Meta-analysis of genetic association studies under heterogeneity

Association between genetic polymorphisms of cadherin 23 and noise-induced hearing loss: a meta-analysis

BACKGROUND

NIHL is one of the most common occupational diseases induced by gene-environment interaction. The CDH23 gene is a candidate gene related to NIHL susceptibility. However, the relationship between CDH23 gene and NIHL is still inconclusive.

AIM

To clarify the association between CDH23 gene and NIHL, a meta-analysis was performed.

SUBJECTS AND METHODS

A search in MEDLINE, PubMed, Web of Science, EBSCO, China National Knowledge Infrastructure (CNKI), and Wanfang Data was implemented to collect data.

RESULTS AND CONCLUSIONS

Six studies were eventually included and all the subjects were Chinese. The results showed that rs1227051, rs1227049, and rs3752752 were not associated with NIHL susceptibility under five genetic models. But rs3802711 reduced the risk of NIHL under the recessive model, and the BB genotype and B allele of rs3802711 were significantly linked to NIHL under recessive, super-dominant, homozygote, and allele genetic models when stratified by the HWE result. Moreover, when not conform to HWE, the BB + AB genotypes and B allele of Exon7 in dominant, super-dominant, homozygote, and allele genetic model increased the risk of NIHL. CDH23 may be a potential gene marker for the prevention and early screening of NIHL in Chinese. Further large and well-designed studies are needed to confirm this association.

Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease

MOTIVATION

Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis.

RESULTS

Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals.

AVAILABILITY AND IMPLEMENTATION

Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance.

Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals

Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m(2)], but their contribution to common obesity (BMI ≥ 30 kg/m(2)) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06-1.24, P = 6.08 × 10(-6)) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04-1.10, P = 3.00 × 10(-7)). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00-0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00-0.03; P = 5.57 × 10(-4)). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.

Testing genotypes-phenotype relationships using permutation tests on association rules

Association rule mining is a knowledge discovery technique which informs researchers about relationships between variables in data. These relationships can be focused to a specific set of response variables. We propose an augmented version of this method to discover groups of genotypes which relate to specific outcomes. We derive the methodology to find these candidate groups of genotypes and illustrate how the method works on data regarding neuroinvasive complications of West Nile virus and through simulation.

Jumping on the Train of Personalized Medicine: A Primer for Non-Geneticist Clinicians: Part 2. Fundamental Concepts in Genetic Epidemiology

With the decrease in sequencing costs, personalized genome sequencing will eventually become common in medical practice. We therefore write this series of three reviews to help non-geneticist clinicians to jump into the fast-moving field of personalized medicine. In the first article of this series, we reviewed the fundamental concepts in molecular genetics. In this second article, we cover the key concepts and methods in genetic epidemiology including the classification of genetic disorders, study designs and their implementation, genetic marker selection, genotyping and sequencing technologies, gene identification strategies, data analyses and data interpretation. This review will help the reader critically appraise a genetic association study. In the next article, we will discuss the clinical applications of genetic epidemiology in the personalized medicine area.

Association between 3801T>C polymorphism of CYP1A1 and idiopathic male infertility risk: a systematic review and meta-analysis

BACKGROUND

Epidemiological studies have evaluated the association between 3801T>C polymorphism of CYP1A1 gene and the risk for idiopathic male infertility, but the results are inconclusive. We aimed to derive a more precise estimation of the relationship by conducting a meta-analysis of case-control studies.

METHODS

This study conformed to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Embase and CNKI databases were searched through November 2013 to identify relevant studies. Pooled odds ratios with 95% confidence intervals were used to assess the strength of the association between CYP1A1 3801T>C polymorphism and idiopathic male infertility risk. Q-test was performed to evaluate between-study heterogeneity and publication bias was appraised using funnel plots. Sensitivity analyses were conducted to evaluate the robustness of meta-analysis findings.

RESULTS

Six studies involving 1,060 cases and 1,225 controls were included in this meta-analysis. Overall, significant associations between 3801T>C polymorphism and idiopathic male infertility risk were observed in allelic comparison (OR = 1.36, 95% CI: 1.01-1.83), homozygous model (OR = 2.18, 95% CI: 1.15-4.12), and recessive model (OR = 1.86, 95% CI: 1.09-3.20), with robust findings according to sensitivity analyses. However, subgroup analyses did not further identify the susceptibility to idiopathic male infertility in all comparisons. Funnel plot inspections did not reveal evidence of publication bias.

CONCLUSIONS

The current meta-analysis provides evidence of a significant association between CYP1A1 3801T>C polymorphism and idiopathic male infertility risk. Considering the limitation inherited from the eligible studies, further confirmation in large-scale and well-designed studies is needed.