CGEN {CGEN} | R Documentation

## An R package for analysis of case-control studies in genetic epidemiology

### Description

The new version extends the additive.test() function by implementing additive gene-gene or gene-environment tests under the trend effect of genotypes (i.e. additive genetic model), extending the previous approaches that were only available for the dominant or recessive genetic models (Han et al. 2012 AJE). Several types of interaction tests under the trend effect of genotype are available – methods with and without the gene-environment independence assumption (de Rochemonteix et al. 2020 AJE) and robust methods based on an empirical Bayes type shrinkage estimator (Sanyal et al. 2021 AJE).

This package is for logistic regression analyses of SNP data in case-control studies. It is designed to give the users flexibility of using a number of different methods for analysis of SNP-environment or SNP-SNP interactions. It is known that power of interaction analysis in case-control studies can be greatly enhanced if it can be assumed that the factors (e.g. two SNPs) under study are independently distributed in the underlying population. The package implements a number of different methods that can incorporate such independence constraints into analysis of interactions in the setting of both unmatched and matched case-control studies. These methods are more general and flexible than the popular case-only method of analysis of interaction that also assumes gene-gene or/and gene-environment independence for the underlying factors in the underlying population. The package also implements various methods, based on shrinkage estimation and conditional-likelihoods, that can automatically adjust for possible violation of the independence assumption that could arise due to direct causal relationship (e.g. between a gene and a behavior exposure) or indirect correlation (e.g due to population stratification). A number of convenient summary and printing functions are included. In its previous version, the package has been updated with the new function `snp.score` that allow testing for disease-SNP association accounting for gene-environment interaction using an array of different types of score-tests. The function can handle both genotyped and imputed SNPs. The latest version of the package has extended the additive.test() function by implementing additive gene by environment tests under the trend effect of genotypes (additive model). Two types of trend tests are available - likelihood ratio tests and Wald tests based on an empirical Bayes type shrinkage estimator. The package will continue to be updated with new methods as they are developed. The methods are currently not suitable for analysis of SNPs on sex chromosomes.

### Details

The main functions for unmatched data are `additive.test`, `snp.logistic` and `snp.score`. Whereas `additive.test`, `snp.logistic` and `snp.score` analyzes one SNP with each function call, `GxE.scan` analyzes a collection of SNPs and writes the summary results to an external file. With `additive.test` and `snp.logistic`, a data frame is input in which the SNP variable must be coded as 0-1-2 (or 0-1). The function `snp.score` can be used with imputed genotypes, where the SNP variable is coded as the expected dosage. The functions `getSummary`, `getWaldTest` and `snp.effects` can be called for creating summary tables, computing Wald tests and joint/stratified effects using the returned object from `snp.logistic` (see `Examples` in `snp.logistic`). With `GxE.scan`, the data is read in from external files defined in `snp.list` and `pheno.list`. The collection of p-values computed in `GxE.scan`, can be plotted using the functions `QQ.plot` and `Manhattan.plot`.
The function for analysis of matched case-control data is `snp.matched`. Optimal matching can be obtained from the function `getMatchedSets`. The current version of the package is only suitable for analysis of SNPs on non-sex chromosomes.

Main functions for single SNP analysis:
`additive.test`
`snp.logistic`
`snp.matched`
`snp.score`

For GWAS analysis:
`GxE.scan`
`GxE.scan.combine`
`GxE.scan.partition`
`pheno.list`
`snp.list`
`subject.list`

Graphics:
`Manhattan.plot`
`QQ.plot`
`snp.effects.plot`

Sample data:
`LocusMapData`
`Xdata`

Miscellaneous:
`getMatchedSets` (Used with `snp.matched`)
`getSummary` (The same as calling `summary`)
`getWaldTest` (For computing Wald tests)
`locusMap.list` (Used with `Manhattan.plot`)
`printEffects` (The same as calling `print`)
`snp.effects` (For computing joint and stratified effects)

### Author(s)

Samsiddhi Bhattacharjee, Summer Han, Minsun Song, Nilanjan Chatterjee, William Wheeler and Nilotpal Sanyal <nsanyal@stanford.edu>

### References

Maximum-likelihood estimation under independence

Chatterjee, N. and Carroll, R. Semiparametric maximum likelihood estimation exploting gene-environment independence in case-control studies. Biometrika, 2005, 92, 2, pp.399-418.

Shrinkage estimation

Mukherjee B, Chatterjee N. Exploiting gene-environment independence in analysis of case-control studies: An empirical Bayes approach to trade-off between bias and efficiency. Biometrics 2008, 64(3):685-94.

Mukherjee B et al. Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs. Genetic Epidemiology, 2008, 32:615-26.

Chen YH, Chatterjee N, Carroll R. Shrinkage estimators for robust and efficient inference in haplotype-based case-control studies. Journal of the American Statistical Association, 2009, 104: 220-233.

Conditional Logistic Regression and Adjustment for Population stratification

Chatterjee N, Zeynep K and Carroll R. Exploiting gene-environmentindependence in family-based case-control studies: Increased power for detecting associations, interactions and joint-effects. Genetic Epidemiology2005; 28:138-156.

Bhattacharjee S, Wang Z, Ciampa J, Kraft P, Chanock S, Yu K, Chatterjee N Using Principal Components of Genetic Variation for Robust and Powerful Detection of Gene-Gene Interactions in Case-Control and Case-Only studies. American Journal of Human Genetics, 2010, 86(3):331-342.

Score tests

Han, S.S., Rosenberg, P., Ghosh, A., Landi M.T., Caporaso N. and Chatterjee, N. An exposure weighted score test for genetic association integrating environmental risk-factors. Biometrics 2015 (Article first published online: 1 JUL 2015 | DOI: 10.1111/biom.12328)

Song M., Wheeler B., Chatterjee, N. Using imputed genotype data in joint score tests for genetic association and gene-environment interactions in case-control studies (In preparation).

Tests for additive interaction

Sanyal, N., Napolioni, V., de Rochemonteix, M., Belloy, M. E., Caporaso, N. E., Landi, M. T., Greicius, M. D., Chatterjee, N., Han, S. S. A robust test for additive gene-environment interaction under the trend effect of genotype using an empirical Bayes-type shrinkage estimator. American Journal of Epidemiology 2021 (in press).

de Rochemonteix M, Napolioni V, Sanyal N, et al. A likelihood ratio test for gene-environment interaction based on the trend effect of genotype under an additive risk model using the gene-environment independence assumption. American Journal of Epidemiology 2021;190(1):129-41.

Han, S. S, Rosenberg P. S, Garcia-Closas M, Figueroa J. D, Silverman D, Chanock S. J, Rothman N, and Chatterjee N. Likelihood ratio test for detecting gene (G) environment (E) interactions under the additive risk model exploiting G-E independence for case-control data. Am J of Epidemiol, 2012; 176:1060-7.

[Package CGEN version 3.28.0 Index]