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.
To investigate the association between common transforming growth factor beta (TGF-β) single nucleotide polymorphisms (SNP) and significant complications of coronary heart disease (CHD).
We performed a meta-analysis of published case-control studies assessing the association of TGF-β SNPs with a range of CHD complications. A random effects model was used to calculate odds ratios and confidence intervals. Analyses were conducted for additive, dominant and recessive modes of inheritance.
Six studies involving 5535 cases and 2970 controls examining the association of common SNPs in TGF-β1 with CHD were identified. Applying a dominant model of inheritance, three TGF-β1 SNPs were significantly associated with CHD complications: The T alleles of rs1800469 (OR = 1.125, 95% CI 1.016–1.247, p = 0.031) and rs1800470 (OR = 1.146, 95% CI 1.026–1.279, p = 0.021); and the C allele of rs1800471 (OR = 1.207, 95% CI 1.037–1.406, p = 0.021).
This meta-analysis suggests that common genetic polymorphisms in TGF-β1 are associated with complications of CHD.
The goal of this study was to apply Bayesian and GBLUP methods to predict genomic breeding values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15th QTL-MAS workshop.
Three methods with models considering dominance and epistasis inheritances were used to fit the data: (i) BayesB with a proportion π = 0.995 of SNPs assumed to have no effect, (ii) BayesCπ, where π is considered as unknown, and (iii) GBLUP, which directly fits animal genetic effects using a genomic relationship matrix.
BayesB, BayesCπ and GBLUP with various fitted models detected 6, 5, and 4 out of 8 simulated QTL, respectively. All five additive QTL were detected by Bayesian methods. When two QTL were in either coupling or repulsion phase, GBLUP only detected one of them and missed the other. In addition, GBLUP yielded more false positives. One imprinted QTL was detected by BayesB and GBLUP despite that only additive gene action was assumed. This QTL was missed by BayesCπ. None of the methods found two simulated additive-by-additive epistatic QTL. Variance components estimation correctly detected no evidence for dominance gene-action. Bayesian methods predicted additive genetic merit more accurately than GBLUP, and similar accuracies were observed between BayesB and BayesCπ.
Bayesian methods and GBLUP mapped QTL to similar chromosome regions but Bayesian methods gave fewer false positives. Bayesian methods can be superior to GBLUP in GEBV prediction when genomic architecture is unknown.
Genetic Analysis Workshop 14 simulated data have been analyzed with MASC(marker association segregation chi-squares) in which we implemented a bootstrap procedure to provide the variation intervals of parameter estimates. We model here the effect of a genetic factor, S, for Kofendrerd Personality Disorder in the region of the marker C03R0281 for the Aipotu population. The goodness of fit of several genetic models with two alleles for one locus has been tested. The data are not compatible with a direct effect of a single-nucleotide polymorphism (SNP) (SNP 16, 17, 18, 19 of pack 153) in the region. Therefore, we can conclude that the functional polymorphism has not been typed and is in linkage disequilibrium with the four studied SNPs. We obtained very large variation intervals both of the disease allele frequency and the degree of dominance. The uncertainty of the model parameters can be explained first, by the method used, which models marginal effects when the disease is due to complex interactions, second, by the presence of different sub-criteria used for the diagnosis that are not determined by S in the same way, and third, by the fact that the segregation of the disease in the families was not taken into account. However, we could not find any model that could explain the familial segregation of the trait, namely the higher proportion of affected parents than affected sibs.
Candidate gene association studies and genome-wide association studies (GWAs) have identified a large number of single nucleotide polymorphisms (SNPs) loci affecting susceptibility to rheumatoid arthritis (RA). However, for the same locus, some studies have yielded inconsistent results. To assess all the available evidence for association, we performed a meta-analysis on previously published case-control studies investigating the association between SNPs and RA.
Two hundred and sixteen studies, involving 125 SNPs, were reviewed. For each SNP, three genetic models were considered: the allele, dominant and recessive effects models. For each model, the effect summary odds ratio (OR) and 95% CIs were calculated. Cochran’s Q-statistics were used to assess heterogeneity. If the heterogeneity was high, a random effects model was used for meta-analysis, otherwise a fixed effects model was used.
The meta-analysis results showed that: (1) 30, 28 and 26 SNPs were significantly associated with RA (P<0.01) for the allele, dominant, and recessive models, respectively. (2) rs2476601 (PTPN22) showed the strongest association for all the three models: OR = 1.605, 95% CI: 1.540–1.672, P<1.00E−15 for the T-allele; OR = 1.638, 95% CI: 1.565–1.714, P<1.00E−15 for the T/T+T/C genotype and OR = 2.544, 95% CI: 2.173–2.978, P<1.00E−15 for the T/T genotype. (3) Only 23 (18.4%), 13 (10.4%) and 15 (12.0%) SNPs had high heterogeneity (P<0.01) for the three models, respectively. (4) For some of the SNPs, there was no publication bias according to Funnel plots and Egger’s regression tests (P<0.01). For the other SNPs, the associations were tested in only a few studies, and may have been subject to publication bias. More studies on these loci are required.
Our meta-analysis provides a comprehensive evaluation of the RA association studies from the past two decades. The detailed meta-analysis results are available at: http://184.108.40.206/DRAP/index.php/Metaanalysis/index.
Stroke is the second most common cause of death and major cause of disability worldwide. The SNP 83 in PDE4D gene has been suggested as a risk factor in ischemic stroke, but direct evidence from genetic association studies remains inconclusive even in Chinese population.
Meta-analysis of case-control studies on the relationship between SNP 83 in PDE4D gene and susceptibility to ischemic stroke in Chinese population published domestically and abroad from January 2003 to September 2012.
9 case-control studies were selected. Meta-analysis results showed that the significant association between SNP 83 and ischemic stroke was found under the dominant model (OR = 1.34, 95% CI: 1.20–1.49) and recessive model (OR = 1.45, 95% CI: 1.19–1.76) in Chinese population. In subgroup meta-analysis, SNP 83 and atherothrombotic stroke, rather than lacunar stroke, showed the significant association under the dominant model (OR = 1.69, 95% CI: 1.41–2.01) and recessive model (OR = 1.47, 95% CI: 1.04–2.06).
The results suggest that SNP 83 in PDE4D gene is significantly associated with susceptibility to ischemic stroke in Chinese population.
Genetic determinants of peripheral arterial disease (PAD) remain largely unknown. To identify genetic variants associated with the ankle-brachial index (ABI), a noninvasive measure of PAD, we conducted a meta-analysis of genome-wide association study data from 21 population-based cohorts.
Methods and Results
Continuous ABI and PAD (ABI≤0.9) phenotypes adjusted for age and sex were examined. Each study conducted genotyping and imputed data to the ~2.5 million SNPs in HapMap. Linear and logistic regression models were used to test each SNP for association with ABI and PAD using additive genetic models. Study-specific data were combined using fixed-effects inverse variance weighted meta-analyses. There were a total of 41,692 participants of European ancestry (~60% women, mean ABI 1.02 to 1.19), including 3,409 participants with PAD and with GWAS data available. In the discovery meta-analysis, rs10757269 on chromosome 9 near CDKN2B had the strongest association with ABI (β= −0.006, p=2.46x10−8). We sought replication of the 6 strongest SNP associations in 5 population-based studies and 3 clinical samples (n=16,717). The association for rs10757269 strengthened in the combined discovery and replication analysis (p=2.65x10−9). No other SNP associations for ABI or PAD achieved genome-wide significance. However, two previously reported candidate genes for PAD and one SNP associated with coronary artery disease (CAD) were associated with ABI : DAB21P (rs13290547, p=3.6x10−5); CYBA (rs3794624, p=6.3x10−5); and rs1122608 (LDLR, p=0.0026).
GWAS in more than 40,000 individuals identified one genome-wide significant association on chromosome 9p21 with ABI. Two candidate genes for PAD and 1 SNP for CAD are associated with ABI.
cohort study; genetic association; genome-wide association study; meta-analysis; peripheral vascular disease
We report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets.
We analysed 967 cases and 343 normoglycaemic controls. The samples were genotyped with the Affymetrix Genome-wide Human SNP array 5.0. Associations of genotyped and imputed markers with type 2 diabetes were tested using a missing data likelihood score test. A fixed-effects meta-analysis including 1,804 cases and 780 normoglycaemic controls was carried out by weighting the effect estimates by their inverse variances.
In the meta-analysis of the two Hispanic studies, markers showing suggestive associations (p<10−5) were identified in two known diabetes genes, HNF1A and KCNQ1, as well as in several additional regions. Meta-analysis of the two Hispanic studies and the recent DIAGRAM+ dataset identified genome-wide significant signals (p<5×10−8) within or near the genes HNF1A and CDKN2A/CDKN2B, as well as suggestive associations in three additional regions, IGF2BP2, KCNQ1 and the previously unreported C14orf70.
We observed numerous regions with suggestive associations with type 2 diabetes. Some of these signals correspond to regions described in previous studies. However, many of these regions could not be replicated in the DIAGRAM datasets. It is critical to carry out additional studies in Hispanic and American Indian populations, which have a high prevalence of type 2 diabetes.
Genome-wide association; Hispanics; Meta-analysis; Type 2 diabetes
In statistical modelling, the effects of single-nucleotide polymorphisms (SNPs) are often regarded as time-independent. However, for traits recorded repeatedly, it is very interesting to investigate the behaviour of gene effects over time. In the analysis, simulated data from the 13th QTL-MAS Workshop (Wageningen, The Netherlands, April 2009) was used and the major goal was the modelling of genetic effects as time-dependent. For this purpose, a mixed model which describes each effect using the third-order Legendre orthogonal polynomials, in order to account for the correlation between consecutive measurements, is fitted. In this model, SNPs are modelled as fixed, while the environment is modelled as random effects. The maximum likelihood estimates of model parameters are obtained by the expectation–maximisation (EM) algorithm and the significance of the additive SNP effects is based on the likelihood ratio test, with p-values corrected for multiple testing. For each significant SNP, the percentage of the total variance contributed by this SNP is calculated. Moreover, by using a model which simultaneously incorporates effects of all of the SNPs, the prediction of future yields is conducted. As a result, 179 from the total of 453 SNPs covering 16 out of 18 true quantitative trait loci (QTL) were selected. The correlation between predicted and true breeding values was 0.73 for the data set with all SNPs and 0.84 for the data set with selected SNPs. In conclusion, we showed that a longitudinal approach allows for estimating changes of the variance contributed by each SNP over time and demonstrated that, for prediction, the pre-selection of SNPs plays an important role.
EM algorithm; Legendre polynomials; Longitudinal data; Maximum likelihood; Prediction; Single-nucleotide polymorphism
To examine the association of previously identified autoimmune disease susceptibility loci with granulomatosis with polyangiitis (GPA, formerly known as Wegener’s granulomatosis), and determine whether genetic susceptibility profiles of other autoimmune diseases are associated with GPA
Genetic data from two cohorts were meta-analyzed. Genotypes for 168 previously identified single nucleotide polymorphisms (SNPs) associated with susceptibility to different autoimmune diseases were ascertained for a total of 880 GPA cases and 1969 controls of European descent. Single marker associations were identified using additive logistic regression models. Multi-SNP associations with GPA were assessed using genetic risk scores based on susceptibility loci for Crohn’s disease, type 1 diabetes, systemic lupus erythematosus, rheumatoid arthritis, celiac disease, and ulcerative colitis. Adjustment for population substructure was performed in all analyses using ancestry informative markers and principal components analysis.
Genetic polymorphisms in CTLA4 were significantly associated with GPA in the single-marker meta-analysis (OR 0.79. 95% CI 0.70–0.89, p=9.8×10−5). A genetic risk score based on rheumatoid arthritis susceptibility markers was significantly associated with GPA (OR 1.05 per 1-unit increase in genetic risk score, 95% CI 1.02–1.08, p=5.1×10−5).
Rheumatoid arthritis and GPA may arise from a similar genetic predisposition. Aside from CTLA4, other loci previously found to be associated with common autoimmune diseases were not statistically associated with GPA in this study.
genetics; vasculitis; granulomatosis with polyangiitis; rheumatoid arthritis; CTLA4
Genome-wide association (GWA) studies identified a series of novel type 2 diabetes risk loci. Most of them were subsequently demonstrated to affect insulin secretion of pancreatic β-cells. Very recently, a meta-analysis of GWA data revealed nine additional risk loci with still undefined roles in the pathogenesis of type 2 diabetes. Using our thoroughly phenotyped cohort of subjects at an increased risk for type 2 diabetes, we assessed the association of the nine latest genetic variants with the predominant prediabetes traits, i.e., obesity, impaired insulin secretion, and insulin resistance.
One thousand five hundred and seventy-eight metabolically characterized non-diabetic German subjects were genotyped for the reported candidate single nucleotide polymorphisms (SNPs) JAZF1 rs864745, CDC123/CAMK1D rs12779790, TSPAN8/LGR5 rs7961581, THADA rs7578597, ADAMTS9 rs4607103, NOTCH2 rs10923931, DCD rs1153188, VEGFA rs9472138, and BCL11A rs10490072. Insulin sensitivity was derived from fasting glucose and insulin concentrations, oral glucose tolerance test (OGTT), and hyperinsulinemic-euglycemic clamp. Insulin secretion was estimated from OGTT data. After appropriate adjustment for confounding variables and Bonferroni correction for multiple comparisons (corrected α-level: p = 0.0014), none of the SNPs was reliably associated with adiposity, insulin sensitivity, or insulin secretion (all p≥0.0117, dominant inheritance model). The risk alleles of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 tended to associate with more than one measure of insulin sensitivity and insulin secretion, respectively, but did not reach formal statistical significance. The study was sufficiently powered (1-β = 0.8) to detect effect sizes of 0.19≤d≤0.25 (α = 0.0014) and 0.13≤d≤0.16 (α = 0.05).
In contrast to the first series of GWA-derived type 2 diabetes candidate SNPs, we could not detect reliable associations of the novel risk loci with prediabetic phenotypes. Possible weak effects of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 on insulin sensitivity and insulin secretion, respectively, await further confirmation by larger studies.
The human Major Histocompatibility Complex (MHC) is a highly polymorphic genomic region occupying approximately 4 Mb on chromosome 6p21.3. The relationship between human MHC and type 1 diabetes (T1D) has been previously investigated. To fine map the disease locus in this region, we carried out both linkage and association analyses using the Type 1 Diabetes Genetics Consortium data.
Two-point linkage analysis was performed with a set of microsatellite markers assuming a fully recessive inheritance model, where we found clustering of high LOD (logarithm of the odds) scores across the MHC region. To narrow down the linkage region, we performed association analyses using both microsatellite and two sets of single nucleotide polymorphism (SNP) markers. We focused on the nuclear families containing a discordant sib-pair (an affected and unaffected sib). For the microsatellite markers, we computed the average repeat length for each individual and carried out a paired t-test.
Microsatellite marker D6S2884 showed the highest association in a sharp peak with a p value of 3.15E–24. We confirmed this finding when using also SNP markers performing a McNemar's test for association. The SNPs that showed the most significant evidence of association mapped to almost the same location as the microsatellite markers.
Besides the main goal of fine mapping of T1D genes, our results also illustrated the differences and the advantage of using both linkage and association analyses. After the identification of a wide peak with linkage analysis, we were able to dramatically narrow down the region by performing association analysis.
association analysis; diabetes; linkage analysis; major histocompatibility complex
Potential functional allele T/C single nucleotide polymorphism (SNP) of Interleukin 10 (IL-10) promoter -819 (rs1800871) has been implicated in gastric cancer risk. We aimed to explore the role of T/C SNP of IL-10 -819 in the susceptibility to gastric cancer through a systematic review and meta-analysis.
Each initially included article was scored for quality appraisal. Desirable data were extracted and registered into databases. 11 studies were ultimately eligible for the meta-analysis of IL-10 -819 T/C SNP. We adopted the most probably appropriate genetic model (recessive model). Potential sources of heterogeneity were sought out via subgroup and sensitivity analyses, and publication biases were estimated.
IL-10 -819 TT genotype is associated with the overall reduced gastric cancer risk among Asians and even apparently observed among high quality subgroup Asians. IL-10-819 TT genotype is not statistically associated with the overall reduced gastric cancer susceptibility in persons with H. pylori infection compared with controls without H. pylori infection. IL-10 -819 TT genotype is reversely associated with diffuse-subtype risk but not in intestinal-subtype risk. IL-10 -819 TT genotype is not reversely associated with non-cardia or cardia subtype gastric cancer susceptibility.
IL-10 -819 TT genotype seems to be more protective from gastric cancer in Asians. Whether IL-10 -819 TT genotype may be protective from gastric cancer susceptibility in persons infected with H. pylori or in diffuse-subtype cancer needs further exploring in the future well-designed high quality studies among different ethnicity populations. Direct sequencing should be more used in the future.
Interleukin 10; Gene; Single nucleotide polymorphism; Association; Gastric cancer
Genome-wide association studies are now widely used tools to identify genes and/or regions which may contribute to the development of various diseases. With case-control data a 2×3 contingency table can be constructed for each SNP to perform genotype-based tests of association. An increasingly common technique to increase the power to detect an association is to collapse each 2×3 table into a table assuming either a dominant or recessive mode of inheritance (2×2 table). We consider three different methods of determining which genetic model to choose and show that each of these methods of collapsing genotypes increases the type I error rate (i.e., the rate of false positives). However, one of these methods does lead to an increase in power compared with the usual genotype- and allele-based tests for most genetic models.
genome-wide association; type I error rate
Diabetes and atherosclerosis may share common genetic determinants. A prior study in Hispanics found association of haplotypes in the diabetes gene calpain-10 (CAPN10) with carotid artery intima-media thickness (CIMT). This study sought to replicate this association in an independent cohort.
Four CAPN10 SNPs were genotyped and haplotypes determined in 487 Hispanic Americans from 143 families ascertained via an index case with hypertension. CIMT was measured from B-mode ultrasound, and glycemic traits quantified from euglycemic clamps. Association of SNPs and haplotypes with CIMT was determined.
The minor alleles of SNP-56 and SNP-63 were associated with increased CIMT in dominant and additive models. The association of haplotype 1112 with increased CIMT was replicated. No associations with fasting insulin, insulin secretion, or insulin sensitivity were observed.
CAPN10 association with CIMT was replicated, further supporting its role as a common genetic determinant of diabetes and atherosclerosis in Hispanics.
The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/site/honcheongso/software/robustsnp.
Genetic models; Association; Genome-wide association studies
Renal interstitial fibrosis and glomerular sclerosis are hallmarks of diabetic nephropathy (DN) and several studies have implicated members of the WNT pathways in these pathological processes. This study comprehensively examined common genetic variation within the WNT pathway for association with DN.
Genes within the WNT pathways were selected on the basis of nominal significance and consistent direction of effect in the GENIE meta-analysis dataset. Common SNPs and common haplotypes were examined within the selected WNT pathway genes in a white population with type 1 diabetes, discordant for DN (cases: n = 718; controls: n = 749). SNPs were genotyped using Sequenom or Taqman assays. Association analyses were performed using PLINK, to compare allele and haplotype frequencies in cases and controls. Correction for multiple testing was performed by either permutation testing or using false discovery rate.
A logistic regression model including collection centre, duration of diabetes, and average HbA1c as covariates highlighted three SNPs in GSK3B (rs17810235, rs17471, rs334543), two in DAAM1 (rs1253192, rs1252906) and one in NFAT5 (rs17297207) as being significantly (P < 0.05) associated with DN, however these SNPs did not remain significant after correction for multiple testing. Logistic regression of haplotypes, with ESRD as the outcome, and pairwise interaction analyses did not yield any significant results after correction for multiple testing.
These results indicate that both common SNPs and common haplotypes of WNT pathway genes are not strongly associated with DN. However, this does not completely exclude these or the WNT pathways from association with DN, as unidentified rare genetic or copy number variants could still contribute towards the genetic architecture of DN.
Diabetic nephropathy; WNT signalling pathway; Association study; End-stage renal disease
Melanocortin-4 receptor (MC4R) plays critical roles in regulating food intake and energy balance. Recent genome wide scans found common variants near MC4R were related to obesity and insulin resistance. We examined the associations of the reported variants rs17782313 (T>C) and rs17700633 (G>A) with dietary intakes, weight change and diabetes risk in 5724 women (1533 with type 2 diabetes) from a prospective cohort. Under an additive inheritance model, SNP rs17782313 was significantly associated with high intakes of total energy (P = 0.028), total fat (P = 0.008) and protein (P = 0.003). Adjustment for age, BMI, diabetes status and other covariates did not appreciably change the associations. The SNP was also associated with significantly increasing trend of percentage of energy from total fat (P for trend = 0.037). The associations between SNP rs17782313 and higher BMI (P = 0.002) were independent of dietary intakes. In addition, carriers of allele-C had 0.2 kg/m2 greater 10-year increase in BMI from cohort baseline 1976 to 1986 (P = 0.028) compared with the non-carriers. Moreover, per allele-C of rs17782313 was associated with 14% (2–32%) increased risk of type 2 diabetes, adjusting for BMI and other covariates. SNP rs1770833 was not significantly associated with either dietary intakes or obesity traits. In conclusion, the common SNP rs17782313 near MC4R gene was significantly associated with higher intakes of total energy and dietary fat. In addition, the SNP was related to greater long-term weight change and increased risk of diabetes in women.
Background and Purpose
Ischemic stroke is a multifactorial disease with a strong genetic component. Pathways including lipid metabolism, systemic chronic inflammation, coagulation, blood pressure regulation, and cellular adhesion have been implicated in stroke pathophysiology, and candidate gene polymorphisms in these pathways have been proposed as genetic risk factors.
We genotyped 105 simple deletions and single nucleotide polymorphisms from 64 candidate genes in 3550 patients and 6560 controls from six case-control association studies conducted in the United States, Europe and China. Genotyping was performed using the same immobilized probe typing system and meta-analyses were based on summary logistic regressions for each study. The primary analyses were fixed-effects meta-analyses adjusting for age and sex with additive, dominant and recessive models of inheritance.
Although seven polymorphisms showed a nominal additive association, none remained statistically significant after adjustment for multiple comparisons. In contrast, after stratification for hypertension, two lymphotoxin-alpha polymorphisms which are in strong linkage disequilibrium were significantly associated among non-hypertensive individuals: for LTA 252A>G (additive model), OR=1.41 with 95% CI, 1.20 to 1.65, p=0.00002; for LTA 26Thr>Asn, OR 1.19 with 95% CI, 1.06 to 1.34, p=0.003. LTA 252A>G remained significant after adjustment for multiple testing using either the false discover rate or by permutation testing. The two SNPs showed no association in hypertensive subjects (eg, LTA 252A>G, OR=0.93; 95%CI, 0.84 to 1.03, p=0.17).
These observations may indicate an important role of LTA-mediated inflammatory processes in the pathogenesis of ischemic stroke.
ischemic stroke; hypertension; inflammation; genetics
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.
Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.
RESEARCH DESIGN AND METHODS
We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.
Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.
We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.
Neuregulin 1 (NRG1) is associated with the pathogenesis of schizophrenia through controlling activation and signaling of neurotransmitter receptors. Influence to schizophrenia development by the NRG1 gene may differ in individuals, and genetic polymorphism is one of the factors affecting their differences. Association between three single nucleotide polymorphisms (SNPs) (rs7014762, -1174 A/T; rs11998176, -788 A/T; rs3924999, Arg253Gln) of NRG1 and the development of schizophrenia was analyzed in 221 schizophrneia and 359 control subjects. Polymerase chain reaction and direct sequencing were performed to obtain genotype data of NRG1 SNPs of the subjects. In analysis of genetic data, multiple logistic regression models (codominant1, codominant2, dominant, recessive, and log-additive model) were applied. SNPStats and SPSS 18.0 were used to calculate odds ratio (OR), 95% confidence interval (CI), and p-value of each model. The genotype distributions of rs3924999 were associated with schizophrenia development (OR=0.67, 95% CI=0.47-0.95, p=0.022 in the dominant model and OR=0.69, 95% CI=0.51-0.93, p=0.013 in the log-addtive model) and allelic distributions also showed significant association (OR=0.70, 95% CI=0.52-0.93, p=0.014). The results suggest that rs3924999 of the NRG1 gene may be associated with schizophrenia susceptibility.
association; neuregulin 1; schizophrenia; single nucleotide polymorphism
The objective of this study was to detect interactions between relevant single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). Data from Problem 1 of the Genetic Analysis Workshop 16 were used. These data consisted of 868 cases and 1,194 controls genotyped with the 500 k Illumina chip. First, machine learning methods were applied for preselecting SNPs. One hundred SNPs outside the HLA region and 1,500 SNPs in the HLA region were preselected using information-gain theory. The software weka was used to reduce colinearity and redundancy in the HLA region, resulting in a subset of 6 SNPs out of 1,500. In a second step, a parametric approach to account for interactions between SNPs in the HLA region, as well as HLA-nonHLA interactions was conducted using a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model incorporating 2,560 covariates. This approach detected some main and interaction effects for SNPs in genes that have previously been associated with RA (e.g., rs2395175, rs660895, rs10484560, and rs2476601). Further, some other SNPs detected in this study may be considered in candidate gene studies.
In genome-wide association studies, high-level statistical analyses rely on the validity of the called genotypes, and different genotype calling algorithms (GCAs) have been proposed. We compared the GCAs Bayesian robust linear modeling using Mahalanobis distance (BRLMM), Chiamo++, and JAPL using the autosomal single-nucleotide polymorphisms (SNPs) from the 500 k Affymetrix Array Set data of the Framingham Heart Study as provided for the Genetic Analysis Workshop 16, Problem 2, and prepared standard quality control (sQC) for each algorithm. Using JAPL, most individuals were retained for the analysis. The lowest number of SNPs that successfully passed sQC was observed for BRLMM and the highest for Chiamo++. All three GCAs fulfilled all sQC criteria for 79% of the SNPs but at least one GCA failed for 18% of the SNPs. Previously undetected errors in strand coding were identified by comparing genotype concordances between GCAs. Concordance dropped with the number of GCAs failing sQC. We conclude that JAPL and Chiamo++ are the GCAs of choice if the aim is to keep as many subjects and SNPs as possible, respectively.
Baseline adiponectin concentrations predict incident Type 2 diabetes mellitus in the Diabetes Prevention Program. We tested the hypothesis that common variants in the genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1, ADIPOR2) would associate with circulating adiponectin concentrations and/or with diabetes incidence in the Diabetes Prevention Program population.
Seventy-seven tagging single-nucleotide polymorphisms (SNPs) in ADIPOQ (24), ADIPOR1 (22) and ADIPOR2 (31) were genotyped. Associations of SNPs with baseline adiponectin concentrations were evaluated using linear modelling. Associations of SNPs with diabetes incidence were evaluated using Cox proportional hazards modelling.
Thirteen of 24 ADIPOQ SNPs were significantly associated with baseline adiponectin concentrations. Multivariable analysis including these 13 SNPs revealed strong independent contributions from rs17366568, rs1648707, rs17373414 and rs1403696 with adiponectin concentrations. However, no ADIPOQ SNPs were directly associated with diabetes incidence. Two ADIPOR1 SNPs (rs1342387 and rs12733285) were associated with ~18% increased diabetes incidence for carriers of the minor allele without differences across treatment groups, and without any relationship with adiponectin concentrations.
ADIPOQ SNPs are significantly associated with adiponectin concentrations in the Diabetes Prevention Program cohort. This observation extends prior observations from unselected populations of European descent into a broader multi-ethnic population, and confirms the relevance of these variants in an obese/dysglycaemic population. Despite the robust relationship between adiponectin concentrations and diabetes risk in this cohort, variants in ADIPOQ that relate to adiponectin concentrations do not relate to diabetes risk in this population. ADIPOR1 variants exerted significant effects on diabetes risk distinct from any effect of adiponectin concentrations.
[Clinical Trials Registry Nos; NCT 00004992 (Diabetes Prevention Program) and NCT 00038727 (Diabetes Prevention Program Outcomes Study)]
adiponectin; diabetes; genetics; polymorphism