Evaluating the association of TRPA1 gene polymorphisms with pain sensitivity: a protocol for an adaptive recall by genotype study

Abstract

Background: Pain is a complex polygenic trait whose common genetic underpinnings are relatively ill-defined due in part to challenges in measuring pain as a phenotype. Pain sensitivity can be quantified, but this is difficult to perform at the scale required for genome wide association studies (GWAS). Existing GWAS of pain have identified surprisingly few loci involved in nociceptor function which contrasts strongly with rare monogenic pain states. This suggests a lack of resolution with current techniques. We propose an adaptive methodology within a recall-by-genotype (RbG) framework using detailed phenotyping to screen minor alleles in a candidate 'nociceptor' gene in an attempt to estimate their genetic contribution to pain.

Methods/design: Participants of the Avon Longitudinal Study of Parents and Children will be recalled on the basis of genotype at five common non-synonomous SNPs in the 'nociceptor' gene transient receptor potential ankylin 1 (TRPA1). Those homozygous for the common alleles at each of the five SNPs will represent a control group. Individuals homozygous for the minor alleles will then be recruited in a series of three sequential test groups. The outcome of a pre-planned early assessment (interim) of the current test group will determine whether to continue recruitment or switch to the next test group. Pain sensitivity will be assessed using quantitative sensory testing (QST) before and after topical application of 10% cinnamaldehyde (a TRPA1 agonist).

Discussion: The design of this adaptive RbG study offers efficiency in the assessment of associations between genetic variation at TRPA1 and detailed pain phenotypes. The possibility to change the test group in response to preliminary data increases the likelihood to observe smaller effect sizes relative to a conventional multi-armed design, as well as reducing futile testing of participants where an effect is unlikely to be observed. This specific adaptive RbG design aims to uncover the influence of common TRPA1 variants on pain sensation but can be applied to any hypothesis-led genotype study where costly and time intensive investigation is required and / or where there is large uncertainty around the expected effect size.

Trial registration: ISRCTN, ISRCTN16294731. Retrospectively registered 25th November 2021.

Keywords: ALSPAC; Adaptive design; Pain; Quantitative sensory testing; Recall by genotype; TRPA1.

Fig. 1

Schematic of Quantitative Sensory Testing (QST) protocol. Top row represents the baseline QST including thermal and mechanical stimulation. The middle row shows capture of baseline cutaneous perfusion using the FLPI in the 4 × 4 cm region of interest, application of 10% cinnamaldehyde and then capture of the post challenge cutaneous perfusion. The bottom row represents the post challenge QST. (Figure adapted from Rolke et al. [7]). CDT, cold detection threshold; WDT, warm detection threshold; CPT, cold pain threshold; HPT; heat pain threshold; MDT, mechanical detection threshold; MPT, mechanical pain threshold; MPS; mechanical pain sensitivity; Brush, presence or absence of brush allodynia; Pressure, deep pressure pain threshold; FLPI, full field laser perfusion imaging; Cinn, cinnamaldehyde

Fig. 2

Schematic of study design showing the 4 potential outcomes of the adaptive design. The trial progresses from left to right until the full sample of 100 is recruited. Each allele cohort is noted with uniquely coloured human icon with the numbers recruited in that phase noted underneath. Interim assesments are marked with a magnifying glass with the effect size ‘d’ criteria to continue the trial noted and alpha thresholds for subsequent t-test. Note that the trial will adapt due to small effect sizes and also if the hypothesis test passess due efficacy. The final “?” represents that this final cohort could be from any of the final cohorts based on assesment of other outcomes. For further details on the outcome criteria see Table 2

Fig. 3

Simulations of the interim analysis. A The number of correct interim decisions—where an interim was stopped where there was no significant finding or continued for effect sizes of 0 and 0.6. Error bars indicate 95% confidence intervals generated by bootstrapping the simulation results sampling 100 results 1000 times. B The change in mean % correct interim decisions as more subjects are added to interim. The interim number 15 was chosen as the relative benefit of adding more decreases above this point. C The effect of number of subjects on overall interim pass rate. This figure displays the % of interims that would go on to recruit a full study from populations of fixed effect sizes