Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy





Purpose


To identify functionally related genes associated with diabetic retinopathy (DR) risk using gene set enrichment analyses applied to genome-wide association study meta-analyses.


Methods


We analyzed DR GWAS meta-analyses performed on 3246 Europeans and 2611 African Americans with type 2 diabetes. Gene sets relevant to 5 key DR pathophysiology processes were investigated: tissue injury, vascular events, metabolic events and glial dysregulation, neuronal dysfunction, and inflammation. Keywords relevant to these processes were queried in 4 pathway and ontology databases. Two GSEA methods, Meta-Analysis Gene set Enrichment of variaNT Associations (MAGENTA) and Multi-marker Analysis of GenoMic Annotation (MAGMA), were used. Gene sets were defined to be enriched for gene associations with DR if the P value corrected for multiple testing (Pcorr) was <.05.


Results


Five gene sets were significantly enriched for numerous modest genetic associations with DR in one method (MAGENTA or MAGMA) and also at least nominally significant (uncorrected P < .05) in the other method. These pathways were regulation of the lipid catabolic process (2-fold enrichment, Pcorr = .014); nitric oxide biosynthesis (1.92-fold enrichment, Pcorr = .022); lipid digestion, mobilization, and transport (1.6-fold enrichment, P = .032); apoptosis (1.53-fold enrichment, P = .041); and retinal ganglion cell degeneration (2-fold enrichment, Pcorr = .049). The interferon gamma ( IFNG ) gene, previously implicated in DR by protein-protein interactions in our GWAS, was among the top ranked genes in the nitric oxide pathway (best variant P = .0001).


Conclusions


These GSEA indicate that variants in genes involved in oxidative stress, lipid transport and catabolism, and cell degeneration are enriched for genes associated with DR risk. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.


D iabetic retinopathy (DR) is a leading cause of blindness. Established risk factors include a longer duration of diabetes and poor glycemic control. Some populations, including African Americans, have been found to have a higher risk of developing DR compared with populations of European ancestry even after adjusting for these established risk factors. Genetic factors are also implicated, with a heritability of 52% for proliferative diabetic retinopathy (PDR). , However, traditional individual candidate gene association studies have not been successful in identifying the underlying genetic architecture for DR. Furthermore, genome-wide association studies (GWAS) of DR to date have not had sufficient power to detect reproducible single DNA variants associations with the disease (tens to hundreds of thousands of individuals needed).


Gene set enrichment analysis (GSEA) applied to GWAS variant data is a method that tests whether sets of functionally related genes are enriched for genetic associations with a polygenic disease or trait. Previous studies have shown that GSEA of GWAS has the potential to detect associations likely missed by single-marker analysis. , GSEA has been used successfully for various multifactorial diseases, such as type 2 diabetes and bipolar disorder, to determine if there is enrichment of genes in pathways implicated in disease pathogenesis among the top ranked genetic associations in GWAS. ,


We have previously collaborated to execute the largest DR GWAS to date. The purpose of this study is to identify functionally related genes that affect risk of DR using GSEA on this GWAS dataset. We hypothesize that common variants associated with DR risk will affect genes that cluster in specific pathogenic pathways or biological processes and that both statistical and explanatory power can be gained by testing for enrichment of numerous modest genetic associations at the gene-set level, using GSEA, compared with testing genetic variants individually. This may be particularly beneficial for studies such as our latest DR GWAS, where only few variants passed genome-wide significance.


METHODS


All studies conformed to the Declaration of Helsinki tenets and were Health Insurance Portability and Accountability Act (HIPAA) compliant. Written informed consent was obtained from all participants. Institutional review board approval was obtained prospectively by each individual study for collection of DNA and genotyping. The Massachusetts Eye and Ear Infirmary institutional review board approved the analysis of deidentified datasets from all the cohorts centrally at the Massachusetts Eye and Ear Infirmary.


GWAS Meta-analyses Analyzed


The study participants were the patients included in the discovery phase of a previously published DR GWAS. These patients were from a consortium of 11 DR genetic studies that included 3246 European and 2611 African American patients. , , All patients had type 2 diabetes that was defined as a fasting plasma glucose ≥126 mg/dL or a hemoglobin A 1C ≥6.5% with onset of the diabetes after age 30 years. Table 1 summarizes the DR phenotyping protocols and covariates by cohort. Phenotyping protocols have been previously described. , , , All of these participants had genome-wide genotyping and were part of the GWAS. The GWAS analyses were performed with liability threshold modeling of duration of diabetes and glycemic control using LTSCORE, and executed separately for the African American and European cohorts. Only variants on the autosomes were analyzed in these DR GWAS; hence, genes on the sex chromosomes were not included in the GSEA. We examined for any differences in the distribution of DR severity between men and women in the European and African American GWAS using a 2-sided Wilcoxon rank sum test in each population.



Table 1

Studies Included in the Gene Set Enrichment Analysis
























































































































































































Study Population Number of Eyes/Number of Fields/Size of Fields Photographed Glycemic Control Measure Cases (ETDRS ≥ 14) Ctrls (ETDRS < 14) Cases (ETDRS ≥ 60) Ctrls (ETDRS < 60) Cases (ETDRS ≥ 30)
AAPDR AA 2/7/30 deg. HbA 1C 274 56 255 75 261
AGES a EUR 2/2/45 deg. HbA 1C 85 222 3 304 8
ARIC AA 1/1/45 deg. HbA 1C 96 265 3 358 73
EUR 1/1/45 deg. HbA 1C 126 632 6 752 80
AUST EUR NA b HbA 1C 522 435 187 770 346
BMES EUR 2/5/30 deg. FPG 124 208 1 331 37
CHS AA 1/1/45 deg. FPG 19 35 4 50 14
EUR 1/1/45 deg. FPG 26 119 4 141 16
FIND-Eye a AA 2/2/45 deg. c HbA 1C 330 167 264 233 303
EUR 2/2/45 deg. c HbA 1C 158 154 115 197 145
JHS AA 2/7/30 deg. HbA 1C 91 160 12 239 57
MESA AA 2/2/45 deg. HbA 1C 101 258 11 348 60
EUR 2/2/45 deg. HbA 1C 38 200 2 236 12
RISE/RIDE EUR 2/7/30 deg. HbA 1C — — 80 117 —
WFU AA NA b HbA 1C — — 548 211 —
TOTAL AA — Varies 911 941 1097 1514 768
TOTAL EUR — Varies 1079 1970 398 2848 644

AA = African American, AAPDR = African American Proliferative Diabetic Retinopathy Study, Affy = Affymetrix, AGES = Age, Gene/Environment Susceptibility Study, ARIC = Atherosclerosis Risk In Communities Study, AUST = Australian Genetics of Diabetic Retinopathy Study, BMES = Blue Mountains Eye Study, CHS = Cardiovascular Health Study, Ctrls = controls, deg. = degrees, ETDRS = Early Treatment Diabetic Retinopathy Study, EUR = European, FIND-Eye = Family Study of Nephropathy and Diabetes-Eye, FPG = fasting plasma glucose, HbA 1C = hemoglobin A 1C , Illum = Illumina, JHS = Jackson Heart Study, MESA = Multiethnic Study of Atherosclerosis, NA = not available, QC = quality control, RIDE/RISE = Ranibizumab Injection in Subjects with Clinically Significant Macular Edema with Center Involvement Secondary to Diabetes, WFU = Wake Forest University..

a Number of genes above 75th percentile enrichment cutoff.


b The AUST study used examination by an ophthalmologist to ascertain diabetic retinopathy. The WFU study used a questionnaire to ascertain diabetic retinopathy.


c Not all FIND-Eye subjects had photographs but all participants had harmonization of examination and clinical data to an ETDRS score.



The GSEA for the present study used the GWAS meta-analysis summary statistics from the previous publication. For this GSEA, we examined 2 DR case-control definitions with different Early Treatment Diabetic Retinopathy Study (ETDRS) score thresholds for cases and controls. The first compared patients with PDR with those without PDR (ETDRS ≥ 60 vs ETDRS < 60, henceforth the PDR analysis). The second compared those with PDR with those without DR (ETDRS ≥ 60 vs ETDRS < 14, henceforth the extremes of DR analysis). We chose to examine these 2 case-control definitions out of the total of 4 case-control definitions originally included in the GWAS paper, because the individual variants with the statistical significance findings came from these 2 case-control definitions that have PDR as their case definition. This is consistent with the fact that PDR has a higher heritability than overall DR. Table 1 shows the available samples by cohort and ETDRS score thresholds. Therefore, in total there were 4 GWAS meta-analysis datasets on which GSEA were run:



  • 1.

    African Americans, PDR analysis


  • 2.

    Europeans, PDR analysis


  • 3.

    African Americans, Extremes of DR analysis


  • 4.

    Europeans, Extremes of DR analysis



Gene Set Enrichment Analyses


Extraction of gene sets


The gene sets that were examined in the GSEA were chosen based on their relevance to the pathophysiology of DR as summarized in Table 2 of a seminal paper on this subject. The 5 pathophysiologic pathways from this table were tissue injury, vascular events, metabolic events and glial dysregulation, neuronal dysfunction, and inflammation. These pathways were broken down into keywords that were used to search in 4 gene set databases: Reactome Pathway Database ( https://reactome.org ), the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg ), Gene Ontology (GO; http://geneontology.org/ ), and Mouse Genome Informatics (MGI; http://www.informatics.jax.org/ ). Supplementary Table 1 lists all the pathophysiologic pathways and the resulting keywords that were queried and the respective gene sets that were identified from those searches. Each keyword was searched individually. Because some of the search terms were very general, the resultant gene sets were pruned by a clinician scientist with expertise in DR (L.S.) to include only those gene sets that truly reflect the pathophysiologic pathways in DR from the seminal paper. We tested a total of 207 gene sets (143 GO, 13 KEGG, 41 MGI, and 10 Reactome Pathway Database gene sets).



Table 2

Gene Sets That Were Significant in Either or Both MAGENTA and MAGMA Gene Set Enrichment Analyses

























































































































































































































































Passed Multiple Hypothesis Correction With MAGENTA MAGENTA MAGMA
Population, GWAS, Gene Window Upstream/ Downstream Database: Gene Set Initial Gene Set Size Effective Gene Set Size Leading Edge Genes a Proposed Number of Disease Associated Genes b Fold Enrichment Uncorrected P Value Benjamini-Hochberg Corrected P Value Effective Gene Set Size Beta Beta Std Uncorrected P Value Benjamini-Hochberg Corrected P Value
AA, PDR analysis, 5 kb/5 kb GO: Regulation of lipid catabolic process 52 51 26 13 2 .0001 c .014 52 0.2051 0.0110 .0345 .823
AA, extremes of DR analysis, 110 kb/40 kb KEGG: VEGF signaling pathway 76 70 29 11 1.61 .0014 c .018 75 0.0827 0.0053 .2464 1.0
AA, PDR analysis, 5 kb/5 kb GO: Regulation of nitric oxide biosynthetic process 53 46 23 11 1.92 .0003 c .022 50 0.3345 0.0175 .0026 .365
AA, PDR analysis, 5 kb/5 kb Reactome: Metabolism of lipids and lipoproteins 478 448 135 23 1.21 .0070 .035 452 0.0185 0.0029 .3121 1.0
AA, PDR analysis, 5 kb/5 kb GO: Tissue development 1518 1444 410 49 1.14 .0008 .038 1457 0.0147 0.0040 .2557 1.0
AA, PDR analysis, 5 kb/5 kb REACTOME: Post-translational protein modification 188 172 59 16 1.37 .0040 c .040 173 0.0751 0.0073 .0942 .942
AA, extremes of DR analysis, 110 kb/40 kb KEGG: Apoptosis 88 75 29 10 1.53 .0063 .041 81 0.1826 0.0122 .0490 .638
AA, extremes of DR analysis, 5 kb/5 kb GO: Regulation of platelet derived growth factor receptor signaling pathway 14 14 10 6 2.5 .0003 c .043 14 0.2568 0.0071 .1044 .67
EU, PDR analysis, 5 kb/5 kb MGI: MP 0030005 increased retinal apoptosis 36 35 12 3 1.33 .1420 .448 35 0.5760 0.0252 .00001 c .0005
EU, PDR analysis, 5 kb/5 kb Reactome: Tight junction interactions 29 27 8 1 1.14 .3590 .599 28 0.5808 0.0227 .0001 c .001
EU, PDR analysis, 5 kb/5 kb MGI: MP 0008507 thin retinal ganglion layer 15 15 6 2 1.5 .1430 .419 15 0.6837 0.0196 .0006 c .012
EU, PDR analysis, 110 kb/40 kb Reactome: HDL mediated lipid transport 15 13 4 1 1.33 .4020 1 15 0.6126 0.0175 .0023 c .023
EU, PDR analysis, 110 kb/40 kb Reactome: Lipid digestion mobilization and transport 46 41 16 6 1.6 .0306 .306 45 0.3046 0.0150 .0064 .032
EU, PDR analysis, 5 kb/5 kb GO: Vascular endothelial growth factor receptor signaling pathway 74 72 23 5 1.28 .1070 .958 72 0.3295 0.0207 .0003 c .045
EU, PDR analysis, 5 kb/5 kb MGI: MP 0008067 retinal ganglion cell degeneration 16 15 8 4 2 .0175 .359 15 0.5462 0.0157 .0048 .049

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Jan 3, 2022 | Posted by in OPHTHALMOLOGY | Comments Off on Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy

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