A multipoint method for meta-analysis of genetic association studies

Susceptibility to leishmaniasis is affected by host SLC11A1 gene polymorphisms: a systematic review and meta-analysis

Leishmaniases are cutaneous, mucocutaneous, and visceral diseases affecting humans and domesticated animals mostly in the tropical and subtropical areas of the planet. Host genetics have been widely investigated for their role in developing various infectious diseases. The SLC11A1 gene has been reported to play a role in neutrophil function and is associated with susceptibility to infectious and inflammatory diseases such as tuberculosis or rheumatoid arthritis. In the present meta-analysis, we investigate the genetic association of SLC11A1 polymorphisms with susceptibility to leishmaniasis. Genotypes and other risk-related data were collected from 13 case-control and family-based studies (after literature search). Conventional random-effects meta-analysis was performed using STATA 13. To pool case-control and family-based data, the weighted Stouffer's method was also applied. Eight polymorphisms were investigated: rs2276631, rs3731865, rs3731864, rs17221959, rs201565523, rs2279015, rs17235409, and rs17235416. We found that rs17235409 (D543N) and rs17235416 (1729 + 55del4) are significantly associated with a risk for cutaneous leishmaniasis (CL), whereas rs17221959, rs2279015, and rs17235409 are associated with visceral leishmaniasis (VL). Our results suggest that polymorphisms in SLC11A1 affect susceptibility to CL and VL. These findings open new pathways in understanding macrophage response to Leishmania infection and the genetic factors predisposing to symptomatic CL or VL that can lead to the usage of predictive biomarkers in populations at risk.

Multivariate Methods for Meta-Analysis of Genetic Association Studies

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

A meta-analysis of FZD3 gene polymorphisms and their association with schizophrenia

OBJECTIVE

The aim of this study was to investigate the potential association of FZD3 polymorphisms with schizophrenia.

METHODS

A systematic review and a meta-analysis were carried out comprising of nine genetic association studies, with both a population-based and a family-based design, and three genome-wide association studies. A total of 1601 family trios, 39 922 schizophrenic patients, and 61 287 healthy individuals were involved in the analysis and six polymorphisms were examined: rs2241802, rs2323019, rs352203, rs3757888, rs880481, and rs960914. A summary-based method for pooling genetic association studies under both family-based and population-based designs was used. Odds ratios along with their 95% confidence intervals were computed to compare the contrast of alleles in patients and controls.

RESULTS

The results indicate a potentially weaker effect of FZD3 polymorphisms on schizophrenia than that suggested originally and possibly limited to Chinese populations. No relationship was identified between all examined polymorphisms and schizophrenia, except for rs352203, which plays a protective role against schizophrenia. However, this effect was mainly attributed to studies including Chinese patients. In the Chinese population, evidence for an elevated risk for schizophrenia linked to the rs2323019 polymorphism was also identified.

CONCLUSION

Given the different linkage disequilibrium patterns observed in Chinese populations, schizophrenia may be related to some other polymorphisms of gene FZD3 that are in stronger linkage disequilibrium to Chinese than to the other populations studied.

Multivariate meta-analysis of the association of G-protein beta 3 gene (GNB3) haplotypes with cardiovascular phenotypes

The objective of the present study was to review previous investigations on the association of haplotypes in the G-protein β3 subunit (GNB3) gene with representative cardiovascular risk factors/phenotypes: hypertension, overweight, and variation in the systolic and diastolic blood pressures (SBP and DBP, respectively) and as well as body mass index (BMI). A comprehensive literature search was undertaken in Pubmed, Web of Science, EMBASE, Biological Abstracts, LILACS and Google Scholar to identify potentially relevant articles published up to April 2011. Six genetic association studies encompassing 16,068 participants were identified. Individual participant data were obtained for all studies. The three most investigated GNB3 polymorphisms (G-350A, C825T and C1429T) were considered. Expectation-maximization and generalized linear models were employed to estimate haplotypic effects from data with uncertain phase while adjusting for covariates. Study-specific results were combined through a random-effects multivariate meta-analysis. After carefully adjustments for relevant confounding factors, our analysis failed to support a role for GNB3 haplotypes in any of the investigated phenotypes. Sensitivity analyses excluding studies violating Hardy-Weinberg expectations, considering gender-specific effects or more extreme phenotypes (e.g. obesity only) as well as a fixed-effects "pooled" analysis also did not disclose a significant influence of GNB3 haplotypes on cardiovascular phenotypes. We conclude that the previous cumulative evidence does not support the proposal that haplotypes formed by common GNB3 polymorphisms might contribute either to the development of hypertension and obesity, or to the variation in the SBP, DBP and BMI.

On the covariance of two correlated log-odds ratios

In many applications two correlated estimates of an effect size need to be considered simultaneously to be combined or compared. Apparently, there is a need for calculating their covariance, which however requires access to the individual data that may not be available to a researcher performing the analysis. We present a simple and efficient method for calculating the covariance of two correlated log-odds ratios. The method is very simple, is based on the well-known large sample approximations, can be applied using only data that are available in the published reports and more importantly, is very general, because it is shown to encompass several previously derived estimates (multiple outcomes, multiple treatments, dose-response models, mutually exclusive outcomes, genetic association studies) as special cases. By encompassing the previous approaches in a unified framework, the method allows easily deriving estimates for the covariance concerning problems that were not easy to be obtained otherwise. We show that the method can be used to derive the covariance of log-odds ratios from matched and unmatched case-control studies that use the same cases, a situation that has been addressed in the past only using individual data. Future applications of the method are discussed.