Chronic pain and major depression are both debilitating conditions, and the two are often comorbid (i.e., occur together). The biological mechanisms of both conditions are poorly understood, although in both cases there is a clear evidence of a genetic contribution. While the genetic architecture of major depression is better understood than that of chronic pain, both are likely to involve a very large number of genetic variants each exerting a very small effect.
It is possible that the relationship between chronic pain and major depression is due to shared genetic influences. McIntosh and colleagues explore this question, using data from two large cohort studies to examine the genetic and environmental contributions to chronic pain and its relationship with major depression. This included both the estimation of relative genetic and environmental influences in family data, and the use of polygenic risk scores.
GS:SFHS is a family-structured, population-based cohort study, recruited at random through general medical practices across Scotland. Participants completed a questionnaire assessment of chronic pain, and a screening questionnaire for major depression.
UKB is a large cohort study that recruited approximately 500,000 people from across the UK between 2006 and 2010. Of these, genetic data are currently available on 112,151 participants. Chronic pain and major depression were again assessed via brief self-report measures.
Since GS:SFHS has a family-based design (i.e., with data from extended family structures) McIntosh and colleagues could use it to estimate the contributions of genetic factors and of shared and unique environmental factors to chronic pain, and its correlation with major depression.
Both GS:SFHS and UKB were then used to test the genetic relationship between chronic pain and major depression. This was done by deriving polygenic risk scores for both chronic pain and major depression from independent genome-wide association studies that did not include either GS:SFHS or UKB.
Polygenic risk scores sum the common genetic risk variants an individual carries for a given trait, weighted by their effect. This approach was used to examine whether a polygenic risk score for chronic pain predicted major depression, and whether a polygenic risk score for major depression predicted chronic pain.
The heritability of chronic pain was substantial, accounting for about 38% of variance (95% CI, 34% to 44%), while shared environment with a spouse accounted for about 19% (95% CI, 10% to 25%).
In GS:SFHS chronic pain and major depression were correlated, and there was evidence of both shared genetic architecture (r = 0.51, SE = 0.054) and of a spouse/partner environment effect (r = 0.53, SE = 0.24).
The polygenic risk scores created for both chronic pain and major depression were associated with both phenotypes in GS:SFHS and UKB, confirming the validity of these scores.
Polygenic risk of chronic pain was not associated with major depression in GS:SFHS. However, polygenic risk of major depression was associated with chronic pain in GS:SFHS and UKB.
This study confirms that genetic factors contribute to chronic pain, and also highlight a role for shared spouse / partner environment. It also suggests that the observed correlation between chronic pain and major depression may be due in part to shared genetic factors as well as an effect of the spouse / partner. The results also suggest that polygenic risk for major depression is associated with chronic pain, but that the reverse is not the case.
The findings with respect to the role of the spouse / partner in the relationship between chronic pain and major depression are intriguing – they imply that the presence of chronic pain in one spouse / partner increases the probability of major depression in the other spouse / partner. This could be due to a number of factors, such as the stress of caring for someone with chronic pain, or the impact of an environmental event that affects both spouses / partners.
The most intriguing aspect of this analysis is perhaps the hint at a directional relationship between major depression and chronic pain, which could be interpreted as the former causing the latter. The authors are cautious with respect to this interpretation, but their analysis is essentially a Mendelian randomization analysis that uses genetic variants to understand causal pathways between exposures (e.g., major depression) and outcomes (e.g., chronic pain) in observational data.
There is one important limitation however, which is that the response rates for the two studies were low (around 12% for GS:SFHS and 5% for UKB). This selection bias could lead to spurious correlations between two variables if each independently influences selection into the study. Since this is plausible here (people with major depression and with chronic pain may be less likely to take part), this kind of bias (known as collider bias) may be a problem.
This is an interesting study using multiple genetically-informed methods: family-based methods to estimate the relative contribution of genetic and environmental factors, and molecular methods using polygenic risk scores for chronic pain and major depression. The large sample sizes and replication across independent cohorts are both important strengths.
The finding that the presence of chronic pain in one spouse / partner increases the probability of major depression in the other spouse / partner is intriguing and plausible, as is the possibility that major depression may causally influence the likelihood of chronic pain. The fact that the reverse association was not seen is also interesting, suggesting chronic pain may not influence risk of major depression.
The real challenge lies in interpreting these results, and the authors are appropriately cautious. For example, as the authors note, any effect of major depression on chronic pain may not be due to a direct effect on pain pathophysiology, but instead on the threshold at which pain is perceived or reported. Disentangling these possibilities could identify appropriate (i.e., causal) targets for intervention.
However, the possibility that selection into these studies might be biasing the results is an important consideration. It is impossible to be certain whether this is the case, and it’s not clear whether any biases of this kind can be corrected for. This is itself the topic of current methodological research, given the wealth of large-scale data that exist, that typically come from selected (i.e., unrepresentative) samples.
McIntosh AM, Hall LS, Zeng Y, Adams MJ, Gibson J, Wigmore E, Hagenaars SP, Davies G, Fernandez-Pujals AM, Campbell AI, Clarke TK, Hayward C, Haley CS, Porteous DJ, Deary IJ, Smith DJ, Nicholl BI, Hinds DA, Jones AV, Scollen S, Meng W, Smith BH, Hocking LJ. (2016) Genetic and Environmental Risk for Chronic Pain and the Contribution of Risk Variants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis. PLoS Med. 201613; (8):e1002090. doi: 10.1371/journal.pmed.1002090