Genome-Wide Association Analysis Identified ANXA1 Associated with Shoulder Impingement Syndrome in UK Biobank Samples

Are commercial genetic injury tests premature?

INTRODUCTION

Several direct-to-consumer (DTC) genetic testing companies have emerged that claim to be able to test for susceptibility for musculoskeletal injuries. Although there are several publications on the emergence of this industry, none have critically evaluated the evidence for the use of genetic polymorphisms in commercial tests. The aim of this review was to identify, where possible, the polymorphisms and to evaluate the current scientific evidence for their inclusion.

RESULTS

The most common polymorphisms included COL1A1 rs1800012, COL5A1 rs12722, and GDF5 rs143383. The current evidence suggests that it is premature or even not viable to include these three polymorphisms as markers of injury risk. A unique set of injury-specific polymorphisms, which do not include COL1A1, COL5A1, or GDF5, identified from genome-wide association studies (GWAS) is used by one company in their tests for 13 sports injuries. However, of the 39 reviewed polymorphisms, 22 effective alleles are rare and absent in African, American, and/or Asian populations. Even when informative in all populations, the sensitivity of many of the genetic markers was low and/or has not been independently validated in follow-up studies.

CONCLUSIONS

The current evidence suggests it is premature to include any of the reviewed polymorphisms identified by GWAS or candidate gene approaches in commercial genetic tests. The association of MMP7 rs1937810 with Achilles tendon injuries, and SAP30BP rs820218 and GLCCI1 rs4725069 with rotator cuff injuries does warrant further investigation. Based on current evidence, it remains premature to market any commercial genetic test to determine susceptibility to musculoskeletal injuries.

Assessing the joint effects of mitochondrial function and human behavior on the risks of anxiety and depression

BACKGROUND

Psychiatric disorders have great health hazards and the exact pathogeny remains elusive now. We aim to explore the potential interaction effects of mitochondrial function and human behavior on the risks of anxiety and depression.

METHODS

The genome-wide association study (GWAS) data of mitochondrial function (N = 383,476-982,072) were obtained from published studies. Individual level genotype and phenotype data of anxiety, depression and behavioral factors (including drinking, smoking and physical activity) were all from the UK Biobank (N = 84,805-85,164). We first calculated the polygenic risk scores (PRS) of mitochondrial function as the instrumental variables, and then constructed linear regression analyses to systematically explore the potential interaction effects of mitochondrial function and human behavior on anxiety and depression.

RESULTS

In total samples, we observed mitochondrial heteroplasmy (MtHz) vs. Drinking (P_GAD-7 = 6.49 × 10^-3; P_PHQ-9 = 1.89 × 10^-3) was positively associated with both anxiety and depression. In males, MtHz vs. Drinking (P_Male = 3.46 × 10^-5) was positively correlated with depression. In females, blood mitochondrial DNA copy number (mtDNA-CN) vs. Drinking (P_Female = 8.63 × 10^-3) was negatively related to anxiety. Furthermore, we identified additional 6 suggestive interaction effects (P < 0.05) for anxiety and depression.

LIMITATIONS

Considering all subjects were from UK Biobank, it should be careful to extrapolate our findings to other populations with different genetic background.

CONCLUSIONS

Our results suggest the significant impacts of mitochondrial function and human behavior interactions on the development of anxiety and depression, providing new clues for clarifying the pathogenesis of anxiety and depression.

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

BACKGROUND

Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study's purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE.

METHODS

A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package.

RESULTS

Compared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA.

CONCLUSIONS

We established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.

A genome-wide association study for shoulder impingement and rotator cuff disease

BACKGROUND

The purpose of the study was to identify genetic variants associated with rotator cuff disease by performing a genome-wide association study (GWAS) for shoulder impingement using the UK Biobank (UKB) cohort and then combining the GWAS data with a prior GWAS for rotator cuff tears. The loci identified by the GWAS and meta-analysis were examined for changes in expression following rotator cuff tearing using RNA sequencing.

METHODS

A GWAS was performed using data from UKB with 3864 cases of shoulder impingement. The summary statistics from shoulder impingement and a prior study on rotator cuff tears were combined in a meta-analysis. Also, the previous association of 2 single-nucleotide polymorphisms (SNPs) with shoulder impingement from a published GWAS using the UKB was tested. Rotator cuff tendon biopsies were obtained from 24 patients with full-thickness rotator cuff tears who underwent arthroscopic rotator cuff repair (cases) and 9 patients who underwent open reduction internal fixation for a proximal humeral fracture (controls). Total RNA was extracted and differential gene expression was measured by RNA sequencing for genes with variants associated with rotator cuff tearing.

RESULTS

The shoulder impingement GWAS identified 4 new loci: LOC100506457, LSP1P3, LOC100506207, and MIS18BP1/LINC00871. Combining data with a prior GWAS for rotator cuff tears in a meta-analysis resulted in the identification of an additional 7 loci: SLC39A8/UBE2D3, C5orf63, ASTN2, STK24, FRMPD4, ACOT9/SAT1, and LINC00890/ALG13. Many of the identified loci have known biologic functions or prior associations with diseases, suggesting possible biologic pathways leading to rotator cuff disease. RNA sequencing experiments show that expression of STK24 increases whereas expression of SAT1 and UBE2D3 decreases following rotator cuff tearing. Two SNPs previously reported to show an association with shoulder impingement from a prior UKB GWAS were not validated in our study.

CONCLUSION

This is the first GWAS for shoulder impingement in which new data from UKB enabled the identification of 4 loci showing a genetic association. A meta-analysis with a prior GWAS for rotator cuff tearing identified an additional 7 loci. The known biologic roles of many of the 11 loci suggest plausible biologic mechanisms underlying the etiology of rotator cuff disease. The risk alleles from each of the genetic loci can be used to assess the risk for rotator cuff disease in individual patients, enabling preventative or restorative actions via personalized medicine.