Genome-wide association meta-analysis identifies novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation

Abstract

Primary open-angle glaucoma (POAG) is a complex eye disease characterized by progressive loss of optic nerve function that, if untreated, ultimately leads to irreversible blindness. To date, the biological mechanisms causing POAG are still unclear. There is disparity in POAG prevalence, clinical presentations and outcomes across ancestries. Here, we aim to identify unique genetics that underlie risk to POAG and evaluate the potential connection with vascular mechanisms. We performed POAG meta-analysis across 15 biobanks that are part of the Global Biobank Meta-analysis Initiative, with two previously published multi-ancestry analyses for a total of 1,478,037 individuals from six ancestries (46,325 cases and 1,431,712 controls). A total of 109 genome-wide significantly associated loci (p<5e-8) were identified, 18 of which were novel. Three of these novel loci are ancestry-specific, two African-specific and the third specific to northern Europeans. We also identified five sex-specific novel loci, four of which are African-specific and one European-specific. To explore biological implications underlying these variant-trait associations, we performed gene enrichment analysis, gene prioritization analysis and transcriptome-wide association studies (TWAS) implicating genes related to vascular-related functions, blood vessels, angiogenesis, and cancer. A fifth of TWAS-prioritized genes with vascular-related and/or cell senescence/proliferation functional roles or have been implicated in vascular or neoplastic diseases are primary ciliary related genes. We further performed extensive statistical validation analysis of genes in the SIX6 and well-known CDKN2B-AS1 loci, previously implicated in POAG, cardiovascular diseases, and cancers across multiple ancestries. We found evidence of significant interaction between SIX6 rs33912345 and causal variants in chr9p21.3, with concomitant effect on expression of a primary cilia gene CDKN2A and CDKN2B at the CDKN2B-AS1 locus. Phenome-wide association analysis of POAG genetic risk burden across five biobanks and genetic correlation analysis also show the shared biology between POAG and vascular and neoplastic traits. In summary, our findings suggest that some POAG risk variants may be ancestry-specific, sex-specific, or both. Our findings further support the contribution of vascular and proliferation genes in POAG and suggest potential involvement of primary cilia in POAG pathogenesis.

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

“…Motivated by successful validation of SLALOM performance, we investigated whether fine-mapping confidence and resolution were improved in the GBMI meta-analyses over individual biobanks. To this end, we used our fine-mapping results 16,17 of nine disease endpoints (asthma 64 , COPD 64 , gout, heart failure 73 , IPF 62 , primary open angle glaucoma 74 , thyroid cancer, stroke 75 , and venous thromboembolism 76 ) in BBJ 58 , FinnGen 20 , and UKBB 19 Europeans that also contributed to the GBMI meta-analyses for the same traits.…”

“…To directly compare with fine-mapping results from the GBMI meta-analyses, we used our fine-mapping results of nine disease endpoints (asthma 64 , COPD 64 , gout, heart failure 73 , IPF 62 , primary open angle glaucoma 74 , thyroid cancer, stroke 75 , and venous thromboembolism 76 ) in BBJ 58 , FinnGen 20 , and UKBB 19 Europeans that were also part of the GBMI meta-analyses for the same traits. For comparison, we computed the maximum PIP for each variant and the minimum size of 95% CS across BBJ, FinnGen, and UKBB.…”

Global biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

“…This suggests that the PRS evaluation may be biased upwards from the prior GWAS for POAG. Also, the phenotypes of POAG across different biobanks are likely more heterogeneous in GBMI than targeted case-control studies 18,32 . The meta-analysis of GBMI with International Glaucoma Genetics Consortium (IGGC) did not lead to substantially improved prediction performance 32 .…”

“…Also, the phenotypes of POAG across different biobanks are likely more heterogeneous in GBMI than targeted case-control studies 18,32 . The meta-analysis of GBMI with International Glaucoma Genetics Consortium (IGGC) did not lead to substantially improved prediction performance 32 . Another concern might be the disproportional case/control ratio of POAG in GBMI, of ∼27,000 cases and ∼1.4M controls, thus POAG-related phenotypes with shared genetics in the controls or possible uncontrolled ancestry differences between cases and controls might confound the GBMI GWAS.…”

“…To boost statistical power, we can meta-analyze GBMI GWAS with other non-overlapping cohorts as shown in other GBMI working groups [33][34][35] . However, we should note that more heterogeneity might be introduced from different resources such as population structure and phenotype definitions, which we cannot control with summary statistics data and that could exacerbate the heterogeneous performance of PRS across target populations.…”