To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.

There are hints of an altered mitochondrial function in obesity. Nuclear-encoded genes are relevant for mitochondrial function (3 gene sets of known relevant pathways: (1) 16 nuclear regulators of mitochondrial genes, (2) 91 genes for oxidative phosphorylation and (3) 966 nuclear-encoded mitochondrial genes). Gene set enrichment analysis (GSEA) showed no association with type 2 diabetes mellitus in these gene sets. Here we performed a GSEA for the same gene sets for obesity. Genome wide association study (GWAS) data from a case-control approach on 453 extremely obese children and adolescents and 435 lean adult controls were used for GSEA. For independent confirmation, we analyzed 705 obesity GWAS trios (extremely obese child and both biological parents) and a population-based GWAS sample (KORA F4, n = 1,743). A meta-analysis was performed on all three samples. In each sample, the distribution of significance levels between the respective gene set and those of all genes was compared using the leading-edge-fraction-comparison test (cut-offs between the 50th and 95th percentile of the set of all gene-wise corrected p-values) as implemented in the MAGENTA software. In the case-control sample, significant enrichment of associations with obesity was observed above the 50th percentile for the set of the 16 nuclear regulators of mitochondrial genes (pGSEA,50 = 0.0103). This finding was not confirmed in the trios (pGSEA,50 = 0.5991), but in KORA (pGSEA,50 = 0.0398). The meta-analysis again indicated a trend for enrichment (pMAGENTA,50 = 0.1052, pMAGENTA,75 = 0.0251). The GSEA revealed that weak association signals for obesity might be enriched in the gene set of 16 nuclear regulators of mitochondrial genes.

Aims

Lipoprotein-associated phospholipase A2 (Lp-PLA2) generates proinflammatory and proatherogenic compounds in the arterial vascular wall and is a potential therapeutic target in coronary heart disease (CHD). We searched for genetic loci related to Lp-PLA2 mass or activity by a genome-wide association study as part of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

Methods and results

In meta-analyses of findings from five population-based studies, comprising 13 664 subjects, variants at two loci (PLA2G7, CETP) were associated with Lp-PLA2 mass. The strongest signal was at rs1805017 in PLA2G7 [P = 2.4 × 10−23, log Lp-PLA2 difference per allele (beta): 0.043]. Variants at six loci were associated with Lp-PLA2 activity (PLA2G7, APOC1, CELSR2, LDL, ZNF259, SCARB1), among which the strongest signals were at rs4420638, near the APOE–APOC1–APOC4–APOC2 cluster [P = 4.9 × 10−30; log Lp-PLA2 difference per allele (beta): −0.054]. There were no significant gene–environment interactions between these eight polymorphisms associated with Lp-PLA2 mass or activity and age, sex, body mass index, or smoking status. Four of the polymorphisms (in APOC1, CELSR2, SCARB1, ZNF259), but not PLA2G7, were significantly associated with CHD in a second study.

Conclusion

Levels of Lp-PLA2 mass and activity were associated with PLA2G7, the gene coding for this protein. Lipoprotein-associated phospholipase A2 activity was also strongly associated with genetic variants related to low-density lipoprotein cholesterol levels.

Background

The SH2B1 gene (Src-homology 2B adaptor protein 1 gene) is a solid candidate gene for obesity. Large scale GWAS studies depicted markers in the vicinity of the gene; animal models suggest a potential relevance for human body weight regulation.

Methods

We performed a mutation screen for variants in the SH2B1 coding sequence in 95 extremely obese children and adolescents. Detected variants were genotyped in independent childhood and adult study groups (up to 11,406 obese or overweight individuals and 4,568 controls). Functional implications on STAT3 mediated leptin signalling of the detected variants were analyzed in vitro.

Results

We identified two new rare mutations and five known SNPs (rs147094247, rs7498665, rs60604881, rs62037368 and rs62037369) in SH2B1. Mutation g.9483C/T leads to a non-synonymous, non-conservative exchange in the beta (βThr656Ile) and gamma (γPro674Ser) splice variants of SH2B1. It was additionally detected in two of 11,206 (extremely) obese or overweight children, adolescents and adults, but not in 4,506 population-based normal-weight or lean controls. The non-coding mutation g.10182C/A at the 3’ end of SH2B1 was only detected in three obese individuals. For the non-synonymous SNP rs7498665 (Thr484Ala) we observed nominal over-transmission of the previously described risk allele in 705 obesity trios (nominal p = 0.009, OR = 1.23) and an increased frequency of the same allele in 359 cases compared to 429 controls (nominal p = 0.042, OR = 1.23). The obesity risk-alleles at Thr484Ala and βThr656Ile/γPro674Ser had no effect on STAT3 mediated leptin receptor signalling in splice variants β and γ.

Conclusion

The rare coding mutation βThr656Ile/γPro674Ser (g.9483C/T) in SH2B1 was exclusively detected in overweight or obese individuals. Functional analyzes did not reveal impairments in leptin signalling for the mutated SH2B1.

Microarray profiling of gene expression is widely applied in molecular biology and functional genomics. Experimental and technical variations make meta-analysis of different studies challenging. In a total of 3358 samples, all from German population-based cohorts, we investigated the effect of data preprocessing and the variability due to sample processing in whole blood cell and blood monocyte gene expression data, measured on the Illumina HumanHT-12 v3 BeadChip array.

Gene expression signal intensities were similar after applying the log2 or the variance-stabilizing transformation. In all cohorts, the first principal component (PC) explained more than 95% of the total variation. Technical factors substantially influenced signal intensity values, especially the Illumina chip assignment (33–48% of the variance), the RNA amplification batch (12–24%), the RNA isolation batch (16%), and the sample storage time, in particular the time between blood donation and RNA isolation for the whole blood cell samples (2–3%), and the time between RNA isolation and amplification for the monocyte samples (2%). White blood cell composition parameters were the strongest biological factors influencing the expression signal intensities in the whole blood cell samples (3%), followed by sex (1–2%) in both sample types. Known single nucleotide polymorphisms (SNPs) were located in 38% of the analyzed probe sequences and 4% of them included common SNPs (minor allele frequency >5%). Out of the tested SNPs, 1.4% significantly modified the probe-specific expression signals (Bonferroni corrected p-value<0.05), but in almost half of these events the signal intensities were even increased despite the occurrence of the mismatch. Thus, the vast majority of SNPs within probes had no significant effect on hybridization efficiency.

In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses.

Mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4) is expressed in all diabetes-relevant tissues and mediates cytokine-induced insulin resistance. We investigated whether common single nucleotide polymorphisms (SNPs) in the MAP4K4 locus associate with glucose intolerance, insulin resistance, impaired insulin release, or elevated plasma cytokines. The best hit was tested for association with type 2 diabetes. Subjects (N = 1,769) were recruited from the Tübingen Family (TÜF) study for type 2 diabetes and genotyped for tagging SNPs. In a subgroup, cytokines were measured. Association with type 2 diabetes was tested in a prospective case-cohort study (N = 2,971) derived from the EPIC-Potsdam study. Three SNPs (rs6543087, rs17801985, rs1003376) revealed nominal and two SNPs (rs11674694, rs11678405) significant associations with 2-hour glucose levels. SNPs rs6543087 and rs11674694 were also nominally associated with decreased insulin sensitivity. Another two SNPs (rs2236936, rs2236935) showed associations with reduced insulin release, driven by effects in lean subjects only. Three SNPs (rs11674694, rs13003883, rs2236936) revealed nominal associations with IL-6 levels. SNP rs11674694 was significantly associated with type 2 diabetes. In conclusion, common variation in MAP4K4 is associated with insulin resistance and β-cell dysfunction, possibly via this gene’s role in inflammatory signalling. This variation’s impact on insulin sensitivity may be more important since its effect on insulin release vanishes with increasing BMI.

A targeted metabolomics approach was used to identify candidate biomarkers of pre-diabetes. The relevance of the identified metabolites is further corroborated with a protein-metabolite interaction network and gene expression data.

Three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine C2) were found with significantly altered levels in pre-diabetic individuals compared with normal controls.Lower levels of glycine and LPC (18:2) were found to predict risks for pre-diabetes and type 2 diabetes (T2D).Seven T2D-related genes (PPARG, TCF7L2, HNF1A, GCK, IGF1, IRS1 and IDE) are functionally associated with the three identified metabolites.The unique combination of methodologies, including prospective population-based and nested case–control, as well as cross-sectional studies, was essential for the identification of the reported biomarkers.

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10−4 to 2.1 × 10−13. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.

Author Summary

Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.

Aim

To investigate whether type 2 diabetes susceptibility genes and body weight influence the development of islet autoantibodies and the rate of progression to type 1 diabetes.

Methods

Genotyping for single nucleotide polymorphisms (SNP) of the type 2 diabetes susceptibility genes CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG, SLC30A8 and TCF7L2 was obtained in 1350 children from parents with type 1 diabetes participating in the BABYDIAB study. Children were prospectively followed from birth for islet autoantibodies and type 1 diabetes. Data on weight and height were obtained at 9 months, 2, 5, 8, 11, and 14 years of age.

Results

None of type 2 diabetes risk alleles at the CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG and SLC30A8 loci were associated with the development of islet autoantibodies or diabetes. The type 2 diabetes susceptible genotype of TCF7L2 was associated with a lower risk of islet autoantibodies (7% vs. 12% by age of 10 years, P = 0.015, Pcorrected = 0.18). Overweight children at seroconversion did not progress to diabetes faster than non-overweight children (HR: 1.08; 95% CI: 0.48–2.45, P>0.05).

Conclusions

These findings do not support an association of type 2 diabetes risk factors with islet autoimmunity or acceleration of diabetes in children with a family history of type 1 diabetes.

OBJECTIVE

Genome-wide association studies have identified gene regions associated with the development of type 1 diabetes. The aim of this study was to determine whether these associations are with the development of autoimmunity and/or progression to diabetes.

RESEARCH DESIGN AND METHODS

Children (n = 1,650) of parents with type 1 diabetes were prospectively followed from birth (median follow-up 10.20 years) for the development of islet autoantibodies, thyroid peroxidase antibodies, tissue transglutaminase antibodies, and diabetes. Genotyping for single-nucleotide polymorphisms of the PTPN22, ERBB3, PTPN2, KIAA0350, CD25, and IFIH1 genes was performed using the MassARRAY system with iPLEX chemistry.

RESULTS

Islet autoantibodies developed in 137 children and diabetes developed in 47 children. Type 1 diabetes risk was associated with the IFIH1 rs2111485 single-nucleotide polymorphism (hazard ratio 2.08; 95% CI 1.16–3.74; P = 0.014). None of the other genes were significantly associated with diabetes development in this cohort. IFIH1 genotypes did not associate with the development of islet autoantibodies (P = 0.80) or autoantibodies against thyroid peroxidase (P = 0.55) and tissue transglutaminase (P = 0.66). Islet autoantibody–positive children with the IFIH1 rs2111485 GG genotype had a faster progression to diabetes (31% within 5 years) than children with the type 1 diabetes protective GA or AA genotypes (11% within 5 years; P = 0.006).

CONCLUSIONS

The findings indicate that IFIH1 genotypes influence progression from autoimmunity to diabetes development, consistent with the notion that protective genotypes downregulate responses to environmental insults after initiation of autoimmunity.

Background

Data from meta-analyses of genome-wide association studies provided evidence for an association of polymorphisms with body mass index (BMI), and gene expression results indicated a role of these variants in the hypothalamus. It was consecutively hypothesized that these associations might be evoked by a modulation of nutritional intake or energy expenditure.

Objective

It was our aim to investigate the association of these genetic factors with BMI in a large homogenous population-based sample to explore the association of these polymorphisms with lifestyle factors related to nutritional intake or energy expenditure, and whether such lifestyle factors could be mediators of the detected single-nucleotide polymorphism (SNP)-association with BMI. It was a further aim to compare the proportion of BMI explained by genetic factors with the one explained by lifestyle factors.

Design

The association of seven polymorphisms in or near the genes NEGR1, TMEM18, MTCH2, FTO, MC4R, SH2B1and KCTD15 was analyzed in 12 462 subjects from the population-based MONICA/KORA Augsburg study. Information on lifestyle factors was based on standardized questionnaires. For statistical analysis, regression-based models were used.

Results

The minor allele of polymorphism rs6548238 C>T (TMEM18) was associated with lower BMI (−0.418 kg/m2, p=1.22×10−8), and of polymorphisms rs9935401 G>A (FTO) and rs7498665 A>G (SH2B1) with increased BMI (0.290 kg/m2, p=2.85×10−7 and 0.145 kg/m2, p=9.83×10−3). The other polymorphisms were not significantly associated. Lifestyle factors were correlated with BMI and explained 0.037 % of the BMI variance as compared to 0.006 % of explained variance by the associated genetic factors. The genetic variants associated with BMI were not significantly associated with lifestyle factors and there was no evidence of lifestyle factors mediating the SNP-BMI association.

Conclusions

Our data first confirm the findings for TMEM18 with BMI in a single study on adults and also confirm the findings for FTO and SH2B1. There was no evidence for a direct SNP-lifestyle association.

Background

Strong evidence exists for an association between socioeconomic status and body mass index (BMI) as well as between genetic variants and BMI. The association of genetic variants with socioeconomic status has not yet been investigated. The aim of this study was to investigate two obesity-related loci - the transmembrane 18 (TMEM18) and the fat mass and obesity-associated (FTO) gene - for their association with educational level and per capita income, and to test whether the detected genotype-BMI association is mediated by these social factors.

Methods

12,425 adults from a large population-based study were genotyped for the polymorphism rs6548238 near TMEM18 and rs9935401 within FTO gene. Data on educational level and per capita income were based on standardized questionnaires.

Results

High educational level and high per capita income were significantly associated with decreased BMI (−1.503 kg/m2, p<.0001 / −0.820 kg/m2, p<.0001). Neither the polymorphism rs6548238 nor rs9935401 nor their combination were significantly associated with educational level (p=0.773 / p=0.827 / p=0.755) or income (p=0.751 / p=0.991 / p=0.820). Adjustment for social factors did not change the association between rs6548238 or rs9935401 and BMI.

Conclusions

As far as the authors know, this is the first study to investigate the association between polymorphisms and socioeconomic status. The polymorphisms rs6548238 and rs9935401 showed no association with educational level or income.

OBJECTIVE

To determine whether HHEX-IDE and CDKAL1 genes, which are associated with birth weight and susceptibility to type 2 diabetes, continue to influence growth during childhood.

RESEARCH DESIGN AND METHODS

BMI, weight, and height at age 8 years expressed as age- and sex-corrected standard deviation scores (SDS) against national reference data and single-nucleotide polymorphism genotyping of HHEX-IDE and CDKAL1 loci were analyzed in 646 prospectively followed children in the German BABYDIAB cohort. All children were singleton full-term births; 386 had mothers with type 1 diabetes, and 260 had fathers with type 1 diabetes and a nondiabetic mother.

RESULTS

Type 2 diabetes risk alleles at the HHEX-IDE locus were associated with reduced BMI-SDS at age 8 years (0.17 SDS per allele; P = 0.004). After stratification for birth weight, both HHEX-IDE and CDKAL1 risk alleles were associated with reduced BMI-SDS (0.45 SDS, P = 0.0002; 0.52 SDS, P = 0.0001) and weight-SDS (0.22 SDS, P = 0.04; 0.56 SDS, P = 0.0002) in children born large for gestational age (>90th percentile) but not children born small or appropriate for gestational age. Within children born large for gestational age, BMI and weight decreased with each additional type 2 diabetes risk allele (∼ −2 kg per allele; >8 kg overall). Findings were consistent in children of mothers with type 1 diabetes (P < 0.0001) and children of nondiabetic mothers (P = 0.008).

CONCLUSIONS

The type 2 diabetes susceptibility alleles at HHEX-IDE and CDKAL1 loci are associated with low BMI at age 8 years in children who were born large for gestational age.

The 46/1 JAK2 haplotype predisposes to V617F-positive myeloproliferative neoplasms, but the underlying mechanism is obscure. We analyzed essential thrombocythemia patients entered into the PT-1 studies and, as expected, found that 46/1 was overrepresented in V617F-positive cases (n = 404) versus controls (n = 1492, P = 3.9 × 10−11). The 46/1 haplotype was also overrepresented in cases without V617F (n = 347, P = .009), with an excess seen for both MPL exon 10 mutated and V617F, MPL exon 10 nonmutated cases. Analysis of further MPL-positive, V617F-negative cases confirmed an excess of 46/1 (n = 176, P = .002), but no association between MPL mutations and MPL haplotype was seen. An excess of 46/1 was also seen in JAK2 exon 12 mutated cases (n = 69, P = .002), and these mutations preferentially arose on the 46/1 chromosome (P = .029). No association between 46/1 and clinical or laboratory features was seen in the PT-1 cohort either with or without V617F. The excess of 46/1 in JAK2 exon 12 cases is compatible with both the “hypermutability” and “fertile ground” hypotheses, but the excess in MPL-mutated cases argues against the former. No difference in sequence, splicing, or expression of JAK2 was found on 46/1 compared with other haplotypes, suggesting that any functional difference of JAK2 on 46/1, if it exists, must be relatively subtle.

By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combinedP < 5 × 10−8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

Circulating glucose levels are tightly regulated. To identify novel glycemic loci, we performed meta-analyses of 21 genome-wide associations studies informative for fasting glucose (FG), fasting insulin (FI) and indices of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 non-diabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with FG/HOMA-B and two associated with FI/HOMA-IR. These include nine new FG loci (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and FAM148B) and one influencing FI/HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB/TMEM195 with type 2 diabetes (T2D). Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify T2D risk loci, as well as loci that elevate FG modestly, but do not cause overt diabetes.

Elevated levels of acute-phase serum amyloid A (A-SAA) cause amyloidosis and are a risk factor for atherosclerosis and its clinical complications, type 2 diabetes, as well as various malignancies. To investigate the genetic basis of A-SAA levels, we conducted the first genome-wide association study on baseline A-SAA concentrations in three population-based studies (KORA, TwinsUK, Sorbs) and one prospective case cohort study (LURIC), including a total of 4,212 participants of European descent, and identified two novel genetic susceptibility regions at 11p15.5-p13 and 1p31. The region at 11p15.5-p13 (rs4150642; p = 3.20×10−111) contains serum amyloid A1 (SAA1) and the adjacent general transcription factor 2 H1 (GTF2H1), Hermansky-Pudlak Syndrome 5 (HPS5), lactate dehydrogenase A (LDHA), and lactate dehydrogenase C (LDHC). This region explains 10.84% of the total variation of A-SAA levels in our data, which makes up 18.37% of the total estimated heritability. The second region encloses the leptin receptor (LEPR) gene at 1p31 (rs12753193; p = 1.22×10−11) and has been found to be associated with CRP and fibrinogen in previous studies. Our findings demonstrate a key role of the 11p15.5-p13 region in the regulation of baseline A-SAA levels and provide confirmative evidence of the importance of the 1p31 region for inflammatory processes and the close interplay between A-SAA, leptin, and other acute-phase proteins.

Author Summary

An elevated level of acute-phase serum amyloid A (A-SAA), a sensitive marker of the acute inflammatory state with high heritability estimates, causes amyloidosis and is a risk factor for atherosclerosis and its clinical complications, type 2 diabetes, as well as various malignancies. This study describes the first genome-wide association study on baseline A-SAA concentrations. In a meta-analysis of four genome-wide scans totalling 4,212 participants of European descent, we identified two novel genetic susceptibility regions on chromosomes 11 and 1 to be associated with baseline A-SAA concentrations. The chromosome 11 region contains the serum amyloid A1 gene and the adjacent genes and explains a high percentage of the total estimated heritability. The chromosome 1 region is a known genetic susceptibility region for inflammation. Taken together, we identified one region, which seems to be of key importance in the regulation of A-SAA levels and represents a novel potential target for the investigation of related clinical entities. In addition, our findings indicate a close interplay between A-SAA and other inflammatory proteins, as well as a larger role of a known genetic susceptibility region for inflammatory processes as it has been assumed in the past.

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.

Summary

Objective

The metabolic syndrome, a major cluster of risk factors for cardiovascular diseases, shows increasing prevalence worldwide. Several studies have established associations of both apolipoprotein A5 (APOA5) gene variants and upstream stimulatory factor 1 (USF1) gene variants with blood lipid levels and metabolic syndrome. USF1 is a transcription factor for APOA5.

Methods

We investigated a possible interaction between these two genes on the risk for the metabolic syndrome, using data from the German population-based KORA survey 4 (1,622 men and women aged 55–74 years). Seven APOA5 single nucleotide polymorphisms (SNPs) were analyzed in combination with six USF1 SNPs, applying logistic regression in an additive model adjusting for age and sex and the definition for metabolic syndrome from the National Cholesterol Education Program's Adult Treatment Panel III (NCEP (AIII)) including medication.

Results

The overall prevalence for metabolic syndrome was 41%. Two SNP combinations showed a nominal gene-gene interaction (p values 0.024 and 0.047). The effect of one SNP was modified by the other SNP, with a lower risk for the metabolic syndrome with odds ratios (ORs) between 0.33 (95% CI = 0.13–0.83) and 0.40 (95% CI = 0.15–1.12) when the other SNP was homozygous for the minor allele. Nevertheless, none of the associations remained significant after correction for multiple testing.

Conclusion

Thus, there is an indication of an interaction between APOA5 and USF1 on the risk for metabolic syndrome.

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.

Meta-analyses of population-based genome-wide association studies (GWAS) in adults have recently led to the detection of new genetic loci for obesity. Here we aimed to discover additional obesity loci in extremely obese children and adolescents. We also investigated if these results generalize by estimating the effects of these obesity loci in adults and in population-based samples including both children and adults. We jointly analysed two GWAS of 2,258 individuals and followed-up the best, according to lowest p-values, 44 single nucleotide polymorphisms (SNP) from 21 genomic regions in 3,141 individuals. After this DISCOVERY step, we explored if the findings derived from the extremely obese children and adolescents (10 SNPs from 5 genomic regions) generalized to (i) the population level and (ii) to adults by genotyping another 31,182 individuals (GENERALIZATION step). Apart from previously identified FTO, MC4R, and TMEM18, we detected two new loci for obesity: one in SDCCAG8 (serologically defined colon cancer antigen 8 gene; p = 1.85×10−8 in the DISCOVERY step) and one between TNKS (tankyrase, TRF1-interacting ankyrin-related ADP-ribose polymerase gene) and MSRA (methionine sulfoxide reductase A gene; p = 4.84×10−7), the latter finding being limited to children and adolescents as demonstrated in the GENERALIZATION step. The odds ratios for early-onset obesity were estimated at ∼1.10 per risk allele for both loci. Interestingly, the TNKS/MSRA locus has recently been found to be associated with adult waist circumference. In summary, we have completed a meta-analysis of two GWAS which both focus on extremely obese children and adolescents and replicated our findings in a large followed-up data set. We observed that genetic variants in or near FTO, MC4R, TMEM18, SDCCAG8, and TNKS/MSRA were robustly associated with early-onset obesity. We conclude that the currently known major common variants related to obesity overlap to a substantial degree between children and adults.

Author Summary

Genome-wide association studies (GWAS) have successfully contributed to the detection of genetic variants involved in body-weight regulation. We jointly analysed two GWAS for early-onset extreme obesity in 2,258 individuals of European origin and followed-up the findings in 3,141 individuals. Evidence for association of markers in two new genetic loci was shown (SDCCAG8 on chromosome 1q43–q44 and between TNKS/MSRA on chromosome 8p23.1). We also re-identified variants in or near FTO, MC4R, and TMEM18 to be associated with extreme obesity. In addition, we assessed the effect of the markers in 31,182 obese, lean, normal weight, and unselected individuals from population-based samples and showed that the variants near FTO, MC4R, TMEM18, and SDCCAG8 were consistently associated with obesity. For variants of TNKS/MSRA, the obesity association was limited to children and adolescents. In summary, we detected two new obesity loci and confirmed that the currently known major common variants related to obesity overlap to a substantial degree between children and adults.

Background

The polycystic ovary syndrome (PCOS), a common endocrine disorder in women of child-bearing age, mainly characterised by chronic anovulation and hyperandrogenism, is often associated with insulin resistance (IR) and obesity. Its etiology and the role of IR and obesity in PCOS are not fully understood. We examined the influence of validated genetic variants conferring susceptibility to obesity and/or type 2 diabetes mellitus (T2DM) on metabolic and PCOS-specific traits in patients with PCOS.

Methods

We conducted an association study in 386 patients with PCOS (defined by the Rotterdam-criteria) using single nucleotide polymorphisms (SNPs) in or in proximity to the fat mass and obesity associated gene (FTO), insulin-induced gene-2 (INSIG2), transcription factor 7-like 2 gene (TCF7L2) and melanocortin 4 receptor gene (MC4R). To compare the effect of FTO obesity risk alleles on BMI in patients with PCOS to unselected females of the same age range we genotyped 1,971 females from the population-based KORA-S4 study (Kooperative Gesundheitsforschung im Raum Augsburg, Survey 4).

Results

The FTO risk allele was associated with IR traits and measures of increased body weight. In addition, the TCF7L2 SNP was associated with body weight traits. For the SNPs in the vicinity of INSIG2 and MC4R and for the other examined phenotypes there was no evidence for an association. In PCOS the observed per risk allele effect of FTO intron 1 SNP rs9939609 on BMI was +1.56 kg/m2, whereas it was +0.46 kg/m2 in females of the same age range from the general population as shown previously.

Conclusion

The stronger effect on body weight of the FTO SNP in PCOS might well have implications for the etiology of the disease.

Background

The orexigenic effects of cannabinoids are limited by activation of the endocannabinoid degrading enzyme fatty acid amide hydrolase (FAAH). The aim of this study was to analyse whether FAAH alleles are associated with early and late onset obesity.

Methods

We initially assessed association of five single nucleotide polymorphisms (SNPs) in FAAH with early onset extreme obesity in up to 521 German obese children and both parents. SNPs with nominal p-values ≤ 0.1 were subsequently analysed in 235 independent German obesity families. SNPs associated with childhood obesity (p-values ≤ 0.05) were further analysed in 8,491 adult individuals of a population-based cohort (KORA) for association with adult obesity. One SNP was further analysed in 985 German obese adults and 588 normal and underweight controls. In parallel, we screened the FAAH coding region for novel sequence variants in 92 extremely obese children using single-stranded-conformation-polymorphism-analysis and denaturing HPLC and assessed the implication of the identified new variants for childhood obesity.

Results

The trio analysis revealed some evidence for an association of three SNPs in FAAH (rs324420 rs324419 and rs873978) with childhood obesity (two-sided p-values between 0.06 and 0.10). Although analyses of these variants in 235 independent obesity families did not result in statistically significant effects (two-sided p-values between 0.14 and 0.75), the combined analysis of all 603 obesity families supported the idea of an association of two SNPs in FAAH (rs324420 and rs2295632) with early onset extreme obesity (p-values between 0.02 and 0.03). No association was, however, found between these variants and adult obesity. The mutation screen revealed four novel variants, which were not associated with early onset obesity (p > 0.05).

Conclusions

As we observed some evidence for an association of the FAAH variants rs2295632 rs324420 with early onset but not adult obesity, we conclude that the FAAH variants analyzed here at least do not seem to play a major role in the etiology of obesity within our samples.

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.