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Citation: Yang, Z.; Zhang, Z.; Zhu, Y.;
Yuan, G.; Yang, J.; Yu, W. Mendelian
Randomization and
Transcriptome-Wide Association
Analysis Identified Genes That Were
Pleiotropically Associated with
Intraocular Pressure. Genes 2023,14,
1027. https://doi.org/10.3390/
genes14051027
Academic Editors: Kai Wang and
Albert Jeltsch
Received: 27 March 2023
Revised: 21 April 2023
Accepted: 28 April 2023
Published: 30 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
genes
G C A T
T A C G
G C A T
Article
Mendelian Randomization and Transcriptome-Wide
Association Analysis Identified Genes That Were
Pleiotropically Associated with Intraocular Pressure
Zhikun Yang 1, Zhewei Zhang 2, Yining Zhu 3, Guangwei Yuan 4, Jingyun Yang 5,6 and Weihong Yu 1, *
1Department of Ophthalmology, Peking Union Medical College Hospital,
Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing 100730, China
2Department of Statistics, The Pennsylvania State University, State College, PA 16802, USA
3School of Mathematical Sciences, Fudan University, Shanghai 200433, China
4College of Professional Studies, Northeastern University, Boston, MA 02115, USA
5Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA;
jingyun_yang@rush.edu
6Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
*Correspondence: yuwh@pumch.cn; Tel.: +86-10-69156351
Abstract:
Background: Intraocular pressure (IOP) is a major modifiable risk factor for glaucoma.
However, the mechanisms underlying the controlling of IOP remain to be elucidated. Objective: To
prioritize genes that are pleiotropically associated with IOP. Methods: We adopted a two-sample
Mendelian randomization method, named summary-based Mendelian randomization (SMR), to
examine the pleiotropic effect of gene expression on IOP. The SMR analyses were based on sum-
marized data from a genome-wide association study (GWAS) on IOP. We conducted separate SMR
analyses using Genotype-Tissue Expression (GTEx) and Consortium for the Architecture of Gene
Expression (CAGE) expression quantitative trait loci (eQTL) data. Additionally, we performed a
transcriptome-wide association study (TWAS) to identify genes whose cis-regulated expression levels
were associated with IOP. Results: We identified 19 and 25 genes showing pleiotropic association
with IOP using the GTEx and CAGE eQTL data, respectively. RP11-259G18.3 (P
SMR
= 2.66
×
10
−6
),
KANSL1-AS1 (P
SMR
= 2.78
×
10
−6
), and RP11-259G18.2 (P
SMR
= 2.91
×
10
−6
) were the top three
genes using the GTEx eQTL data. LRRC37A4 (P
SMR
= 1.19
×
10
−5
), MGC57346 (P
SMR
=1.19
×
10
−5
),
and RNF167 (P
SMR
= 1.53
×
10
−5
) were the top three genes using the CAGE eQTL data. Most of the
identified genes were found in or near the 17q21.31 genomic region. Additionally, our TWAS analysis
identified 18 significant genes whose expression was associated with IOP. Of these, 12 and 4 were also
identified by the SMR analysis using the GTEx and CAGE eQTL data, respectively. Conclusions: Our
findings suggest that the 17q21.31 genomic region may play a critical role in the regulation of IOP.
Keywords:
intraocular pressure; expression quantitative trait loci; summary-based Mendelian
randomization; genome-wide association study; transcriptome-wide association study
1. Introduction
Intraocular pressure (IOP) refers to the fluid pressure inside the eye, which is affected
by the production and drainage of the aqueous humor. Excessive production or insufficient
drainage of aqueous humor can lead to ocular hypertension (OHT), which has a prevalence
of approximately 1.6% in the general population over 30 years old [
1
]. The prevalence of
OHT can be higher in persons over 40 years old, ranging from 2.6% to 3.6% [
2
–
4
]. IOP can be
modified by various treatments, such as eye drops, laser, and surgery [
5
]. Previous studies
have shown that each 1 mm Hg drop of IOP can lower the risk of glaucoma progression
by 10% [
6
,
7
]. Therefore, IOP represents a major modifiable risk factor of glaucoma [
8
], the
most common cause of irreversible blindness worldwide [9].
Genes 2023,14, 1027. https://doi.org/10.3390/genes14051027 https://www.mdpi.com/journal/genes
Genes 2023,14, 1027 2 of 13
Previous research indicated that both environmental and genetic factors affect IOP [
10
].
Smoking, alcohol consumption, and dietary omega-3 fatty acids have shown epidemio-
logical associations with primary open-angle glaucoma (POAG) [
11
–
13
]. Twin and family
studies have estimated the heritability of IOP to be between 40% and 70% [
14
,
15
]. Multi-
ple genetic variants, mutations, and genomic loci have been found to be associated with
IOP [
10
,
16
–
27
]. Despite these informative findings, the mechanisms underlying the control
of aqueous humor dynamics and IOP regulation are still poorly understood. More studies
are needed to explore the complex mechanisms underlying the regulation of IOP.
In this paper, we investigated genes that are pleiotropically associated with IOP by
using a recently developed two-sample Mendelian randomization (MR), named summary-
based Mendelian randomization (SMR), that integrates genome-wide association study
(GWAS) summary data for IOP and cis-eQTL (expression quantitative trait loci) data. We
also conducted a transcriptome-wide association study (TWAS) to identify genes whose
cis-regulated expression levels are associated with IOP.
2. Methods
2.1. Editorial Policies and Ethical Considerations
This study utilized GWAS summary results for IOP and eQTL data. All the data were
publicly available. As a result, ethical considerations are not needed. The analytical process
of the present study is illustrated in Figure 1.
Genes 2023, 14, x FOR PEER REVIEW 2 of 15
glaucoma progression by 10% [6,7]. Therefore, IOP represents a major modifiable risk fac-
tor of glaucoma [8], the most common cause of irreversible blindness worldwide [9].
Previous research indicated that both environmental and genetic factors affect IOP
[10]. Smoking, alcohol consumption, and dietary omega-3 fay acids have shown epide-
miological associations with primary open-angle glaucoma (POAG) [11–13]. Twin and
family studies have estimated the heritability of IOP to be between 40% and 70% [14,15].
Multiple genetic variants, mutations, and genomic loci have been found to be associated
with IOP [10,16–27]. Despite these informative findings, the mechanisms underlying the
control of aqueous humor dynamics and IOP regulation are still poorly understood. More
studies are needed to explore the complex mechanisms underlying the regulation of IOP.
In this paper, we investigated genes that are pleiotropically associated with IOP by
using a recently developed two-sample Mendelian randomization (MR), named sum-
mary-based Mendelian randomization (SMR), that integrates genome-wide association
study (GWAS) summary data for IOP and cis-eQTL (expression quantitative trait loci)
data. We also conducted a transcriptome-wide association study (TWAS) to identify genes
whose cis-regulated expression levels are associated with IOP.
2. Methods
2.1. Editorial Policies and Ethical Considerations
This study utilized GWAS summary results for IOP and eQTL data. All the data were
publicly available. As a result, ethical considerations are not needed. The analytical pro-
cess of the present study is illustrated in Figure 1.
Figure 1. Flow chart for the SMR analyses. (A) SMR analysis using GTEx eQTL data for blood; and
(B) SMR analysis using CAGE eQTL data for blood. CAGE, Consortium for the Architecture of Gene
Expression; eQTL, expression quantitative trait loci; GWAS, genome-wide association studies;
GTEx, Genotype-Tissue Expression; LD, linkage disequilibrium; IOP, intraocular pressure; SMR,
summary-based Mendelian randomization; SNP, single nucleotide polymorphisms.
2.2. GWAS Data for IOP
The GWAS summary data for IOP were provided by a recent multi-trait genome-
wide association meta-analysis of optic disc parameters [28]. The results were based on ge-
netic data imputed using the Haplotype Reference Consortium (HRC) and included 11 co-
horts from the International Glaucoma Genetics Consortium (IGGC). A total of 31,269 partici-
pants of European ancestry were included in the meta-analysis. An additive genetic model
was assumed by all the participating studies, adjusting for age, sex, and at least the first five
principal components, as well as cohort-specific covariates when necessary. The GWAS
Figure 1.
Flow chart for the SMR analyses. (
A
) SMR analysis using GTEx eQTL data for blood; and
(
B
) SMR analysis using CAGE eQTL data for blood. CAGE, Consortium for the Architecture of
Gene Expression; eQTL, expression quantitative trait loci; GWAS, genome-wide association studies;
GTEx, Genotype-Tissue Expression; LD, linkage disequilibrium; IOP, intraocular pressure; SMR,
summary-based Mendelian randomization; SNP, single nucleotide polymorphisms.
2.2. GWAS Data for IOP
The GWAS summary data for IOP were provided by a recent multi-trait genome-
wide association meta-analysis of optic disc parameters [
28
]. The results were based on
genetic data imputed using the Haplotype Reference Consortium (HRC) and included
11 cohorts
from the International Glaucoma Genetics Consortium (IGGC). A total of
31,269 participants
of European ancestry were included in the meta-analysis. An addi-
tive genetic model was assumed by all the participating studies, adjusting for age, sex,
and at least the first five principal components, as well as cohort-specific covariates when
necessary. The GWAS summarized data are publicly available and can be downloaded
from http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST0
10000/GCST009413 (accessed on 26 July 2021).
2.3. eQTL Data
The SMR analyses utilized cis-eQTL genetic variants as instrumental variables (IVs)
for gene expression. Because eQTL data for the eye were unavailable, eQTL data for
blood from Genotype-Tissue Expression (GTEx) and Consortium for the Architecture of Gene
Genes 2023,14, 1027 3 of 13
Expression (CAGE) were used for the SMR analyses. The V7 release of the GTEx eQTL data
for blood [
29
] included 338 participants, while the CAGE eQTL data for blood [
30
] included
2765 participants. The eQTL data can be downloaded at https://cnsgenomics.com/data/
SMR/#eQTLsummarydata (accessed on 26 July 2021).
2.4. SMR Analysis
The Mendelian analyses were conducted using the method implemented in the SMR
software version 1.3.1 [
31
]. SMR applies the principles of MR, integrating GWAS and eQTL
summary statistics to explore the pleiotropic association between gene expression and
a trait. In SMR, genetic variants are used as the IVs, and estimation of the pleiotropic
association can be made because the inherited genetic variants are independent of poten-
tially confounding factors [
32
]. Here, ‘pleiotropic association’ between gene expression
and IOP refers to either pleiotropy (i.e., both gene expression and IOP are affected by the
same causal variant) or causality (i.e., the effect of a causal variant on IOP is mediated
by gene expression). In either case, gene expression is the exposure but not an outcome
(
i.e., SNP > gene
expression). SMR is not designed to examine whether a trait influences
gene expression.
The SMR analysis followed a similar approach as described in a previous publica-
tion [
33
], using all the default settings in SMR software. Details about the settings adopted
in the SMR analyses can be found in Table S1. The multiple testing was adjusted by the
false discovery rate (FDR).
2.5. TWAS Analysis
We also conducted a TWAS analysis [
34
] integrating the GWAS summary statistics
of IOP and pre-computed gene expression weights to further explore genes whose cis-
regulated expression is associated with IOP. This approach imputed gene expression from
summarized GWAS data to test its association with the phenotype of interest. The effect
size of the expression of a gene for a trait can be viewed as a linear combination of genetic
effects on a trait, with weights calculated based on the correlation between SNPs and
gene expression while accounting for linkage disequilibrium (LD) among the SNPs. The
weights are generally pre-computed using data from a relatively small set of reference
individuals for whom both gene expression and genetic variations (SNPs) are available [
34
].
Although this TWAS approach is conceptually similar to SMR, it is less strict than SMR
in that it aims to identify genes whose genetically controlled expression is associated
with a disease, while SMR aims to identify genes that are pleiotropically associated with
a disease. As we observed multiple significant genes located in 17q21.31, for this risk
locus, we performed joint/conditional analysis, a process to identify which genes represent
independent associations (i.e., jointly significant), and which genes are not significant
after accounting for the predicted expression of other genes in the region (i.e., marginally
significant). The analyses were performed by using FUSION software. We applied the
weights that were pre-computed from the GTEx v7 whole-blood reference expression panel
and adopted all the default settings in FUSION.
The data curation and statistical/bioinformatical analysis were performed using R
version 4.1.1, R Foundation for Statistical Computing, Vienna, Austria (https://www.r-
project.org/, accessed on 27 April 2023), PLINK 1.9 (https://www.cog-genomics.org/
plink/1.9/, accessed on 27 April 2023), SMR version 1.3.1 (https://yanglab.westlake.edu.
cn/software/smr, accessed on 27 April 2023), and FUSION (http://gusevlab.org/projects/
fusion/, accessed on 27 April 2023).
Genes 2023,14, 1027 4 of 13
3. Results
3.1. Basic Information of the Summarized Data
The CAGE eQTL data have a much larger number of participants compared to the
GTEx eQTL data (2765 vs. 338), as well as a larger number of eligible probes (8524 vs. 4543).
After checking the allele frequencies between the datasets and performing LD pruning,
there were more than 6.2 million eligible SNPs in each SMR analysis (Table 1).
3.2. Pleiotropic Association with IOP
Using the GTEx eQTL data, our SMR analysis identified 19 genes that are pleiotrop-
ically associated with IOP (Tables 2and S2), with RP11-259G18.3 (ENSG00000262539.1;
β
[SE] = 0.12 [0.03], P
SMR
= 2.66
×
10
−6
; Figure 2), KANSL1-AS1 (ENSG00000214401.4;
β[SE] = 0.13 [0.03]
, P
SMR
= 2.78
×
10
−6
; Figure 2), and RP11-259G18.2 (ENSG00000262500.1;
β
[SE] = 0.14 [0.03], P
SMR
= 2.91
×
10
−6
; Figure 2) being the top three genes. Most
of the identified genes are located on chromosome 17, except for a few such as ABO
(ENSG00000175164.9,
β
[SE] = 0.21 [0.05], P
SMR
= 6.40
×
10
−6
; Supplementary
Figure S1
).
Using the CAGE eQTL data, our SMR analysis identified 25 unique genes that are pleio-
tropically associated with IOP (Tables 2and S3), with LRRC37A4 (ILMN_2393693;
β[SE] = −0.12 [0.03]
, P
SMR
= 1.19
×
10
−5
; Figure 3), MGC57346 (ILMN_1784428;
β[SE] = 0.15
[0.03], P
SMR
= 1.19
×
10
−5
; Figure 4), and RNF167 (ILMN_1794726;
β
[SE] = 0.15 [0.03],
P
SMR
= 1.53
×
10
−5
; Figure 5) being the top three genes. Similarly, most of the identi-
fied genes are located on chromosome 17, except for a few such as SGTB (ILMN_2109343;
β[SE] = −0.16 [0.04]
, P
SMR
= 3.45
×
10
−5
; Supplementary Figure S2). Four genes, including
DAB2,LOC644297,LRRC37A4, and NSF, were each tagged by two probes.
Genes 2023, 14, x FOR PEER REVIEW 6 of 15
ILMN_1701998 AFAP1 4 rs62290601 1.26 × 10−179 8.11 × 10−5 −0.127 0.033 9.23 × 10−5 0.009 3.35 × 10−4
ILMN_2330845 NSF 17 rs199446 1.03 × 10−10 7.35 × 10−7 0.736 0.187 8.50 × 10−5 0.257 3.35 × 10−4
ILMN_1706511 TEF 22 rs4822025 1.02 × 10−27 5.19 × 10−5 0.324 0.085 1.49 × 10−4 0.381 4.80 × 10−4
ILMN_1737195 CENP
K
5 rs154940 3.27 × 10−299 1.58 × 10−4 −0.100 0.027 1.75 × 10−4 0.002 5.09 × 10−4
The GWAS summarized data were provided by the study of Bonnemaijer et al. [28] and can be
downloaded at hp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-
GCST010000/GCST009413 (accessed on 26 July 2021). The CAGE and GTEx eQTL data can be down-
loaded at hps://cnsgenomics.com/data/SMR/#eQTLsummarydata (accessed on 26 July 2021). Only
the top ten significant results are shown for each SMR analysis. For the complete significant results,
please see Tables S1 and S2. Top SNP means the top cis-eQTL and was used as the instrument vari-
able. PeQTL is the p-value of the top associated cis-eQTL in the eQTL analysis, and PGWAS is the p-value
for the top associated cis-eQTL in the GWAS analysis. Beta is the estimated effect size in the SMR
analysis, SE is the corresponding standard error, PSMR is the p-value for the SMR analysis, PHEIDI is
the p-value for the HEIDI test, and the Q value is the adjusted p-value found using FDR. The FDR
was calculated at p = 10−3 threshold. CHR, chromosome; HEIDI, heterogeneity in dependent instru-
ments; IOP, intraocular pressure; SNP, single-nucleotide polymorphism; SMR, summary-based
MendelianrRandomization; QTL, quantitative trait loci; FDR, false discovery rate; GWAS, genome-
wide association studies.
Figure 2. Pleiotropic association of RP11-259G18.3, KANSL1-AS1, and RP11-259G18.2 with IOP. Top
plot, grey dots represent the -log10 (p values) for SNPs from the GWAS of IOP, with solid rhombuses
indicating that the probes pass the HEIDI test. Middle plot, eQTL results. Boom plot, location of
genes tagged by the probes. GWAS, genome-wide association studies; SMR, summary-based Men-
delian randomization; HEIDI, heterogeneity in dependent instruments; eQTL, expression quantita-
tive trait loci; IOP, intraocular pressure.
Figure 2.
Pleiotropic association of RP11-259G18.3,KANSL1-AS1, and RP11-259G18.2 with IOP.
Top plot, grey dots represent the
−
log10 (pvalues) for SNPs from the GWAS of IOP, with solid
rhombuses indicating that the probes pass the HEIDI test. Middle plot, eQTL results. Bottom plot,
location of genes tagged by the probes. GWAS, genome-wide association studies; SMR, summary-
based Mendelian randomization; HEIDI, heterogeneity in dependent instruments; eQTL, expression
quantitative trait loci; IOP, intraocular pressure.
Genes 2023,14, 1027 5 of 13
Table 1. Basic information on the eQTL and GWAS data.
Data Source Total Number of Participants Number of Eligible Genetic Variants or Probes
SMR using GTEx eQTL data
eQTL 338 4543
GWAS 31,269 6,432,507
SMR using CAGE eQTL data
eQTL 2765 8524
GWAS 31,269 6,212,127
CAGE, Consortium for the Architecture of Gene Expression; eQTL, expression quantitative trait loci; GWAS, genome-
wide association studies; GTEx, Genotype-Tissue Expression; SMR, summary-based Mendelian randomization.
Table 2. The top ten genes showing significant pleiotropic association with IOP.
eQTL Data Probe Gene CHR TopSNP PeQTL PGWAS Beta SE PSMR PHEIDI Q Value
GTEx
ENSG00000214401.4 KANSL1-AS1 17 rs199534 2.42 ×10−76 1.24 ×10−60.125 0.027 2.78 ×10−60.022 1.84 ×10−5
ENSG00000262500.1 RP11-259G18.2 17 rs199451 2.19 ×10−66 1.19 ×10−60.141 0.030 2.91 ×10−60.049 1.84 ×10−5
ENSG00000262539.1 RP11-259G18.3 17 rs199534 5.96 ×10−81 1.24 ×10−60.120 0.026 2.66 ×10−60.046 1.84 ×10−5
ENSG00000175164.9 ABO 9 rs12216891 1.20 ×10−22 3.70 ×10−70.208 0.046 6.40 ×10−60.412 3.04 ×10−5
ENSG00000264070.1 DND1P1 17 rs112578465 1.20 ×10−60 5.00 ×10−60.120 0.027 1.07 ×10−50.370 4.08 ×10−5
ENSG00000214425.2 LRRC37A4P 17 rs79501144 1.11 ×10−102 1.04 ×10−5−0.115 0.027 1.57 ×10−50.526 4.50 ×10−5
ENSG00000263503.1 RP11-707O23.5 17 rs111273167 1.88 ×10−75 1.08 ×10−50.118 0.028 1.90 ×10−50.530 4.50 ×10−5
ENSG00000204650.9 CRHR1-IT1 17 rs1724390 1.21 ×10−53 7.53 ×10−60.251 0.058 1.66 ×10−50.893 4.50 ×10−5
ENSG00000238083.3 LRRC37A2 17 rs17426174 6.85 ×10−38 1.63 ×10−50.161 0.039 4.28 ×10−50.002 9.04 ×10−5
ENSG00000196526.6 AFAP1 4 rs62290601 7.45 ×10−80 8.11 ×10−5−0.096 0.025 1.12 ×10−40.030 2.12 ×10−4
CAGE
ILMN_1794726 RNF167 17 rs238243 2.22 ×10−175 1.23 ×10−50.147 0.034 1.53 ×10−50.627 1.48 ×10−4
ILMN_2393693 LRRC37A4 17 rs113661667 5.03 ×10−228 9.86 ×10−6−0.124 0.028 1.19 ×10−50.003 1.48 ×10−4
ILMN_1784428 MGC57346 17 rs62057067 2.43 ×10−157 8.99 ×10−60.146 0.033 1.19 ×10−50.010 1.48 ×10−4
ILMN_1680353 NSF 17 rs199442 5.94 ×10−15 3.84 ×10−70.612 0.144 2.11 ×10−50.108 1.53 ×10−4
ILMN_2109343 SGTB 5 rs42884 4.56 ×10−137 2.68 ×10−5−0.159 0.038 3.45 ×10−50.015 1.67 ×10−4
ILMN_1743621 C17orf69 17 rs113029914 8.60 ×10−34 1.01 ×10−50.334 0.081 3.32 ×10−50.005 1.67 ×10−4
ILMN_1701998 AFAP1 4 rs62290601 1.26 ×10 −179 8.11 ×10−5−0.127 0.033 9.23 ×10−50.009 3.35 ×10−4
ILMN_2330845 NSF 17 rs199446 1.03 ×10−10 7.35 ×10−70.736 0.187 8.50 ×10−50.257 3.35 ×10−4
ILMN_1706511 TEF 22 rs4822025 1.02 ×10−27 5.19 ×10−50.324 0.085 1.49 ×10−40.381 4.80 ×10−4
ILMN_1737195 CENPK 5 rs154940 3.27 ×10−299 1.58 ×10−4−0.100 0.027 1.75 ×10−40.002 5.09 ×10−4
The GWASsummarized data were provided by the study of Bonnemaijer et al. [
28
] and can be downloaded at http:
//ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009413 (accessed
on 26 July 2021). The CAGE and GTEx eQTL data can be downloaded at https://cnsgenomics.com/data/SMR/
#eQTLsummarydata (accessed on 26 July 2021). Only the top ten significant results are shown for each SMR
analysis. For the complete significant results, please see Tables S1 and S2. Top SNP means the top cis-eQTL and
was used as the instrument variable. P
eQTL
is the p-value of the top associated cis-eQTL in the eQTL analysis, and
P
GWAS
is the p-value for the top associated cis-eQTL in the GWAS analysis. Beta is the estimated effect size in
the SMR analysis, SE is the corresponding standard error, P
SMR
is the p-value for the SMR analysis, P
HEIDI
is the
p-value for the HEIDI test, and the Q value is the adjusted p-value found using FDR. The FDR was calculated at
p= 10−3
threshold. CHR, chromosome; HEIDI, heterogeneity in dependent instruments; IOP, intraocular pressure;
SNP, single-nucleotide polymorphism; SMR, summary-based MendelianrRandomization; QTL, quantitative trait
loci; FDR, false discovery rate; GWAS, genome-wide association studies.
3.3. Cis-Regulated Gene Expression in Association with IOP
We found 18 significant genes whose expression was associated with IOP by FU-
SION analysis after correction for multiple testing (FDR < 0.05; Table S4), with RP11-
259G18.2 (ENSG00000262500.1; Z = 4.87, P
TWAS
= 1.13
×
10
−6
), RP11-259G18.3 (ENSG000-
00262539.1; Z = 4.85, P
TWAS
= 1.26
×
10
−6
), and NUP160 (ENSG00000030066.9; Z =
−
4.62,
PTWAS = 3.83 ×10−6
) being the top three genes. Most of the significant genes are located
on chromosome 17. Of the 18 significant genes, 12 were also identified by the SMR analysis
using the GTEx eQTL data, including RP11-259G18.2,RP11-259G18.3,ABO,CRHR1-IT1,
KANSL1-AS1,RP11-707O23.5,LRRC37A2,DND1P1,LRRC37A4P,AFAP1,TEF, and MEI1.
Genes 2023,14, 1027 6 of 13
Four were identified by the SMR analysis using the CAGE eQTL data, including LRRC37A2,
AFAP1,TEF, and MEI1. Therefore, these four genes were identified by FUSION and
the two SMR analyses (Table S4). The joint/conditional tests for 17q21.31 indicated that
RP11
−
259G18.2 was jointly significant (i.e., independent association), and seven genes were
marginally significant, including RP11-259G18.3,CRHR1-IT1,KANSL1-AS1,RP11-707O23.5,
LRRC37A2,DND1P1, and LRRC37A4P (Figure 6).
Genes 2023, 14, x FOR PEER REVIEW 7 of 15
Figure 3. Pleiotropic association of LRRC37A4 with IOP. Top plot, grey dots represent the -log10 (p
values) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the HEIDI
test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS, genome-
wide association studies; SMR, summary-based Mendelian randomization; HEIDI, heterogeneity in de-
pendent instruments; eQTL, expression quantitative trait loci; IOP, intraocular pressure.
Figure 4. Pleiotropic association of MGC57346 with IOP. Top plot, grey dots represent the -log10 (p val-
ues) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the HEIDI
test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS, genome-
wide association studies; SMR, summary-based Mendelian randomization; HEIDI, heterogeneity in de-
pendent instruments; eQTL, expression quantitative trait loci; IOP, intraocular pressure.
Figure 3.
Pleiotropic association of LRRC37A4 with IOP. Top plot, grey dots represent the
−
log10
(
pvalues
) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the
HEIDI test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS,
genome-wide association studies; SMR, summary-based Mendelian randomization; HEIDI, hetero-
geneity in dependent instruments; eQTL, expression quantitative trait loci; IOP,
intraocular pressure
.
Genes 2023, 14, x FOR PEER REVIEW 7 of 15
Figure 3. Pleiotropic association of LRRC37A4 with IOP. Top plot, grey dots represent the -log10 (p
values) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the HEIDI
test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS, genome-
wide association studies; SMR, summary-based Mendelian randomization; HEIDI, heterogeneity in de-
pendent instruments; eQTL, expression quantitative trait loci; IOP, intraocular pressure.
Figure 4. Pleiotropic association of MGC57346 with IOP. Top plot, grey dots represent the -log10 (p val-
ues) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the HEIDI
test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS, genome-
wide association studies; SMR, summary-based Mendelian randomization; HEIDI, heterogeneity in de-
pendent instruments; eQTL, expression quantitative trait loci; IOP, intraocular pressure.
Figure 4.
Pleiotropic association of MGC57346 with IOP. Top plot, grey dots represent the
−
log10
(
pvalues
) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the
HEIDI test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS,
genome-wide association studies; SMR, summary-based Mendelian randomization; HEIDI, hetero-
geneity in dependent instruments; eQTL, expression quantitative trait loci; IOP,
intraocular pressure
.
Genes 2023,14, 1027 7 of 13
Genes 2023, 14, x FOR PEER REVIEW 8 of 15
Figure 5. Pleiotropic association of RNF167 with IOP. Top plot, grey dots represent the -log10 (p values)
for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the HEIDI test.
Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS, genome-wide
association studies; SMR, summary-based Mendelian randomization; HEIDI, heterogeneity in depend-
ent instruments; eQTL, expression quantitative trait loci; IOP, intraocular pressure.
3.3. Cis-Regulated Gene Expression in Association with IOP
We f oun d 18 sig nificant genes whose expression was associated with IOP by FUSION
analysis after correction for multiple testing (FDR < 0.05; Table S4), with RP11-259G18.2
(ENSG00000262500.1; Z = 4.87, P
TWAS
= 1.13 × 10
−6
), RP11-259G18.3 (ENSG00000262539.1; Z
= 4.85, P
TWAS
= 1.26 × 10
−6
), and NUP160 (ENSG00000030066.9; Z = −4.62, P
TWAS
= 3.83 × 10
−6
)
being the top three genes. Most of the significant genes are located on chromosome 17. Of
the 18 significant genes, 12 were also identified by the SMR analysis using the GTEx eQTL
data, including RP11-259G18.2, RP11-259G18.3, ABO, CRHR1-IT1, KANSL1-AS1, RP11-
707O23.5, LRRC37A2, DND1P1, LRRC37A4P, AFAP1, TEF, and MEI1. Four were identified
by the SMR analysis using the CAGE eQTL data, including LRRC37A2, AFAP1, TEF, and
MEI1. Therefore, these four genes were identified by FUSION and the two SMR analyses
(Table S4). The joint/conditional tests for 17q21.31 indicated that RP11−259G18.2 was
jointly significant (i.e., independent association), and seven genes were marginally signif-
icant, including RP11-259G18.3, CRHR1-IT1, KANSL1-AS1, RP11-707O23.5, LRRC37A2,
DND1P1, and LRRC37A4P (Figure 6).
Figure 5.
Pleiotropic association of RNF167 with IOP. Top plot, grey dots represent the
−
log10
(
pvalues
) for SNPs from the GWAS of IOP, with solid rhombuses indicating that the probes pass the
HEIDI test. Middle plot, eQTL results. Bottom plot, location of genes tagged by the probes. GWAS,
genome-wide association studies; SMR, summary-based Mendelian randomization; HEIDI, hetero-
geneity in dependent instruments; eQTL, expression quantitative trait loci; IOP,
intraocular pressure
.
Genes 2023, 14, x FOR PEER REVIEW 9 of 15
Figure 6. Joint/conditional analysis of TWAS significant loci on 17q21.31. The top panel of the
joint/conditional plot displays all genes that are in the loci (usually gray), with marginally significant
TWAS association genes highlighted in blue, and jointly significant genes in green. The boom panel
is the Manhaan plot of the original GWAS summary statistics data before (gray) and after (blue)
conditioning on the green genes. TWAS, transcriptome-wide association study; GWAS, genome-
wide association study.
4. Discussion
In this study, we identified multiple genes showing a pleiotropic association with
IOP through SMR and TWAS approaches. Our results confirmed findings from previous
studies and revealed novel genes related to IOP regulation.
In our SMR analyses, the IVs were based on eQTL data, and the exposure was (tran-
scriptome-wide) gene expression. The GWAS used genetic data imputed based on the
HRC and included 11 cohorts from the International Glaucoma Genetics Consortium
(IGGC) [28]. The GTEx eQTL data were based on deceased donors [29], and the CAGE
eQTL data were based on samples from five cohorts [30]. These cohorts were not part of
the GWAS summary results for IOP. Therefore, there is no overlap between the samples.
MR can be conducted based on data from one sample or two samples. The summary as-
sociation results came from the same individuals in the one-sample MR, and from differ-
ent, potentially overlapping sets of individuals in the two-sample MR [35]. We chose the
‘two-sample MR’ over the ‘one-sample MR’ for several reasons: (1) The eQTL data are
unavailable for the subjects in the GWAS data; (2) using the association results from the
same or partially overlapping samples may introduce weak instrument bias [35]; and, (3)
the power of an MR can be greatly increased by using a two-sample MR approach [36,37].
In our study, most of the genes showing a pleiotropic association with IOP are near
17q21.31 (chr17: 40900001-44900000, GRCh37/hg19), a structurally complex and evolu-
tionarily dynamic region of the genome [38–40]. This region contains a ~970 kb inversion
of the MAPT locus in populations of European ancestry [41]. MAPT (microtubule-associ-
ated protein tau) is associated with both the ganglion cell inner plexiform layer (GCIPL)
and the retinal nerve fiber layer (RNFL), indicating that it might impact glaucoma patho-
genesis through modulation of retinal thickness [42]. The MAPT locus has two divergent
haplotypes, H1 (direct orientation) and H2 (inverted orientation), with distinct functional
impacts [38]. Although both GTEx and CAGE do have eQTL data for MAPT, it was
dropped in the SMR analysis because SMR only includes cis-eQTL with a p-value < 5 ×
10−8 (The minimum p-value is 1.41 × 10−4 for cis-eQTL in the GTEx and 2.62 × 10−7 in the
CAGE). Despite that, some of the genes identified in our study were reported to be asso-
ciated with MAPT haplotypes. For example, LRRC37A4 is the top-hit gene in the SMR
analysis using the CAGE eQTL data, and the H1 haplotype of MAPT is associated with an
Figure 6.
Joint/conditional analysis of TWAS significant loci on 17q21.31. The top panel of the
joint/conditional plot displays all genes that are in the loci (usually gray), with marginally significant
TWAS association genes highlighted in blue, and jointly significant genes in green. The bottom panel
is the Manhattan plot of the original GWAS summary statistics data before (gray) and after (blue)
conditioning on the green genes. TWAS, transcriptome-wide association study; GWAS, genome-wide
association study.
4. Discussion
In this study, we identified multiple genes showing a pleiotropic association with IOP
through SMR and TWAS approaches. Our results confirmed findings from previous studies
and revealed novel genes related to IOP regulation.
In our SMR analyses, the IVs were based on eQTL data, and the exposure was
(transcriptome-wide) gene expression. The GWAS used genetic data imputed based on
the HRC and included 11 cohorts from the International Glaucoma Genetics Consortium
(IGGC) [
28
]. The GTEx eQTL data were based on deceased donors [
29
], and the CAGE
eQTL data were based on samples from five cohorts [
30
]. These cohorts were not part of the
Genes 2023,14, 1027 8 of 13
GWAS summary results for IOP. Therefore, there is no overlap between the samples. MR
can be conducted based on data from one sample or two samples. The summary association
results came from the same individuals in the one-sample MR, and from different, poten-
tially overlapping sets of individuals in the two-sample MR [
35
]. We chose the ‘two-sample
MR’ over the ‘one-sample MR’ for several reasons: (1) The eQTL data are unavailable for
the subjects in the GWAS data; (2) using the association results from the same or partially
overlapping samples may introduce weak instrument bias [
35
]; and, (3) the power of an
MR can be greatly increased by using a two-sample MR approach [36,37].
In our study, most of the genes showing a pleiotropic association with IOP are near
17q21.31 (chr17: 40900001-44900000, GRCh37/hg19), a structurally complex and evolution-
arily dynamic region of the genome [
38
–
40
]. This region contains a ~970 kb inversion of
the MAPT locus in populations of European ancestry [
41
]. MAPT (microtubule-associated
protein tau) is associated with both the ganglion cell inner plexiform layer (GCIPL) and
the retinal nerve fiber layer (RNFL), indicating that it might impact glaucoma pathogen-
esis through modulation of retinal thickness [
42
]. The MAPT locus has two divergent
haplotypes, H1 (direct orientation) and H2 (inverted orientation), with distinct functional
impacts [
38
]. Although both GTEx and CAGE do have eQTL data for MAPT, it was dropped
in the SMR analysis because SMR only includes cis-eQTL with a p-value < 5
×
10
−8
(The
minimum p-value is 1.41
×
10
−4
for cis-eQTL in the GTEx and 2.62
×
10
−7
in the CAGE).
Despite that, some of the genes identified in our study were reported to be associated with
MAPT haplotypes. For example, LRRC37A4 is the top-hit gene in the SMR analysis using
the CAGE eQTL data, and the H1 haplotype of MAPT is associated with an increased
expression of it [
41
]. Moreover, several other identified genes in the 17q21.31 region are
either associated with IOP or act collectively in influencing IOP or associated traits. For
instance, a GWAS identified 139 genetic loci associated with the macular thickness (MT), in-
cluding genetic variants in KANSL1,LRRC37A4P-MAPK8IP1P2, and NSF [
43
]. In addition,
KANSL1-AS1 (identified in GTEx), LRRC37A2 (identified in CAGE), and OR7E14P were
found to form a regulatory cluster in influencing both IOP and MT [
44
]. Together, these
findings suggest that 17q21.31 is crucial for IOP regulation. However, the exact pathogene-
sis remains unclear. Further investigation is needed to elucidate the exact functions of this
region and examine its biological role in influencing IOP and the pathogenesis
of glaucoma
.
We found a significant pleiotropic association between ABO and IOP using GTEx
eQTL data. ABO (Alpha 1-3-N-Acetylgalactosaminyltransferase and Alpha 1-3-Galactosyl-
transferase) is located on chromosome 9q34.2 and encodes proteins related to the first
discovered blood group system [
45
]. It has seven exons and six introns [
46
]. A variation in
ABO forms the basis of the ABO blood group [
47
]. Genetic variants in ABO are associated
with various health conditions, such as diabetes, thromboembolism, myocardial infrac-
tions, atherosclerosis, and stroke [
48
,
49
]. In a previous multi-ancestry meta-analysis, the
International Glaucoma Genetics Consortium (IGGC) revealed a novel ABO polymorphism
(rs8176693) associated with IOP [
50
]. A later meta-analysis showed that the ABO polymor-
phism rs8176741 was significantly associated with IOP, vertical cup–disc ratio (VCDR), and
cup area [
51
]. Despite these encouraging findings, the exact mechanisms underlying the
observed association between genetic variants in ABO and IOP remain to be elucidated.
More research is needed to understand the functions and roles of ABO in influencing IOP.
In our study, we found that some genes, such as AFAP1, showed a pleiotropic associa-
tion with IOP. AFAP1 (Actin Filament Associated Protein 1) is located on 4p16.1 and encodes
a protein that binds and crosslinks filaments [
52
,
53
]. Actin cytoskeleton-modulating signals
are involved in the regulation of aqueous outflow and IOP [
54
,
55
]. Two SNPs in AFAP1
(rs4619890 and rs11732100) have been reported to be associated with POAG in GWAS
studies [
56
,
57
]. In European-ancestry populations, the two SNPs are moderately associated
with another SNP in AFAP1 (rs28795989) linked to IOP [
25
]. Another GWAS found that
rs6816389 in AFAP1 is associated with IOP in European-ancestry participants [
58
]. More-
over, the expression of AFAP1 was detected in the trabecular meshwork, retina (including
retinal ganglion cells [RGCs]), and optic nerve of a normal human eye and a glaucoma-
Genes 2023,14, 1027 9 of 13
tous eye [
57
]. Together, existing evidence implies AFAP1’s potential involvement in the
pathogenesis of glaucoma.
We also identified several other genes that are not on 17q21.31 but show a significant
pleiotropic association with IOP regulation, such as SGTB and TEF. The SGTB gene, also
known as the small glutamine-rich tetratricopeptide repeat (TPR)-containing beta, is located
on 5q12.3 and belongs to the SGT (small glutamine-rich TPR-containing protein) family.
SGT proteins have been associated with a variety of biological processes, including neuronal
synaptic transmission, cell cycle regulation, protein folding, and apoptosis [
59
–
61
]. Previous
research discovered that SGTB interacts with Brother of CDO (BOC) and modulates its
surface presence. This subsequently leads to JNK activation, which, in turn, facilitates
neuronal differentiation and the growth of neurites [
62
]. It was found that genetic variants
near SGTB are related to IOP or corneal thickness (CCT) [
63
,
64
]. The TEF gene, also known
as thyrotroph embryonic factor, is located on 22q13.2. The TEF protein belongs to the
PAR (proline and acidic amino acid-rich) subfamily of bZIP transcription factors [
65
]. The
proteins encoded by these genes have recently been demonstrated to regulate the expression
of many enzymes and molecules involved in detoxification and drug metabolism [
8
]. A
previous study found that the genetic polymorphism rs6519240 near TEF is associated with
refractive error [
66
]. However, studies on the relationship between SGTB,TEF, and IOP
regulation are scarce. Further investigation is necessary to elucidate the role of these genes
in IOP regulation.
An SMR analysis relies on three key assumptions. First, the genotype is associated
with gene expression. Second, confounding factors that bias the associations between gene
expression and IOP are not associated with the genotype. Third, the genotype is related
to IOP only through its association with gene expression. For assumption 1, we used
the default p-value threshold of 5
×
10
−8
to select the top associated eQTL in our SMR
analyses. Therefore, the genetic variants selected as IVs are indeed strongly associated with
gene expression, and weak instruments are unlikely to be a big concern. Assumption 2
is difficult to verify directly, as SMR analyses use summarized data. The assumption is
often based on the biological belief that genotypes are unrelated to confounding factors,
such as socioeconomics and behavioral characteristics [
32
]. For assumption 3, horizontal
pleiotropy was found in about half of the significant causal relationships in MR, which
could introduce distortions as high as 201% in the causal estimates. Horizontal pleiotropy
could induce false-positive causal findings in up to 10% of the relationships [
67
]. We
observed some pleiotropic associations with significant HEIDI tests, suggesting horizon-
tal pleiotropy (Table 2). Therefore, caution should be exercised when interpreting the
corresponding findings.
Our study has limitations. The eQTL data are based on limited sample sizes, which
may affect the statistical power. Additionally, the eQTL data have limited eligible probes.
As a result, we may have missed some important genes. Despite this, the power of SMR
has been examined through extensive simulation studies. The simulations showed that
SMR was equivalent to MR analysis if the genotype, gene expression, and phenotype data
were from the same sample. The power of SMR could be greatly increased if the eQTL
data and GWAS summary results were from two independent samples with very large
sample sizes [
31
]. We believe that concerns about the power of our SMR analyses are
minimal, especially in the SMR analysis using the CAGE eQTL data. The SMR approach
cannot differentiate between pleiotropy and causality. We used eQTL data from blood
because usable eQTL data from the eye are unavailable. Future studies using eye eQTL
data are needed to validate our findings. Our SMR analyses used data from participants
of European ancestry. Since the prevalence of POAG is ethnicity-specific, it is reasonable
to postulate that the GWAS results might also be ethnicity-specific. Therefore, our results
might not be generalized to other ethnic populations.
Genes 2023,14, 1027 10 of 13
5. Conclusions
We identified several genes that are pleiotropically associated with IOP. Our results
indicate that the 17q21.31 genomic region could be crucial for IOP regulation. Future
studies are necessary to clarify the collective actions of the genes identified in the 17q21.31
genomic region and the roles of the identified genes on the other chromosomes in the
IOP regulation.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/genes14051027/s1, Figure S1: Pleiotropic association of ABO
with IOP; Figure S2: Pleiotropic association of SGTB with IOP; Table S1: Default parameters in the
SMR analyses; Table S2: Genes showing significantly pleiotropic association with IOP using GTEx
eQTL data; Table S3: Genes showing significantly pleiotropic association with IOP using CAGE eQTL
data; Table S4: Genes that showed transcriptome-wide significant associations with IOP.
Author Contributions:
Conceptualization: Z.Y., J.Y. and W.Y. methodology: Z.Z., Y.Z., G.Y. and J.Y.;
formal analysis and investigation: Z.Z., Y.Z., G.Y. and J.Y.; writing—original draft preparation: Z.Y.
and J.Y.; writing—review and editing: J.Y. and W.Y.; funding acquisition: W.Y.; supervision: W.Y. All
authors have read and agreed to the published version of the manuscript.
Funding:
The study was supported by the Beijing Tianjin Hebei Basic Research Cooperation Project
(J200006), the Pharmaceutical Collaborative Innovation Research Project of the Beijing Science
and Technology Commission (L192062), and the National Key Research and Development Project
(2018YFC2000803). Dr. Jingyun Yang’s research was supported by NIH/NIA grants P30AG10161,
R01AG15819, R01AG17917, R01AG033678, R01AG36042, U01AG61356, and 1RF1AG064312–01.
Institutional Review Board Statement:
Ethical review and approval were waived for this study
because the study only used publicly available summary data.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All the data generated or analyzed during this study are publicly
available as specified in Section 2of this paper. Specifically, the eQTL data can be downloaded at https:
//cnsgenomics.com/data/SMR/#eQTLsummarydata (accessed on 26 July 2021), and the GWAS
summarized data can be downloaded at http://ftp.ebi.ac.uk/pub/databases/gwas/summary_
statistics/GCST009001-GCST010000/GCST009413 (accessed on 26 July 2021).
Conflicts of Interest: The authors declare no conflict of interest.
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