A study in scarlet: MC1R as the main predictor of red hair and exemplar of the flip-flop effect.

Genetic variation in melanocortin-1 receptor (MC1R) is a known contributor to disease-free red hair in humans. Three loss-of-function single-nucleotide variants (rs1805007, rs1805008 and rs1805009) have been established as strongly correlated with red hair. The contribution of other loss-of-function MC1R variants (in particular rs1805005, rs2228479 and rs885479) and the extent to which other genetic loci are involved in red hair colour is less well understood. Here, we used the UK Biobank cohort to capture a comprehensive list of MC1R variants contributing to red hair colour. We report a correlation with red hair for both strong-effect variants (rs1805007, rs1805008 and rs1805009) and weak-effect variants (rs1805005, rs2228479 and rs885479) and show that their coefficients differ by two orders of magnitude. On the haplotype level, both strong- and weak-effect variants contribute to the red hair phenotype, but when considered individually, weak-effect variants show a reverse, negative association with red hair. The reversal of association direction in the single-variant analysis is facilitated by a distinguishing structure of MC1R, in which loss-of-function variants are never found to co-occur on the same haplotype. The other previously reported hair colour genes' variants do not substantially improve the MC1R red hair colour predictive model. Our best model for predicting red versus other hair colours yields an unparalleled area under the receiver operating characteristic of 0.96 using only MC1R variants. In summary, we present a comprehensive statistically derived characterization of the role of MC1R variants in red hair colour and offer a powerful, economical and parsimonious model that achieves unsurpassed performance.

Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling.

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SU), substance use disorder (SUD), and other (non-SU/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD. We created single-factor measurement models-each describing SUB, SUD, or BD traits--based on mutually exclusive sets of European ancestry genome-wide association study (GWAS) statistics exploring externalizing variables. We then applied trivariate Cholesky decomposition to these factors in order to identify BD-specific genomic variation and assess the partitioning of BD's genetic covariance with each of the other facets. Even when the residuals for indicators relating to the same substance were correlated across the SUB and SUD factors, the two factors yielded a large zero-order correlation (rg=.803). BD correlated strongly with the SUD (rg=.774) and SUB factors (rg=.778). In our initial decompositions, 33% of total BD variance remained after removing variance associated with SUD and SUB. The majority of covariance between BD and SU and between BD and SUD was shared across all factors. When only nicotine/tobacco, cannabis, and alcohol were included for the SUB/SUD factors, their zero-order correlation increased to rg=.861; in corresponding decompositions, BD-specific variance decreased to 27%. In summary, BD, SU, and SUD were highly genetically correlated at the latent factor level, and a significant minority of genomic BD variation was not shared with SU and/or SUD. Further research can better elucidate the properties of BD-specific variation by exploring its genetic/molecular correlates.

Characterization of Common Genetic Variants in P2RX7 and Their Contribution to Chronic Pain Conditions.

The adenosine triphosphate (ATP)-gated channel P2X7 is encoded by a gene enriched for common nonsynonymous variants. Many of these variants have functional cellular effects, and some have been implicated in chronic pain. In this study, we first systematically characterized all 17 common nonsynonymous variants using whole-cell patch clamp electrophysiology. Then, we analyzed these variants for statistical association with chronic pain phenotypes using both individual P2RX7 variants as predictors and cumulative allele counts of same-direction cellular effect in univariate models. Association and validation analyses were conducted in the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) cohort (N = 3260) and in the Complex Persistent Pain Conditions (CPPC) cohort (N = 900), respectively. Our results showed an association between allele A of rs7958311 and an increased risk of chronic pelvic pain, with convergent evidence for contribution to fibromyalgia and irritable bowel syndrome, confirmed in a meta-analysis. This allelic variant produced a unique cellular phenotype: a gain-of-function in channel opening, and a loss-of-function in pore opening. A computational study using a 12-state Markov model of ATP binding to the P2X7 receptor suggested that this cellular phenotype arises from an increased ATP binding affinity and an increased open channel conductance combined with a loss of sensitization. Cumulative allele count analysis did not provide additional insights. In conclusion, our results go beyond reproducing association for rs7958311 with chronic pain and suggest that its unique combination of gain-of-function in channel and loss-of-function in pore activity may explain why it is likely the only common P2RX7 variant with contribution to chronic pain. PERSPECTIVE: This study characterizes all common P2RX7 variants using cellular assays and statistical association analyses with chronic pain, with Markov state modeling of the most robustly associated variant.

Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling.

ABSTRACT: Chronic pain conditions frequently co-occur, suggesting common risks and paths to prevention and treatment. Previous studies have reported genetic correlations among specific groups of pain conditions and reported genetic risk for within-individual multisite pain counts (≤7). Here, we identified genetic risk for multiple distinct pain disorders across individuals using 24 chronic pain conditions and genomic structural equation modeling (Genomic SEM). First, we ran individual genome-wide association studies (GWASs) on all 24 conditions in the UK Biobank ( N ≤ 436,000) and estimated their pairwise genetic correlations. Then we used these correlations to model their genetic factor structure in Genomic SEM, using both hypothesis- and data-driven exploratory approaches. A complementary network analysis enabled us to visualize these genetic relationships in an unstructured manner. Genomic SEM analysis revealed a general factor explaining most of the shared genetic variance across all pain conditions and a second, more specific factor explaining genetic covariance across musculoskeletal pain conditions. Network analysis revealed a large cluster of conditions and identified arthropathic, back, and neck pain as potential hubs for cross-condition chronic pain. Additionally, we ran GWASs on both factors extracted in Genomic SEM and annotated them functionally. Annotation identified pathways associated with organogenesis, metabolism, transcription, and DNA repair, with overrepresentation of strongly associated genes exclusively in brain tissues. Cross-reference with previous GWASs showed genetic overlap with cognition, mood, and brain structure. These results identify common genetic risks and suggest neurobiological and psychosocial mechanisms that should be targeted to prevent and treat cross-condition chronic pain.

Detangling red hair from pain: phenotype-specific contributions from different genetic variants in melanocortin-1 receptor.

Genetic variation in melanocortin-1 receptor (MC1R) has a known role in red hair. Studies on responses to noxious stimuli in red-haired individuals have also been conducted, with mixed findings. To investigate a possible divergence between variants responsible for red hair and pain sensitivity, we performed a genewide association analysis in the Orofacial Pain: Prospective Evaluation and Risk Assessment cohort. All genotyped (17) MC1R variants were tested for association with heat pain temporal summation and sensitivity. Our analyses showed an association for pain sensitivity with the 5'-UTR, tagged by rs3212361, and 1 missense variant, rs885479 (R163Q), previously shown to be weakly associated with red hair. For both variants, the minor allele was protective. These results were validated in the 500,000-person UK Biobank cohort, where the minor alleles of rs3212361 and rs885479 were associated with a reduced count of persistent pain conditions as well as individual pain conditions. Haplotype association analysis revealed a possible joint effect from the 2 individual variants. The 5'-UTR variant rs3212361 was further identified as an expression quantitative trait locus, associated with reduced transcript levels of MC1R in the brain and in the peripheral tibial nerve. Hair colour association analysis of the loss-of-function 5'-UTR rs3212361 allele identified association with red hair, and red hair colour itself was associated with a reduced count of persistent pain conditions. Together, our results suggest that primarily different mechanisms-affecting expression levels vs protein activity-mediated by different genetic variants in the MC1R locus contribute to red hair and pain.

Genetic studies of human neuropathic pain conditions: a review.

Numerous studies have shown associations between genetic variants and neuropathic pain disorders. Rare monogenic disorders are caused by mutations of substantial effect size in a single gene, whereas common disorders are likely to have a contribution from multiple genetic variants of mild effect size, representing different biological pathways. In this review, we survey the reported genetic contributors to neuropathic pain and submit them for validation in a 150,000-participant sample of the U.K. Biobank cohort. Successfully replicated association with a neuropathic pain construct for 2 variants in IL10 underscores the importance of neuroimmune interactions, whereas genome-wide significant association with low back pain (P = 1.3e-8) and false discovery rate 5% significant associations with hip, knee, and neck pain for variant rs7734804 upstream of the MAT2B gene provide evidence of shared contributing mechanisms to overlapping pain conditions at the molecular genetic level.

Human pain genetics database: a resource dedicated to human pain genetics research.

The Human Pain Genetics Database (HPGDB) is a comprehensive variant-focused inventory of genetic contributors to human pain. After curation, the HPGDB currently includes 294 studies reporting associations between 434 distinct genetic variants and various pain phenotypes. Variants were then submitted to a comprehensive analysis. First, they were validated in an independent high-powered replication cohort by testing the association of each variant with 10 different pain phenotypes (n = 1320-26,973). One hundred fifty-five variants replicated successfully (false discovery rate 20%) in at least one pain phenotype, and the association P values of the HPGDB variants were significantly lower compared with those of random controls. Among the 155 replicated variants, 21 had been included in the HPGDB because of their association with analgesia-related and 13 with nociception-related phenotypes, confirming analgesia and nociception as pathways of vulnerability for pain phenotypes. Furthermore, many genetic variants were associated with multiple pain phenotypes, and the strength of their association correlated between many pairs of phenotypes. These genetic variants explained a considerable amount of the variance between different pairs of pain phenotypes, indicating a shared genetic basis among pain phenotypes. In addition, we found that HPGDB variants show many pleiotropic associations, indicating that genetic pathophysiological mechanisms are also shared among painful and nonpainful conditions. Finally, we demonstrated that the HPGDB data set is significantly enriched for functional variants that modify gene expression, are deleterious, and colocalize with open chromatin regions. As such, the HPGDB provides a validated data set that represents a valuable resource for researchers in the human pain field.

Exploitation of Conformational Dynamics in Imparting Selective Inhibition for Related Matrix Metalloproteinases.

Matrix metalloproteinases (MMPs) have numerous physiological functions and share a highly similar catalytic domain. Differential dynamical information on the closely related human MMP-8, -13, and -14 was integrated onto the benzoxazinone molecular template. An in silico library of 28,099 benzoxazinones was generated and evaluated in the context of the molecular-dynamics information. This led to experimental evaluation of 19 synthesized compounds and identification of selective inhibitors, which have potential utility in delineating the physiological functions of MMPs. Moreover, the approach serves as an example of how dynamics of closely related active sites may be exploited to achieve selective inhibition by small molecules and should find applications in other enzyme families with similar active sites.

Genetic predictors of human chronic pain conditions.

Chronic pain conditions are multifactorial disorders with a high frequency in the population. Their pathophysiology is often unclear, and treatment is inefficient. During the last 20years, genetic linkage analysis and association studies have made considerable strides toward identifying key molecular contributors to the onset and maintenance of chronic pain. Here, we review the genetic variants that have been implicated in chronic pain conditions, divided into the following etiologically-grouped categories: migraine, musculoskeletal pain disorders, neuropathic pain disorders, and visceral pain disorders. In rare familial monogenic pain conditions several strong-effect mutations have been identified. In contrast, the genetic landscape of common chronic pain conditions suggests minor contributions from a large number of single nucleotide polymorphisms representing different functional pathways. A comprehensive survey of up-to-date genetic association results reveals migraine and musculoskeletal pain to be the most investigated chronic pain disorders, in which nearly half of identified genetic variability alters neurotransmission pathways.