesity in adults (OR = 1.50, P = 0.006 or OR = 1.37, P = 0.02, respectively) and T2D (OR = 1.56, P = 0.00002). The Genotype IBD Sharing Test suggested that this obesity-associated ENPP1 risk haplotype contributes to the observed chromosome 6q linkage with childhood obesity. The haplotype confers a higher risk of glucose intolerance and T2D to obese children and their parents and associates with increased serum levels of soluble ENPP1 protein in children. Expression of a long ENPP1 mRNA isoform, which includes the obesity-associated A-->G+1044TGA SNP, was specific for pancreatic islet beta cells, adipocytes and liver. These findings suggest that several variants of ENPP1 have a primary role in mediating insulin resistance and in the development of both obesity and T2D, suggesting that an underlying molecular mechanism is common to both conditions.

Meta-analysis of genome scans of age-related macular degeneration

A genetic contribution to the development of age-related macular degeneration (AMD) is well established. Several genome-wide linkage studies have identified a number of putative susceptibility loci for AMD but only a few of these regions have been replicated in independent studies. Here, we perform a meta-analysis of six AMD genome screens using the genome-scan meta-analysis method, which allows linkage results from several studies to be combined, providing greater power to identify regions that show only weak evidence for linkage in individual studies. Results from non-parametric analysis for a broad AMD clinical phenotype (including two studies with quantitative traits) were extracted. For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted according to study size and summed across all studies; the summed rank (SR) for each bin was assessed empirically for significance using permutation methods. A high SR indicates a region with consistent evidence for linkage across studies. The strongest evidence for an AMD susceptibility locus was found on chromosome 10q26 where genome-wide significant linkage was observed (P=0.00025). Several other regions met the empirical significance criteria for bins likely to contain linked loci including adjacent pairs of bins on chromosomes 1q, 2p, 3p and 16. Several of the regions identified here showed only weak evidence for linkage in the individual studies. These results will help prioritize regions for future positional and functional candidate gene studies in AMD.

Genetics of Type 2 diabetes

Type 2 diabetes (T2D) has become a health-care problem worldwide, with the rise in disease prevalence being all the more worrying as it not only affects the developed world but also developing nations with fewer resources to cope with yet another major disease burden. Furthermore, the problem is no longer restricted to the ageing population, as young adults and children are also being diagnosed with T2D. In recent years, there has been a surge in the number of genetic studies of T2D in attempts to identify some of the underlying risk factors. In this review, I highlight the main genes known to cause uncommon monogenic forms of diabetes (e.g. maturity-onset diabetes of the young--MODY--and insulin resistance syndromes), as well as describe some of the main approaches used to identify genes involved in the more common forms of T2D that result from the interaction between environmental risk factors and predisposing genotypes. Linkage and candidate gene studies have been highly successful in the identification of genes that cause the monogenic variants of diabetes and, although progress in the more common forms of T2D has been slow, a number of genes have now been reproducibly associated with T2D risk in multiple studies. These are discussed, as well as the main implications that the diabetes gene discoveries will have in diabetes treatment and prevention.

Meta-analysis in psychiatric genetics

The article reviews literature on methods for meta-analysis of genetic linkage and association studies, and summarizes and comments on specific meta-analysis findings for psychiatric disorders. The Genome Scan Meta-Analysis and Multiple Scan Probability methods assess the evidence for linkage across studies. Multiple Scan Probability analysis suggested linkage of two chromosomal regions (13q and 22q) to schizophrenia and bipolar disorder, whereas Genome Scan Meta-Analysis on a larger sample identified at least 10 schizophrenia linkage regions, but none for bipolar disorder. Meta-analyses of pooled ORs support association of schizophrenia to the Ser311Cys polymorphism in DRD2 and the T102C polymorphism in HTR2A, and of attention deficit hyperactivity disorder to the 48-bp repeat in DRD4. The 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4) may contribute to the risk of bipolar disorder, suicidal behavior, and neuroticism, but association to the lifetime risk of major depression has not been shown. Meta-analyses support linkage of schizophrenia to regions where replicable associations to candidate genes have been identified through positional cloning methods. There are additional supported regions where susceptibility genes are likely to be identified. Linkage meta-analysis has had less clear success for bipolar disorder based on a smaller dataset. Meta-analysis can guide the prioritization of regions for study, but proof of association requires biological confirmation of hypotheses about gene actions. Elucidation of causal mechanisms will require more comprehensive study of sequence variation in candidate genes, better statistical and meta-analytic methods to take all variation into account, and biological strategies for testing etiologic hypotheses.

A note on generalized Genome Scan Meta-Analysis statistics

BACKGROUND

Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies.

RESULTS

We provide an Edgeworth approximation to the null distribution of the weighted GSMA statistic, and, we examine the limiting distribution of the GSMA statistics under the order statistic formulation, and quantify the relevance of the pairwise correlations of the GSMA statistics across different bins on this limiting distribution. We also remark on aggregate criteria and multiple testing for determining significance of GSMA results.

CONCLUSION

Theoretical considerations detailed herein can lead to clarification and simplification of testing criteria for generalizations of the GSMA statistic.

Heterogeneity testing in meta-analysis of genome searches

Genome searches for identifying susceptibility loci for the same complex disease often give inconclusive or inconsistent results. Genome Search Meta-analysis (GSMA) is an established non-parametric method to identify genetic regions that rank high on average in terms of linkage statistics (e.g., lod scores) across studies. Meta-analysis typically aims not only to obtain average estimates, but also to quantify heterogeneity. However, heterogeneity testing between studies included in GSMA has not been developed yet. Heterogeneity may be produced by differences in study designs, study populations, and chance, and the extent of heterogeneity might influence the conclusions of a meta-analysis. Here, we propose and explore metrics that indicate the extent of heterogeneity for specific loci in GSMA based on Monte Carlo permutation tests. We have also developed software that performs both the GSMA and the heterogeneity testing. To illustrate the concept, the proposed methodology was applied to published data from meta-analyses of rheumatoid arthritis (4 scans) and schizophrenia (20 scans). In the first meta-analysis, we identified 11 bins with statistically low heterogeneity and 8 with statistically high heterogeneity. The respective numbers were 9 and 6 for the schizophrenia meta-analysis. For rheumatoid arthritis, bins 6.2 (the HLA region that is a well-documented susceptibility locus for the disease) and 16.3 (16q12.2-q23.1) had both high average ranks and low between-study heterogeneity. For schizophrenia, this was seen for bin 3.2 (3p25.3-p22.1) and heterogeneity was still significantly low after adjusting for its high average rank. Concordance was high between the proposed metrics and between weighted and unweighted analyses. Data from genome searches should be synthesized and interpreted considering both average ranks and heterogeneity between studies.

Linkage analysis of diabetes status among hypertensive families: the Hypertension Genetic Epidemiology Network study

Type 2 diabetes susceptibility is determined by multiple genetic and environmental factors. Genome-wide linkage scans have localized common regions, possibly harboring susceptibility genes on chromosomes 1, 2, 12, and 20. Variability in linkage findings underscores the probable genetic heterogeneity of type 2 diabetes. Thus, we conducted a genome scan of diabetes status using maximum likelihood methods that model affection status by a liability threshold model. Hypertensive sibships and their offspring and/or parents in the Hypertension Genetic Epidemiology Network study were recruited from five field centers. The diabetes phenotype was derived using the World Health Organization criteria and adjusted for race/study center, age, age2, sex, and with and without percent body fat. In total, 567 diabetic participants were identified in 437 families. Variance component linkage analysis was performed among 1,545 Caucasians and 1,608 African Americans using race-specific marker allele frequencies. We detected a quantitative trait loci (QTLs) influencing diabetes variance (logarithm of odds = 3.4) on chromosome 22, which overlaps a positive type 2 diabetes finding among Canadian Oji-Cree Indians. We also observed suggestive evidence for linkage on chromosomes 1, 2, 5, 8, 14, 17, and 19. The identification and replication of type 2 diabetes QTLs will bring us closer to the detection of functional genes that influence diabetes susceptibility.

Quantitative trait locus determining dietary macronutrient intakes is located on human chromosome 2p22

BACKGROUND

Obesity is generally accompanied by increased food intake.

OBJECTIVE

We sought to identify the genes influencing variation in dietary macronutrient intakes in Mexican Americans.

DESIGN

We conducted a genome-wide scan by using data derived from food-frequency questionnaires in 816 participants from the San Antonio Family Heart Study. Household effect was simultaneously estimated in a variance component model with the use of SOLAR.

RESULTS

All dietary intake measures (total calories, proteins, fat, saturated fat, monounsaturated fat, polyunsaturated fat, carbohydrates, and sucrose) were moderately heritable. Household effect was insignificant except on total calories and sucrose. Suggestive evidence of linkage with saturated fat intake was found on chromosome 2p22 near marker D2S1346 [logarithm of odds (LOD) = 2.62]. Intakes of total calories, fat, protein, and monounsaturated fat were also suggestively linked to the same marker. A significant linkage signal on chromosome 2p22 for leptin concentrations and fat mass was localized in this population, so we used leptin or fat mass as a covariate. Multipoint LOD scores for saturated fat dropped to 1.27 and 1.90, respectively, which suggested that this region on chromosome 2p contributes to both saturated fat intake and body adiposity. This chromosomal region contains the proopiomelanocortin gene (POMC). However, 2 polymorphisms in exon 3 of the POMC gene showed no association with saturated fat intake.

CONCLUSIONS

The results strengthen the hypothesis that chromosome 2p22 harbors genes that influence a variety of obesity-related phenotypes, including macronutrient intakes.

SREBP transcription factors: master regulators of lipid homeostasis

Sterol regulatory element binding proteins (SREBPs) are a family of transcription factors that regulate lipid homeostasis by controlling the expression of a range of enzymes required for endogenous cholesterol, fatty acid (FA), triacylglycerol and phospholipid synthesis. The three SREBP isoforms, SREBP-1a, SREBP-1c and SREBP-2, have different roles in lipid synthesis. In vivo studies using transgenic and knockout mice suggest that SREBP-1c is involved in FA synthesis and insulin induced glucose metabolism (particularly in lipogenesis), whereas SREBP-2 is relatively specific to cholesterol synthesis. The SREBP-1a isoform seems to be implicated in both pathways. SREBP transcription factors are synthetized as inactive precursors bound to the endoplasmic reticulum (ER) membranes. Upon activation, the precursor undergoes a sequential two-step cleavage process to release the NH(2)-terminal active domain in the nucleus (designated nSREBPs). SREBP processing is mainly controlled by cellular sterol content. When sterol levels decrease, the precursor is cleaved to activate cholesterogenic genes and maintain cholesterol homeostasis. This sterol-sensitive process appears to be a major point of regulation for the SREBP-1a and SREBP-2 isoforms but not for SREBP-1c. Moreover, the SREBP-1c isoform seems to be mainly regulated at the transcriptional level by insulin. The unique regulation and activation properties of each SREBP isoform facilitate the co-ordinate regulation of lipid metabolism; however, further studies are needed to understand the detailed regulation pathways that specifically regulate each SREBP isoform.

Clustering patterns of LOD scores for asthma-related phenotypes revealed by a genome-wide screen in 295 French EGEA families

A genome-wide scan for asthma phenotypes was conducted in the whole sample of 295 EGEA families selected through at least one asthmatic subject. In addition to asthma, seven phenotypes involved in the main asthma physiopathological pathways were considered: SPT (positive skin prick test response to at least one of 11 allergens), SPTQ score being the number of positive skin test responses to 11 allergens, Phadiatop (positive specific IgE response to a mixture of allergens), total IgE levels, eosinophils, bronchial responsiveness (BR) to methacholine challenge and %predicted FEV(1). Four regions showed evidence for linkage (P</=0.001): 6q14 for %FEV(1), 12p13 for IgE, 17q22-q24 for SPT and 21q21 for both SPTQ and %FEV(1). Nine other regions indicated smaller linkage signals (0.001<P</=0.005). While most of these regions have been reported by previous asthma and lung function screens, 6q14 appears to be a new region potentially linked to %FEV(1). To determine which of these various asthma phenotypes are more likely to share common genetic determinants, a principal component analysis was applied to the genome-wide LOD scores. This analysis revealed clustering of LODs for asthma, SPT and Phadiatop on one axis and clustering of LODs for %FEV(1), BR and SPTQ on the other, while LODs for IgE and eosinophils appeared to be independent from all other LODs. These results provide new insights into the potential sharing of genetic determinants by asthma-related phenotypes.

Effect of SNPs in protein kinase Cz gene on gene expression in the reporter gene detection system

AIM: To investigated the effects of the SNPs (rs411021, rs436045, rs427811, rs385039 and rs809912) on gene expression and further identify the susceptibility genes of type 2 diabetes.

METHODS: Ten allele fragments (49 bp each) were synthesized according to the 5 SNPs mentioned above. These fragments were cloned into luciferase reporter gene vector and then transfected into HepG2 cells. The activity of the luciferase was assayed. Effects of the SNPs on RNA splicing were analyzed by bioinformatics.

RESULTS: rs427811T allele and rs809912G allele enhanced the activity of the reporter gene expression. None of the 5 SNPs affected RNA splicing.

CONCLUSION: SNPs in protein kinase Cz (PKCZ) gene probably play a role in the susceptibility to type 2 diabetes by affecting the expression level of the relevant genes.

SREBF-1 gene polymorphisms are associated with obesity and type 2 diabetes in French obese and diabetic cohorts

Sterol regulatory element-binding protein (SREBP)-1 transcription factors play a central role in energy homeostasis by promoting glycolysis, lipogenesis, and adipogenesis. The sterol regulatory element-binding protein gene (SREBF)-1 is a good candidate gene for obesity and obesity-related metabolic traits such as type 2 diabetes and dyslipidemia. The SREBF-1 molecular screening of 40 unrelated obese patients by PCR/single-strand conformation polymorphism revealed 19 single nucleotide polymorphisms (SNPs). Six SNPs were genotyped for an association study in large French obese and nonobese cohorts. Case-control studies using two independent nonobese cohorts indicated that SNP17 (54G/C, exon 18c) is associated with morbid obesity (odds ratio 1.5, P = 0.006 and P = 0.02, respectively). SNP3 (-150G/A, exon 1a), SNP5 (-36delG, exon 1a), and SNP17 are found in high linkage disequilibrium (D' > 0.8). The haplotype including wild-type alleles of these SNPs (C/G/G/T/C/G, HAP2) is identified as a risk factor for morbid obesity (P = 0.003). In the obese group, SNP3, SNP5, and SNP17 are associated with male-specific hypertriglyceridemia (P = 0.07, P = 0.01, and P = 0.05, respectively). SNP17 is also associated with type 2 diabetes (P = 0.03) and increased prevalence of nephropathy (P = 0.028) in a diabetic cohort. Our results indicate a role of the SREBF-1 gene in genetic predisposition of metabolic diseases such as obesity, type 2 diabetes, and dyslipidemia.

A Note on the Genome Scan Meta-Analysis Statistic

Wise and colleagues (Ann. Hum. Genet. (1999) 63: 263-72) introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. We provide an alternative derivation of the null distribution of the GSMA statistic, with extensions, and we suggest approximations to the distribution of the GSMA statistic that may be useful in applications.