Rare genetic variants and treatment response: sample size and analysis issues

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 non-responders 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.

Star Allele-Based Haplotyping versus Gene-Wise Variant Burden Scoring for Predicting 6-Mercaptopurine Intolerance in Pediatric Acute Lymphoblastic Leukemia Patients

“…To evaluate the validity of GVB, independent replication studies for an expanded gene–drug set with sufficient sample sizes in diverse ethnic groups are required as no novel variant was identified in the current study. A conventional single variant-based association test of rare variants requires infeasible magnitude of sample sizes (Bansal et al, 2010), but approaches that aggregate common, rare, and novel variants jointly will substantially reduce a required effective sample sizes (Witte, 2012). The robustness of the analysis framework shall further be improved as novel prognostic markers on 6-MP DIP are acquired.…”

Rare genetic variants in interleukin-37 link this anti-inflammatory cytokine to the pathogenesis and treatment of gout

“…For serum urate-associated loci and gout, it has also been shown that rare variants contribute to disease pathogenesis 24 25. However, statistical confirmation of rare variants requires high-quality in-depth sequencing and large sample sizes, which is not the case for most GWAS performed to date in gout patients 26–28. With recent advancements in sequencing technologies, the study of rare-frequency and low-frequency variants has remarkably improved.…”

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

“…While recent technological advances have presented an opportunity for rare variant discovery, rare and low-frequency variant analysis requires special consideration. As these variants are individually uncommon, they are often statistically underpowered for detecting phenotypic association 9 . To circumvent this challenge, binning or collapsing methodologies are often utilized.…”