Association of C47T polymorphism in <Emphasis Type="Italic">SOD2</Emphasis> gene with coronary artery disease: a case–control study and a meta-analysis

Oxidative damage promotes atherosclerosis. SOD2 is an important antioxidant enzyme. A case–control study and a meta-analysis were performed to assess the association of C47T polymorphism in SOD2 gene with premature, late-onset and overall coronary artery disease (CAD) risk. A hospital-based case–control study was conducted with 269 premature CAD cases, 278 late-onset CAD cases and 299 healthy controls. Polymerase chain reaction (PCR) and Pyrosequencing were used to detect the polymorphism. Multinomial logistic regression model was performed to estimate odds ratio (OR) with 95% confidence intervals (CIs) and adjust potential confounders. A meta-analysis was performed using eight outcomes including our result. Fixed or random effect pooled measure was selected on the basis of homogeneity test among studies. Heterogeneity among studies was evaluated using I ². Meta-regression was used to explore potential sources of between-study heterogeneity. Publication bias was estimated using Peters’s linear regression test. In our case–control study, compared with the TT as the reference, the mutant genotype of CC + TC was significantly associated with a reduced premature CAD risk both in univariate (OR = 0.60, 95% CI = 0.41–0.87) and multivariate (OR = 0.59, 95% CI = 0.40–0.87) logistic regressions, but not with late-onset CAD risk. After excluding one article that deviated from Hardy–Weinberg equilibrium in controls, this meta-analysis showed a significant association of the C allele with reduced risk of CAD in dominant (FEM: OR = 0.69, 95% CI = 0.61–0.78), recessive (OR = 0.64, 95% CI = 0.50–0.82), and codominant (FEM: OR = 0.73, 95% CI = 0.65–0.80) models. Our study suggested that the mutant genotype of CC + TC was significantly associated with a reduced CAD risk.     

Cytomegalovirus infection and coronary heart disease risk: a meta-analysis

The chronic inflammatory process including cytomegalovirus (CMV) infection has been hypothesized to induce the progression of atherosclerosis in coronary heart disease (CHD). Numbers studies were conducted to analyze the association between CMV infection and risk of CHD, but no clear consensus had been reached. To assess this relationship more precisely, a meta-analysis was performed. The electronic databases PubMed, Embase, and CNKI were searched; data were extracted and analyzed independently by two investigators. Ultimately, 55 studies, involving 9,000 cases and 8,608 controls from six prospective studies (all with a nested case–control design) and 49 retrospective case–control studies were included. Overall, people exposed to CMV infection had an odds ratio (OR) of 1.67 (95% CI, 1.56–1.79) for CHD risk, relative to those not exposed. CMV infection was clearly identified as a risk factor for CHD in both prospective studies (OR, 1.31; 95% CI, 1.132–1.517) and retrospective studies (OR, 1.79; 95% CI, 1.659–1.939), and in both Asian group (OR, 2.69; 95% CI, 2.304–3.144) and non-Asian group (OR, 1.48; 95% CI, 1.371–1.600). Interestingly, in the subgroup analyses by detection methods of CMV, the increased risk (OR, 8.121) was greater among studies using polymerase chain reaction than the risk (OR, 1.561) among studies using enzyme-linked immunosorbent assay. In conclusion, this meta-analysis suggested that CMV infection is associated with an increased risk for CHD, especially among Asian populations.     

Combining data from 2 nested case-control studies of overlapping cohorts to improve efficiency

Researchers subject to time and budget constraints may conductsmall nested case–control studies with individually matchedcontrols to help optimize statistical power. In this paper,we show how precision can be improved considerably by combiningdata from a small nested case–control study with datafrom a larger nested case–control study of a differentoutcome in the same or overlapping cohort. Our approach is basedon the inverse probability weighting concept, in which the log-likelihoodcontribution of each individual observation is weighted by theinverse of its probability of inclusion in either study. Weillustrate our approach using simulated data and an applicationwhere we combine data sets from 2 nested case–controlstudies to investigate risk factors for anorexia nervosa ina cohort of young women in Sweden.     

A Two-Step Penalized Regression Method with Networked Predictors

Penalized regression incorporating prior dependency structure of predictors can be effective in high-dimensional data analysis (Li and Li in Bioinformatics, 24:1175–1118, 2008). Pan et al. (Biometrics, 66:474–484, 2010) proposed a penalized regression method for better outcome prediction and variable selection by smoothing parameters over a given predictor network, which can be applied to analysis of microarray data with a given gene network. In this paper, we develop two modifications to their method for further performance enhancement. First, we employ convex programming and show its improved performance over an approximate optimization algorithm implemented in their original proposal. Second, we perform bias reduction after initial variable selection through a new penalty, leading to better parameter estimates and outcome prediction. Simulations have demonstrated substantial performance improvement of the proposed modifications over the original method.     

Power analysis for case–control association studies of samples with known family histories

Genome-wide case–control studies have been widely used to identify genetic variants that predispose to human diseases. Such studies are powerful in detecting common genetic variants with moderate effects, but quickly lose power as allele frequency and genotype relative risk decrease. Because patients with one or more affected relatives are more likely to inherit disease-predisposing alleles of a genetic disease than patients without family histories of the disease, sampling patients with affected relatives almost always increases the frequency of disease predisposing alleles in cases and improves the power of case–control association studies. This paper evaluates the power of case–control studies that select cases and/or controls according to their family histories of disease. Our results showed that this study design can dramatically increase the power of a case–control association study for a wide range of disease types. Because each additional affected relative of a patient reduces the required sample size roughly by a pair of case and control, inclusion of cases with affected relatives can dramatically decrease the required sample size and thus the cost of such studies.     

MTHFR 677C>T and 1298A>C polymorphisms and male infertility risk: a meta-analysis

Epidemiological studies have evaluated the association between MTHFR 677C>T and 1298A>C polymorphisms and risk of male infertility. However, the results from the published studies on the association between these two MTHFR polymorphisms and male infertility risk are conflicting. To derive a more precise estimation of association between the MTHFR polymorphisms and risk of male infertility, we performed a meta-analysis. A comprehensive search was conducted to identify all case–control studies of MTHFR polymorphisms and male infertility risk. We used odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of the association. Overall, we found that both 677C>T and 1298A>C polymorphisms were not significantly associated with male infertility risk. However, in stratified analysis by ethnicity, we found that the 677C>T polymorphism was significantly associated with the risk of male infertility in Asian population (TT vs. CC: OR = 1.57, 95% CI: 1.05–2.37, P = 0.03; TT vs. TC + CC: OR = 1.40, 95% CI: 1.05–1.86, P = 0.02; TT + TC vs. CC: OR = 1.34, 95% CI: 1.01–1.77, P = 0.04). Although some modest bias could not be eliminated, this meta-analysis suggested that the MTHFR 677T allele might be a low-penetrant risk factor for male infertility, especially in Asian population.     

Case contamination in electronic health records based case‐control studies

Clinically relevant information from electronic health records (EHRs) permits derivation of a rich collection of phenotypes. Unlike traditionally designed studies where scientific hypotheses are specified a priori before data collection, the true phenotype status of any given individual in EHR‐based studies is not directly available. Structured and unstructured data elements need to be queried through preconstructed rules to identify case and control groups. A sufficient number of controls can usually be identified with high accuracy by making the selection criteria stringent. But more relaxed criteria are often necessary for more thorough identification of cases to ensure achievable statistical power. The resulting pool of candidate cases consists of genuine cases contaminated with noncase patients who do not satisfy the control definition. The presence of patients who are neither true cases nor controls among the identified cases is a unique challenge in EHR‐based case‐control studies. Ignoring case contamination would lead to biased estimation of odds ratio association parameters. We propose an estimating equation approach to bias correction, study its large sample property, and evaluate its performance through extensive simulation studies and an application to a pilot study of aortic stenosis in the Penn medicine EHR. Our method holds the promise of facilitating more efficient EHR studies by accommodating enlarged albeit contaminated case pools.     

Lipoprotein-associated phospholipase A2 gene V279F polymorphisms and coronary heart disease: a meta-analysis

Lipoprotein-associated phospholipase A2 (LP-PLA2) may play an important role in the pathophysiology of coronary heart disease (CHD). The polymorphism of LP-PLA2 gene caused LP-PLA2 enzyme activity depressing or lost. But there is not a definite conclusion for the association of between the LP-PLA2 gene polymorphism and CHD risk. To assess the relationship between LP-PLA2 gene V279F polymorphism and CHD, a comprehensive Meta-analysis was performed. All the case–control studies evaluating the association of between the LP-PLA2 gene V279F polymorphism and CHD risk were identified. Seven case–control studies involving 3,614 patients with CHD and 4,334 controls were included. The crude odds ratios (ORs) of meta-analysis under the different gene model were not significant. But in the stratified analysis by study size, ethnicity, cases definition, and source of controls under the additive model, the association was evident in ethnicity for Japanese group (OR = 1.38, 95%CI = 1.22–1.56), cases definition for MI (OR = 1.22, 95%CI = 1.01–1.49), source of controls for the based-hospital (OR = 1.42, 95%CI = 1.24–1.59). These data suggested that the V279F polymorphism in LP-PLA2 gene may contribute to CHD development. But there is necessary that more well-designed large studies are required for the validation of this association.     

DNMT3B polymorphisms and cancer risk: a meta analysis of 24 case–control studies

The association between polymorphism of DNA methyltransferases 3B and cancer risk has been widely studied recently, and no consensus conclusion is available up to now. We perform a comprehensive search using the databases of Medline, ISI Web of Knowledge and Embase. The odds ratio (OR) and its 95% confidence interval (95% CI) are used to investigate the strength of the association. A total of 24 case–control studies with 15,647 individuals are included in this meta-analysis. For −149C > T (17 studies, 5229 cases and 6910 controls), no evidence indicate that individuals carrying the variant genotypes (CC + CT), relative to those carrying the wild homozygote TT genotype, have an increased risk of cancer (OR = 1.03; 95% CI = 0.84–1.26; P = 0.76). Similarly, no cancer risk is found in the subgroup analyses. For −579G > T (11 studies, 3513 cases and 3714 controls), significantly decreased risks of cancer are observed, and the ORs (95% CI) are 0.70 (0.56–0.87) for GT versus TT, 0.70 (0.57–0.85) for GG + GT versus TT and 0.76 (0.63–0.93) for G-allele versus T-allele, respectively. Subgroup analyses stratified by ethnicity and types of cancer are also performed, and results indicated that −579G > C polymorphism is associated with risk of cancer in Asians [0.68 (0.53–0.87) for GT vs. TT] but not in Europeans [0.82 (0.63–1.07) for GT vs. TT]. We also observe that the −579G is associated with decreased risk of colorectal cancer [0.49(0.38–0.62) for GT vs. TT]. More studies with larger sample size were needed to provide more precise evidence.     

Screening effects in risk studies of thyroid cancer after the Chernobyl accident

In this article scenarios have been developed, which simulate screening effects in ecological and cohort studies of thyroid cancer incidence among Ukrainians, whose thyroids have been exposed to ¹³¹I in the aftermath of the Chernobyl accident. If possible, the scenarios were based on directly observed data, such as the population size, dose distributions and thyroid cancer cases. Two scenarios were considered where the screening effect on baseline cases is either equal to or larger than that of radiation-related thyroid cancer cases. For ecological studies in settlements with more than ten measurements of the ¹³¹I activity in the human thyroid in May–June 1986, the screening bias appeared small (<19%) for all risk quantities. In the cohort studies, the excess absolute risk per dose was larger by a factor of 4 than in the general population. For an equal screening effect on baseline and radiation-related cancer (Scenario 1) the excess relative risk was about the same as in the general population. However, a differential screening effect (Scenario 2) produced a risk smaller by a factor of 2.5. A comparison with first results of the Ukrainian–US-American cohort study did not give any indication that a differential screening effect has a marked influence on the risk estimates. The differences in the risk estimates from ecological studies and cohort studies were explained by the different screening patterns in the general population and in the much smaller cohort. The present investigations are characterized by dose estimates for many settlements which are very weakly correlated with screening, the confounding variable. The results show that under these conditions ecological studies may provide risk estimates with an acceptable bias.     

Sample size for positive and negative predictive value in diagnostic research using case-control designs

Important properties of diagnostic methods are their sensitivity,specificity, and positive and negative predictive values (PPVand NPV). These methods are typically assessed via case–controlsamples, which include one cohort of cases known to have thedisease and a second control cohort of disease-free subjects.Such studies give direct estimates of sensitivity and specificitybut only indirect estimates of PPV and NPV, which also dependon the disease prevalence in the tested population. The motivatingexample arises in assay testing, where usage is contemplatedin populations with known prevalences. Further instances includebiomarker development, where subjects are selected from a populationwith known prevalence and assessment of PPV and NPV is crucial,and the assessment of diagnostic imaging procedures for rarediseases, where case–control studies may be the only feasibledesigns. We develop formulas for optimal allocation of the samplebetween the case and control cohorts and for computing samplesize when the goal of the study is to prove that the test procedureexceeds pre-stated bounds for PPV and/or NPV. Surprisingly,the optimal sampling schemes for many purposes are highly unbalanced,even when information is desired on both PPV and NPV.     

Methods for Analyzing Odds Ratios in a 2× C Contingency Table

This paper presents produrea for the analysis of the odds ratio and log odds ratio for a 2×C contingency table. The proposed procedurea provide a more unified approach to study of the trend of the odds ratio as a measure of association between a risk indioator and an “outcome” variable than traditional form of analyeis. The methodology is illustrated with an example from the epidemiologic literature.     

Association between C1431T polymorphism in peroxisome proliferator-activated receptor-γ gene and coronary artery disease in Chinese Han population

The C1431T polymorphism in peroxisome proliferator-activated receptor-γ (PPARγ) has been shown to be associated with diabetes, obesity, and metabolic syndrome. However, it is unclear whether this polymorphism is associated with coronary artery disease (CAD). Therefore, we conducted a hospital-based case–control study with 864 CAD patients and 1008 controls to explore the association between the PPARγ C1431T polymorphism and risk of CAD in Chinese Han population. Subjects with the variant genotypes (CT + TT) had a 39% decreased risk of CAD relative to CC carriers (adjusted odds ratio, 0.61; 95% confidence interval, 0.49–0.76). Our results suggested that the C1431T polymorphism was associated with a higher body mass index in both CAD patients and controls. Moreover, this polymorphism was also found to be associated with a higher HDL cholesterol level and a lower blood glucose level in CAD patients. In stratified analyses, the T allele was significantly associated with reduced risk of CAD in males, subjects with age <62 years, and non-smokers. In conclusion, the PPARγ C1431T polymorphism is associated with decreased risk of CAD in Chinese Han population.     

CXCL12 G801A polymorphism and breast cancer risk: a meta-analysis

The G801A polymorphism in the CXCL12 gene has been implicated in breast cancer risk. However, the published findings are inconsistent. We therefore performed a meta-analysis to investigate this relationship. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the strength of the association. The pooled ORs were performed for codominant model, dominant model, and recessive model, respectively. Five published case–control studies, including 1,058 breast cancer cases and 1,023 controls were identified. No study had a deviation from the Hardy–Weinberg equilibrium (HWE) in controls. We found that the CXCL12 G801A (rs1801157) polymorphism was associated with a significantly increased risk of breast cancer risk when all studies were pooled into the meta-analysis (codomiant model: AA versus GG, OR = 1.64, 95% CI = 1.16–2.33; GA versus GG, OR = 1.42, 95% CI = 1.18–1.71; dominant model: AA/GA versus GG, OR = 1.44, 95% CI = 1.21–1.72). Furthermore, Egger’s test did not show any evidence of publication bias (P > 0.05 for the dominant model). In conclusion, the results suggest that the CXCL12 G801A polymorphism may be a low-penetrant risk factor for developing breast cancer.     

Interferon gamma +874 T/A polymorphism contributes to cancer susceptibility: a meta-analysis based on 17 case–control studies

Interferon gamma (IFN-γ) plays a pivotal role in antiproliferative, antitumor and antiviral activities. The +874 polymorphism in IFN gene region reportedly affects cancer risk. However, pertinent studies offer conflicting results. To derive a more precise estimation, we performed a meta-analysis based on 1,929 cases and 2,830 controls from 17 published case–control studies, assessing the strength of the association using odds ratios with 95% confidence intervals. Our meta-analysis showed the evidence that IFN-γ +874 T/A was not associated with increased cancer risk in ethnicity and source of controls. However, stratified analysis by cancer type indicated a significantly increased risk of cervical cancer (AT vs. TT: OR = 1.10, 95% CI = 1.02–1.19, P = 0.961 for heterogeneity). Further prospective researches with a larger single study are required to evaluate any association with other types of cancer or in other populations.     

Neutron versus Á-ray risk estimates

While it is recognized that neutrons contributed to the excess cancer incidence and mortality among the atomic bomb survivors in Hiroshima, there is no possibility to deduce the magnitude of this contribution from the data. This remains true even if the neutron doses in the dosimetry system DS86 are corrected upwards in line with recent neutron activation measurements. In spite of this fact, important information can be obtained in the form of an inverse relation of the risk coefficients for γ-rays and neutrons. Such an interrelation must apply because the observed excess incidence or mortality is made up of a γ-ray and a neutron component; increased attribution to neutrons decreases the attribution to photons. Computations with the uncorrected and the corrected DS86 are performed for the mortality and the incidence of solid tumors combined. They refer to doses up to 2 Gy and employ the constant relative risk model and a linear-quadratic dose dependence with variable ratio – the neutron relative biological effectiveness (RBE) at low doses – of the linear component for neutrons and γ-rays. In line with past analyses, no quadratic component is obtained with the uncorrected DS86, but it is seen, even in these calculations, that the assumption of increased neutron RBEs does not translate into proportional increases of the risk coefficients of neutrons, because it leads to substantially reduced risk estimates for γ-rays. Calculations with the corrected dosimetry bring out this reciprocity even more clearly. High values of the neutron RBE reduce – in line with recent suggestions by Rossi and Zaider – the risk estimates for γ-rays substantially. Even a purely quadratic dose relation for γ-rays is consistent with the data; it requires no major increase of the nominal risk coefficients for neutrons over the currently assumed values. The cancer data from Hiroshima can still provide `prudent' risk estimates for photons, but with the corrected DS86, they do not prove that there is a linear component in the dose dependence for photons. Received: 20 January 1997 / Accepted in revised form: 14 March 1997     

XPA A23G polymorphism and lung cancer risk: a meta-analysis

Case–control studies on the association between XPA A23G and lung cancer have provided either controversial or inconclusive results. To clarify the effect of XPA A23G on the risk of lung cancer, a meta-analysis of all case–control observational studies was performed. Pooled odds ratios (ORs) for various polymorphisms were estimated using random and fixed effects models. The Q-statistic was used to evaluate the homogeneity, and Egger and Begg tests were used to assess publication bias. For the homozygote GG and G allele carriers (GA + GG), the pooled ORs were 1.24 (95% CI 1.05–1.46; P = 0.27 for heterogeneity) and 1.30 (95% CI 1.13–1.51; P = 0.45 for heterogeneity) compared to the homozygous genotype (AA). In the stratified analysis by ethnicity, the ORs of the G allele carriers and the homozygote GG were 1.28 (95% CI 1.10–1.49; P = 0.07 for heterogeneity) and 1.42 (95% CI 1.04–1.93; P = 0.39 for heterogeneity) among non-Caucasians. No significant associations were found in the Caucasian population in any of the genetic models. When studies that were not in Hardy–Weinberg equilibrium (HWE) were corrected, the pattern of the results remained the same. Our results indicated a significantly decreased risk of lung cancer in non-Caucasians with the G allele.     

Varied association of prothrombin G20210A polymorphism with coronary artery disease susceptibility in different ethnic groups: evidence from 15,041 cases and 21,507 controls

Published data on the association between prothrombin G20210A polymorphism and coronary artery disease (CAD) risk are inconclusive. To derive a more precise estimation of the relationship, a meta-analysis was performed. A total of 42 case–control studies including 15,041 cases and 21,507 controls were included in this meta-analysis. Overall, significantly elevated CAD risk was associated with prothrombin G20210A polymorphism (OR, 1.22; 95% CI 1.07–1.40; P = 0.003) when 39 eligible studies were pooled into the meta-analysis. In the subgroup analysis, borderline statistically increased risk was found for myocardial infarction in 22 case–control studies (OR, 1.27; 95% CI 1.00–1.61; P = 0.05). When stratified by ethnicity, significantly elevated risk was found in Europeans (OR, 1.19; 95% CI, 1.02–1.38; P = 0.02). However, no statistical differences were found among Americans and Asians. In summary, this meta-analysis indicated that prothrombin G20210A allele is a low-penetrant risk factor for developing CAD in Europeans.     

Association of the GNAS locus with severe malaria

Functional studies have demonstrated an interaction between the stimulatory G protein alpha subunit (G-alpha-s) and the malaria parasite at a cellular level. Obstruction of signal transduction via the erythrocyte G-alpha-s subunit reduced invasion by Plasmodium falciparum parasites. We sought to determine whether this signal pathway had an impact at the disease level by testing polymorphisms in the gene encoding G-alpha-s (GNAS) for association with severe malaria in a large multi-centre study encompassing family and case–control studies from The Gambia, Kenya and Malawi, and a case–control study from Ghana. We gained power to detect association using meta-analysis across the seven studies, with an overall sample size approximating 4,000 cases and 4,000 controls. Out of 12 SNPs investigated in the 19 kb GNAS region, four presented signals of association (P < 0.05) with severe malaria. The strongest single-locus association demonstrated an odds ratio of 1.13 (1.05–1.21), P = 0.001. Three of the loci presenting significant associations were clustered at the 5-prime end of the GNAS gene. Accordingly, haplotypes constructed from these loci demonstrated significant associations with severe malaria [OR = 0.88 (0.81–0.96), P = 0.005 and OR = 1.12 (1.03–1.20), P = 0.005]. The evidence presented here indicates that the influence of G-alpha-s on erythrocyte invasion efficacy may, indeed, alter individual susceptibility to disease.