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 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.

Figure 1

Odds ratios and sample sizes for genetic variants association with binary traits from genome-wide association studies (P≤10⁻⁶). Red crosses are results from studies of treatment response (details given in Table 1). Blue circles are the 1,173 findings from all other binary traits reported in the NIH catalog of GWAS [14].

Figure 2

Sample size required to detect associations between less common and rare variants and a binary outcome (e.g., treatment response). All results are for power =0.80 and alpha-level = 10⁻⁶, assuming the population risk of the outcome = 0.0001.