Meta analysis definition biology software#

Hessian matrices) from the private data and share the parameters with a central server. In federated learning, the cohorts extract model parameters (e.g.

Each computing party computes the final results given all intermediate results. The computing parties calculate intermediate results from the secret shares and exchange the intermediate results with each other. In SMPC, there are several computing parties and the cohorts extract a separate secret share (anonymized chunk) from the private data and send it to a computing party. In HE, the cohorts encrypt their private data and share it with a single server, which performs operations on the encrypted data from the cohorts to compute the association test results. To address the aforementioned shortcomings, privacy-aware collaborative GWAS can be developed using homomorphic encryption (HE), secure multi-party computation (SMPC), and federated learning. Second and more importantly, the statistical power of meta-analysis can be adversely affected in the presence of cross-study heterogeneity, leading to inaccurate estimation of the joint results and yielding misleading conclusions. Cochran’s Q or the I 2 statistic), the covariates to be considered, etc. fixed effect or random effect), meta-analysis tool (e.g., METAL or GWAMA), heterogeneity metric (e.g. the raw data is not shared with third parities, they suffer from two main constraints: first, they rely on detailed planning and agreement of cohorts on various study parameters such as meta-analysis model (e.g.

Although meta-analysis approaches are privacy-aware, i.e.

Meta analysis definition biology software#

There are several software packages such as METAL, GWAMA, and PLINK that implement different meta-analysis models including fixed or random effect models. Hence, the field has established methods for meta-analysis of individual studies, where only the results and summary statistics of the individual analyses have to be exchanged (Fig. 1a) however, this is close to impossible due to privacy restrictions and data protection issues, especially concerning genetic and medical data. If multiple cohorts want to conduct collaborative GWAS to take advantage of larger sample sizes, they can pool their data for a joint analysis (Fig. The major limitation of PLINK is that it can only perform association tests on local data. PLINK is a widely used open source software tool for GWAS. GWAS sample size is an important factor in detecting associations, and larger sample sizes lead to identifying more associations and more accurate genetic predictors. They have led to considerable achievements over the past decade including better comprehension of the genetic structure of complex diseases and the discovery of SNPs playing a role in many traits or disorders. Genome-wide association studies (GWAS) test millions of single nucleotide polymorphisms (SNPs) to identify possible associations between a specific SNP and disease.