catmap: Case-control And TDT Meta-Analysis Package
© Nicodemus; licensee BioMed Central Ltd. 2008
Received: 30 July 2007
Accepted: 28 February 2008
Published: 28 February 2008
Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect sizes are modest. 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.
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. In addition, catmap may be used to create forest and funnel plots and to perform sensitivity analysis and cumulative meta-analysis. catmap is available from the Comprehensive R Archive Network http://www.r-project.org.
catmap 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.
Two study designs are commonly employed in genetic association studies: a case-control and a family-based approach. The case-control design compares frequencies of alleles carried among cases with a disease and among controls that are free of disease. 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). 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. 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. Although a few methods have been developed to combine family-based data with data from unrelated controls and/or unrelated cases obtained by the same study [1–10], most of these methods are not specifically designed to pool results from multiple independent studies and thus do not have a built-in test for heterogeneity of effect, which may be used to determine whether pooling of independent samples is reasonable. Exceptions to this statement include the method implemented in Genie[7, 8], which conducts a genotype-based test that allows for the pooling of multiple study types across independent samples (meta-analysis), relying on empirical p-values which can be computationally intensive and SCOUT, which allows for the pooling of family-based data with data from unrelated cases and/or controls and performs a formal test of whether differing data types can be sensibly pooled. The likelihood-based method implemented in SCOUT assumes that pooled samples are from the same source population with the same mating-type frequencies, and thus is inappropriate to use in a meta-analysis setting where independent samples have been drawn from differing populations; however, the use of additional programs such as the R package meta  subsequent to using SCOUT may be performed to pool estimates. A recent paper outlined a method for the combination of odds ratios (ORs) from independent case-control and TDT-based studies using a fixed-effect approach , using an allele-based model. Although several programs exist to conduct meta-analyses of case-control genetic data, no software exists that implements the Kazeem & Farrall (2005) method to conduct case-control and TDT meta-analyses of independent samples. I have implemented this model , plus extended the method to the random-effects model of DerSimonian and Laird  in the freely-available R  package catmap.
and the standard error of the random-effects pooled ln(ORRE) is simply 1/(Σr i )1/2.
Results and Discussion
Pooling studies of genetic polymorphisms data via meta-analysis may improve the ability of researchers to detect modest association signals. Since case-control studies are not robust to spurious effects caused by confounding via population stratification, before combining results from case-control studies researchers should assess evidence for substructure in individual studies and also examine the similarity of effect sizes and allele frequencies across study type and population, along with the formal test of heterogeneity of genetic effect implemented in catmap. catmap implements a random-effects estimate of the pooled odds ratio, which assumes a Normal distribution of genetic effects across studies instead of a single genetic effect, which may be preferred when combining studies across sub-groups or when the heterogeneity χ2 indicates significant between-study differences in effect sizes, although when evidence for heterogeneity exists the pooling of data may not be indicated. Since two types of study design (case-control and family-based) are common in statistical genetics, methods and freely-available software that can combine estimates from both designs should prove useful to applied researchers.
Availability and Requirements
Project name: catmap
Project home page: Contributed package at http://www.r-project.org.
Operating systems: MS Windows, Linux, Mac
Programming language: R
Other requirements: R 2.4 or higher
Any restrictions to use by non-academics: none
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