Next Article in Journal
Combination of EphA2- and Wee1-Targeted Therapies in Endometrial Cancer
Previous Article in Journal
In Vitro Photoinactivation of Fusarium oxysporum Conidia with Light-Activated Ammonium Phthalocyanines
Previous Article in Special Issue
Microbiome Dysbiosis: A Pathological Mechanism at the Intersection of Obesity and Glaucoma
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 4
10.3390/ijms24043925
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Phenotypic and Genetic Links between Body Fat Measurements and Primary Open-Angle Glaucoma

by
Shi Song Rong
1,* and
Xinting Yu
2
1
Department of Ophthalmology, Massachusetts Eye and Ear, Mass General Brigham, Harvard Medical School, Boston, MA 02114, USA
2
Department of Medicine, Brigham and Women’s Hospital, Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(4), 3925; https://doi.org/10.3390/ijms24043925
Submission received: 1 January 2023 / Revised: 9 February 2023 / Accepted: 11 February 2023 / Published: 15 February 2023
(This article belongs to the Special Issue Cross-Talk between Obesity and Eye Diseases: Pathogenesis and Preventive/Therapeutic Strategies)
Browse Figures
Versions Notes

Abstract

:
The phenotypic and genetic links between body fat phenotypes and primary open-angle glaucoma (POAG) are unclear. We conducted a meta-analysis of relevant longitudinal epidemiological studies to evaluate the phenotypic link. To identify genetic links, we performed genetic correlation analysis and pleiotropy analysis of genome-wide association study summary statistics datasets of POAG, intraocular pressure (IOP), vertical cup-to-disc ratio, obesity, body mass index (BMI), and waist-to-hip ratio. In the meta-analysis, we first established that obese and underweight populations have a significantly higher risk of POAG using longitudinal data. We also discovered positive genetic correlations between POAG and BMI and obesity phenotypes. Finally, we identified over 20 genomic loci jointly associated with POAG/IOP and BMI. Among them, the genes loci CADM2, RP3-335N17.2, RP11-793K1.1, RPS17P5, and CASC20 showed the lowest false discovery rate. These findings support the connection between body fat phenotypes and POAG. The newly identified genomic loci and genes render further functional investigation.

1. Introduction

Obesity is a condition characterized by excess body fat. Defined by a body mass index (BMI) of at least 30 kg/m2, it is one of the largest global health problems [1], is the most important risk factor for chronic disease in the United States [2], and is associated with more than 200 related complications [3], including primary open-angle glaucoma (POAG) [4,5] and increased intraocular pressure (IOP) [6]. POAG is an ocular neurodegenerative disorder and a leading cause of global irreversible blindness [7,8]. Under the direct influences of IOP, patients with POAG gradually develop retinal ganglion cell (RGC) injury, retinal nerve fiber layer thinning, characteristic optic disc cupping, and corresponding visual field defects [7,9].
The association between obesity and POAG is more sophisticated than a linear relationship. On one hand, epidemiological and clinical data support a positive association of POAG risk with obesity. Newman-Casey, Jung Y., and Chen W.D. reported from populations of European and Asian ancestries that obese patients had a 6% to 54% increase in POAG risk after multivariable adjustment [4,10,11]. Clinical evidence from two separate groups revealed a substantial decrease in IOP after successful weight management through bariatric surgery [12,13]. On the other hand, population-based studies also reported a negative or an insignificant association between obesity/body fat measurements and POAG. Na K.S. found 8% reduced risk of developing POAG in obese subjects [14], while Pasquale L.R. showed a negligible association between obesity and POAG [15]. Using BMI and waist-to-hip ratio (WHR), studies also generated a mixed picture of the POAG–obesity relationship. For the association between POAG and BMI/WHR, Jiang X. reported a positive association [16], while Pasquale L.R. and Ramdas reported a negative association [15,17]. In addition, some studies detected a significant increase in POAG risk in underweight subjects [4,14], which further complicated the picture. Therefore, efforts should be made to sift through the existing epidemiological evidence and determine the true relationship between POAG risk and body fat measurements.
Obesity is multifactorial and occurs due to complex interactions between genetics and the environment. The high heritability (h2) for different measures of obesity and body fat—BMI (h2 = 0.4–0.7) and WHR (h2~0.45)—underlines the strong effects of genetic factors in the phenotype [18]. Since 2006, significant associations of hundreds of single nucleotide polymorphisms (SNPs) with obesity, BMI, and WHR have been identified by genome-wide association studies (GWASs) [19,20]. For POAG, thus far, GWASs have mapped more than 100 gene loci that are significantly associated with POAG or IOP [21,22,23]. These discoveries have provided substantial insights into the genetic underpinnings of both disease phenotypes. These genomic data also offer a unique opportunity to the evaluation of the relationship between obesity/body fat measurements and POAG. For example, two studies have reported significant causal effects of obesity [24] and BMI [25] on POAG phenotype using two-sample Mendelian randomization methods. Therefore, further uncovering the specific gene or genomic loci that are directly responsible for the association between both phenotypes will improve the understanding of disease pathogenesis, classification and risk profiling while suggesting uncharacterized biological mechanisms.
In this study, we first confirmed the significantly increased risk of POAG in obese and underweighted populations using the existing longitudinal data and meta-analysis. We then found positive genetic correlations of POAG phenotypes with BMI and obesity phenotypes. Finally, we identified more than 20 genomic loci jointly associated with POAG and BMI. Based on epidemiological and genomic data, this study provided new evidence supporting the link between body fat phenotypes and POAG. Moreover, new target genomic loci and genes were highlighted for further functional investigations.

2. Results

2.1. Both Obesity and Underweight Increases POAG Risk in Longitudinal Epidemiology Studies

The literature search yielded 940 records. Among them, we identified 12 independent longitudinal cohorts in nine studies (Figure S1 and Table S2) [4,5,6,10,11,14,15,16,17]. Seven of the cohorts were of European ancestries [6,11,15,16,17], and five were recruited from Asian populations [4,5,10,14].
Association of obesity and incidence of POAG was tested in seven large cohorts (Table S2) [4,10,11,14,15]. Subsequent meta-analysis supported a significantly increased risk of developing POAG in the obese population (HR = 1.10, 95% CI 1.00–1.21, p = 0.03) (Figure 1A). Opposite to obesity, we further tested the associations of underweight (BMI < 18.5 kg/m2) with the development of POAG. In contrast to obesity, a meta-analysis of two independent cohorts from the Korean population [4,14] also linked underweight to a significantly increased risk of developing POAG (HR = 1.13, 95% CI 1.10–1.16, p < 0.001) (Figure 1B). These results partially explained the inconclusive correlations between BMI/WHR and POAG risk that have been reported in five large cohorts, where two were positive correlations (p < 0.03) [5,16], one negative correlation (p = 0.03) [17], and two insignificant findings [15]. Our meta-analysis of BMI’s effect on glaucoma risk, with BMI as a continuous variable, showed no significant relationship (p = 0.79). This refuted a simple linear link between BMI and POAG risk (Figure 1C).
All meta-analyzed studies had a Newcastle Ottawa Scale (NOS, accessed via http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp, accessed on 1 December 2022) score of six and above (Table S3), suggesting a low risk of biases in the pooled outcomes. In sensitivity analysis, the pooled results remained unchanged after removing the two studies that scored six in NOS [4,14] and the relative risk/risk ratio (RR) outcomes [15].
These results suggested that risk of developing POAG may have a U-shaped correlation with BMI, which corresponded to the raised POAG risk at the lower (underweight) and the upper (obesity) end of the BMI range.

2.2. Genome-Wide Association Studies

In subsequent genetic analysis, we included 10 GWAS summary statistics datasets from eight GWASs, representing the target phenotypes POAG (open-angle glaucoma [28], IOP [29], and vertical cup-to-disc ratio (VCDR) [30]) and body fat measurements (obesity [31,32], BMI [33,34], and WHR [35]). GWAS summary datasets for the phenotype underweight—the lower end of the BMI measurement—were not available. Therefore, we were not able to analyze this phenotype. Features of the included GWASs and corresponding datasets are summarized in Table S4.

2.3. Genetic Correlation

Linkage disequilibrium score regression was performed to evaluate the genetic correlation between POAG phenotypes and body fat measurements. We found suggestive positive genetic correlations of POAG, IOP, and VCDR with BMI and obesity, but not with BMI-adjusted WHR in either gender (p > 0.05) (Figure 2). The statistical significance was segregated between POAG and BMI/adult obesity (p values ranged from 0.03 to 0.002) (Figure 2). The average genetic correlation coefficient between adult obesity and POAG/IOP was higher than that between adult BMI and POAG/IOP (0.18 vs. 0.061; Pt-test = 0.0016). These results supported the existence of shared genetic factors between BMI/obesity and POAG phenotypes. Moreover, the lower correlation coefficient between adult BMI and POAG/IOP than that between POAG/IOP and obesity might be partially explained by the U-shaped correlation between POAG and BMI when both obese and underweight subjects were analyzed in the BMI GWAS datasets.
In addition, expectedly, the POAG, IOP, and VCDR phenotypes significantly correlated with each other (p < 0.001), and most of the body fat measurements, including BMI, obesity, and WHR, also significantly correlated with each other (Figure 2). These results supported the validity of the genetic correlation results.

2.4. Shared Genetic Factors between Body Fat and POAG

2.4.1. Conditional Q-Q Plot

Based on the above results, we further focused on identifying the specific genetic overlaps between body fat measurements and POAG. We used the GWAS summary statistics datasets of the POAG [28], IOP (OS) [29], and adult BMI [33] phenotypes.
We generated a conditional Q-Q plot to visualize the cross-trait enrichment between POAG/IOP and BMI. If there was cross-trait enrichment, successive leftward deflections can be seen in conditional Q-Q plots as levels of SNP associations with the BMI phenotype increase. Our conditional Q-Q plots showed noticeable cross-trait enrichment of genetic variants between POAG/IOP and BMI (Figure 3).

2.4.2. Shared Genetic Loci between POAG/IOP and BMI

At conjFDR <0.01, we identified 23 distinct genomic loci jointly associated with POAG and BMI (Figure 4 and Table S5). Moreover, we found 11 independent genomic loci jointly associated with IOP and BMI (Figure 4 and Table S6). Among the identified genomic loci, six showed association with POAG, IOP, and BMI (Figure 4, Tables S5 and S6). Seventeen of the identified genomic loci were not reported in previously published GWASs of glaucoma or IOP (Tables S5 and S6).

2.4.3. Functional Annotations to the Identified Genomic Loci

We first obtained the expression profiles and known gene functions of the nearby genes to the independently significant SNPs (Table S7 and Figure S2). In subsequent GO enrichment analysis, none of the GO terms survived correction for multiple testing (corrected p values >0.05).

3. Discussion

In this study, we first established that obese and underweight populations have a significantly higher risk of POAG using existing longitudinal data and meta-analysis. We also discovered positive genetic correlations between POAG, BMI, and obesity phenotypes. Finally, we identified over 20 genomic loci that are jointly associated with POAG and BMI. Our study, which combines both epidemiological and genomic data, strongly supports the connection between body fat phenotypes and POAG. Additionally, we identified new genomic loci and genes that are worthy of further functional investigation.
In the GO term enrichment analysis, we did not identify a significant functional cluster from the list of potential genes prioritized by our analysis, which suggested that each genomic loci/gene could play a different role that links POAG/IOP and BMI. Noticeably, half of the prioritized genes are also expressed ubiquitously in different human tissue types, which is in line with their association with BMI, a systemic parameter, but their specific effects on the ocular condition—POAG—should be further studied.
ATXN2, for example, is important for multiple cellular processes [36], including (1) repressing mTORC1 signaling pathway to limit cell size, protein synthesis, fat and glycogen utilization; (2) assisting mitochondrial autophagy and maintaining mitochondrial precursors, etc. One possibility that ATXN2 may alter glaucoma risk is through causing mitochondrial dysfunction, because one of the significant aspects in glaucoma pathogenesis is the structural and functional impairment of mitochondria in retinal ganglion cells and their axons and synapses [37]. In addition to its role in glaucoma, ATXN2 has been linked to body fat regulation. Animal studies in mice have shown that ataxin-2 is involved in the regulation of energy metabolism including fatty acid metabolism [38]. However, more research is needed to fully understand the molecular mechanisms underlying the involvement of ATXN2 and the other potential genes/genomic loci in body fat control and glaucoma.
There are some limitations in our study. First, the available GWAS summary statistics datasets were limited for underweight phenotype and obesity. Although we included two GWAS datasets for obesity, the sample size and statistical power of the two GWASs were incomparable to the datasets for POAG, IOP, BMI or WHR. This limited our ability to identify shared genomic loci for these two phenotypes and to test if obesity and underweight would have different shared loci with POAG. Second, in our pleiotropy analysis, we employed GWASs with potential overlapping samples, causing FDR inflation. To minimize the risk of false enrichment from population stratification or relatedness, we employed methods such as an established genomic control procedure with intergenic SNPs to control FDR inflation. Third, the discoveries were made using the GWASs conducted mainly in populations of the European ancestries. The results should be further verified in populations of other ancestral origins.

4. Materials and Methods

4.1. Meta-Analysis

We searched for original studies evaluating the association between obesity phenotypes and POAG in the PubMed database on 20 December 2022. Search strategies are shown in Table S1 [39,40]. In addition, we manually scanned the citations of relevant articles and reviews. We summarized the studies met the following criteria: (1) longitudinal study design–incident of POAG was reported; (2) evaluated the risk of developing open-angle glaucoma or its progression under exposure to obesity, different BMI levels, or WHR levels; (3) reported outcomes in odds ratio (OR), hazard ratio (HR), or relative risk (RR); (4) written in English. We excluded animal studies, case reports, reviews, abstracts, conference proceedings, editorials, and studies with incomplete data. All records were reviewed, and data were extracted/cross-checked by two reviewers (S.S.R. and X.T.Y.). We used the Newcastle–Ottawa Scale (NOS, accessed via http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp, accessed on 1 December 2022) to assess the quality of the cohort studies (Appendix A) [41,42], which informed our risk of bias assessments.
Meta-analysis of longitudinal studies was conducted following our previously published methodologies [43,44,45]. Briefly, we combined study outcomes of comparable definitions using RevMan 5 (https://training.cochrane.org/online-learning/core-software/revman/, accessed on 1 December 2022). Obesity and underweight were defined as BMI ≥ 30 kg/m2 and BMI < 18.5 kg/m2, respectively. We treated each cohort as an independent record when one study reported multiple cohorts. Only fully adjusted outcomes were combined using a random-effects model—the DerSimonian and Laird method [46]. In all the cohorts, the incidence of POAG was <5% during the follow-up period. Therefore, we included relative risk or OR in our meta-analyses of obesity and BMI outcomes, assuming the relative risk, odds ratio, and hazard ratio were comparable under a very low incidence of POAG [26,27]. In sensitivity analysis, we recalculated the combined outcomes by excluding the RR results, or via leaving each cohort at a time. Heterogeneity between studies was evaluated using Q-statistic and I2 [47,48]. Funnel plots were generated [49,50,51].

4.2. GWAS Quality Control and Imputation

The study-specific design for each GWAS [28,29,30,31,32,33,34,35], including sample collection, quality control procedures, and criteria for inclusion or exclusion of SNPs, was described in the corresponding publication. In general, SNPs with call rates less than 95% and a minor allele frequency of less than 0.5% were excluded. Software used for imputation included Minimac3 and SNP2HLA, etc.

4.3. Genetic Correlation Analysis Using Linkage Disequilibrium Score Regressions

Linkage disequilibrium score regression (LDSR) regresses LD scores on the χ2 statistics of the SNPs from a GWAS to infer SNP-based heritability. Genetic correlations are estimated from the LDSR slope between two phenotypes [52]. To test genetic correlation between POAG and body fat measurements, GWAS summary statistics datasets of European ancestries for POAG, IOP [53,54], VCDR [30], obesity [31,32], BMI [33,34], and BMI-adjusted WHR [35] were curated. LD scores were computed using 1000 Genomes European data [55]. The LDSR analysis was completed using the ldsc tool kit and the following published guidelines (https://github.com/bulik/ldsc, accessed on 1 December 2022) [56].

4.4. Pleiotropy Analysis

In general, we performed pleiotropy analysis following established protocols and tools [57,58,59]. To reduce the risk of spurious enrichment due to population stratification or relatedness [60], we applied a genomic inflation control procedure using intergenic SNPs, which are relatively depleted of true associations [61]. This procedure was used to correct all test statistics. Moreover, we used n = 20 iterations of random pruning with an LD threshold r2 = 0.1 to define LD-blocks throughout the genome. We also excluded the major histocompatibility complex (MHC) region in the analysis due to strong SNP associations within the long-range LD region [62].

4.4.1. Conditional Quantile-Quantile Plots

Conditional Q-Q plots depict the differential enrichment between pre-specified strata of SNPs. Conditional Q-Q plot was constructed by creating subsets of SNPs based on the level of association with the BMI phenotype (i.e., p < 0.1, p < 0.01, and p < 0.001). The data points on the Q-Q plot were weighted according to the LD structure around the corresponding SNP.
If there was no cross-trait enrichment, the nominal p values of POAG associations will form a straight line shown as a function of their empirical distribution. With the existence of cross-trait enrichment, successive leftward deflections can be seen in conditional Q-Q plots as levels of SNP associations with the BMI phenotype increase, i.e., p ≤ 1, p < 0.1, p < 0.01, and p < 0.001. We created Q-Q plots by considering BMI as the primary phenotype and POAG/IOP as the conditional phenotype and then by reversing the roles of the primary and conditional phenotypes.

4.4.2. Conditional and Conjunction False Discovery Rates

To identify the specific pleiotropic SNPs that were significant for both POAG and body fat phenotypes, we applied conditional and conjunctional false discovery rates (FDRs) analysis (condFDR and conjFDR, respectively). FDR is a statistical method for correcting for multiple hypothesis testing and was used in pleiotropy analysis to account for the possibility of non-pleiotropy for a particular SNP [58]. To increase the power of detecting pleiotropy associated with the primary phenotype (e.g., POAG) while leveraging the association with the second phenotype (e.g., a body fat phenotype), we used the Bayesian conditional FDR method (condFDR) [57,63].
CondFDR is a modified version of FDR that takes into account the associations between genetic variants and the secondary phenotype to recalculate the P values for the primary phenotype. In our study, we used condFDR analysis to identify genetic variants associated with POAG that are dependent on the body fat phenotype. If the primary phenotype (e.g., POAG) and the second phenotype (e.g., body fat phenotype) are genetically correlated, the condFDR will rearrange the SNPs in a different order from the ranking obtained when considering the primary phenotype alone [59]. The condFDR of the primary phenotype (e.g., POAG) conditioned on body fat phenotype (POAG|body fat phenotype) and the reversing condFDR (body fat phenotype|POAG) were calculated as follows [64]:
c F D R p i | p j = Pr ( H 0 i | P i ≤ p i , P j ≤ p j )
where pi is the association of a specific SNP with the principal disease, pj is with the conditional disease. Pi is the random variable of the p value for trait i among all SNPs. H0(i) represents the null hypothesis that a specific SNP is not associated with trait i.
To identify pleiotropic SNPs that are jointly significant for both phenotypes, we calculated conjFDR, which is an extension of condFDR [65]. ConjFDR is defined as the larger of the two condFDR values and serves as a conservative estimate of the FDR for a pleiotropic SNP associated with both phenotypes. In our study, we used conjFDR analysis to identify shared SNPs between POAG and body fat phenotypes based on the previously calculated condFDR. The threshold for conjFDR was set at <0.01, and p values were corrected using the genomic inflation control procedure as described in previous studies [57,64,66]. For SNPs with multiple conjFDR values, we used the average values.

4.5. Functional Annotation of Shared Loci and Genes

We used FUMA [67] and DAVID [68] tools for functional annotation of the target genes. GO enrichment analysis was conducted using the PANTHER classification system [69].

5. Conclusions

Primary open-angle glaucoma and body fat have significant phenotypic and genetic overlaps. The shared genomic loci by POAG and body fat phenotypes render further functional investigation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24043925/s1.

Author Contributions

Conceptualization, S.S.R. and X.Y.; methodology, S.S.R.; formal analysis, S.S.R.; data curation, S.S.R. and X.Y.; writing—original draft preparation, S.S.R.; writing—review and editing, S.S.R. and X.Y.; visualization, S.S.R.; supervision, S.S.R.; funding acquisition, S.S.R. and X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to usage of published GWAS summary statistics datasets and epidemiological studies.

Informed Consent Statement

Written informed consent has been obtained in the original publications.

Data Availability Statement

We used publicly available software for the analyses and provided a list of the specific programs used in the methods section.

Acknowledgments

We would like to express our sincere gratitude to all the authors of the genome-wide association studies for generously sharing their summary statistics. Your contributions have been invaluable to our research.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Newcastle–Ottawa Quality Assessment Scale for Cohort Studies

Note: A study can be awarded a maximum of one star for each numbered item within the Selection and Outcome categories. A maximum of two stars can be given for Comparability.
  • Selection
    (1)
    Representativeness of the exposed cohort
    (a)
    Truly representative of the average _______________ (describe) in the community *
    (b)
    Somewhat representative of the average ______________ in the community *
    (c)
    Selected group of users (e.g., nurses, volunteers)
    (d)
    No description of the derivation of the cohort
    (2)
    Selection of the nonexposed cohort
    (a)
    Drawn from the same community as the exposed cohort *
    (b)
    Drawn from a different source
    (c)
    No description of the derivation of the non exposed cohort
    (3)
    Ascertainment of exposure
    (a)
    Secure record (e.g., surgical records) *
    (b)
    Structured interview *
    (c)
    Written self-report
    (d)
    No description
    (4)
    Demonstration that outcome of interest was not present at start of study
    (a)
    Yes *
    (b)
    No
  • Comparability
    (1)
    Comparability of cohorts on the basis of the design or analysis
    (a)
    Study controls for _____________ (select the most important factor) *
    (b)
    Study controls for any additional factor * (these criteria could be modified to indicate specific control for a second important factor)
  • Outcome
    (1)
    Assessment of outcome
    (a)
    Independent blind assessment *
    (b)
    Record linkage *
    (c)
    Self-report
    (d)
    No description
    (2)
    Was follow-up long enough for outcomes to occur
    (a)
    Yes (select an adequate follow-up period for outcome of interest) *
    (b)
    No
    (3)
    Adequacy of follow-up of cohorts
    (a)
    Complete follow-up—all subjects accounted for *
    (b)
    Subjects lost to follow-up unlikely to introduce bias—small number lost—>____% (select an adequate %) follow-up, or description provided of those lost) *
    (c)
    Follow-up rate <____% (select an adequate %) and no description of those lost
    (d)
    No statement

References

  1. WHO. Obesity and Overweight. Fact Sheet. Available online: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 19 December 2022).
  2. Garvey, W.T.; Mechanick, J.I.; Brett, E.M.; Garber, A.J.; Hurley, D.L.; Jastreboff, A.M.; Nadolsky, K.; Pessah-Pollack, R.; Plodkowski, R. American Association of Clinical Endocrinologists and American College of Endocrinology Comprehensive Clinical Practice Guidelines for Medical Care of Patients with Obesity. Endocr. Pract. 2016, 22 (Suppl. S3), 1–203. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Yuen, M.; Lui, D.; Kaplan, L. A Systematic Review and Evaluation of Current Evidence Reveals 195 Obesity-Associated Disorders (OBAD). In Proceedings of the Obesity Week 2016, New Orleans, LA, USA, 31 October–4 November 2016. Poster T-P-3166. [Google Scholar]
  4. Jung, Y.; Han, K.; Park, H.Y.L.; Lee, S.H.; Park, C.K. Metabolic Health, Obesity, and the Risk of Developing Open-Angle Glaucoma: Metabolically Healthy Obese Patients versus Metabolically Unhealthy but Normal Weight Patients. Diabetes Metab. J. 2020, 44, 414–425. [Google Scholar] [CrossRef] [PubMed]
  5. Kim, Y.K.; Choi, H.J.; Jeoung, J.W.; Park, K.H.; Kim, D.M. Five-year incidence of primary open-angle glaucoma and rate of progression in health center-based Korean population: The Gangnam Eye Study. PLoS ONE 2014, 9, e114058. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Marshall, H.; Berry, E.C.; Torres, S.D.; Mullany, S.; Schmidt, J.; Thomson, D.; Nguyen, T.T.; Knight, L.S.; Hollitt, G.; Qassim, A.; et al. Association Between Body Mass Index and Primary Open Angle Glaucoma in Three Cohorts. Am. J. Ophthalmol. 2022, 245, 126–133. [Google Scholar] [CrossRef]
  7. Tham, Y.C.; Li, X.; Wong, T.Y.; Quigley, H.A.; Aung, T.; Cheng, C.Y. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology 2014, 121, 2081–2090. [Google Scholar] [CrossRef]
  8. WHO. Blindness and Vision Impairment. Available online: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment (accessed on 19 December 2022).
  9. Jonas, J.B.; Aung, T.; Bourne, R.R.; Bron, A.M.; Ritch, R.; Panda-Jonas, S. Glaucoma. Lancet 2017, 390, 2183–2193. [Google Scholar] [CrossRef]
  10. Chen, W.D.; Lai, L.J.; Lee, K.L.; Chen, T.J.; Liu, C.Y.; Yang, Y.H. Is Obesity a Risk or Protective Factor for Open-Angle Glaucoma in Adults? A Two-Database, Asian, Matched-Cohort Study. J. Clin. Med. 2021, 10, 4021. [Google Scholar] [CrossRef]
  11. Newman-Casey, P.A.; Talwar, N.; Nan, B.; Musch, D.C.; Stein, J.D. The relationship between components of metabolic syndrome and open-angle glaucoma. Ophthalmology 2011, 118, 1318–1326. [Google Scholar] [CrossRef] [Green Version]
  12. Burgansky-Eliash, Z.; Achiron, A.; Hecht, I.; Shimonov, M. Reduction of intraocular pressure after bariatric surgery. Acta Ophthalmol. 2018, 96, e592–e595. [Google Scholar] [CrossRef] [Green Version]
  13. Viljanen, A.; Hannukainen, J.C.; Soinio, M.; Karlsson, H.K.; Salminen, P.; Nuutila, P.; Vesti, E. The effect of bariatric surgery on intraocular pressure. Acta Ophthalmol. 2018, 96, 849–852. [Google Scholar] [CrossRef]
  14. Na, K.S.; Kim, J.H.; Paik, J.S.; Cho, W.K.; Ha, M.; Park, Y.G.; Yang, S.W. Underweight increases the risk of primary open-angle glaucoma in diabetes patients: A Korean nationwide cohort study. Medicine 2020, 99, e19285. [Google Scholar] [CrossRef] [PubMed]
  15. Pasquale, L.R.; Willett, W.C.; Rosner, B.A.; Kang, J.H. Anthropometric measures and their relation to incident primary open-angle glaucoma. Ophthalmology 2010, 117, 1521–1529. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Jiang, X.; Varma, R.; Wu, S.; Torres, M.; Azen, S.P.; Francis, B.A.; Chopra, V.; Nguyen, B.B. Baseline risk factors that predict the development of open-angle glaucoma in a population: The Los Angeles Latino Eye Study. Ophthalmology 2012, 119, 2245–2253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Ramdas, W.D.; Wolfs, R.C.; Hofman, A.; de Jong, P.T.; Vingerling, J.R.; Jansonius, N.M. Lifestyle and risk of developing open-angle glaucoma: The Rotterdam study. Arch. Ophthalmol. 2011, 129, 767–772. [Google Scholar] [CrossRef] [Green Version]
  18. Herrera, B.M.; Lindgren, C.M. The genetics of obesity. Curr. Diab. Rep. 2010, 10, 498–505. [Google Scholar] [CrossRef] [Green Version]
  19. Buniello, A.; MacArthur, J.A.L.; Cerezo, M.; Harris, L.W.; Hayhurst, J.; Malangone, C.; McMahon, A.; Morales, J.; Mountjoy, E.; Sollis, E.; et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019, 47, D1005–D1012. [Google Scholar] [CrossRef] [Green Version]
  20. Fall, T.; Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Mol. Cell. Endocrinol. 2014, 382, 740–757. [Google Scholar] [CrossRef]
  21. Wiggs, J.L.; Pasquale, L.R. Genetics of glaucoma. Hum. Mol. Genet. 2017, 26, R21–R27. [Google Scholar] [CrossRef] [Green Version]
  22. Aboobakar, I.F.; Wiggs, J.L. The genetics of glaucoma: Disease associations, personalised risk assessment and therapeutic opportunities-A review. Clin. Exp. Ophthalmol. 2022, 50, 143–162. [Google Scholar] [CrossRef]
  23. Wang, Z.; Wiggs, J.L.; Aung, T.; Khawaja, A.P.; Khor, C.C. The genetic basis for adult onset glaucoma: Recent advances and future directions. Prog. Retin. Eye Res. 2022, 90, 101066. [Google Scholar] [CrossRef]
  24. Lin, Y.; Zhu, X.; Luo, W.; Jiang, B.; Lin, Q.; Tang, M.; Li, X.; Xie, L. The Causal Association Between Obesity and Primary Open-Angle Glaucoma: A Two-Sample Mendelian Randomization Study. Front. Genet. 2022, 13, 835524. [Google Scholar] [CrossRef] [PubMed]
  25. Yuan, R.; Liu, K.; Cai, Y.; He, F.; Xiao, X.; Zou, J. Body shape and risk of glaucoma: A Mendelian randomization. Front. Med. 2022, 9, 999974. [Google Scholar] [CrossRef] [PubMed]
  26. Zhang, J.; Yu, K.F. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 1998, 280, 1690–1691. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Thompson, D.M.; Reid, J.E. The hazard ratio is interpretable as an odds or a probability under the assumption of proportional hazards. arXiv 2021, arXiv:2109.12207. [Google Scholar]
  28. Gharahkhani, P.; Jorgenson, E.; Hysi, P.; Khawaja, A.P.; Pendergrass, S.; Han, X.; Ong, J.S.; Hewitt, A.W.; Segre, A.V.; Rouhana, J.M.; et al. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries. Nat. Commun. 2021, 12, 1258. [Google Scholar] [CrossRef]
  29. Pan-UKB_Team. Pan-UK Biobank. Available online: https://pan.ukbb.broadinstitute.org/ (accessed on 1 December 2022).
  30. Springelkamp, H.; Iglesias, A.I.; Mishra, A.; Hohn, R.; Wojciechowski, R.; Khawaja, A.P.; Nag, A.; Wang, Y.X.; Wang, J.J.; Cuellar-Partida, G.; et al. New insights into the genetics of primary open-angle glaucoma based on meta-analyses of intraocular pressure and optic disc characteristics. Hum. Mol. Genet. 2017, 26, 438–453. [Google Scholar] [CrossRef]
  31. Bradfield, J.P.; Taal, H.R.; Timpson, N.J.; Scherag, A.; Lecoeur, C.; Warrington, N.M.; Hypponen, E.; Holst, C.; Valcarcel, B.; Thiering, E.; et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat. Genet. 2012, 44, 526–531. [Google Scholar] [CrossRef] [Green Version]
  32. Jiang, L.; Zheng, Z.; Fang, H.; Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 2021, 53, 1616–1621. [Google Scholar] [CrossRef]
  33. Yengo, L.; Sidorenko, J.; Kemper, K.E.; Zheng, Z.; Wood, A.R.; Weedon, M.N.; Frayling, T.M.; Hirschhorn, J.; Yang, J.; Visscher, P.M.; et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum. Mol. Genet. 2018, 27, 3641–3649. [Google Scholar] [CrossRef]
  34. Vogelezang, S.; Bradfield, J.P.; Ahluwalia, T.S.; Curtin, J.A.; Lakka, T.A.; Grarup, N.; Scholz, M.; van der Most, P.J.; Monnereau, C.; Stergiakouli, E.; et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet. 2020, 16, e1008718. [Google Scholar] [CrossRef]
  35. Pulit, S.L.; Stoneman, C.; Morris, A.P.; Wood, A.R.; Glastonbury, C.A.; Tyrrell, J.; Yengo, L.; Ferreira, T.; Marouli, E.; Ji, Y.; et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 2019, 28, 166–174. [Google Scholar] [CrossRef] [PubMed]
  36. Auburger, G.; Sen, N.E.; Meierhofer, D.; Basak, A.N.; Gitler, A.D. Efficient Prevention of Neurodegenerative Diseases by Depletion of Starvation Response Factor Ataxin-2. Trends Neurosci. 2017, 40, 507–516. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Ju, W.K.; Perkins, G.A.; Kim, K.Y.; Bastola, T.; Choi, W.Y.; Choi, S.H. Glaucomatous optic neuropathy: Mitochondrial dynamics, dysfunction and protection in retinal ganglion cells. Prog. Retin. Eye Res. 2022, 84, 101136. [Google Scholar] [CrossRef] [PubMed]
  38. Meierhofer, D.; Halbach, M.; Sen, N.E.; Gispert, S.; Auburger, G. Ataxin-2 (Atxn2)-Knock-Out Mice Show Branched Chain Amino Acids and Fatty Acids Pathway Alterations. Mol. Cell. Proteom. MCP 2016, 15, 1728–1739. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Darbandi, M.; Pasdar, Y.; Moradi, S.; Mohamed, H.J.J.; Hamzeh, B.; Salimi, Y. Discriminatory Capacity of Anthropometric Indices for Cardiovascular Disease in Adults: A Systematic Review and Meta-Analysis. Prev. Chronic Dis. 2020, 17, E131. [Google Scholar] [CrossRef] [PubMed]
  40. Bennett, W.L.; Wilson, R.F.; Zhang, A.; Tseng, E.; Knapp, E.A.; Kharrazi, H.; Stuart, E.A.; Shogbesan, O.; Bass, E.B.; Cheskin, L.J. Methods for Evaluating Natural Experiments in Obesity: Systematic Evidence Review. Comp. Eff. Rev. 2018, 168, 791–800. [Google Scholar] [CrossRef]
  41. Kmet, L.M.; Lee, R.C.; Cook, L.S. Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields; Alberta Heritage Foundation for Medical Research: Edmonton, AB, Canada, 2004; p. 22. [Google Scholar]
  42. Khan, K.S.; Riet, G.t.; Popay, J.; Nixon, J.; Kleijnen, J. Conducting the review: Phase 5 study quality assessment. In Undertaking Systematic Reviews of Research Effectiveness CDC’s Guidance for Those Carrying Out or Commissioning Reviews; Centre for Reviews and Dissemination (CRD) at the University of York: York, UK, 2001; p. 20. [Google Scholar]
  43. Rong, S.S.; Lee, B.Y.; Kuk, A.K.; Yu, X.T.; Li, S.S.; Li, J.; Guo, Y.; Yin, Y.; Osterbur, D.L.; Yam, J.C.S.; et al. Comorbidity of dementia and age-related macular degeneration calls for clinical awareness: A meta-analysis. Br. J. Ophthalmol. 2019, 103. [Google Scholar] [CrossRef]
  44. Rong, S.S.; Tang, F.Y.; Chu, W.K.; Ma, L.; Yam, J.C.; Tang, S.M.; Li, J.; Gu, H.; Young, A.L.; Tham, C.C.; et al. Genetic Associations of Primary Angle-Closure Disease: A Systematic Review and Meta-analysis. Ophthalmology 2016, 123, 1211–1221. [Google Scholar] [CrossRef]
  45. Rong, S.S.; Peng, Y.; Liang, Y.B.; Cao, D.; Jhanji, V. Does cigarette smoking alter the risk of pterygium? A systematic review and meta-analysis. Investig. Ophthalmol. Vis. Sci. 2014, 55, 6235–6243. [Google Scholar] [CrossRef] [Green Version]
  46. DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef]
  47. Higgins, J.P.; Thompson, S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 2002, 21, 1539–1558. [Google Scholar] [CrossRef] [PubMed]
  48. Higgins, J.P.; Thompson, S.G.; Deeks, J.J.; Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 2003, 327, 557–560. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Peters, J.L.; Sutton, A.J.; Jones, D.R.; Abrams, K.R.; Rushton, L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J. Clin. Epidemiol. 2008, 61, 991–996. [Google Scholar] [CrossRef] [PubMed]
  50. Higgins, J.P.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.A. (Eds.) Cochrane Handbook for Systematic Reviews of Interventions; John Wiley & Sons Ltd.: Oxford, UK, 2019; Available online: www.cochrane-handbook.org (accessed on 20 November 2022).
  51. Sterne, J.A.; Gavaghan, D.; Egger, M. Publication and related bias in meta-analysis: Power of statistical tests and prevalence in the literature. J. Clin. Epidemiol. 2000, 53, 1119–1129. [Google Scholar] [CrossRef]
  52. Bulik-Sullivan, B.; Finucane, H.K.; Anttila, V.; Gusev, A.; Day, F.R.; Loh, P.R.; ReproGen Consortium; Psychiatric Genomics Consortium; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3; Duncan, L.; et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 2015, 47, 1236–1241. [Google Scholar] [CrossRef] [Green Version]
  53. Sudlow, C.; Gallacher, J.; Allen, N.; Beral, V.; Burton, P.; Danesh, J.; Downey, P.; Elliott, P.; Green, J.; Landray, M.; et al. UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015, 12, e1001779. [Google Scholar] [CrossRef] [Green Version]
  54. UK Biobank. Available online: http://www.nealelab.is/uk-biobank (accessed on 2 November 2022).
  55. Genomes Project, C.; Auton, A.; Brooks, L.D.; Durbin, R.M.; Garrison, E.P.; Kang, H.M.; Korbel, J.O.; Marchini, J.L.; McCarthy, S.; McVean, G.A.; et al. A global reference for human genetic variation. Nature 2015, 526, 68–74. [Google Scholar] [CrossRef] [Green Version]
  56. Schorsch, E. LDSC (LD SCore). Available online: https://github.com/bulik/ldsc (accessed on 24 July 2022).
  57. Andreassen, O.A.; Thompson, W.K.; Schork, A.J.; Ripke, S.; Mattingsdal, M.; Kelsoe, J.R.; Kendler, K.S.; O’Donovan, M.C.; Rujescu, D.; Werge, T.; et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 2013, 9, e1003455. [Google Scholar] [CrossRef] [Green Version]
  58. Smeland, O.B.; Frei, O.; Shadrin, A.; O’Connell, K.; Fan, C.C.; Bahrami, S.; Holland, D.; Djurovic, S.; Thompson, W.K.; Dale, A.M.; et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum. Genet. 2020, 139, 85–94. [Google Scholar] [CrossRef]
  59. Desikan, R.S.; Schork, A.J.; Wang, Y.; Thompson, W.K.; Dehghan, A.; Ridker, P.M.; Chasman, D.I.; McEvoy, L.K.; Holland, D.; Chen, C.H.; et al. Polygenic Overlap Between C-Reactive Protein, Plasma Lipids, and Alzheimer Disease. Circulation 2015, 131, 2061–2069. [Google Scholar] [CrossRef] [Green Version]
  60. Devlin, B.; Roeder, K. Genomic control for association studies. Biometrics 1999, 55, 997–1004. [Google Scholar] [CrossRef] [PubMed]
  61. Schork, A.J.; Thompson, W.K.; Pham, P.; Torkamani, A.; Roddey, J.C.; Sullivan, P.F.; Kelsoe, J.R.; O’Donovan, M.C.; Furberg, H.; The Tobacco and Genetics Consortium; et al. All SNPs are not created equal: Genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 2013, 9, e1003449. [Google Scholar] [CrossRef]
  62. Price, A.L.; Weale, M.E.; Patterson, N.; Myers, S.R.; Need, A.C.; Shianna, K.V.; Ge, D.; Rotter, J.I.; Torres, E.; Taylor, K.D.; et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 2008, 83, 132–135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Liley, J.; Wallace, C. A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics. PLoS Genet. 2015, 11, e1004926. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Zheng, C.; Liu, S.; Zhang, X.; Hu, Y.; Shang, X.; Zhu, Z.; Huang, Y.; Wu, G.; Xiao, Y.; Du, Z.; et al. Shared genetic architecture between the two neurodegenerative diseases: Alzheimer’s disease and glaucoma. Front. Aging Neurosci. 2022, 14, 880576. [Google Scholar] [CrossRef] [PubMed]
  65. Witoelar, A.; Jansen, I.E.; Wang, Y.; Desikan, R.S.; Gibbs, J.R.; Blauwendraat, C.; Thompson, W.K.; Hernandez, D.G.; Djurovic, S.; Schork, A.J.; et al. Genome-wide Pleiotropy Between Parkinson Disease and Autoimmune Diseases. JAMA Neurol. 2017, 74, 780–792. [Google Scholar] [CrossRef] [PubMed]
  66. Bahrami, S.; Shadrin, A.; Frei, O.; O’Connell, K.S.; Bettella, F.; Krull, F.; Fan, C.C.; Rossberg, J.I.; Hindley, G.; Ueland, T.; et al. Genetic loci shared between major depression and intelligence with mixed directions of effect. Nat. Hum. Behav. 2021, 5, 795–801. [Google Scholar] [CrossRef]
  67. Watanabe, K.; Umicevic Mirkov, M.; de Leeuw, C.A.; van den Heuvel, M.P.; Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 2019, 10, 3222. [Google Scholar] [CrossRef] [Green Version]
  68. Sherman, B.T.; Hao, M.; Qiu, J.; Jiao, X.; Baseler, M.W.; Lane, H.C.; Imamichi, T.; Chang, W. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022, 50, W216–W221. [Google Scholar] [CrossRef]
  69. Mi, H.; Muruganujan, A.; Casagrande, J.T.; Thomas, P.D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 2013, 8, 1551–1566. [Google Scholar] [CrossRef]
Ijms 24 03925 g001 550
Figure 1. Associations of obesity, underweight, and BMI with incidence of open-angle glaucoma. (A) Incidence of POAG was significantly higher in obese subjects (BMI ≥ 30 kg/m2) when compared to individuals with normal BMI (p = 0.04) [4,10,11,14,15]. (B) Incidence of POAG was significantly higher in underweight individuals (p < 0.001) [4,14]. (C) The association of BMI with the risk of POAG was negligible when BMI was analyzed as a continuous independent variable (p = 0.79) [5,15,17]. BMI, body mass index; CI, confidence interval; IV, inverse variance; POAG, primary open-angle glaucoma; SE, standard error. * In Pasquale LR’s study, the incidence of POAG was <1% during the follow-up period [15]. Therefore, we included the reported relative risk in our meta-analyses of obesity and BMI outcomes, assuming that the risk ratio, odds ratio, and hazard ratio were comparable under a very low incidence of POAG [26,27]. In sensitivity analysis, the results remained unchanged after excluding the risk ratios reported in Pasquale LR’s report [15].
Figure 1. Associations of obesity, underweight, and BMI with incidence of open-angle glaucoma. (A) Incidence of POAG was significantly higher in obese subjects (BMI ≥ 30 kg/m2) when compared to individuals with normal BMI (p = 0.04) [4,10,11,14,15]. (B) Incidence of POAG was significantly higher in underweight individuals (p < 0.001) [4,14]. (C) The association of BMI with the risk of POAG was negligible when BMI was analyzed as a continuous independent variable (p = 0.79) [5,15,17]. BMI, body mass index; CI, confidence interval; IV, inverse variance; POAG, primary open-angle glaucoma; SE, standard error. * In Pasquale LR’s study, the incidence of POAG was <1% during the follow-up period [15]. Therefore, we included the reported relative risk in our meta-analyses of obesity and BMI outcomes, assuming that the risk ratio, odds ratio, and hazard ratio were comparable under a very low incidence of POAG [26,27]. In sensitivity analysis, the results remained unchanged after excluding the risk ratios reported in Pasquale LR’s report [15].
Ijms 24 03925 g001
Ijms 24 03925 g002 550
Figure 2. Genetic correlations of primary open-angle glaucoma-related phenotypes and body fat measurements. Obesity and BMI were positively correlated with POAG, IOP, and VCDR with suggestively significant p values. Glaucoma phenotypes and body fat measurements showed strong genetic correlations within their own cluster, which suggested that the genetic correlation results were valid and robust. The larger the correlation coefficient’s absolute value was, the larger the circle. The direction and value of the correlation coefficients were indicated by the color bar below the plot. BMI, body mass index; IOP, intraocular pressure (corneal-compensated, OD and OS); OD, right eye; OS, left eye; POAG, primary open-angle glaucoma; VCDR, vertical cup-to-disc ratio; WHR, waist-to-hip ratio * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 2. Genetic correlations of primary open-angle glaucoma-related phenotypes and body fat measurements. Obesity and BMI were positively correlated with POAG, IOP, and VCDR with suggestively significant p values. Glaucoma phenotypes and body fat measurements showed strong genetic correlations within their own cluster, which suggested that the genetic correlation results were valid and robust. The larger the correlation coefficient’s absolute value was, the larger the circle. The direction and value of the correlation coefficients were indicated by the color bar below the plot. BMI, body mass index; IOP, intraocular pressure (corneal-compensated, OD and OS); OD, right eye; OS, left eye; POAG, primary open-angle glaucoma; VCDR, vertical cup-to-disc ratio; WHR, waist-to-hip ratio * p < 0.05, ** p < 0.01, *** p < 0.001.
Ijms 24 03925 g002
Ijms 24 03925 g003 550
Figure 3. Conditional Q-Q plots showed cross-trait enrichment between POAG/IOP and BMI. (A) Conditional Q-Q plot of nominal—log10(p) for SNPs association with POAG as a function of statistical significance for SNPs association with BMI at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between POAG and BMI. (B) Conditional Q-Q plot of nominal—log10(p) for SNPs association with BMI as a function of statistical significance for SNPs association with POAG at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between BMI and POAG. (C) Conditional Q-Q plot of nominal—log10(p) for SNPs association with IOP as a function of statistical significance for SNPs association with BMI at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between IOP and BMI. (D) Conditional Q-Q plot of nominal—log10(p) for SNPs association with BMI as a function of statistical significance for SNPs association with IOP at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between BMI and IOP.
Figure 3. Conditional Q-Q plots showed cross-trait enrichment between POAG/IOP and BMI. (A) Conditional Q-Q plot of nominal—log10(p) for SNPs association with POAG as a function of statistical significance for SNPs association with BMI at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between POAG and BMI. (B) Conditional Q-Q plot of nominal—log10(p) for SNPs association with BMI as a function of statistical significance for SNPs association with POAG at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between BMI and POAG. (C) Conditional Q-Q plot of nominal—log10(p) for SNPs association with IOP as a function of statistical significance for SNPs association with BMI at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between IOP and BMI. (D) Conditional Q-Q plot of nominal—log10(p) for SNPs association with BMI as a function of statistical significance for SNPs association with IOP at the predefined stratum of p values. The lines with sharp leftward deviated tails showed the true associations between BMI and IOP.
Ijms 24 03925 g003
Ijms 24 03925 g004 550
Figure 4. Common genetic variants jointly associated with POAG/IOP and BMI at conjFDR <0.01. (A). Twenty-three independent genomic loci were identified to be jointly associated with POAG and BMI. (B). Ten independent genomic loci were identified to be jointly associated with IOP and BMI. * There were six overlapping genomic loci that were jointly associated with POAG, IOP, and BMI. The nearby genes included RP3-335N17.2, RP11-793K1.1, MADD, CADM2, LMX1B, and SMG6.
Figure 4. Common genetic variants jointly associated with POAG/IOP and BMI at conjFDR <0.01. (A). Twenty-three independent genomic loci were identified to be jointly associated with POAG and BMI. (B). Ten independent genomic loci were identified to be jointly associated with IOP and BMI. * There were six overlapping genomic loci that were jointly associated with POAG, IOP, and BMI. The nearby genes included RP3-335N17.2, RP11-793K1.1, MADD, CADM2, LMX1B, and SMG6.
Ijms 24 03925 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rong, S.S.; Yu, X. Phenotypic and Genetic Links between Body Fat Measurements and Primary Open-Angle Glaucoma. Int. J. Mol. Sci. 2023, 24, 3925. https://doi.org/10.3390/ijms24043925

AMA Style

Rong SS, Yu X. Phenotypic and Genetic Links between Body Fat Measurements and Primary Open-Angle Glaucoma. International Journal of Molecular Sciences. 2023; 24(4):3925. https://doi.org/10.3390/ijms24043925

Chicago/Turabian Style

Rong, Shi Song, and Xinting Yu. 2023. "Phenotypic and Genetic Links between Body Fat Measurements and Primary Open-Angle Glaucoma" International Journal of Molecular Sciences 24, no. 4: 3925. https://doi.org/10.3390/ijms24043925

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop