Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls. Hum Genet. 2008;123:1-14. [PMID: 18026754 DOI: 10.1007/s00439-007-0445-9]

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia

Meta-analysis is used to aggregate the effects of interest across multiple studies, while its methodology is largely underexplored in mediation analysis, particularly in estimating the total mediation effect of high-dimensional omics mediators. Large-scale genomic consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program, comprise multiple cohorts with diverse technologies to elucidate the genetic architecture and biological mechanisms underlying complex human traits and diseases. Leveraging the recent established asymptotic standard error of the R-squared (R2)-based mediation effect estimation for high-dimensional omics mediators, we have developed a novel meta-analysis framework requiring only summary statistics and allowing inter-study heterogeneity. Whereas the proposed meta-analysis can uniquely evaluate and account for potential effect heterogeneity across studies due to, for example, varying genomic profiling platforms, our extensive simulations showed that the developed method was more computationally efficient and yielded satisfactory operating characteristics comparable to analysis of the pooled individual-level data when there was no inter-study heterogeneity. We applied the developed method to 5 TOPMed studies with over 5800 participants to estimate the mediation effects of gene expression on age-related variation in systolic blood pressure and sex-related variation in high-density lipoprotein (HDL) cholesterol. The proposed method is available in R package MetaR2M on GitHub.

A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study

The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform ( https://nf-co.re/omicsgenetraitassociation ).

Genetic variations in immune mediators and prostate cancer risk: A field synopsis with Bayesian calculations

OBJECTIVE

Our study aimed to revaluate the significant data from meta-analyses on genetic variations in immune mediators and the risk of prostate cancer (PCa) by Bayesian approaches.

METHODS

We performed a search on the literature before September 5th, 2023, for meta-analytic studies on polymorphisms in immune mediator genes and the risk of PCa. Two Bayesian approaches were used to assess the level of noteworthiness in the meta-analytic data: the False-Positive Rate Probability (FPRP) and the Bayesian False Discovery Probability (BFDP) with a statistical power of 1.2 and 1.5 of Odds Ratio at a prior probability of 10-3 and 10-6. The quality evaluation of studies was performed with the Venice criteria. Gene-gene and protein-protein networks were designed for the genes and products enrolled in the results.

RESULTS

As results, 18 meta-analyses on 17 polymorphisms in several immune mediator genes were included (IL1B rs16944/rs1143627, IL4 rs2243250/rs2227284/rs2070874, IL6 1800795/rs1800796/rs1800797, IL8 rs4073, IL10 rs1800896/rs1800871/rs1800872, IL18 rs1946518, COX2 rs2745557, TNFA rs361525 and PTGS2 rs20417/689470). The Bayesian calculations showed the rs1143627 and the rs1946518 polymorphisms in IL1B and IL18 genes, respectively, were noteworthy. The Venice criteria showed that only four studies received the highest level of evidence. The gene-gene and protein-protein networks reinforced the findings on IL1B and IL18 genes.

CONCLUSION

In conclusion, this current Bayesian revaluation showed that the rs1143627 and the rs1946518 polymorphisms in the IL1B and IL18 genes, respectively, were noteworthy biomarker candidates for PCa risk.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).

Discerning asthma endotypes through comorbidity mapping

Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different ethnicity subsamples in UK Biobank, US BioVU, and BioBank Japan. Fourteen loci confer asthma risk in multiple subgroups and in all subgroups combined. Importantly, another six loci confer asthma risk in only one subgroup. The strength of association between asthma and each of 44 health-related phenotypes also varies dramatically across subgroups. This work reveals subpopulations of asthma patients distinguished by comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes, and so reveals different asthma endotypes.

Amoxicillin-Resistant Streptococci Carriage in the Mouths of Children: A Systematic Review and Meta-Analysis

Streptococcal bacteremia that occurs during invasive dental procedures can lead to infective endocarditis (IE) in children with certain heart diseases. Prior to such procedures, antibiotic prophylaxis (AP) with amoxicillin (AMPC) is recommended. However, the detection of amoxicillin-resistant strains (AMPC-RS) in the mouths of children with heart diseases raises the concern that they would be uncovered by the action of standard AP. This work carried out a systematic review and meta-analysis regarding AMPC-RS carriage in the mouths of children. We consulted databases covering studies between the years 2000 and 2021, following the PRISMA declaration. A meta-analysis was carried out to assess the prevalence of children carrying AMPC-RS in the mouths. The antimicrobial tests were carried out by microdilution (46.2% of articles), disk diffusion (38.3%), and the E-test (15.4%). Streptococcus mitis and S. sanguinis were bacteria with the most found resistance phenotype, with MIC reaching values of 128 µg/mL. Of the 13 selected articles, only 6 presented results that made it possible to calculate the prevalence of children carrying AMPC-RS in their mouths, ranging from 5.5% to 86.3%. Most of the studies were classified as high quality, and the collected data demonstrate the presence of streptococcal strains with different levels of resistance in the collected samples, such as the dental plaque. The meta-analysis pointed to evidence of AMPC-RS being carried, with a prevalence of 21.3% (I² = 0%, p = 0.705). There is an important prevalence of AMPC-RS carriage in the mouths of children. Specific attention should be directed to AP in those susceptible to IE.

Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis

Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e⁻¹⁴) and myometrial relaxation and contraction (p = 3e⁻⁰⁸) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.

Influence of the presence of mannose-binding lectin polymorphisms on the occurrence of leishmaniasis: a systematic review and meta-analysis

BACKGROUND

Leishmaniasis is caused by an intracellular protozoan of the Leishmania genus. Mannose-binding lectin (MBL) is a serum complement protein and recognizes lipoprotein antigens in protozoa and the bacterial plasma membrane. Nucleotide variants in the promoter region and exon 1 of the MBL gene can influence its expression or change its molecular structure.

OBJECTIVE

To evaluate, through a systematic review, case-control studies of the genetic association of variants in the MBL2 gene and the risk of developing leishmaniasis.

METHODS

This review carried out a search in PubMed, Science Direct, Cochrane Library, Scopus and Lilacs databases for case-control publications with six polymorphisms in the mannose-binding Lectin gene. The following strategy was used: P = Patients at risk of leishmaniasis; I = Presence of polymorphisms; C = Absence of polymorphisms; O = Occurrence of leishmaniasis. Four case/control studies consisting of 791 patients with leishmaniasis and 967 healthy subjects (Control) are included in this meta-analysis. The association of variants in the mannose-binding Lectin gene and leishmaniasis under the allelic genetic model, -550 (Hvs. L), -221 (X vs. Y), +4 (Q vs. P), CD52 (A vs. D), CD54 (A vs. B), CD57 (A vs. C) and A/O genotype (A vs. O) was evaluated. International Prospective Register of Systematic Reviews (PROSPERO): CRD42020201755.

RESULTS

The meta-analysis results for any allelic genetic model showed no significant association for the variants within the promoter, the untranslated region, and exon 1, as well as for the wild-type A allele and mutant allele O with leishmaniasis.

STUDY LIMITATIONS

Caution should be exercised when interpreting these results, as they are based on a few studies, which show divergent results when analyzed separately.

CONCLUSIONS

This meta-analysis showed a non-significant association between the rs11003125, rs7096206, rs7095891, rs5030737, rs1800450, and rs1800451 polymorphisms of the Mannose-binding Lectin gene and leishmaniasis in any allelic and heterogeneous evaluation.

Efficacy and Safety of Second-line Treatments in Patients with Advanced Hepatocellular Carcinoma after Sorafenib Failure: A Meta-analysis

BACKGROUND AND AIMS

In the last decade, several second-line therapies followed by sorafenib in patients with advanced hepatocellular carcinoma (HCC) have been reported. But the outcomes were different from each other. This meta-analysis aimed to evaluate the efficacy and safety of the second-line therapies followed by sorafenib in patients with advanced HCC.

METHODS

Embase (1974 to October 2019) and Ovid MEDLINE (1946 to October 2019) were searched for randomized clinical trials on second-line therapies followed by sorafenib in patients with advanced HCC. The quality of each study was assessed by the modified Jadad scale. Statistical analysis was carried out by RevMan5.3 software. Efficacy and safety were analyzed. Efficacy included overall survival (OS), disease control rate, time to progression, and progression-free survival.

RESULTS

Eight studies involving 3,173 patients were eligible. No difference in OS was found between the second-line treatment group and the control group (HR=0.87, 95% CI: 0.74-1.01, p=0.06). Disease control rate (relative risk (RR)=1.36, 95% CI: 1.16-1.60, p=0.0002), time to progression (HR=0.64, 95% CI: 0.51-0.81, p=0.0002) and progression-free survival (HR=0.60, 95% CI: 0.46-0.77, p<0.0001) were significantly improved by the second-line therapies. There was a slight difference in adverse events of any grade (RR=1.07, 95% CI: 1.00-1.14, p=0.03) between the two groups.

CONCLUSIONS

These second-line therapies followed by sorafenib may potentially improve the prognosis in patients with advanced HCC. Compared with other second-line therapies, regorafenib seemed to be more effective.

A Systematic Review and Meta-Analysis of the Prevalence of Congenital Myopathy

Background: Congenital myopathy constitutes a heterogeneous group of orphan diseases that are mainly classified on the basis of muscle biopsy findings. This study aims to estimate the prevalence of congenital myopathy through a systematic review and meta-analysis of the literature.

Methods: The PubMed, MEDLINE, Web of Science, and Cochrane Library databases were searched for original research articles published in English prior to July 30, 2021. The quality of the included studies was assessed by a checklist adapted from STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). To derive the pooled epidemiological prevalence estimates, a meta-analysis was performed using the random effects model. Heterogeneity was assessed using the Cochrane Q statistic as well as the I² statistic.

Results: A total of 11 studies were included in the systematic review and meta-analysis. Of the 11 studies included, 10 (90.9%) were considered medium-quality, one (9.1%) was considered low-quality, and no study was assessed as having a high overall quality. The pooled prevalence of congenital myopathy in the all-age population was 1.62 (95% CI, 1.13–2.11) per 100,000, while the prevalence in the child population was 2.76 (95% CI, 1.34–4.18) per 100,000. In the pediatric population, the prevalence among males was 2.92 (95% CI, −1.70 to 7.55) per 100,000, while the prevalence among females was 2.47 (95% CI, −1.67 to 6.61) per 100,000. The prevalence estimates of the all-age population per 100,000 were 0.20 (95% CI 0.10–0.35) for nemaline myopathy, 0.37 (95% CI 0.21–0.53) for core myopathy, 0.08 (95% CI −0.01 to 0.18) for centronuclear myopathy, 0.23 (95% CI 0.04–0.42) for congenital fiber-type disproportion myopathy, and 0.34 (95% CI, 0.24–0.44) for unspecified congenital myopathies. In addition, the prevalence estimates of the pediatric population per 100,000 were 0.22 (95% CI 0.03–0.40) for nemaline myopathy, 0.46 (95% CI 0.03–0.90) for core myopathy, 0.44 (95% CI 0.03–0.84) for centronuclear myopathy, 0.25 (95% CI −0.05 to 0.54) for congenital fiber-type disproportion myopathy, and 2.63 (95% CI 1.64–3.62) for unspecified congenital myopathies.

Conclusions: Accurate estimates of the prevalence of congenital myopathy are fundamental to supporting public health decision-making. The high heterogeneity and the lack of high-quality studies highlight the need to conduct higher-quality studies on orphan diseases.

The effectiveness of goal setting on glycaemic control for people with type 2 diabetes and prediabetes: A systematic review and meta-analysis

AIM

This review assessed the effectiveness of interventions using a goal-setting approach on glycaemic control for people diagnosed with prediabetes or type 2 diabetes.

DESIGN

A systematic review guided by the Joanna Briggs Institute methodology for conducting systematic reviews of primary research studies was conducted.

DATA SOURCES

Randomized controlled trials and experimental studies with a minimum follow-up period of 6 months were considered for inclusion. The primary outcome was change in glycaemic control as measured by glycated haemoglobin (%) and/or fasting plasma glucose (mg/dl). A systematic search of seven electronic databases was completed in October 2020.

REVIEW METHODS

Papers meeting the inclusion criteria were critically appraised using the Joanna Briggs Institute tools for critical appraisal followed by data extraction. A Grading of Recommendations Assessment, Development and Evaluation assessment was conducted to assess the overall certainty of the evidence. Fixed-effect meta-analyses were completed to demonstrate the mean effect for each outcome of interest.

RESULTS

Twenty one studies were included in this review. Goal setting was more effective than usual care for glycaemic control in prediabetes at 6 months and at 12 months for fasting plasma glucose (mg/dl) and glycated haemoglobin (%). Goal setting was more effective than usual care for glycaemic control in type 2 diabetes for fasting plasma glucose (mg/dl) at 6 months, fasting plasma glucose (mg/dl) at 12 months, glycated haemoglobin (%) at 6 months and glycated haemoglobin (%) at 12 months.

CONCLUSION

The evidence suggests goal setting is effective in supporting people to achieve glycaemic targets in prediabetes and type 2 diabetes.

A survey of gene expression meta-analysis: methods and applications

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.

Non-significant association between - 330 T/G polymorphism in interleukin-2 gene and chronic periodontitis: findings from a meta-analysis

BACKGROUND

Chronic periodontitis (CP) is an immune-inflammatory disease that promotes tissue damage around the teeth. Among the several inflammatory mediators that orchestrate the periodontitis, there is the interleukin (IL)-2. Genetic variations in IL2 gene may be associated with the risk and severity of the disease. Contrary results are available in the literature with inconclusive findings and none meta-analysis to gather these data.

METHODS

A literature search was performed for studies published before June 11, 2019 in diverse scientific and educational databases. The data was extracted by two investigators and the statistical evaluation was performed by Review Manager statistical program with heterogeneity (I²) and Odds Ratio (OR) with 95% of Confidence Intervals (CI) calculations and a sensitive analysis to assess the accuracy of the obtained results. The publication bias was evaluated by Begg' and Egger's test with Comprehensive meta-analysis software. The value of P < 0.05 was considered as significant.

RESULTS

Five studies were identified in diverse ethnical groups with 1425 participants. The - 330 T/G polymorphism in IL2 gene was not significantly associated with CP in allelic evaluation (P > 0.05) as well as in the genotypic comparisons (P = 0.15). The Begg's test and the linear regression Egger's test did not show any evidence of publication bias risk (P > 0.05) which was corroborated by the absence of obvious asymmetry in Funnel plot graphic.

CONCLUSIONS

This meta-analysis showed a non-significant association between - 330 T/G polymorphism in IL2 gene and CP in any allelic evaluation.

Do Genomic Factors Play a Role in Diabetic Retinopathy?

Although there is strong clinical evidence that the control of blood glucose, blood pressure, and lipid level can prevent and slow down the progression of diabetic retinopathy (DR) as shown by landmark clinical trials, it has been shown that these factors only account for 10% of the risk for developing this disease. This suggests that other factors, such as genetics, may play a role in the development and progression of DR. Clinical evidence shows that some diabetics, despite the long duration of their diabetes (25 years or more) do not show any sign of DR or show minimal non-proliferative diabetic retinopathy (NPDR). Similarly, not all diabetics develop proliferative diabetic retinopathy (PDR). So far, linkage analysis, candidate gene studies, and genome-wide association studies (GWAS) have not produced any statistically significant results. We recently initiated a genomics study, the Diabetic Retinopathy Genetics (DRGen) Study, to examine the contribution of rare and common variants in the development of different phenotypes of DR, as well as their responsiveness to anti-VEGF treatment in diabetic macular edema (DME). Our preliminary findings reveal a novel set of genetic variants involved in the angiogenesis and inflammatory pathways that contribute to DR progression or protection. Further investigation of variants can help to develop novel biomarkers and lead to new therapeutic targets in DR.

Role of CD226 Rs763361 Polymorphism in Susceptibility to Multiple Autoimmune Diseases

Background: Gly307Ser (rs763361) polymorphism in Cluster of Differentiation 226 (CD226) gene has been implicated in susceptibility to autoimmune diseases (ADs) with controversial results. This study aimed to conduct a meta-analysis for examining the relationship between CD226 rs763361 polymorphism and ADs risk. Methods: a literature search was performed to identify relevant studies published in Embase, PubMed, Wanfang, and China National Knowledge Infrastructure. In the most appropriate genetic models, pooled odds ratio (OR) with 95% confidence interval (CI) was calculated for evaluating the strength of the associations. Besides standard meta-analysis, cumulative meta-analysis was also conducted to assess the trend in OR over time. Also, we performed subgroup and sensitivity analysis, and checked for the heterogeneity and publication bias. Results: Twenty-nine reports with 51 independent studies, comprising 18157 cases and 29904 controls, were enrolled in this meta-analysis. Among overall and various ethnic populations (Europeans, Asians, Africans, and South Americans), CD226 rs763361 polymorphism was significantly associated with ADs susceptibility; in the subgroup analysis by disease type, rs763361 polymorphism revealed significant associations with the risk of RA, SLE, T1D, and MS. The sensitivity analysis and cumulative meta-analysis confirmed the stability and robustness of these significant results. However, no evidence of stable significant association emerged in the subgroup analysis of SSc. Conclusion: These findings demonstrate that CD226 rs763361 polymorphism confers susceptibility to ADs in the overall population, Europeans, Asians, Africans, and South Americans. rs763361 polymorphism in CD226 gene may be a potential susceptible predictor of ADs especially RA, SLE, T1D, and MS.

Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits

In genome-wide association studies (GWAS), sample size is the most important factor affecting statistical power that is under control of the investigator, posing a major challenge in understanding the genetics underlying difficult-to-measure traits. Combining data sets available from different populations for joint or meta-analysis is a promising alternative to increasing sample sizes available for GWAS. Simulation studies indicate statistical advantages from combining raw data or GWAS summaries in enhancing quantitative trait loci (QTL) detection power. However, the complexity of genetics underlying most quantitative traits, which itself is not fully understood, is difficult to fully capture in simulated data sets. In this study, population-specific and combined-population GWAS as well as a meta-analysis of the population-specific GWAS summaries were carried out with the objective of assessing the advantages and challenges of different data-combining strategies in enhancing detection power of GWAS using milk fatty acid (FA) traits as examples. Gas chromatography (GC) quantified milk FA samples and high-density (HD) genotypes were available from 1,566 Dutch, 614 Danish, and 700 Chinese Holstein Friesian cows. Using the joint GWAS, 28 additional genomic regions were detected, with significant associations to at least 1 FA, compared with the population-specific analyses. Some of these additional regions were also detected using the implemented meta-analysis. Furthermore, using the frequently reported variants of the diacylglycerol acyltransferase 1 (DGAT1) and stearoyl-CoA desaturase (SCD1) genes, we show that significant associations were established with more FA traits in the joint GWAS than the remaining scenarios. However, there were few regions detected in the population-specific analyses that were not detected using the joint GWAS or the meta-analyses. Our results show that combining multi-population data set can be a powerful tool to enhance detection power in GWAS for seldom-recorded traits. Detection of a higher number of regions using the meta-analysis, compared with any of the population-specific analyses also emphasizes the utility of these methods in the absence of raw multi-population data sets to undertake joint GWAS.

Association between F+1 polymorphism in a disintegrin and metalloprotease 33 (ADAM33) gene and chronic obstructive pulmonary disease susceptibility: An evidence-based meta-analysis

BACKGROUND

The F+1 (rs511898 G>A) polymorphism in a disintegrin and metalloprotease 33 (ADAM33) gene has been implicated in susceptibility of chronic obstruction pulmonary disease (COPD). However, a series of studies have reported inconclusive. The aim of this study is to explore the association between the F+1 (rs511898) of ADAM33 gene and COPD susceptibility by using the method of meta-analysis.

METHOD

PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure database (CNKI), Chongqing VIP database, Wanfang and China Biology Medicine (CBM) were searched comprehensively to obtain the related cohort studies and case-control studies. The included studies were selected according to inclusion criteria. The pooled odds ratios were performed respectively for allele comparison, additive model, dominant genetic model and recessive genetic model. The association between the F+1 polymorphism of ADAM33 gene and COPD susceptibility was measured by OR and 95%CI by STATA 12.0. The subgroup analysis was distinguished according to the ethnicity. The publication bias was tested by funnel plots and Egger's linear regression method.

RESULTS

Twelve case-control studies were included in the meta-analysis, which study is comprised of 6935 participants (2454 patients with COPD and 4481 controls). The meta results showed significant association between ADAM33 F+1 polymorphism and COPD susceptibility in allele model OR _total = 1.16(95% CI 1.04-1.30, P = 0.007), OR _Asian = 1.14(95% CI 1.02-1.27, P = 0.022), additive model OR _total = 1.27 (95% CI 1.13-1.43, P = 0.000), OR _Asian = 1.25 (95% CI 1.08-1.45, P = 0.003), recessive model OR _total = 1.49 (95% CI 1.16-1.91, P = 0.002), OR _Asian = 1.56(95% CI 1.09-2.22, P = 0.014), but not significant in Caucasians.

CONCLUSION

The ADAM33 F+1 mutant gene A may increase the risk of COPD among the Asian population, while it may not associate with the European population.

Effect of Autologous Bone Marrow Stem Cell Therapy in Patients with Liver Cirrhosis: A Meta-analysis

Background and Aims: Although autologous bone marrow stem cell (BMSC) transplantation is an effective treatment for liver cirrhosis, there are few reports describing the optimal delivery route and number of injected BMSCs. Methods: A literature search was conducted using PubMed, ISI Web of Science, Cochrane Central Register of Controlled Trials, and EBSCO. A meta-analysis was performed to assess the effect of BMSCs on liver and coagulation function indices. Subgroup analysis was performed based on number of injected BMSCs, delivery route, and length of follow-up. Results: A total of 15 studies were selected from among 1903 potential studies for analysis. Autologous BMSC transplantation significantly improved aspartate aminotransferase, total bilirubin, albumin, prothrombin time, prothrombin activity, prothrombin concentration, Child-Pugh score, and model for end-stage liver disease. In the subgroup analysis of cell numbers, all four of the indices were significantly improved when the number of BMSCs was >4 × 108. The subgroup analysis referring to the delivery route showed that arterial infusion increased the therapeutic effect over venous infusion. Finally, in the subgroup analysis of follow-up length, the results showed that BMSC therapy significantly improved liver function at 2 weeks after transplantation. In addition, this therapy improved coagulation 4 weeks after the transplant, with a maintenance of efficacy for up to 24 weeks. Conclusions: Autologous BMSC therapy is beneficial for liver improvement and coagulation in patients with liver cirrhosis. The therapeutic effect was generated at 2-4 weeks after transplantation. The effect lasted for 24 weeks but no more than 48 weeks. The greatest benefit to patients was observed with a 4 × 108 autologous BMSC transplant via the hepatic artery.

Generalized meta-analysis for multiple regression models across studies with disparate covariate information

Meta-analysis is widely popular for synthesizing information on common parameters of interest across multiple studies because of its logistical convenience and statistical efficiency. We develop a generalized meta-analysis approach to combining information on multivariate regression parameters across multiple studies that have varying levels of covariate information. Using algebraic relationships among regression parameters in different dimensions, we specify a set of moment equations for estimating parameters of a maximal model through information available from sets of parameter estimates for a series of reduced models from the different studies. The specification of the equations requires a reference dataset for estimating the joint distribution of the covariates. We propose to solve these equations using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. We describe extensions of the iterated reweighted least-squares algorithm for fitting generalized linear regression models using the proposed framework. Based on the same moment equations, we also develop a diagnostic test for detecting violations of underlying model assumptions, such as those arising from heterogeneity in the underlying study populations. The proposed methods are illustrated with extensive simulation studies and a real-data example involving the development of a breast cancer risk prediction model using disparate risk factor information from multiple studies.

Statistical Methods and Software for Substance Use and Dependence Genetic Research

BACKGROUND

Substantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

METHODS

Various statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants.

CONCLUSION

In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Renin-Angiotensin-Aldosterone System Gene Polymorphisms and Type 2 Diabetic Nephropathy in Asian Populations: An Updated Meta-analysis

BACKGROUND

The association of polymorphisms in the renin-angiotensin-aldosterone system candidate genes, namely Angiotensin-Converting Enzyme (ACE) Insertion/Deletion (I/D), Angiotensinogen (AGT) M235T and Angiotensin II Receptor Type 1 (AGTR1) A1166C with Diabetic Nephropathy (DN) has been studied for decades.

OBJECTIVE

This meta-analysis aimed to assess the updated pooled effects of these polymorphisms with DN among Asian populations with type 2 diabetes mellitus.

METHODS

The PubMed electronic database was searched without duration filter until August 2017 and the reference list of eligible studies was screened. The association of each polymorphism with DN was examined using odds ratio and its 95% confidence interval based on dominant, recessive and allele models. Subgroup analyses were conducted based on region, DN definition and DM duration.

RESULTS

In the main analysis, the ACE I/D (all models) and AGTR1 A1166C (dominant model) showed a significant association with DN. The main analysis of the AGT M235T polymorphism did not yield significant findings. There were significant subgroup differences and indication of significantly higher odds for DN in terms of DM duration (≥10 years) for ACE I/D (all models), AGT M235T (recessive and allele models) and AGTR1 A1166C (recessive model). Significant subgroup differences were also observed for DN definition (advanced DN group) and region (South Asia) for AGTR1 A1166C (recessive model).

CONCLUSION

In the Asian populations, ACE I/D and AGTR1 A1166C may contribute to DN susceptibility in patients with T2DM by different genetic models. However, the role of AGT M235T needs to be further evaluated.

Comparative analysis of blood parameters of the erythrocyte lineage between patients with chronic periodontitis and healthy patients: Results obtained from a meta-analysis

Objective Quantitatively evaluate blood parameters of the erythrocyte lineage in patients with chronic periodontitis and healthy controls by means of a meta-analysis. Material and methods A review of literature was performed in the scientific databases for studies published before September 2016. The abstracts were evaluated and the extraction of data performed by two examiners. The parameters assessed were: hemoglobin levels, number of circulating red blood cells, hematocrit, mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration in patients with chronic periodontitis and healthy controls. The calculations were obtained through the statistical software Review Manager version 5.3, with calculation of Mean Difference, heterogeneity (I²) and funnel plot with P < 0.05. Results Nine case/control studies composed the results with 342 patients with chronic periodontitis and 359 healthy controls. There was a significant decrease in hemoglobin levels (MD = -1.60, 95% CI: -2.72, -0.48, P = 0.005) and number of circulating red blood cells (MD = -0.51, 95% CI: -0.78, -0.24, P = 0.0002), hematocrit (MD = -4.41, IC 95%: -7.13, -1.68, P = 0.002), mean corpuscular hemoglobin (MD = -0.80; IC 95%: -1.38, -0,22, P = 0.007) and mean corpuscular hemoglobin concentration (MD = -0.93, IC 95%: -1.63, -0.23, P = 0.009) in patients with chronic periodontitis when compared to healthy controls. No publication bias was found through the funnel plot asymmetry. Conclusion There is an associated of the reduction of hematological parameters (such as levels of hemoglobin and circulating red blood cells, hematocrit, mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration) to patients with chronic periodontitis.

Vitamin D receptor FokI polymorphism and the risks of colorectal cancer, inflammatory bowel disease, and colorectal adenoma

Based on an inverse association between vitamin D levels and the risks of colorectal diseases, a functional start codon polymorphism in the vitamin D receptor (VDR) gene is speculated to affect the risks for these diseases. To validate this hypothesis, we first conducted a case-control study of 695 colorectal cancer patients and 1,397 controls. The association of VDR FokI polymorphism with colorectal cancer risk was analyzed using a logistic regression model. In the present case-control study, compared to the F allele, the f allele seemed to be associated with lower risks of colon cancer and advanced colorectal cancer. Additionally, a meta-analysis of 27 studies was conducted to combine findings from previous studies investigating the association of FokI polymorphism with colorectal disease using a random effects model. In the present meta-analysis, the f allele was positively associated with the risk of inflammatory bowel disease, including Crohn’s disease and ulcerative colitis. However, this allele was inversely associated with colon cancer and was not associated with the risk of rectal cancer or colorectal adenoma. In conclusion, the findings from this study imply that the role of VDR FokI polymorphism may differ based on the type and severity of colorectal disease.

A data mining paradigm for identifying key factors in biological processes using gene expression data

A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer's Disease

Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost, whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized LMM (GLMM) in a Bayesian framework, called Bayes-GLMM Bayes-GLMM has four major features: (1) support of categorical, binary, and quantitative variables; (2) cohesive integration of previous GWAS results for related traits; (3) correction for sample relatedness by mixed modeling; and (4) model estimation by both Markov chain Monte Carlo sampling and maximal likelihood estimation. We applied Bayes-GLMM to the whole-genome sequencing cohort of the Alzheimer's Disease Sequencing Project. This study contains 570 individuals from 111 families, each with Alzheimer's disease diagnosed at one of four confidence levels. Using Bayes-GLMM we identified four variants in three loci significantly associated with Alzheimer's disease. Two variants, rs140233081 and rs149372995, lie between PRKAR1B and PDGFA The coded proteins are localized to the glial-vascular unit, and PDGFA transcript levels are associated with Alzheimer's disease-related neuropathology. In summary, this work provides implementation of a flexible, generalized mixed-model approach in a Bayesian framework for association studies.

Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy

In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.

Systematic reviews and cancer research: a suggested stepwise approach

Systematic reviews, with or without meta-analysis, play an important role today in synthesizing cancer research and are frequently used to guide decision-making. However, there is now an increase in the number of systematic reviews on the same topic, thereby necessitating a systematic review of previous systematic reviews. With a focus on cancer, the purpose of this article is to provide a practical, stepwise approach for systematically reviewing the literature and publishing the results. This starts with the registration of a protocol for a systematic review of previous systematic reviews and ends with the publication of an original or updated systematic review, with or without meta-analysis, in a peer-reviewed journal. Future directions as well as potential limitations of the approach are also discussed. It is hoped that the stepwise approach presented in this article will be helpful to both producers and consumers of cancer-related systematic reviews and will contribute to the ultimate goal of preventing and treating cancer.

Common genetic variant rs3802842 in 11q23 contributes to colorectal cancer risk in Chinese population

A genome-wide association study identified a common genetic variant rs3802842 at 11q23 to be associated with CRC risk with OR=1.1 and P = 5.80E-10 in European population. In Chinese population, several genetic association studies have investigated the association between rs3802842 variant and CRC risk. However these studies reported both positive and negative association results. It is still necessary to evaluate a specific variant in a specific population, which would be informative to reveal the disease mechanism. Until recently, there is no a systemic study to evaluate the potential association between rs3802842 and CRC risk in Chinese population by a meta-analysis method. Here, we aim to evaluate this association in Chinese population by a meta-analysis method using 12077 samples including 5816 CRC cases and 6261 controls. We identified the T allele of rs3802842 to be significantly related with an increase CRC risk (P=2.22E-05, OR=1.14, 95% CI 1.07-1.21) in Chinese population.

SMAD7 rs4939827 variant contributes to colorectal cancer risk in Chinese population

A genome-wide association study identified a common genetic variant rs4939827 at 18q21 in SMAD7 to be related with colorectal cancer (CRC) risk with OR=1.2 and P =7.80E-28. Until recently, several meta-analysis studies have been conducted, and reported significant association between rs4939827 and CRC risk. However none of these studies evaluated the potential association between rs4939827 and CRC risk in Chinese population. In this study, we evaluated this association by a meta-analysis using 12077 samples including 5816 CRC cases and 6261 controls. In the end, we identified the T allele of rs4939827 to be significantly related with an increase CRC risk (P=2.22E-05, OR=1.14, 95% CI 1.07-1.21) in Chinese population.

T1 polymorphism in a disintegrin and metalloproteinase 33 (ADAM33) gene may contribute to the risk of childhood asthma in Asians

OBJECTIVE

Polymorphisms in ADAM33 gene have been implicated in susceptibility to the risk of childhood asthma. However, the results remain controversial. We performed meta-analyses to clarify the relationship between them.

METHODS

Relevant articles were searched in PubMed, Embase, Wanfang, and China National Knowledge Infrastructure. The Odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of the associations.

RESULTS

Fourteen studies with five ADAM33 polymorphisms (F + 1, T1, T2, S2, and V4) were identified, involving 2687 cases and 2996 controls. ADAM33 F + 1, T2, and T1 polymorphisms showed significant associations with asthma risks in the overall and Caucasian children, Asian children, and Caucasian and Chinese children, respectively; however, these significant results were unstable in sensitivity analysis. T1 revealed significant and stable associations with asthma risks among Asian children in the dominant (OR = 2.00, 95% CI = 1.40-2.87, P = 0.0002) and codominant (OR = 3.06, 95% CI = 1.71-5.50, P = 0.0002) models; in cumulative meta-analyses, these significant results were robust. Concerning S2 or V4 polymorphism, no significant associations were observed.

CONCLUSION

These findings demonstrate that ADAM33 T1 polymorphism might be a potential susceptible predictor of asthma for Asian children. Further functional studies between this polymorphism and asthma risks are warranted.

Gln223Arg polymorphism in the Caucasian population and Pro1019Pro polymorphism in the Chinese population are risk factors for OSAS: An updated meta-analysis of 1159 subjects

BACKGROUND

We conducted a meta-analysis of published literature to identify the correlation between leptin receptor gene polymorphisms and the risk of obstructive sleep apnea syndrome (OSAS).

METHODS

Five different single nucleotide polymorphisms (SNPs) were studied. Only Gln223Arg and Pro1019Pro had multiple studies. Nine studies focused on the correlation between Gln223Arg and Pro1019Pro polymorphisms and OSAS risk. Fixed-effects model or random-effects model was used to calculate the pooled odds ratio (ORs) and its corresponding 95% confidence interval (95% CI). The Begg's, Egger's, Perter's and Harbord tests were used to measure publication bias. Sensitivity analysis was also performed to ensure the robustness of the findings.

RESULTS

Six studies on Gln223Arg polymorphisms (661 cases and 498 controls) and three studies on Pro1019Pro polymorphisms (561 cases and 561 controls) were extracted. There was no correlation between the leptin receptor Gln223Arg polymorphism and the risk of OSAS (odd ratio=0.86, 95% CI=0.68-1.10, P=0.23). However, Caucasian OSAS patients had a higher Arg allele frequency; whereas Chinese population with G genotype were more susceptible to OSAS (odd ratio=1.28, 95% CI=1.04-1.57, P=0.02) in the studies on Pro1019Pro polymorphisms.

CONCLUSION

The Gln223Arg polymorphisms in the Caucasian population and the Pro1019Pro polymorphisms in the Chinese population are risk factors for OSAS.

Association of IL4, IL13, and IL4R polymorphisms with gastrointestinal cancer risk: A meta-analysis

Background

Previous studies have suggested that IL4, IL13, and IL4R are associated with serum IgE levels and allergies, and common variants of these genes may alter cancer risk. To clarify these associations, we conducted a meta-analysis to investigate the associations of IL4, IL13, and IL4R polymorphisms with gastrointestinal cancer risk.

Methods

We used 27 eligible case–control studies describing the associations of six polymorphisms of IL4, IL13, and IL4R with gastrointestinal cancer risk to calculate summary odds ratios (ORs) and 95% confidence intervals (CIs) using five different genetic models. The Q-statistic and I² statistic were calculated to examine heterogeneity.

Results

The IL4 rs2070874 T allele seems to be associated with an increased risk of gastrointestinal cancer (OR 1.11; 95% CI, 1.00–1.24 for T allele vs. C allele). This association was significant in studies conducted outside of Asia (OR 1.28; 95% CI, 1.03–1.58 for T allele vs. C allele) and in studies investigating the association with gastric cancer (OR 1.17; 95% CI, 1.03–1.34 for T allele vs. C allele). However, the IL4R rs1801275 heterozygote seems to be associated with a reduced risk of gastrointestinal cancer (OR 0.79; 95% CI, 0.65–0.96 for AG vs. AA). Other polymorphisms did not show any significant associations with gastrointestinal cancer risk in any of the genetic models and subgroup analyses.

Conclusions

Our results suggest that certain polymorphisms of IL4 and IL4R may affect susceptibility to gastrointestinal cancer. However, further studies are required to confirm these findings.

•

This meta-analysis included 27 eligible case–control studies.

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IL4 rs2070874 T allele may increase the risk of gastrointestinal cancer.

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IL4R rs1801275 variant may reduce the risk of gastrointestinal cancer.

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Polymorphisms in IL4 and IL4R may affect susceptibility to gastrointestinal cancer.

Cross-Phenotype Association Analysis Using Summary Statistics from GWAS

For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.

Meta-analysis for genome-wide association studies using case-control design: application and practice

This review aimed to arrange the process of a systematic review of genome-wide association studies in order to practice and apply a genome-wide meta-analysis (GWMA). The process has a series of five steps: searching and selection, extraction of related information, evaluation of validity, meta-analysis by type of genetic model, and evaluation of heterogeneity. In contrast to intervention meta-analyses, GWMA has to evaluate the Hardy-Weinberg equilibrium (HWE) in the third step and conduct meta-analyses by five potential genetic models, including dominant, recessive, homozygote contrast, heterozygote contrast, and allelic contrast in the fourth step. The 'genhwcci' and 'metan' commands of STATA software evaluate the HWE and calculate a summary effect size, respectively. A meta-regression using the 'metareg' command of STATA should be conducted to evaluate related factors of heterogeneities.

Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures

This paper shows that the effects of causal SNPs on lifespan, estimated through GWAS, may be confounded and the genetic structure of the study population may be responsible for this effect. Simulation experiments show that levels of linkage disequilibrium (LD) and other parameters of the population structure describing connections between two causal SNPs may substantially influence separate estimates of the effect of the causal SNPs on lifespan. This study suggests that differences in LD levels between two causal SNP loci within two study populations may contribute to the failure to replicate previous GWAS findings. The results of this paper also show that successful replication of the results of genetic association studies does not necessarily guarantee proper interpretation of the effect of a causal SNP on lifespan.

Association Between the IL-6 rs1800795 Polymorphism and the Risk of Cervical Cancer: A Meta-Analysis of 1210 Cases and 1525 Controls

Cervical cancer is the second most common cancer and the third leading cause of cancer-related death among females in less developed countries. Studies have shown that the single-nucleotide polymorphisms of interleukin 6 might be associated with cervical cancer risk. A total of 710 articles from EMBASE, EBSCO, Web of science, PubMed, Springer link, and Chinese National Knowledge Infrastructure databases were reviewed in our study. A meta-analysis on the associations between interleukin 6 rs1800795 polymorphism and cervical cancer risk was carried out by comparison using 5 genetic models. In this systematic review, 5 studies were analyzed. The pooled population included 2735 participants (1210 cases and 1525 controls). The overall odds ratio (G vs C alleles) using fixed-effects model was 0.85 (95% confidence interval 0.75-0.97), P = .02. Our results show that the C genotype of interleukin 6 rs1800795 is associated with higher cervical cancer risk. Our results indicate that interleukin 6 rs1800795 polymorphism might be associated with susceptibility to cervical cancer.

Meta-analysis of the serotonin transporter promoter variant (5-HTTLPR) in relation to adverse environment and antisocial behavior

Several studies have suggested an association between antisocial, aggressive, and delinquent behavior and the short variant of the serotonin transporter gene polymorphism (5-HTTLPR). Yet, genome wide and candidate gene studies in humans have not convincingly shown an association between these behaviors and 5-HTTLPR. Moreover, individual studies examining the effect of 5-HTTLPR in the presence or absence of adverse environmental factors revealed inconsistent results. We therefore performed a meta-analysis to test for the robustness of the potential interaction effect of the "long-short" variant of the 5-HTTLPR genotype and environmental adversities, on antisocial behavior. Eight studies, comprising of 12 reasonably independent samples, totaling 7,680 subjects with an effective sample size of 6,724, were included in the meta-analysis. Although our extensive meta-analysis resulted in a significant interaction effect between the 5-HTTLPR genotype and environmental adversities on antisocial behavior, the methodological constraints of the included studies hampered a confident interpretation of our results, and firm conclusions regarding the direction of effect. Future studies that aim to examine biosocial mechanisms that influence the etiology of antisocial behavior should make use of larger samples, extend to genome-wide genetic risk scores and properly control for covariate interaction terms, ensuring valid and well-powered research designs. © 2016 Wiley Periodicals, Inc.

Ischemic Stroke and Six Genetic Variants in CRP, EPHX2, FGA, and NOTCH3 Genes: A Meta-Analysis

BACKGROUND

Ischemic stroke (IS) is a leading cause of death and disability worldwide. As genetic heritability for IS is estimated at about 35%-40%, the identification of genetic variants associated with IS risk is of great importance. The main objective of this study was to carry out a meta-analysis for polymorphisms in CRP, EPHX2, FGA, and NOTCH3 genes and the risk for IS.

METHODS

Literature search for 6 candidate polymorphisms and IS was conducted using HuGE Navigator, PubMed, and Google Scholar databases. Meta-Analyst program was used to calculate pooled odds ratios (ORs) with a random effects model.

RESULTS

Twenty-five published studies for 6 candidate polymorphisms were included: CRP-rs1800947 (5 studies), CRP-rs1205 (3 studies), EPHX2-rs751141 (5 studies), FGA-rs6050 (6 studies), NOTCH3-rs3815188 (3 studies), and NOTCH3-rs1043994 (3 studies), for a total number of 7,825 IS cases and 56,532 control subjects. We did not find significant pooled ORs (P values > .05) for any of the genetic variants evaluated in this work.

CONCLUSIONS

Our meta-analysis results did not show significant associations between these 6 polymorphisms in 4 candidate genes and IS, despite the functional role of some of these single nucleotide polymorphisms (e.g., rs6050 in FGA gene). Future studies are needed to identify additional main genetic risk factors for IS in different populations.

Understanding and evaluating meta-analysis

BACKGROUND

Meta-analysis refers to statistical methodology used to combine data from many studies to obtain an overall assessment of disease risk or treatment outcomes. In this article, the authors review basic methods, interpretation, and limitations of meta-analysis.

METHODS

Investigators use meta-analysis approaches to combine data from available studies to obtain an answer to a specific question. An investigator uses a fixed model if there is homogeneity among the combined studies and a random-effects model if there is heterogeneity. The random-effects model results in wider confidence limits and more conservative estimates of overall results. A meta-analysis can be biased because studies with negative results (no differences in treatment outcomes) are less likely to be published (publication bias).

RESULTS

A meta-analysis should include a well-specified and reproducible set of procedures, including description of data abstraction procedures, attempts to include unpublished studies, and appropriate statistical analysis that includes thorough consideration of heterogeneity and potential bias.

CONCLUSIONS

Meta-analysis cannot correct shortcomings of existing studies or data. However, if potential pitfalls are recognized, meta-analysis can be a useful tool for summarizing existing studies, providing a means to address conflicting reports. Meta-analysis can lead to increased precision, providing greater power to detect existing relationships or treatment effects. Furthermore, meta-analysis may make it possible to address questions that cannot be answered by means of individual studies.

PRACTICAL IMPLICATIONS

Meta-analysis provides an objective, quantitative synthesis of available studies but needs to be understood and assessed critically by those who use it to assess risk or make treatment decisions.

A Case Study of Fixed-Effects and Random-Effects Meta-Analysis Models for Genome-Wide Association Studies in Celiac Disease

BACKGROUND/AIMS

Amongst the many approaches to genome-wide association study (GWAS) meta-analysis (MA), the most popular methods are based on fixed-effects (FE) modeling because it tends to be the statistically most powerful approach in the absence of heterogeneity. However, FE-based MA ignores the potential heterogeneity that may exist between studies. The purpose of our analysis was to test whether results from random effects (RE)-based methods that account for heterogeneity differed significantly from the results that were originally published.

METHODS

We reanalyzed two GWAS FE-based MAs of celiac disease with RE-based methods: (1) a two-stage GWAS MA that includes 9,451 celiac disease cases and 16,434 controls from 12 collections and (2) a single-stage GWAS MA using a custom dense genotyping platform to capture low-frequency and rare variants in 12,041 cases and 12,228 controls from 7 collections.

RESULTS

We present evidence that SNPs at loci that were previously reported to be genome-wide significant (GWS; p < 5 × 10(-8)) in either the two-stage GWAS MA or the single-stage GWAS MA were not GWS when heterogeneity was accounted for by an RE MA method.

CONCLUSION

This case study highlights the strengths of RE MA methods in the presence of heterogeneity and of pooled FE methods.