Rare genetic variants and treatment response: sample size and analysis issues
- 27 June 2012
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 31 (25), 3041-3050
- https://doi.org/10.1002/sim.5428
Abstract
Incorporating information about common genetic variants may help improve the design and analysis of clinical trials. For example, if genes impact response to treatment, one can pregenotype potential participants to screen out genetically determined nonresponders and substantially reduce the sample size and duration of a trial. Genetic associations with response to treatment are generally much larger than those observed for development of common diseases, as highlighted here by findings from genome‐wide association studies. With the development and decreasing cost of next generation sequencing, more extensive genetic information — including rare variants — is becoming available on individuals treated with drugs and other therapies. We can use this information to evaluate whether rare variants impact treatment response. The sparseness of rare variants, however, raises issues of how the resulting data should be best analyzed. As shown here, simply evaluating the association between each rare variant and treatment response one‐at‐a‐time will require enormous sample sizes. Combining the rare variants together can substantially reduce the required sample sizes, but require a number of assumptions about the similarity among the rare variants’ effects on treatment response. We have developed an empirical approach for aggregating and analyzing rare variants that limit such assumptions and work well under a range of scenarios. Such analyses provide a valuable opportunity to more fully decipher the genomic basis of response to treatment. Copyright © 2012 John Wiley & Sons, Ltd.Keywords
This publication has 42 references indexed in Scilit:
- Comparison of statistical tests for disease association with rare variantsGenetic Epidemiology, 2011
- Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association TestAmerican Journal of Human Genetics, 2011
- Next generation genome-wide association tool: Design and coverage of a high-throughput European-optimized SNP arrayGenomics, 2011
- A map of human genome variation from population-scale sequencingNature, 2010
- Pooled Association Tests for Rare Variants in Exon-Resequencing StudiesAmerican Journal of Human Genetics, 2010
- Association tests using kernel‐based measures of multi‐locus genotype similarity between individualsGenetic Epidemiology, 2009
- Potential etiologic and functional implications of genome-wide association loci for human diseases and traitsProceedings of the National Academy of Sciences, 2009
- Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence DataAmerican Journal of Human Genetics, 2008
- Common and rare variants in multifactorial susceptibility to common diseasesNature Genetics, 2008
- Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controlsNature, 2007