A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test (metaUSAT) software for genetically correlated phenotypes (N = 397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and four loci were newly suggested. The LDL receptor related protein 1 (LRP1)-Signal Transducer and Activator of Transcription 6 (STAT6)-S hort chain D ehydrogenase/R eductase family 9C member 7 (SDR9C7) region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24 × 10-62 of rs11172113. The One Cut homeobox 2 (ONECUT2) gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29 × 10-9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches. This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00078-7.

Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts

BACKGROUND

To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors.

METHODS

The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy.

RESULTS

Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN.

CONCLUSIONS

Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model's performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.

A genome-wide association study finds genetic variants associated with neck or shoulder pain in UK Biobank

Background

Common types of musculoskeletal conditions include pain in the neck and shoulder areas. This study seeks to identify the genetic variants associated with neck or shoulder pain based on a genome-wide association approach using 203 309 subjects from the UK Biobank cohort and look for replication evidence from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and TwinsUK.

Methods

A genome-wide association study was performed adjusting for age, sex, BMI and nine population principal components. Significant and independent genetic variants were then sent to GS:SFHS and TwinsUK for replication.

Results

We identified three genetic loci that were associated with neck or shoulder pain in the UK Biobank samples. The most significant locus was in an intergenic region in chromosome 17, rs12453010, having P = 1.66 × 10⁻¹¹. The second most significant locus was located in the FOXP2 gene in chromosome 7 with P = 2.38 × 10⁻¹⁰ for rs34291892. The third locus was located in the LINC01572 gene in chromosome 16 with P = 4.50 × 10⁻⁸ for rs62053992. In the replication stage, among four significant and independent genetic variants, rs2049604 in the FOXP2 gene and rs62053992 in the LINC01572 gene were weakly replicated in GS:SFHS (P = 0.0240 and P = 0.0202, respectively).

Conclusions

We have identified three loci associated with neck or shoulder pain in the UK Biobank cohort, two of which were weakly supported in a replication cohort. Further evidence is needed to confirm their roles in neck or shoulder pain.

Trends in gabapentinoid prescribing, co-prescribing of opioids and benzodiazepines, and associated deaths in Scotland

BACKGROUND

Gabapentinoid drugs (gabapentin and pregabalin) are effective in neuropathic pain, which has a prevalence of ∼7%. Concerns about increased prescribing have implications for patient safety, misuse, and diversion. Drug-related deaths (DRDs) have increased and toxicology often implicates gabapentinoids. We studied national and regional prescribing rates (2006-2016) and identified associated sociodemographic factors, co-prescriptions and mortality, including DRDs.

METHODS

National data from the Information Service Division, NHS Scotland were analysed for prescribing, sociodemographic, and mortality data from the Health Informatics Centre, University of Dundee. DRDs in which gabapentinoids were implicated were identified from National Records of Scotland and Tayside Drug Death Databases.

RESULTS

From 2006 to 2016, the number of gabapentin prescriptions in Scotland increased 4-fold (164 630 to 694 293), and pregabalin 16-fold (27 094 to 435 490). In 2016 'recurrent users' (three or more prescriptions) had mean age 58.1 yr, were mostly females (62.5%), and were more likely to live in deprived areas. Of these, 60% were co-prescribed an opioid, benzodiazepine, or both (opioid 49.9%, benzodiazepine 26.8%, both 17.1%). The age-standardised death rate in those prescribed gabapentinoids was double that in the Scottish population (relative risk 2.16, 95% confidence interval 2.08-2.25). Increases in gabapentinoids contributing to cause of DRDs were reported regionally and nationally (gabapentin 23% vs 15%; pregabalin 21% vs 7%). In Tayside, gabapentinoids were implicated in 22 (39%) of DRDs, 17 (77%) of whom had not received a prescription.

CONCLUSIONS

Gabapentinoid prescribing has increased dramatically since 2006, as have dangerous co-prescribing and death (including DRDs). Older people, women, and those living in deprived areas were particularly likely to receive prescriptions. Their contribution to DRDs may be more related to illegal use with diversion of prescribed medication.

DOLORisk: study protocol for a multi-centre observational study to understand the risk factors and determinants of neuropathic pain

Background: Neuropathic pain is an increasingly prevalent condition and has a major impact on health and quality of life. However, the risk factors for the development and maintenance of neuropathic pain are poorly understood. Clinical, genetic and psychosocial factors all contribute to chronic pain, but their interactions have not been studied in large cohorts. The DOLORisk study aims to study these factors. Protocol: Multicentre cross-sectional and longitudinal cohorts covering the main causes leading to neuropathic pain (e.g. diabetes, surgery, chemotherapy, traumatic injury), as well as rare conditions, follow a common protocol for phenotyping of the participants. This core protocol correlates answers given by the participants on a set of questionnaires with the results of their genetic analyses. A smaller number of participants undergo deeper phenotyping procedures, including neurological examination, nerve conduction studies, threshold tracking, quantitative sensory testing, conditioned pain modulation and electroencephalography. Ethics and dissemination: All studies have been approved by their regional ethics committees as required by national law. Results are disseminated through the DOLORisk website, scientific meetings, open-access publications, and in partnership with patient organisations. Strengths and limitations: Large cohorts covering many possible triggers for neuropathic painMulti-disciplinary approach to study the interaction of clinical, psychosocial and genetic risk factorsHigh comparability of the data across centres thanks to harmonised protocolsOne limitation is that the length of the questionnaires might reduce the response rate and quality of responses of participants.

DOLORisk: study protocol for a multi-centre observational study to understand the risk factors and determinants of neuropathic pain

Background: Neuropathic pain is an increasingly prevalent condition and has a major impact on health and quality of life. However, the risk factors for the development and maintenance of neuropathic pain are poorly understood. Clinical, genetic and psychosocial factors all contribute to chronic pain, but their interactions have not been studied in large cohorts. The DOLORisk study aims to study these factors.

Protocol: Multicentre cross-sectional and longitudinal cohorts covering the main causes leading to neuropathic pain (e.g. diabetes, surgery, chemotherapy, traumatic injury), as well as rare conditions, follow a common protocol for phenotyping of the participants. This core protocol correlates answers given by the participants on a set of questionnaires with the results of their genetic analyses. A smaller number of participants undergo deeper phenotyping procedures, including neurological examination, nerve conduction studies, threshold tracking, quantitative sensory testing, conditioned pain modulation and electroencephalography.

Ethics and dissemination: All studies have been approved by their regional ethics committees as required by national law. Results are disseminated through the DOLORisk website, scientific meetings, open-access publications, and in partnership with patient organisations.

Strengths and limitations:

Large cohorts covering many possible triggers for neuropathic pain

Multi-disciplinary approach to study the interaction of clinical, psychosocial and genetic risk factors

High comparability of the data across centres thanks to harmonised protocols

One limitation is that the length of the questionnaires might reduce the response rate and quality of responses of participants

A genome-wide association study suggests new evidence for an association of the NADPH Oxidase 4 (NOX4) gene with severe diabetic retinopathy in type 2 diabetes

Purpose

Diabetic retinopathy is the most common eye complication in patients with diabetes. The purpose of this study is to identify genetic factors contributing to severe diabetic retinopathy.

Methods

A genome‐wide association approach was applied. In the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) datasets, cases of severe diabetic retinopathy were defined as type 2 diabetic patients who were ever graded as having severe background retinopathy (Level R3) or proliferative retinopathy (Level R4) in at least one eye according to the Scottish Diabetic Retinopathy Grading Scheme or who were once treated by laser photocoagulation. Controls were diabetic individuals whose longitudinal retinopathy screening records were either normal (Level R0) or only with mild background retinopathy (Level R1) in both eyes. Significant Single Nucleotide Polymorphisms (SNPs) were taken forward for meta‐analysis using multiple Caucasian cohorts.

Results

Five hundred and sixty cases of type 2 diabetes with severe diabetic retinopathy and 4,106 controls were identified in the GoDARTS cohort. We revealed that rs3913535 in the NADPH Oxidase 4 (NOX4) gene reached a p value of 4.05 × 10⁻⁹. Two nearby SNPs, rs10765219 and rs11018670 also showed promising p values (p values = 7.41 × 10⁻⁸ and 1.23 × 10⁻⁸, respectively). In the meta‐analysis using multiple Caucasian cohorts (excluding GoDARTS), rs10765219 and rs11018670 showed associations for diabetic retinopathy (p = 0.003 and 0.007, respectively), while the p value of rs3913535 was not significant (p = 0.429).

Conclusion

This genome‐wide association study of severe diabetic retinopathy suggests new evidence for the involvement of the NOX4 gene.

A Genome-Wide Association Study Finds Genetic Associations with Broadly-Defined Headache in UK Biobank (N=223,773)

BACKGROUND

Headache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort.

METHODS

We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls.

RESULTS

We identified 3343 SNPs which reached the genome-wide significance level of P<5×10-8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92×10-47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87×10-15 in the LINC02210-CRHR1 gene was the top SNP. Significant relationships between multiple brain tissues and genetic associations were identified through tissue expression analysis. We also identified significant positive genetic correlations between headache and many psychological traits.

CONCLUSIONS

Our results suggest that brain function is closely related to broadly-defined headache. In addition, we found that many psychological traits have genetic correlations with headache.

Dense genotyping of immune-related susceptibility loci reveals new insights into the genetics of psoriatic arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis and, despite the larger estimated heritability for PsA, the majority of genetic susceptibility loci identified to date are shared with psoriasis. Here, we present results from a case-control association study on 1,962 PsA patients and 8,923 controls using the Immunochip genotyping array. We identify eight loci passing genome-wide significance, secondary independent effects at three loci and a distinct PsA-specific variant at the IL23R locus. We report two novel loci and evidence of a novel PsA-specific association at chromosome 5q31. Imputation of classical HLA alleles, amino acids and SNPs across the MHC region highlights three independent associations to class I genes. Finally, we find an enrichment of associated variants to markers of open chromatin in CD8(+) memory primary T cells. This study identifies key insights into the genetics of PsA that could begin to explain fundamental differences between psoriasis and PsA.