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Mendelian randomization (MR) is becoming a popular approach to estimate the causal effect of an exposure on an outcome overcoming limitations of observational epidemiology. The advent of genome-wide association studies and the increasing accumulation of summarized data from large genetic consortia make MR a powerful technique. In this review, we give a primer in MR methodology, describe efficient MR designs and analytical strategies, and focus on methods and practical guidance for conducting an MR study using summary association data. We show that the analysis is straightforward utilizing either the MR-base platform or available packages in R. However, further research is required for the development of specialized methodology to assess MR assumptions.

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.