Abstract
Previous studies have demonstrated the genetic correlations between
type 2 diabetes, obesity and dyslipidemia, and indicated that many
genes have pleiotropic effects on them. However, these pleiotropic
genes have not been well-defined. It is essential to identify
pleiotropic genes using systematic approaches because systematically
analyzing correlated traits is an effective way to enhance their
statistical power. To identify potential pleiotropic genes for these
three disorders, we performed a systematic analysis by incorporating
GWAS (genome-wide associated study) datasets of six correlated traits
related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA
(meta-analysis using canonical correlation analysis). Meta-CCA is an
emerging method to systematically identify potential pleiotropic genes
using GWAS summary statistics of multiple correlated traits. 2,720
genes were identified as significant genes after multiple testing
(Bonferroni corrected p value < 0.05). Further, to refine the
identified genes, we tested their relationship to the six correlated
traits using VEGAS-2 (versatile gene-based association study-2). Only
the genes significantly associated (Bonferroni corrected p value <
0.05) with more than one trait were kept. Finally, 25 genes (including
two confirmed pleiotropic genes and eleven novel pleiotropic genes)
were identified as potential pleiotropic genes. They were enriched in 5
pathways including the statin pathway and the PPAR (peroxisome
proliferator-activated receptor) Alpha pathway. In summary, our study
identified potential pleiotropic genes and pathways of type 2 diabetes,
obesity and dyslipidemia, which may shed light on the common biological
etiology and pathogenesis of these three disorders and provide
promising insights for new therapies.
Introduction
Type 2 diabetes is a serious chronic metabolic disorder characterized
by hyperglycemia, insulin resistance (IR), destruction of pancreatic
beta cells and impairment in insulin secretion [[44]1]. Obesity,
another serious universal health problem characterized by excess
visceral fat and high Waist-Hip Ratio (WHR)) and general obesity
(defined as having a Body Mass Index (BMI) of 25 or higher, is related
to several chronic diseases including type 2 diabetes [[45]2].(Both
type 2 diabetes and obesity are associated with dyslipidemia [[46]2,
[47]3], which is characterized by hypertriglyceridemia,
hypercholesterolemia, decreased HDL (high-density lipoprotein) and/or
increased LDL (low-density lipoprotein). The coexistence of the three
diseases is common among the populations, which increases the
prevalence of other serious fatal diseases such as CVD (cardiovascular
diseases) [[48]4, [49]5] and stroke [[50]6–[51]8].
These three disorders are closely connected. Firstly, clinical
observation and epidemiological data show that a number of type 2
diabetes patients are obese with dyslipidemia before receiving
intervention or therapy [[52]9]. Secondly, these three disorders have
common risk factors and pathophysiological bases. For instance, a long
term high fat diet is a significant risk factor for type 2 diabetes
[[53]10], obesity and dyslipidemia [[54]2]. IR is one of the common
pathophysiological bases for these three diseases [[55]1, [56]11].
Moreover, previous studies have reported genetic correlations among
these three disorders and indicated that many genes have pleiotropic
effects [[57]12–[58]14]. For example, the SREBF1 gene was identified as
a pleiotropic gene in the progression of type 2 diabetes, obesity, and
dyslipidemia [[59]12]. In addition, common pathways that could
influence these three disorders were discovered in recent years, such
as the AMPK signal pathway (AMP-activated protein kinase signal
pathway) and the JNK-1 pathway [[60]11, [61]15, [62]16].
The pleiotropic genes and pathways of the three diseases can partially
explain the common biological pathogenesis of the three diseases. It is
essential to identify pleiotropic genes that exert their influence on
potentially common biological etiology and pathogenesis of these three
disorders using systematic analysis approaches. Pleiotropic genes and
their effects have been successfully identified in bivariate analyses
of type 2 diabetes with obesity, type 2 diabetes with dyslipidemia, and
obesity with dyslipidemia. Hasstedt et al [[63]17] performed bivariate
analyses of type 2 diabetes with BMI and type 2 diabetes with WHR,
which identified significant pleiotropy loci (chromosome 13 at 26–30
MB) of type 2 diabetes with both. Li et al [[64]3] found a stronger
correlation between dyslipidemia associated genes (APOB, APOE-C1-C2,
CETP, CYP7A1 GCKR, MLXIPL, PLTP, TIMD4) and glycemic traits including
FG (fasting glucose) and HOMA-IR (homeostasis model assessment for IR,
an important index for evaluating IR, calculated by FG* FI(fasting
insulin)/22.5), which revealed the pleiotropic effects of
dyslipidemia-associated genes on glycemic traits. Despite the bivariate
analyses of these three diseases which have identified pleiotropic
genes in recent years, multivariate analyses have not yet been
performed. Undertaking multivariate analysis is desirable because it
allows us to more systemically explore the common underlying genetic
architecture and common etiology of these three disorders. Another
important advantage of multivariate analysis is that it increases the
statistical power for identifying associated genes exerting influences
on multiple traits, which leads to more novel insights for drug gable
gene targets compared to univariate and bivariate analyses [[65]18].
Meta-CCA (meta-analysis using canonical correlation analysis) [[66]19],
a new systematical multivariate analysis tool recently proposed by Anna
Cichonska, allows multivariate analyses between multiple SNPs and
multiple traits [[67]19], which enriches the pleiotropic information by
combining correlation signals among multiple traits.
Inspired by this, we performed the current work to identify potential
pleiotropic genes for type 2 diabetes, obesity, and dyslipidemia using
Meta-CCA. We used six correlated quantitative traits reported to be
established related factors for type 2 diabetes, obesity or
dyslipidemia, including FG, FI, BMI, WHR, HDL and triglyceride (TG).
Interestingly, our findings indicated that five pathways and 25 genes
(including two confirmed pleiotropic genes and eleven novel pleiotropic
genes) were potential pleiotropic genes for type 2 diabetes, obesity,
and dyslipidemia. These findings yielded some genetic basis for the
common biological etiology and pathogenesis, and thus provided
promising insights for a potential common therapy for the three
disorders.
Materials and methods
GWAS datasets and processing
Step1: Annotation genes to SNPs, and SNP prune
The large-scale GWAS datasets of the six correlated traits in the
present study were downloaded from
[68]http://diagram-consortium.org/2015_ENGAGE_1KG/ [[69]20, [70]21].
The glycemic traits (FG and FI) data [[71]20] were derived from a
meta-analysis of 13 original GWAS studies, including FG data for 46,694
individuals and FI data for 24,245 individuals. The obesity-related
data (BMI and WHR) [[72]20] were derived from a meta-analysis of 22
original GWAS studies, including BMI data for 87,048 individuals and
WHR data for 54,572 individuals. The lipids (HDL and TG) data [[73]21]
were derived from a meta-analysis of 22 original GWAS studies,
including 62,166 individuals for both HDL and TG. The large-scale GWAS
datasets were collected by the European Network for Genetic and Genomic
Epidemiology (ENGAGE) Consortium, and all samples were from individuals
of European ancestry (The details were shown in [74]Table 1). The
large-scale GWAS datasets were the largest datasets that included all
six correlated traits and contained all the information needed to
conduct the analyses in the Meta-CCA framework in one ethnicity
(Caucasians). We selected the overlapped SNPs (9,411,134 SNPs) of the
six traits to perform the multivariate analysis.
Table 1. Details and phenotypic pearson correlation coefficients of the six
traits in European ancestry.
Traits Number of SNPs Number of individuals The phenotypic correlation
structures between traits
FG FI BMI WHR HDL TG
FG 9,967,161 46,694 1 0.35 0.24 0.17 -0.15 0.19
FI 9,837,043 24,245 0.35 1 0.52 0.39 -0.37 0.40
BMI 9,953,164 87,048 0.24 0.52 1 0.51 -0.32 0.30
WHR 9,954,793 54,572 0.17 0.39 0.51 1 -0.30 0.33
HDL 9,549,054 62,166 -0.15 -0.37 -0.32 -0.30 1 -0.52
TG 9,544,498 62,166 0.19 0.40 0.30 0.33 -0.52 1
[75]Open in a new tab
FG Stands for: Fasting glucose.
FI Stands for: Fasting insulin.
BMI Stands for: Body Mass Index.
WHR Stands for: Waist-Hip Ratio.
HDL Stands for: High-density lipoprotein.
TG Stands for: Triglyceride.
The analytical workflow of our study is presented in [76]Fig 1.
Firstly, we completed the gene annotation according to the 1000 Genome
datasets using PLINK1.9. We downloaded the reference data, which
contained 26,291 genes from the website:
[77]https://www.cog-genomics.org/static/bin/plink/glist-hg19. We
recognized the transcript including all SNPs (both exonic and intronic
SNPs) in the region as genes. We selected the overlapped SNPs between
the six traits and the reference data in our study. After the gene
annotation of SNPs, we pruned the SNPs for each gene using the
parameter r^2 = 0.01 [[78]22]. r is the Pearson correlation coefficient
between any of the two SNPs in one region. The purpose of SNP pruning
is to reduce potential biases caused by the linkage disequilibrium (LD)
among SNPs [[79]22].
Fig 1. The analytical workflow of the present work.
[80]Fig 1
[81]Open in a new tab
For the present work, the GWAS datasets we used provided not only the p
value of the SNPs but also the regression coefficient β and the
standard error (SE). We normalized the regression coefficient β before
conducting the Meta-CCA using the following equation (n is the sample
size in the corresponding GWAS dataset of each trait):
[MATH:
βnormal=β
n*SE
:MATH]
(1)
Statistical analysis
Step 2: Meta-CCA analysis
The present work was conducted by the Meta-CCA R package, and each gene
as a unit for Meta-CCA analysis. We recognized the transcript including
all SNPs (both exonic and intronic SNPs) in the region as genes. The
program of Meta-CCA required three basic data inputs: the genotypic
correlation structures between SNPs (∑XX), the correlation coefficients
between SNPs and traits (∑XY), and the phenotypic correlation
structures between traits (∑YY) ([82]Table 1) [[83]19]. In Meta-CCA,
∑XX was estimated using a reference SNP dataset such as the HapMap data
or the 1000 Genomes data representing the study population. It is
better if ∑XX was estimated from the target population or the same
ethnicity from the database used [[84]19]. As all of the participants
in our study were Caucasians, we calculated the ∑XX based on the 1000
Genomes data and downloaded the reference data from 1000 Genomes
Project (Phase 3 European (CEU) population reference data
([85]https://www.cog-genomics.org/static/bin/plink/glist-hg19). ∑XY was
estimated by the normalized regression coefficient β:
[MATH: ∑XY=XTYN−1=<
mo>(β11β12⋯β
1Pβ21β22⋯β2P⋮⋮<
/mo>⋱⋮βG1βG2⋯βGP) :MATH]
(2)
G and P are the number of genotypic and phenotypic variables,
respectively.
And ∑YY was estimated based on the Phenotypic Pearson Correlation
Coefficients (YY), which was shown in [86]Table 1 [[87]23]. In the
original study, all of the participants had the clinical data of the
six traits (FG, FI, BMI, WHR, HDL and TG). The phenotypic Pearson
correlation coefficients between the six traits were calculated from
the same corresponding individuals [[88]23]. The details of the exact
procedures of Meta-CCA program were described in Anna Cichonska’s paper
[[89]19]. After Meta-CCA, we obtained the output data of the
relationship between the genes and the six traits. We used a p value <
0.05 (after Bonferroni correction) as the nominal significance
threshold.
Step 3: Gene-based association analysis
To refine the identified genes by Meta-CCA, we tested their specific
relationships with the six traits respectively using VEGAS-2 (Versatile
Gene-based Association Study–2) [[90]24, [91]25], a gene-based
algorithm widely used for gene-based p value calculation using GWAS
summary statistics. VEGAS-2, an approach provides the correlation of
all the SNPs in one gene region for one single trait, also has lower
false positive rates compared with other gene-based approaches [[92]26,
[93]27]. We selected the potential pleiotropic genes significantly
associated with more than one trait (P value< 0.05/2720, Bonferroni
correction) [[94]28] after obtaining the gene-based p-value of each
gene for the six traits using VEGAS-2.
Functional annotation and gene enrichment analyses
Step 4: Pathway and GO (Gene Ontology) term enrichment analyses of the
potential pleiotropic genes
To explore the biological functions of the identified potential
pleiotropic genes, we performed pathway enrichment analyses and GO
enrichment analyses for the potential pleiotropic genes using Enrichr
(a web server tool for gene set enrichment analysis:
[95]http://amp.pharm.mssm.edu/Enrichr/) [[96]29]. The Pathway and GO
Term enrichment analyses also provide a better understanding of the
polygenic associations and the potential mechanisms of the biological
process. With this program, we used hypergeometric tests and Fisher’s
exact tests for the statistical analysis. Benjamini-Hochberg corrected
p value <0.05 in the enrichment analysis is used as the threshold for
significance.
Results
After gene annotation and SNP pruning, 21,209 genes were left to
conduct the Meta-CCA analysis in our study. The number of SNPs in each
gene ranged from 1 to 280; the average was 13. We used the threshold of
0.05/21209 (Bonferroni correction) as our target alpha level for the
Meta-CCA analysis [[97]28]. For the Meta-CCA analysis, 2,720 genes with
the p value < 0.05/21209 were identified as potential pleiotropic genes
for the six correlated quantitative traits. After Meta-CCA analysis, we
tested the 2,720 genes’ relationship to the six traits using VEGAS-2.
Only the genes significantly associated (Bonferroni corrected p value <
0.05) with more than one trait were kept [[98]24, [99]25]. There were
31, 0, 75, 1, 225 and 185 significant genes (Bonferroni corrected p
value < 0.05) for FG, FI, BMI, WHR, HDL, and TG respectively. By
screening the genes based on the results of gene-based p value, a total
of 25 associated genes related to more than one trait in VEGAS-2
analysis were identified as potential pleiotropic genes for type 2
diabetes, obesity and dyslipidemia. The details are shown in [100]Table
2.
Table 2. The features of the significant potential pleiotropic genes.
Genes r[101]^* P-value for one single trait
BMI[102]^# WHR[103]^# FG[104]^# FI[105]^# HDL[106]^# TG[107]^#
SIK3[108]^a[[109]30, [110]31] 2.63E-01 6.08E-01 9.69E-02 4.19E-01
5.79E-01 1.00E-06 1.00E-06
CETP[111]^a[[112]3, [113]32] 2.48E-01 2.70E-01 4.04E-01 5.90E-01
6.25E-01 1.00E-06 1.00E-06
LIPC[114]^a[[115]33, [116]34] 2.27E-01 2.02E-01 6.78E-01 3.58E-02
2.05E-01 1.00E-06 1.00E-06
GALNT2[117]^a[[118]21, [119]35] 2.05E-01 1.93E-01 6.87E-01 6.66E-01
3.09E-01 1.00E-06 1.00E-06
SNX17[120]^a[[121]36] 1.10E-01 3.08E-01 6.89E-03 6.00E-06 1.19E-03
9.79E-02 1.00E-06
GCKR[122]^b[[123]21, [124]37, [125]38] 9.73E-02 2.06E-01 6.89E-03
1.00E-06 6.10E-05 1.09E-01 1.00E-06
LIPC-AS1[126]^c 9.07E-02 5.01E-01 7.92E-01 1.09E-01 1.71E-01 1.00E-06
1.00E-06
LPL[127]^a[[128]39, [129]40] 9.05E-02 5.51E-01 3.83E-01 1.57E-01
6.69E-01 1.00E-06 1.00E-06
HLA-DQA1[130]^d[[131]21, [132]41] 8.42E-02 6.47E-01 2.18E-01 4.26E-01
8.84E-01 1.20E-05 1.00E-06
IFT172[133]^c 8.40E-02 3.57E-01 6.32E-03 4.00E-06 3.13E-04 8.69E-02
1.00E-06
KRTCAP3[134]^c 8.28E-02 3.30E-01 7.51E-03 1.00E-05 1.46E-03 1.30E-01
1.00E-06
CSGALNACT1[135]^c 8.26E-02 6.89E-02 7.66E-01 6.93E-01 2.44E-01 1.00E-05
1.00E-06
APOA5[136]^a[[137]42, [138]43] 8.23E-02 5.21E-01 3.53E-01 2.22E-01
4.35E-01 1.00E-06 1.00E-06
EIF2B4[139]^c 7.28E-02 3.48E-01 7.18E-03 5.00E-06 1.48E-03 7.09E-02
1.00E-06
GTF3C2[140]^c 7.07E-02 4.26E-01 1.19E-02 1.20E-05 1.61E-03 7.19E-02
1.00E-06
ZNF513[141]^c 7.00E-02 3.19E-01 6.02E-03 1.10E-05 1.41E-03 9.69E-02
1.00E-06
NRBP1[142]^c 6.21E-02 3.22E-01 9.40E-03 9.00E-06 1.60E-03 1.46E-01
1.00E-06
FNDC4[143]^c 6.19E-02 3.39E-01 5.16E-03 1.00E-06 1.00E-04 1.02E-01
1.00E-06
APOA1[144]^a[[145]44, [146]45] 5.52E-02 5.43E-01 2.43E-01 3.54E-01
4.51E-01 1.00E-06 1.00E-06
FADS1[147]^b[[148]21, [149]46, [150]47] 5.22E-02 8.79E-02 5.49E-01
3.00E-06 6.44E-01 1.00E-06 1.00E-06
TMEM258[151]^c 5.15E-02 7.69E-02 6.77E-01 1.00E-06 7.05E-01 1.00E-06
1.00E-06
FEN1[152]^d[[153]21, [154]48] 4.82E-02 1.35E-01 6.67E-01 1.00E-06
6.54E-01 1.00E-06 1.00E-06
ZPR1[155]^d[[156]21, [157]49] 4.36E-02 5.01E-01 3.51E-01 2.39E-01
4.41E-01 1.00E-06 1.00E-06
APOC2[158]^d[[159]21, [160]50] 4.04E-02 1.10E-01 5.32E-01 6.99E-02
7.43E-01 1.00E-06 1.00E-06
CLPTM1[161]^c 3.98E-02 1.96E-01 6.07E-01 9.79E-02 7.72E-01 1.00E-06
1.00E-06
[162]Open in a new tab
* Stands for: Canonical correlation value for the six correlated traits
which is the result of Meta-CCA.
^# Stands for: P-value for each trait which is the result of gene-based
analysis.
^a Stands for: This gene hasn’t been identified by any previous GWAS
studies for type 2 diabetes and obesity, but has been reported to be
associated with hyperglycemia, obesity and dyslipidemia in other types
of previous studies.
^b Stands for: This gene was previously reported to be associated with
type 2 diabetes, obesity and dyslipidemia, which was confirmed by our
present study.
^c Stands for: Novel pleotropic gene for type 2 diabetes, obesity and
dyslipidemia.
^d Stands for: This gene hasn’t been identified by any previous studies
for obesity, but has been reported to be associated with type 2
diabetes and dyslipidemia in previous study.
Interestingly, four of the top five significant genes (GALNT2, SNX17,
CETP, LIPC) were regarded as dyslipidemia associated genes in the
original GWAS study [[163]21]. In particular, two (GALNT2, SNX17) were
also suggested to be associated with type 2 diabetes and obesity in
previous studies [[164]35, [165]36]. All 25 potential pleiotropic genes
were identified as the associated genes/loci (with at least one SNP p
value < 5*10^−8) for TG in the original GWAS study, while eight of
these 25 genes (GALNT2, GCKR, LPL, FADS1, LIPC, CETP, APOA5, ZPR1) were
reported to be TG associated genes in the original GWAS study after
validation [[166]21]. Specifically, two of these 16 genes (FADS1, GCKR)
have been identified as susceptibility candidate genes for type 2
diabetes in early GWAS studies [[167]37, [168]47, [169]51].
For the results of the pathway enrichment analyses, significant
enrichment was observed in five human pathways conforming to the
up-to-date 2016 Wiki-pathway database ([170]Table 3) [[171]29,
[172]52], such as Statin Pathway (WP430), Composition of Lipid
Particles (WP3601), Triacylglyceride Synthesis (WP325), PPAR
(Peroxisome proliferator-activated receptor) Alpha Pathway (WP2878),
Fatty Acid Beta Oxidation (WP143). The most significant pathway was the
statin pathway (WP430), which contains six potential pleiotropic genes
(CETP, LIPC, APOC2, APOA1, LPL, APOA5), suggesting a close relationship
between the statin pathway and the three disorders.
Table 3. Pathway enrichment analysis of the potential pleiotropic genes.
Term (Pathway) P-value Benjamini-Hochberg P-value Genes
Statin Pathway(WP430) 1.44E-12 4.03E-10 CETP, LIPC, APOC2, APOA1, LPL,
APOA5
Composition of Lipid Particles(WP3601) 1.44E-07 1.35E-06 CETP, LPL,
APOA1
Triacylglyceride Synthesis(WP325) 4.07E-04 1.89E-03 LIPC, LPL
PPAR Alpha Pathway(WP2878) 4.79E-04 1.91E-03 APOA1, APOA5
Fatty Acid Beta Oxidation(WP143) 8.21E-04 2.55E-03 LIPC, LPL
[173]Open in a new tab
GO enrichment analyses (conforming to the up-to-date 2017 database)
[[174]29, [175]52] revealed that the biological functions of these
pleiotropic genes were mainly involved in the metabolism of lipids. For
the GO biological process, the top five significant GO terms were
Triglyceride homeostasis (GO:0070328), Cellular triglyceride
homeostasis (GO:0035356), Positive regulation of lipoprotein lipase
activity (GO:0051006), Cholesterol homeostasis (GO:0042632) and Reverse
cholesterol transport (GO:0043691). For the GO cellular component, the
top five significant GO terms were Very-low-density lipoprotein
particle (GO:0034361), Spherical high-density lipoprotein particle
(GO:0034366), Early endosome (GO:0005769), Early endosome lumen
(GO:0031905) and Integral component of Golgi medial cisterna membrane
(GO:1990703). For the GO molecular function, the top five significant
GO terms were Intermembrane cholesterol transfer activity (GO:0120020),
Cholesterol transporter activity (GO:0017127), Cholesterol binding
(GO:0015485), Phosphatidylcholine-sterol O-acyltransferase activator
activity (GO:0060228), and High-density lipoprotein particle receptor
binding (GO:0070653). The results of the GO enrichment analysis are
summarized in [176]Table 4. GO Term is the gene collection of different
arborescence types [[177]53]. Therefore, some GO Term is the branch of
others. As a result, there is a considerable overlap of genes between
related GO-terms such as “Triglyceride homeostasis” and “Cellular
triglyceride homeostasis”.
Table 4. Top five significant GO term enrichment analysis of the potential
pleiotropic genes.
Term (GO Biological Process) P-value Benjamini-Hochberg P-value Genes
Triglyceride homeostasis (GO:0070328) 3.19E-16 2.44E-13 CETP, GCKR,
LIPC, APOC2, LPL, APOA1, APOA5
Cellular triglyceride homeostasis (GO:0035356) 2.21E-15 8.42E-13 CETP,
GCKR, LIPC, APOC2, LPL, APOA1, APOA5
Positive regulation of lipoprotein lipase activity (GO:0051006)
2.29E-11 5.83E-09 LIPC, APOC2, LPL, APOA1, APOA5
Cholesterol homeostasis (GO:0042632) 3.06E-11 5.83E-09 CETP, LIPC,
APOC2, LPL, APOA1, APOA5
Reverse cholesterol transport (GO:0043691) 1.58E-10 2.01E-08 CETP,
LIPC, APOC2, APOA1, APOA5
Term (GO Cellular Component) P-value Benjamini-Hochberg P-value Genes
Very-low-density lipoprotein particle (GO:0034361) 7.77E-07 1.17E-04
APOC2, APOA1, APOA5
Spherical high-density lipoprotein particle (GO:0034366) 4.18E-05
3.16E-03 APOC2, APOA1
Early endosome (GO:0005769) 8.43E-04 2.09E-02 SNX17, APOC2, APOA1
Early endosome lumen (GO:0031905) 1.11E-03 2.09E-02 SNX17, APOC2, APOA1
Integral component of Golgi medial cisterna membrane (GO:1990703)
1.08E-03 2.09E-02 CSGALNACT1, GALNT2
Term (GO Molecular Function) P-value Benjamini-Hochberg P-value Genes
Intermembrane cholesterol transfer activity (GO:0120020) 9.56E-07
1.71E-04 CETP, APOA1, APOA5
Cholesterol transporter activity (GO:0017127) 4.95E-06 4.43E-04 CETP,
APOA1, APOA5
Cholesterol binding (GO:0015485) 2.06E-05 6.69E-04 CETP, APOA1, APOA5
Phosphatidylcholine-sterol O-acyltransferase activator activity
(GO:0060228) 2.24E-05 6.69E-04 APOA1, APOA5
High-density lipoprotein particle receptor binding (GO:0070653)
2.24E-05 6.69E-04 APOA1, APOA5
[178]Open in a new tab
In summary, our present work identified twenty-five potential
pleiotropic genes as well as the enriched pathways and GO terms of
potential pleiotropic genes for type 2 diabetes, obesity and
dyslipidemia.
Discussion
The present study, the first systemically multivariate analysis of
genomics data for type 2 diabetes, obesity and dyslipidemia jointly
using Meta-CCA, identified potential pleiotropic genes as well as
enriched pathways and GO terms. Importantly, two of the 25 identified
genes (GCKR, FADS1) were reported to be associated with type 2
diabetes, obesity and dyslipidemia in different prior studies [[179]21,
[180]37, [181]38, [182]46, [183]47], validated by our present study.
Other significant genes, excluding the genes that were reported to be
associated with hyperglycemia, obesity and dyslipidemia in other types
of previous studies, might be novel pleiotropic candidate genes (such
as LIPC-AS1, IFT172, KRTCAP3, CSGALNACT1, EIF2B4, GTF3C2, ZNF513,
NRBP1, FNDC4, TMEM258, and CLPTM1) for the three disorders. We
preformed the functional protein-protein interaction network analysis
for the potential pleiotropic candidate genes by using STRING 10.5
([184]https://string-db.org/cgi/input.pl). [185]Fig 2 shows that there
are interactions between most of the potential pleiotropic candidate
genes. The results not only revealed some of the shared genetic
components but also provided novel insights for exploring the potential
common biological pathogenesis of these three disorders.
Fig 2. The nodes represent proteins which were encoded by corresponding
genes, edges represent the protein-protein association, line color represents
types of interaction evidence (e.g., text mining, co-expression and so on).
[186]Fig 2
[187]Open in a new tab
All of the interacting proteins with an interaction score ≥ 0.15 (based
on previous study).
Many genes and pathways have pleiotropic effects on more than one
disease, a common phenomenon supported by this study of type 2
diabetes, obesity and dyslipidemia. Recently, animal experiments and
cross-sectional population-based studies have shown evidence of large
shared gene components. Pleiotropic genes have been successfully
identified in bivariate analyses of type 2 diabetes with obesity, type
2 diabetes with dyslipidemia, and obesity with dyslipidemia. However,
multivariate analysis had not previously been conducted for these three
disorders simultaneously. Systemically exploring the pleiotropic genes
and their effects on these three disorders is essential, and is
possible because of the accessibility of the GWAS summary statistics.
The advantages of Meta-CCA are listed as follows. Firstly, Meta-CCA can
detect correlations between multiple variants and multiple traits based
on GWAS summary statistics [[188]19], which might provide richer clues
for finding novel gene targets in multivariate analyses compared to the
univariate and bivariate analysis [[189]18]. For example, TMEM258, a
gene for adipose tissue regulation, was not identified by any previous
GWAS studies for type 2 diabetes and obesity, but was one of the novel
pleiotropic candidate genes for type 2 diabetes, obesity and
dyslipidemia identified in this study. Secondly and notably, Meta-CCA
can identify novel candidates, since some of the associations become
detectable only when multiple variants and multiple traits are tested
jointly [[190]19]. For example, CETP, a well-known gene for
dyslipidemia, was not identified by any previous GWAS studies for type
2 diabetes, but it was one of the novel findings in our study. Last but
not least, Meta-CCA is a cost-effective analytical method based on the
data of GWAS summary statistics, which provides an enlarged larger
effective sample size to detect potential pleiotropic genes for
multivariate traits. Meta-CCA and similar types of analyses are an
emerging and powerful tool for detection of pleiotropic genes of
multiple correlated traits using GWAS summary statistics.
Among the 25 potential pleiotropic genes, two genes, GCKR, and FADS1,
were suggested to be pleiotropic genes for type 2 diabetes, obesity and
dyslipidemia based on the results of previous studies [[191]21,
[192]37, [193]38, [194]46, [195]47]. GCKR, located in 2p23.3, which
encodes the protein belonging to the glucosidase regulatory subfamily,
which in turn inhibits glucosidase by binding to enzymes in pancreatic
islet cells and liver. A recent study found that the variants of GCKR
were associated with obesity in postmenopausal women [[196]38]. FADS1,
located in 11q12.2, encodes a protein that is a member of fatty acid
desaturases. FADS1 was reported to be related to type 2 diabetes by a
previous GWAS study, but the mechanism was still unknown [[197]51].
From a biochemistry point of view, eight (APOA5, APOA1, APOC2, CETP,
LPL, LIPC, GCKR, GALNT2) of the twenty-five potential pleiotropic genes
were involved in important metabolic routes. Details are summarized in
[198]Fig 3. APOA5, APOA1, and APOC2 encode lipoproteins which mainly
ferry TG, HDL, and VLDL (very low density lipoprotein), respectively.
CETP encodes cholesteryl ester-transfer protein, which transfers HDL
into VLDL and IDL (intermediate density lipoprotein) by involving the
transportation of cholesteryl ester. LPL is a lipoprotein lipase which
plays a critical role in lipid metabolism such as transferring VLDL
into IDL. The function of the protein hepatic triglyceride lipase
encoded by LIPC is important in catabolism of lipids, including
transferring IDL into LDL. GALNT2 and GCKR are involved in the
metabolism of glucose as mentioned above.
Fig 3. Eight potential pleiotropic genes (the italic) affected the three
disorders through these important metabolic routes.
[199]Fig 3
[200]Open in a new tab
From a biochemistry point of view, eight (APOA5, APOA1, APOC2, CETP,
LPL, LIPC, GCKR, GALNT2) of the twenty-five potential pleiotropic genes
were involved in important metabolic routes. APOA5, APOA1, and APOC2
encode lipoproteins which mainly ferry TG, HDL, and VLDL, respectively.
CETP encodes cholesteryl ester-transfer protein, which transfers HDL
into VLDL and IDL by involving the transportation of cholesteryl ester.
LPL is a lipoprotein lipase which plays a critical role in lipid
metabolism such as transferring VLDL into IDL. The function of the
protein hepatic triglyceride lipase encoded by LIPC is important in
catabolism of lipids, including transferring IDL into LDL. GALNT2 and
GCKR are involved in the metabolism of glucose. The dotted line stands
for the complex metabolic routes of gluconeogenesis.
Our results, as previously described, have identified 25 genes and five
pathways associated with type 2 diabetes, obesity, and dyslipidemia.
Interestingly, all 25 genes were identified as the associated
genes/loci for TG in the original GWAS study [[201]21], though just
eight of these 25 genes were refined in the validation stage [[202]21].
All five pathways were associated with the metabolism of lipids.
Specifically, two types of lipid-lowering drugs successfully targeted
the statin pathway and PPAR Alpha pathway respectively, suggesting that
abnormal plasma levels of lipids play a critical role in the common
biological pathogenesis of the three disorders. Some drugs targeted on
the statin pathway have been used successfully in therapy for type 2
diabetes patients with dyslipidemia. Another significant pathway is the
PPAR Alpha pathway. The PPAR pathway family, which includes the PPAR
Alpha pathway, the PPAR Beta pathway, and the PPAR Gamma pathway, plays
a key role in substance metabolism (including glucose metabolism, lipid
metabolism, and protein metabolism). Specifically, PPAR Alpha was a
core factor for fatty acid oxidation in liver, which was activated by
ligands or drugs such as fibrates, resulting in a decrease in serum
level of TG [[203]54]. PPAR Gamma was also an important factor for the
etiology of IR [[204]55]. Drugs targeting PPAR Gamma, such as
thiazolidinedione, were effective in the control of IR [[205]56].
Our present study also indicated that TG played an important role in
these three disorders, as all 25 potential pleiotropic genes were
identified as associated genes/loci for TG in the original GWAS study
(though eight of these 25 genes were reported to be TG associated genes
after validation) [[206]21]. For type 2 diabetes, hypertriglyceridemia
is the most common type of dyslipidemia [[207]57], which is mainly
induced by IR and impairment in insulin secretion. Further, genomic
studies [[208]57, [209]58] have indicated that hypertriglyceridemia has
a higher genetic correlation with type 2 diabetes than other types of
dyslipidemia. For obesity, most of the plasma TG is determined by the
level of VLDL-TG (the balance between synthesis and clearance of
VLDL-TG), and the synthesis of VLDL-TG is associated with total fat
mass and liver fat [[210]59]. Thus, the large amount of fat mass in
obese patients leads to increasing synthesis of VLDL-TG, but the
clearance of VLDL-TG remains unchanged. Hypertriglyceridemia is a
principal characteristic of dyslipidemia and is linked to many other
types of dyslipidemia such as decreased HDL level and increased small
dense LDL level [[211]60]. Above all, the metabolism of TG seems to
play a core role in the common biological pathogenesis of these three
disorders.
Our study not only provides a better understanding of the shared
genetic background for the three disorders, but also produced a list of
potential novel pleiotropic candidate genes for follow-up study in
further biological experiments. Some of the 25 pleiotropic genes
(LIPC-AS1, IFT172, KRTCAP3, CSGALNACT1, EIF2B4, GTF3C2, ZNF513, NRBP1,
FNDC4, TMEM258, and CLPTM1) were first reported to be associated with
type 2 diabetes, obesity and dyslipidemia. The findings of our present
work were not completely consistent with the findings in previous GWAS
studies or other types of systematically analysis studies in type 2
diabetes and other metabolic related diseases (The details of the
overlapped identified genes and the novel potential pleiotropic
candidate genes were shown in [212]Table 2). The reason for the
different findings in type 2 diabetes might be the using of different
datasets and different methods in these studies. For example, one
published work that using integrative omics data shown that 15 SNPs and
the corresponding genes were associated with type 2 diabetes [[213]61].
However, these genes were not identified by our present work. Our
present work did not identify all the genes that were identified in
GWASs or other types of studies, as it was only a supplementary study
to identify the potential pleiotropic genes for chronic complex
diseases. We hope that the potential novel pleiotropic candidate genes
can provide some clues for molecular biologists performing future
functional validation studies to determine whether the findings truly
have pathophysiological significance for type 2 diabetes, obesity and
dyslipidemia.
Conclusion
In this study, we identified and assessed some potential pleiotropic
genes and pathways for type 2 diabetes, obesity and dyslipidemia using
novel Meta-CCA analysis. The findings validated two previously
identified pleiotropic genes (GCKR, FADS1) for these three disorders
and highlighted another eleven significant genes (LIPC-AS1, IFT172,
KRTCAP3, CSGALNACT1, EIF2B4, GTF3C2, ZNF513, NRBP1, FNDC4, TMEM258, and
CLPTM1) as potential novel pleiotropic candidate genes for the three
disorders. Further, the potential pleiotropic genes were significantly
enriched in five pathways including the statin pathway and PPAR Alpha
pathway. In conclusion, our findings may yield novel insights into
exploring the common biological pathogenesis of these three disorders,
which ultimately may lead to the development of effective drug
therapies.
Acknowledgments