Meta-analysis allows for the pooling of independent studies that examine similar hypotheses for example, that a particular allele at a SNP is associated with disease status, and thus may improve power to detect moderate effect sizes. Complex diseases are likely to be under the influence of several genetic risk factors therefore, the contribution of a single gene to risk of disease is expected to be modest. The family-based design compares the frequency of alleles transmitted to an affected offspring by their parents with alleles carried by the parents but not passed to the offspring this type of statistical analysis is often called a transmission disequilibrium test (TDT). The case-control design compares frequencies of alleles carried among cases with a disease and among controls that are free of disease.
Two study designs are commonly employed in genetic association studies: a case-control and a family-based approach.
ConclusionĬatmap allows researchers to synthesize data to assess evidence for association in studies of genetic polymorphisms, facilitating the use of pooled data analyses which may increase power to detect moderate genetic associations. catmap is available from the Comprehensive R Archive Network. In addition, catmap may be used to create forest and funnel plots and to perform sensitivity analysis and cumulative meta-analysis. I introduce the package catmap for the R statistical computing environment that implements fixed- and random-effects pooled estimates for case-control and transmission disequilibrium methods, allowing for the use of genetic association data across study types.
Although many software options are available for meta-analysis of genetic case-control data, no currently available software implements the method described by Kazeem and Farrall (2005), which combines data from independent family-based and case-control studies. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect sizes are modest. Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects.
0 kommentar(er)
