Download Identification of pleiotropic genes and gene sets underlying growth

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Animal (2016), 10:4, pp 550–557 © The Animal Consortium 2015
doi:10.1017/S1751731115002761
Identification of pleiotropic genes and gene sets underlying
growth and immunity traits: a case study on Meishan pigs
Z. Zhang1,2, Z. Wang1,2, Y. Yang1,2, J. Zhao1,2, Q. Chen1,2, R. Liao1,2, Z. Chen1,2, X. Zhang1,2,
M. Xue3, H. Yang3, Y. Zheng3, Q. Wang1,2† and Y. Pan1,2†
1
Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China; 2Shanghai Key Laboratory of
Veterinary Biotechnology, Shanghai 200240, PR China; 3National Station of Animal Husbandry, Beijing 100125, PR China
(Received 21 June 2015; Accepted 10 November 2015; First published online 22 December 2015)
Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a
valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL
intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an
effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and
pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs.
In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes
were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition,
19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343
growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also
sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide
polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to
the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency
between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL
overlapping intervals and can be generalized to explore the relationships between other traits.
Keywords: QTL overlaps, pleiotropy, growth, immunity
Implications
The relationship between immunity and growth traits is
interesting in pig breeding programs because it appears that
improvement in growth traits often compromises immune
performance. With genomic data of Meishan and three
European pig breeds, we identified putative genetic loci that
were related to immunity and growth and also defined some
loci that might play different directional roles in growth and
immune traits. The results of this study may help breeders
design balanced plans for breeding, and the methods can be
generalized to explore the relationships between other traits.
Introduction
Growth traits such as average daily gain and BW have
been improved because of the development of breeding
†
E-mail: [email protected]; [email protected]
technology for commercial needs. However, for immune
traits, little genetic progress has been made, which is mainly
due to the following reasons: ignorance of the improvement
in disease-resistance traits, the complexity of these traits and
the identification of having not enough trait-specific genes or
pathways (Zhao et al., 2012). Rauw had reviewed that the
new breeding technology could improve economic production efficiency as well as result in immunological problems
(Rauw et al., 1998). This type of trade-off exists in European
pigs and Meishan pigs. European pig breeds often perform
better than Meishan pigs in terms of growth traits (Müller
et al., 2000), but Meishan pigs perform better than European
pigs in terms of immune traits – for example, Meishan pigs
have higher neutrophil and monocyte counts than Large
White pigs (Clapperton et al., 2005). This phenomenon
suggests that making a balanced selection plan for the two
traits is important for breeders.
However, before a balanced selection plan can be
developed, the genetic background of the relationship
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Pleiotropy of growth and immunity on Meishan pigs
between the two trait complexes should be explored.
Genetic relationships among multiple traits often result from
pleiotropy of a gene and linkage disequilibrium (LD) between
the genes for the different traits (Bolormaa et al., 2014). The
former is known as biological pleiotropy, whereas the latter
is a type of spurious pleiotropy (Solovieff et al., 2013). In
this study, we investigated the genetic background of
relationships between immunity and growth not only from
the view of biological pleiotropy and spurious pleiotropy but
also with the idea that genes that are involved in the same
pathway could interact with one another, which can be
regarded as another type of pleiotropy – namely, mediated
pleiotropy (as reviewed by Solovieff et al., 2013).
In this study, we conducted our research via the exploration of quantitative trait loci (QTL) overlapping intervals.
With more and more QTLs having been integrated, a QTL
database (Hu et al., 2013) can be a potential resource
for studying the genetic mechanisms that underlie growth
and immune traits. Until now, there are more than 500
growth-related QTLs and ~300 immunity-related QTLs in the
pig QTL database. Moreover, many overlaps formed by these
growth-related and immunity-related QTLs have inspired us
to mine the information of the QTL database in order to
study the genetic relationships between them. QTL intervals,
however, often involve too many candidate genes to find the
true causal genes. Therefore, effective annotation methods
are needed to further identify and validate pleiotropic genes
and gene sets in the overlapping intervals of growth-related
and immunity-related QTLs. The objective of this study was
to explore the relationship between growth and immunity
traits from the view of QTL overlapping intervals.
Material and methods
Bioinformatics pipeline for the identification of pleiotropic
genes and gene sets
The general pipeline for the identification of pleiotropic gene
sets is shown in Figure 1. The details are described as
follows.
QTL data collection and pre-processing. Growth-related and
immunity-related QTLs were extracted from the pig QTL
database (http://www.animalgenome.org/cgi-bin/QTLdb/SS/
index). A Perl script was used to find overlaps of the two
types of QTLs. Some overlapping intervals could also overlap
with each other, and therefore we merged them into several
intervals that have no adjoining region between nonredundant intervals. The genes involved in these overlaps
and their Gene Ontology (GO) annotation were retrieved
using the BiomaRt package (Durinck et al., 2005).
Identification of pleiotropic genes. GOSim (Fröhlich et al.,
2007), which allows calculating the semantic similarity
between GO terms, was used to investigate whether the
genes identified by previous steps are related to growth,
immunity or both. First, we obtained the exact concepts
‘growth’ and ‘development’ that correspond to ‘growth’ and
many immune processes corresponding to ‘immunity’ using
MeSH database (Lowe and Barnett, 1994). We then selected
GO terms ‘development process,’ ‘growth’ and ‘immune
system process’ as the ‘model’ terms, and the semantic
similarity between these genes’ GO terms and ‘model’ terms
were evaluated by GOSim. As a result, whether a gene has
a relation to growth, immunity, both or neither can be
determined. The genes with both growth and immunity
function were regarded as pleiotropic genes. The relative
importance of these pleiotropic genes was evaluated to
classify them into three levels. The genes of the first level
are related to ‘development,’ ‘growth’ and ‘immunity’
simultaneously. Less important genes are related to ‘growth’
and ‘immunity,’ and those that belong to the third level are
genes related to ‘development’ and ‘immunity.’
Identification of pleiotropic linkage gene blocks. To further
explore the information of the genes involved in the overlaps,
we took the extent of LD between these genes into consideration. If the growth-related genes and immunity-related
genes defined in the previous step are both in an LD region,
the region can be considered to be a pleiotropic block with
2
growth and immune function. LD with average r0:3
values,
which is known as useful LD (Aerts et al., 2006), is ~100kb
for Western pigs and 10kb for Chinese pigs (Ai et al.,
2013 and 2015), and thus the length of the LD was set
conservatively as 10kb in this study.
Identification of pleiotropic pathways. In addition to
pleiotropic genes and pleiotropic gene linkage blocks, pathways can also be regarded as gene sets that include growthrelated genes and immunity-related genes simultaneously. In
the first step, genes involved in pig pathways were obtained
using KEGG’s API (Kyoto Encyclopedia of Genes and
Genomes’ application programming interface; Kanehisa and
Goto, 2000), and subsequently they were filtered according
to three criteria: (1) genes that cannot be mapped to overlaps
that are formed by growth-related and immunity-related
QTLs; (2) genes that are pleiotropic genes related to growth
and immunity; and (3) genes that are in the same LD regions,
which were defined in the previous step. Finally, we undertook
a statistical step to identify enriched pathways from all the
pathways that contain the remaining growth-related and
immunity-related genes simultaneously. For each pathway, we
calculated the P-value using a hypergeometric test (Mao et al.,
2005): (1) if there are N background genes that are involved in
all of the pig pathways, and among them there are n growthrelated and immunity-related QTL genes, and (2) if there are
m genes that can be mapped to a certain pathway and this
pathway includes M genes in total, then the P-value for this
certain pathway can be calculated as follows:
M
NM
m1
X i
ni
P ¼ 1
N
i¼0
n
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Zhang, Wang, Yang, Zhao, Chen, Liao, Chen, Zhang, Xue, Yang, Zheng, Wang and Pan
Figure 1 The pipeline flow chart for the identification of the three pleiotropic gene sets. QTLs = quantitative trait loci; LD = linkage disequilibrium.
We adjusted the P-value by ‘BH’ method in R (Benjamini and
Hochberg, 1995) and set 0.05 to be the default cut-off.
Identification of pleiotropic single nucleotide polymorphisms
(SNPs) in Meishan and European pigs
This step was designed to investigate the real genetic variants
located in these pleiotropic genes identified by previous steps.
Sequencing data from 284 individuals (parts of these data were
published by Wang et al., 2015) that belonged to four swine
breeds (161 Chinese Meishan, 49 Duroc, 37 Landrace and
37 Yorkshire pigs) were used to identify pleiotropic SNPs in
Meishan and European pigs. The DNA samples were sequenced
and genotyped using the genotyping by genome reducing
and sequencing protocol (http://klab.sjtu.edu.cn/GGRS/) (Chen
et al., 2013). A quality control test was performed based on the
criterion that minor allele frequency should be >0.01.
SNPs that passed the quality control test were mapped to
the following: (1) pleiotropic gene sets (pleiotropic genes,
pleiotropic linkage blocks and genes involved in significant
pathways) to obtain pleiotropic SNPs; and to (2) genes only
related growth or immunity to obtain only growth and
only immune SNPs. A Fisher’s exact test (Upton, 1992) was
performed to identify the SNPs whose allele frequency
differed between Meishan and European pigs (false discovery
rate (FDR) < 0.01). After the test, the significant SNPs
involved in all of the growth and immune genes can be
regarded as the cause for the difference between Meishan
and European pigs for the two traits. However, Chinese and
European pigs are so different that there will be frequency
differences for most SNPs, and therefore a hypergeometric
test using all of the SNPs as a negative control was
performed to validate our findings. The test was performed
for the following: (1) pleiotropic SNPs; (2) only growth and
only immune SNPs; and (3) the aggregate sets of 1 and 2.
Results
Overlaps formed by growth and immunity QTLs
A total of 2414 overlaps formed by 575 growth-related and
262 immunity-related QTLs were identified across the
porcine genome. Figure 2 shows whether growth-related and
immunity-related QTLs are involved in these overlaps. We
found that the number of overlapping QTLs takes up a large
portion of the two types of QTLs (79.8% for growth-related
QTLs and 88.1% for immunity-related QTLs), which indicates
that a relationship between growth and immune traits exists.
Furthermore, except for a few traits, most or all of the specific
QTLs are included in overlaps. After merging repeated
intervals, 55 non-redundant overlapping intervals were
finally identified. The distribution of all 2414 overlaps in the
non-redundant regions and across the genome is shown
in Figure 3. The results of discrete distribution χ 2 test
(P < 2.0 × 10 − 16) indicated that these overlaps were not
randomly distributed in non-redundant intervals (Figure 3a).
Identification of pleiotropic genes and gene sets
There are 1893 growth-related genes and 713 immunityrelated genes identified by GOSim in the overlapping intervals. As shown in Figure 4, 405 pleiotropic genes are shared
by the two gene sets. Moreover, there are 19 and 343
growth-related genes involved in pleiotropic LD blocks and
pathways, respectively. For immunity-related gene sets, 19
and 144 genes are included in pleiotropic LD blocks and
pathways, respectively. The number of all pleiotropic genes
involved in pleiotropic gene sets accounts for 40.5% and
79.7% for the size of the growth and immune gene
sets, respectively. For the pleiotropic genes, we identified
72 genes in the first level, nine genes in the second level and
324 genes in the third level according to their functional
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Pleiotropy of growth and immunity on Meishan pigs
Figure 2 The distribution of growth quantitative trait loci (QTLs) (a) and immune QTLs (b) within or beyond overlapping intervals. The x-axis represents
the QTLs’ abbreviated names and their full names are available in the pig QTL database (http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). The
y-axis represents the number of specific QTLs. Different colors stand for different statuses as to whether these QTLs are involved in overlapping intervals
(blue) or not (red). As a result, 79.8% of growth-related QTLs and 88.1% of immunity-related QTLs are within overlapping intervals.
Figure 3 The distribution of the number of overlaps included in the non-redundant intervals (a) and genome (b). The x-axis of (a) represents 55 nonredundant intervals. The y-axis of (a) and (b) both represent the number of all of the 2414 overlaps formed by growth-related quantitative trait loci
(QTLs) and immunity-related QTLs, located in either non-redundant intervals or chromosomes.
annotation importance. Limited by space, Supplementary
Table S1 lists only pleiotropic genes of the first level whose
annotation terms have at least one quantified similarity value
that is >0.5 between each ‘model’ term; more details are
available upon request. Their GO annotation accessions with
the maximum quantified similarity value between each
‘model’ term are also listed. The number of genes in the third
level is larger than those in the other two levels due to the
broader concept of development compared with growth.
These 81 pleiotropic genes in the first and second levels are
probably more important than those in the third level
because ‘growth’ is a more accurate term related to the
growth concept than ‘development.’
Moreover, 19 pleiotropic gene linkage blocks were
identified that involved 38 genes in total (Table 1). The size
of pleiotropic gene blocks is not very large because the
length of the linkage block was set to a conservative value
(10 kb). Growth-related genes in these blocks are all actually
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Zhang, Wang, Yang, Zhao, Chen, Liao, Chen, Zhang, Xue, Yang, Zheng, Wang and Pan
Figure 4 The classification of growth-related genes (a) and immunity-related genes (b) that we determined. This pie plot illustrates the situations of the
number of growth-related genes or immunity-related genes that are distributed in different classes. Except for the ‘growth only’ or ‘immune only’ class,
the pleiotropic gene set is composed of genes in the other three classes. Comparing the two pie plots, we can find that pleiotropic genes account for a
larger fraction of the immunity-related genes compared with the growth-related genes.
Table 1 Pleiotropic linkage disequilibrium blocks and the genes
involved
Block
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Chromosomes
Growth-related
genes
Immunity-related
genes
2
2
2
4
4
4
6
7
7
7
7
9
12
12
12
13
15
17
18
ZGLP1
GPX4
SLC23A1
SNAPIN
PPGRP-S
HORMAD1
B9D2
EHMT2
BYSL
TDRD6
CHURC1
CHI3L1
MKS1
POLDIP2
CTNS
ATG7
ABCB6
TRIB3
SMARCD3
ICAM-1
SBNO2
MZB1
ILF2
S100A9
CTSS
EXOSC5
C2
CCND3
PAF-AH
GPX2
ADORA1
EPX
VTN
SHPK
HRH1
ATG9A
RBCK1
NUB1
annotated by GO terms linked to ‘development;’ therefore,
these pleiotropic blocks play roles mainly in development
and immunity.
A total of 120 pathways were identified that included growth
genes and immune genes simultaneously, and 67 were
statistically significant (available upon request). Table 2 lists
pleiotropic pathways with P-values of ⩽1.0 × 10−8. The
distribution of the number of genes that were involved in
pleiotropic gene sets in the genome is shown in Figure 5. This
distribution does not fully correspond to the distribution of
overlaps in the genome (Figure 3b). For example, the number of
overlaps in chromosome 12, 13 and 14 are not very large, but a
large number of genes are distributed in these chromosomes,
which indicate that there are either long intervals for these
overlaps or an enrichment of genes in these chromosomes.
Pleiotropic gene sets in Meishan and European pigs
A total of 72 542 SNPs passed the quality control test, among
which 53 584 SNPs showed a statistically significant difference between Meishan and European pigs in terms of allele
frequencies by Fisher’s exact test (FDR < 0.01). A total of 971
high-confidence SNPs could be mapped to pleiotropic gene
sets, and among them 743 SNPs were statistically significant.
A total of 1603 SNPs were included in genes that were
related to only growth and related to only immunity, among
which 1260 SNPs were statistically significant.
The hypergeometric test results show that the pleiotropic
SNPs (P = 0.031), only growth and only immune SNPs
(P = 4.88 × 10−6) and their aggregate sets (P = 1.32 × 10−6),
are all statistically significant. Table 3 lists the summary
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Pleiotropy of growth and immunity on Meishan pigs
Table 2 Pleiotropic pathways with P-value ⩽ 1.00 × 10 −8 and the number of genes involved
Pathway ID
path:ssc04060
path:ssc04611
path:ssc04921
path:ssc04620
path:ssc05320
path:ssc04310
path:ssc04062
path:ssc04151
path:ssc04360
path:ssc05205
P-value
<2.00 × 10−16
<2.00 × 10−16
<2.00 × 10−16
8.16 × 10−12
5.86 × 10−11
5.57 × 10−10
1.37 × 10−9
3.17 × 10−9
3.89 × 10−9
1.00 × 10−8
Pathway definition
Cytokine–cytokine receptor interaction
Platelet activation
Oxytocin signaling pathway
Toll-like receptor signaling pathway
Autoimmune thyroid disease
Wnt signaling pathway
Chemokine signaling pathway
PI3K-Akt signaling pathway
Axon guidance
Proteoglycans in cancer
Number of growth
genes1
Number of immune
genes1
Number of all
genes2
20
19
20
5
1
37
13
52
34
47
53
3
3
31
23
1
32
15
1
2
258
132
161
101
51
128
173
328
119
212
1
The number of growth-related or immunity-related genes that we identified to be involved in the pathway.
The number of all the genes that are involved in the pathway.
2
Table 3 The number of single nucleotide polymorphisms (SNPs) and
genes or linkage disequilibrium (LD) blocks that are involved in each
gene set
Category Pleiotropy
SNP
Gene1
LD
Pathway Growth only Immunity only
329 (249) 36 (26) 606 (468) 1473 (1158)
129 (114) 10 (9) 192 (167) 413 (370)
130 (102)
46 (40)
The number of significant ones is shown in parenthesis.
1
The number of ‘genes’ for ‘LD’ is actually the count of LD blocks.
Figure 5 Distribution of the number of pleiotropic genes in the genome.
The x-axis represents different chromosomes. The y-axis stands for the
number of genes in the different pleiotropic gene sets. This figure offers a
perspective in terms of the genomes to view the distribution of
pleiotropic genes.
of these results, and more detailed information is available
on request.
Discussion
The relationship between the immune trait and growth trait
has been suggested by evidence not only from the standpoint
of physiology (Klasing, 1988; Borghetti et al., 2006;
Galina-Pantoja et al., 2006) but also from the viewpoint
of genetics (van der Most et al., 2011). Moreover, the
heritability of some immune traits and genetic relationships
that are associated with average daily gain was evaluated
under different environments (Clapperton et al., 2009).
Therefore, in this study, we considered the fact that a
relationship between immunity and growth as a starting
point exists, and we defined pleiotropic genes and gene sets
in relation to immunity and growth from the pig QTL
database and mapped real sequencing data to them to
demonstrate our findings on Meishan and European pigs.
QTL is a type of statistical association between genetic
variation and a trait. QTL intervals are often very large that
real causal variants may be obscured among many genes.
However, the annotation method enabled us to downsize the
long intervals and improve the confidence of the genes that
we identified. The number of overlapping QTLs accounts for a
higher portion of immune QTLs than growth QTLs (Figure 2a
and b), which most likely indicates that a correlation
between the two traits has a larger effect on immunity than
on growth. This phenomenon was proven in poultry – for
example, by means of meta-analysis, van der Most et al.
(2011) found that the selection for growth traits in poultry
indeed compromised the immune function, but the
selection for immunity traits did not consistently affect the
growth traits.
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Zhang, Wang, Yang, Zhao, Chen, Liao, Chen, Zhang, Xue, Yang, Zheng, Wang and Pan
GOSim can quantify the relationship between two GO
terms, which allows us to evaluate the genes’ functions.
Growth is the enlargement of a tissue or organism and is a
narrower biological concept than development. Thus, we
regarded the pleiotropic genes in the first and second levels
to be more important than those in the third level. As a
result, the 81 pleiotropic genes in the first and second levels
that were identified in our present study are most likely more
important genes. The result of GO annotation can also help
us understand the functional direction of the pleiotropic
genes or blocks. If a pleiotropic gene or linkage gene block
has a positive function of growth and a negative function of
immunity and vice versa, a selection of that gene must occur
cautiously. Although we cannot find these genes or gene
blocks directly as a result of a lack of expression profile data
or quantified research, we can resort to GO annotation and
publications to find some clues – for example, the pleiotropic
gene TNFAIP3 that we defined in the third level has the GO
terms ‘negative regulation of B cell activation’ and ‘negative
regulation of innate immune response,’ but it has been
proven to be a stimulus of cell growth, especially in cancer
cells (Vereecke et al., 2009). In addition, the gene HIF1A
(Supplementary Table S1) is annotated as ‘negative regulation of growth,’ but this gene actually improves the immune
capacity by activating embryonic hematopoiesis (Adelman
et al., 1999). There are some pleiotropic genes that have
the same functional direction as well – for example, the
pleiotropic gene SIX1 (Supplementary Table S1) plays a
positive role in many organs’ development, including the
thymus, which is very important for immune capacity (Klein
and Nikolaidis, 2005), and the gene DAB2IP, which we
defined in the third level with the GO term ‘negative
regulation of toll-like receptor 4 signaling pathway,’ can also
decrease the development speed of hepatocellular carcinoma
(Zhang et al., 2012). For pleiotropic genes with the same
functional direction, such as SIX1 and DAB2IP, it is much
easier than genes that have different functional directions for
breeding programs because the functional directions of the
concerned traits are not incompatible.
In the linkage analysis, the LD threshold was set at 10 kb.
In fact, the standards for determining whether genes are in a
LD region are dependent on many conditions. Even so, some
regulations had been found in which the difference of the
extent of LD between the European and Chinese pig breeds
extending up to 400 kb in Europe and ~ 10 kb in China
(Amaral et al., 2008). Ai et al. (2013 and 2015) also reported
2
that LD with average r0:3
values is ~100 kb for Western pigs
and 10 kb for Chinese pigs (Ai et al., 2013 and 2015). We
should note that many QTL mapping designs used families
that were developed by crossing European Wild Boar with a
commercial breed or crossing the Chinese Meishan breed
with a commercial breed (Rothschild et al., 2007). For
example, an overlap could be formed by a growth-related
QTL identified in a family generated by cross-breeding of
Duroc and Pietrain (Liu et al., 2007) and an immunity-related
QTL in a family generated by cross-breeding of Pietrain
and Meishan (Reiner et al., 2008). Therefore, to decrease
false-positive findings, we set the threshold to be a
conservative value – 10 kb.
Pathway annotation is to discover pleiotropic genes in a
biological function set. From another perspective, pathways
with growth-related genes and immunity-related genes that
are enriched could also be pleiotropic. In the present
study, 67 pathways that were statistically significant were
identified, and several of them have been proven by other
researchers. For example, ‘Chemokine signaling pathway
(path:ssc04062)’ (Table 2) was discovered to be a mediator
of immunity (Le et al., 2004) and has also been proven to be
related to cell growth and differentiation (Wong and Fish,
2003). A similar situation has also been found in the most
significant pathway – ‘Cytokine–cytokine receptor interaction (path:ssc04060)’ (Deverman and Patterson, 2009). Some
of these are concerned with cancer, such as ‘Pathways in
cancer (path:ssc05200)’ (Table 2). The reason is that some
immune processes or cytokines play roles in cancer regulation, and the growth of functional biological molecules can
also play an important part in tumor growth, such as with
‘Proteoglycans in cancer (path:ssc05205).’ Furthermore, the
significant pathway ‘Jak-STAT signaling pathway (path:
ssc04630)’ plays an important role in growth and immunity
because many cytokines and growth factors mediate their
effects via activation of this pathway (Wong and Fish, 2003).
The difference in the immune and growth traits between
Meishan and European pig breeds might be caused by the
statistically significant differences in genetic variants,
which can be mapped to immunity-related genes and
growth-related genes. These genes include pleiotropic genes,
pleiotropic gene sets and genes that are linked to either
growth or immunity. One of the causes of differences is that
breeders in developed countries pay attention to improving
production traits and care less about immune traits. Therefore, if an SNP is included in the pleiotropic gene sets and its
allele frequency differs between Meishan and European
pigs, then the reason could be that its advantageous allele
frequencies for growth traits are improving in the European
pig population and its advantageous allele frequency for
immune traits are retained in the Chinese Meishan pig
population. In other words, these pleiotropic SNPs are
putative contradictive loci that play an opposite directional
role in growth and immunity traits. The directional role of
these pleiotropic SNPs on immune and growth traits must
also be further validated based on large sample sizes and
phenotypic data; therefore, a breeding program for these
significant pleiotropic SNPs should be more careful than
considering only significantly different SNPs that are included
in genes that are related to either growth or immunity.
In conclusion, this study provides the viewpoint of
overlapping growth and immune QTLs to understand the
relationship between growth and immunity traits of pigs.
With many pleiotropic genes and gene sets having been
identified, the pleiotropic polymorphisms could also be
determined. Pig breeders might design a balanced breeding
program by making full use of the polymorphisms of
pleiotropic genes, LD blocks and pathways. The method used
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Pleiotropy of growth and immunity on Meishan pigs
in this study might be generalized to explore the relationships between other traits in the initial steps, especially when
there is a lack of phenotypic or expression profile data.
Fröhlich H, Speer N, Poustka A and Beissbarth T 2007. GOSim – an R-package
for computation of information theoretic GO similarities between terms and
gene products. BMC Bioinformatics 8, 166.
Acknowledgments
This work was supported by the National Natural Science
Foundation of China (No. 31472069, 31370043, U1402266),
National Key Technology Support Program (2012BAD12B08),
Shanghai agriculture science and technology project (2012(2-3))
and Shanghai Jiao Tong University Agri-X fund to YCP.
Hu ZL, Park CA, Wu XL and Reecy JM 2013. Animal QTLdb: an improved
database tool for livestock animal QTL/association data dissemination in the
post-genome era. Nucleic Acids Research 41, D871–D879.
Kanehisa M and Goto S 2000. KEGG: Kyoto Encyclopedia of Genes and
Genomes. Nucleic Acids Research 28, 27–30.
Klasing KC 1988. Nutritional aspects of leukocytic cytokines. The Journal of
Nutrition 118, 1436–1446.
Klein J and Nikolaidis N 2005. The descent of the antibody-based immune
system by gradual evolution. Proceedings of the National Academy of Sciences
of the United States of America 102, 169–174.
Supplementary material
For supplementary material/s referred to in this article, please
visit http://dx.doi.org/10.1017/S1751731115002761
References
Adelman DM, Maltepe E and Simon MC 1999. Multilineage embryonic
hematopoiesis requires hypoxic ARNT activity. Genes & Development 13, 2478–2483.
Aerts J, Megens H, Veenendaal T, Ovcharenko I, Crooijmans R, Gordon L, Stubbs L
and Groenen M 2006. Extent of linkage disequilibrium in chicken. Cytogenetic and
Genome Research 117, 338–345.
Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L, Zhang F, Zhang L, Cui L and He W
2015. Adaptation and possible ancient interspecies introgression in pigs
identified by whole-genome sequencing. Nature Genetics 47, 217–225.
Ai H, Huang L and Ren J 2013. Genetic diversity, linkage disequilibrium and
selection signatures in Chinese and Western pigs revealed by genome-wide SNP
markers. PLoS One 8, e56001.
Amaral AJ, Megens H-J, Crooijmans RP, Heuven HC and Groenen MA 2008.
Linkage disequilibrium decay and haplotype block structure in the pig. Genetics
179, 569–579.
Benjamini Y and Hochberg Y 1995. Controlling the false discovery rate: a
practical and powerful approach to multiple testing. Journal of the Royal
Statistical Society. Series B (Methodological) 57, 289–300.
Bolormaa S, Pryce JE, Reverter A, Zhang Y, Barendse W, Kemper K, Tier B, Savin K,
Hayes BJ and Goddard ME 2014. A multi-trait, meta-analysis for detecting
pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle.
PLoS Genetics 10, e1004198.
Borghetti P, De Angelis E, Saleri R, Cavalli V, Cacchioli A, Corradi A, Mocchegiani E
and Martelli P 2006. Peripheral T lymphocyte changes in neonatal piglets:
relationship with growth hormone (GH), prolactin (PRL) and cortisol changes.
Veterinary Immunology and Immunopathology 110, 17–25.
Galina-Pantoja L, Mellencamp MA, Bastiaansen J, Cabrera R, Solano-Aguilar G
and Lunney JK 2006. Relationship between immune cell phenotypes and pig
growth in a commercial farm. Animal Biotechnology 17, 81–98.
Le Y, Zhou Y, Iribarren P and Wang J 2004. Chemokines and chemokine
receptors: their manifold roles in homeostasis and disease. Cellular & Molecular
Immunology 1, 95–104.
Liu G, Jennen DG, Tholen E, Juengst H, Kleinwachter T, Holker M, Tesfaye D,
Un G, Schreinemachers HJ, Murani E, Ponsuksili S, Kim JJ, Schellander K and
Wimmers K 2007. A genome scan reveals QTL for growth, fatness, leanness
and meat quality in a Duroc-Pietrain resource population. Animal Genetics 38,
241–252.
Lowe HJ and Barnett GO 1994. Understanding and using the medical subject
headings (MeSH) vocabulary to perform literature searches. The Journal of the
American Medical Association 271, 1103–1108.
Mao X, Cai T, Olyarchuk JG and Wei L 2005. Automated genome annotation and
pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 21, 3787–3793.
Müller E, Moser G, Bartenschilager H and Geldermann H 2000. Trait values of
growth, carcass and meat quality in Wild Boar, Meishan and Pietrain pigs as well
as their crossbred generations. Journal of Animal Breeding and Genetics 117,
189–202.
Rauw W, Kanis E, Noordhuizen-Stassen E and Grommers F 1998. Undesirable
side effects of selection for high production efficiency in farm animals: a review.
Livestock Production Science 56, 15–33.
Reiner G, Fischer R, Hepp S, Berge T, Kohler F and Willems H 2008. Quantitative
trait loci for white blood cell numbers in swine. Animal Genetics 39, 163–168.
Rothschild MF, Hu Z-l and Jiang Z 2007. Advances in QTL mapping in pigs.
International Journal of Biological Sciences 3, 192–197.
Solovieff N, Cotsapas C, Lee PH, Purcell SM and Smoller JW 2013. Pleiotropy in
complex traits: challenges and strategies. Nature Reviews Genetics 14, 483–495.
Upton GJ 1992. Fisher’s exact test. Journal of the Royal Statistical Society. Series
A (Statistics in Society) 155, 395–402.
van der Most PJ, de Jong B, Parmentier HK and Verhulst S 2011. Trade-off
between growth and immune function: a meta-analysis of selection
experiments. Functional Ecology 25, 74–80.
Chen Q, Ma Y, Yang Y, Chen Z, Liao R, Xie X, Wang Z, He P, Tu Y and Zhang X
2013. Genotyping by genome reducing and sequencing for outbred animals.
PLoS One 8, e67500.
Vereecke L, Beyaert R and van Loo G 2009. The ubiquitin-editing enzyme A20
(TNFAIP3) is a central regulator of immunopathology. Trends in Immunology
30, 383–391.
Clapperton M, Bishop S and Glass E 2005. Innate immune traits differ between
Meishan and Large White pigs. Veterinary Immunology and Immunopathology
104, 131–144.
Wang Z, Chen Q, Yang Y, Liao R, Zhao J, Zhang Z, Chen Z, Zhang X, Xue M and
Yang H 2015. Genetic diversity and population structure of six Chinese
indigenous pig breeds in the Taihu Lake region revealed by sequencing data.
Animal Genetics 46, 697–701.
Clapperton M, Diack AB, Matika O, Glass EJ, Gladney CD, Mellencamp MA,
Hoste A and Bishop SC 2009. Traits associated with innate and adaptive
immunity in pigs: heritability and associations with performance under different
health status conditions. Genetics Selection Evolution 41, 54.
Wong MM and Fish EN 2003. Chemokines: attractive mediators of the immune
response. Seminars in Immunology 15, 5–14.
Deverman BE and Patterson PH 2009. Cytokines and CNS development. Neuron
64, 61–78.
Zhang X, Li N, Li X, Zhao W, Qiao Y, Liang L and Ding Y 2012. Low expression of
DAB2IP contributes to malignant development and poor prognosis in hepatocellular carcinoma. Journal of Gastroenterology and Hepatology 27, 1117–1125.
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A and Huber W
2005. BioMart and Bioconductor: a powerful link between biological databases
and microarray data analysis. Bioinformatics 21, 3439–3440.
Zhao S, Zhu M and Chen H 2012. Immunogenomics for identification of disease
resistance genes in pigs: a review focusing on gram-negative bacilli. Journal of
Animal Science and Biotechnology 3, 1–13.
557
Downloaded from https:/www.cambridge.org/core. IP address: 88.99.165.207, on 18 Jun 2017 at 14:17:29, subject to the Cambridge Core terms of use, available at
https:/www.cambridge.org/core/terms. https://doi.org/10.1017/S1751731115002761