




































In ternationa l
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African Journal of Pig Farming ISSN 2375-0731 Vol. 6 (7), pp. 001-008, July, 2018. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 

 

Full Length Research Paper 

 

Identification of pig reproductive QTL genes 
based on gene set enrichment analysis of mouse 

microarray dataset 

 
Kan He1,2, Fan Yang1,2, Minghui Wang1,2, Qishan Wang1,2, Yuchun Pan1,2 and Yufang Ma1,2* 

 
1
School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China. 
2
Shanghai Key Laboratory for Veterinary Biotechnology, Shanghai, 200240, China. 

 
Accepted 23 January, 2018 

 
Nowadays, abundantly different transcripts related to reproductive traits using mouse model have been identified by 
microarray analysis with the robustness, which were needed of re-evaluation with quantitive real time PCR (qPCR). 
Meanwhile, pig QTL databases were established but with large spans in the confidence interval for QTL location. 
Therefore, a genome-wide comparative and functional analysis of the association between mouse microarrays 
analysis results and reported pig QTLs is needed to better elucidate the genetic and physiological background of pig 
enhanced reproductive performance in genomics. Here, we employed a microarray dataset of a high fertility mouse 
line and applied gene set enrichment analysis method to identify significant mouse genes in a pathway level. Next we 
used a comparative genomic approach to find the homologous pig genes and locate them to the interval of the 
reported QTL for pigs’ traits based on AnimalQTLdb. Finally, we identified some pathways participating in pig high 
fertility, and some pig genes were further identified within the reported QTL location related to reproductive 
performance. Combining microarray analysis of mouse high fertility dataset by GSEA with pigQTLdb will substantially 
help us to identify candidate genes in reported QTL regions related to pig reproduction that are eventually responsible 
for increased fertility performance in pig and are also helpful for understanding the genetic information of pig 
reproduction in genomics. Furthermore, we can also provide some novel pig genes in identified pathways with 
relationship of reproduction. 

 
Key words: Reproductive, microarray, pig, pathway, endocrine. 

 
INTRODUCTION 

 
It is well known that the number of ovulation is an 
important parameter for reproductive performance in 
polytocous species as mouse and pig. Folliculogenesis, 
ovulation, and luteinization are regulated by some 
important endocrine factors including the hypothalamic 
gonadotropin releasing hormone, follicle stimulating 
hormone (FSH) and luteinizing hormone from the pituitary 
gland, and steroid and peptide hormones generated 
within the ovary as estrogen, progesterone, inhibin, 
activin, and follistatin (Bastida et al., 2005; Rice et al., 
1993; Emmen et al., 2005). Nowadays, mice have been 
successfully used as models for swine selection systems.  
 
 

 
*Corresponding author. E-mail: mayufang@sjtu.edu.cn Tel: 86-
021-3420-6147. 

 
 
 

 
Especially for studying the regulatory mechanism of 
reproduction in genomics, the expression profiles of 
animal model mouse reproductive organs have 
extensively been used to detect genetic factors involved 
in reproductive performance. However, abundantly 
different transcripts were identified by microarray analysis 
with the robustness, which were needed of re-evaluation 
with quantitive real-time PCR (qPCR). Whereas DNA 
microarray technology has extended to all fields of 
genomic research and has become practically the 
primary tool for gene expression analysis, the platforms 
of livestock are still not perfect, especially for pigs. 
Therefore, comparative analysis of mouse model 
microarray datasets may help us to better understand the 
regulatory mechanism of pig reproductive performance.  

Furthermore, there is a wealth of information that has 
been collated from many QTL studies over the last 



   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Figure 1. Six major steps in our analysis strategy for pig QTL genes identification. 
 

 

decade, summarized in resources such as AnimalQTLdb 
(Hu et al., 2005). However, even for relatively large 
effects with a saturated marker map, the confidence 
interval for QTL location by linkage analysis spans tens of 
map units, or hundreds of genes. This requires further 
linkage disequilibrium mapping to target their gene of 
interest (Meuwissen et al., 2004). Therefore, a genome-
wide comparative and functional analysis of the 
association between mouse microarrays analysis results 
and pig QTLs is needed to better elucidate the genetic 
and physiological background of enhanced reproductive 
performance, including reproductive traits, reproductive 
organ, litter size and endocrine.  

Currently the most well-known Gene set enrichment 
analysis (GSEA) method has been widely used to 
analysis gene expression profiles, especially to identify 
pre-defined gene sets which exhibited significant 
differences in expression between samples from control 
and treated (Keller et al., 2007; Subramanian et al., 
2005). The algorithms calculate the statistical significance 
of the expression changes across groups or pathways 
rather than individual gene, thus allowing identification of 
groups or pathways most strongly affected by the 
observed expression changes. The analysis based on a 

 
 

 

group of relevant genes instead of on an individual gene 
increases the likelihood for investigators to identify the 
critical functional processes under the biological 
phenomena under study. GSEA is likely to be more 
powerful than conventional single-gene methods in the 
study of complex diseases in which many genes make 
subtle contributions. The animal quantitative trait locus  
(QTL) database (AnimalQTLdb, 
http://www.animalgenome.org/ QTLdb) is designed to 
house all publicly available QTL data on livestock animal 
species for easily locating and making comparisons 
within and between species (Hu et al., 2005).  

Here, we employed a microarray dataset of a high-
fertility mouse line and applied gene set enrichment 
analysis method to identify significant pathways and 
genes in mouse. Next we used a comparative genomic 
approach to find the homologous pig genes and locate 
them to the interval of the reported QTLs for pigs’ traits 
based on AnimalQTLdb. 
 
 
MATERIALS AND METHODS 
 
There were six major steps in our analysis strategy showing in 
Figure 1. 



 
 
 

 
Data collection and preprocessing 

 
We searched the public database GEO 
(www.ncbi.nlm.nih.gov/geo/) for the gene expression profiling 
studies related to reproductive performance. Finally, we chose the 
dataset GSE11113 for our re-analysis, which was contributed by 
Vanselow et al. (2008) for mouse high fertility performance study. In 
this dataset, ovaries from 30 animals of line FL1 (selected for high 
fertility performance) and of the control line DUKsi were collected at 
the metestrous stage, combined to 6 pools each, and processed for 
microarray analysis. The samples of GSM280489 to GSM280500 
were from line FL1 and GSM280501 to GSM280512 were from line 
DUKsi, which were hybridized onto Affymetrix Mouse Expression 
430A Array. For the assessment of the influence of preprocessing 
on the comparison, data preprocessing was performed using 
software packages developed in version 2.6.0 of Bioconductor and 
R version 2.10.1 (Gentleman et al., 2004). Each Affymetrix dataset 
was background adjusted, normalized and log2 probe-set 
intensities calculated using the Robust multichip averaging (RMA) 
algorithm in Affy package (Gautier et al., 2004).  

During the previous study a high fertility mouse line FL1, which 
was generated by index trait selection over more than 130 
generations was comparatively analyzed with the non-selected 
control line, DUKsi, and both lines have been derived from the 
same genetic pool (Spitschak et al., 2007). The mean number of 
corpora lutea at the first day of FL1 pregnancy was 18.2 ± 6.8 
(mean ± std, n = 10) compared to 12.2 ± 5.5 (n = 10) in the control 
line (p = 0.0437). The mean litter size and litter weight at birth 
increased during the selection period to 17.3 ± 7.7 (n = 60) and 27.8 
± 4.6 g, respectively in FL1 compared to 9.81 ± 2.03 (n = 75) and 
15.1 ± 6.2 g in the control line (p < 0.0001 for litter size and weight). 
It suggested that the body weight of individual newborn pups 
however was not significantly reduced in FL1 despite of the strongly 
increased number of pups per litter. 

 

Gene set enrichment analysis 
 
Our gene set enrichment analysis of pathways and genes were 
performed using category package in version 2.6.0 of Bioconductor 
(Chiaretti et al., 2004). The goal of GSEA is to determine whether 
the members of a gene set S randomly distributed throughout the 
entire reference gene list L or are primarily found at the top or 
bottom in a pathway level. One of the advantages of GSEA is the 
relative robustness to noise and outliers in the data. The gene sets 
represented by less than 10 genes were excluded. The t-statistic 
mean of the genes was computed in each pathway. Using a 
permutation test with 1000 times, the significantly changed 
pathways were identified with p-value less than 0.001. Accordingly, 
the significant pathways and genes between line FL1 and line 
DUKsi were then identified. 
 
 
Significance analysis of each pathway and Retrieving the 
sequences of significant genes 
 
Based on GSEA, we have identified the significantly up- or down-
regulated pathways and ranked genes list in each pathway. Then 
we retrieved each significant gene’s Entrez ID and used biomaRt 
package (http://www.biomart.org/) to mine the sequence information 
of significantly identified genes in each significant pathway. 
 
 
Comparative analysis with the pig genome and QTL mapping 

 
To identify pig genes homologous to the aforementioned mouse 
pathway targets and to predict their map locations in Sus scrofa, the 
mouse sequences were Blast searched against the pig genome 

 
 
 
 

 
using Blast 2.2.23. The homologous gene was identified when the 
pig’s transcript or gene with the homology higher than 85% and 
alignment region longer than 300 bp. Then we wrote a Perl script to 
relatively locate genes to the interval of the QTL for pigs’ traits 
within PigQTLdb. 
 
 
RESULTS AND DISCUSSION 
 
Significantly up or down regulated pathways by 
GSEA of mouse dataset 
 
The previous GSEA method was applied to the dataset of 
GSE11113. Based on the cutoff of the permutation p-
values, there were 5 up-regulated and 69 down-regulated 
pathways under the comparison of FL1 to DUKsi. The 
analysis results including significant genes in each 
identified pathway were summarized in Appendices 1 and  
2. Mapping these ovarian genes to such pathway terms 
could elucidate the line-specific differences due to the 
genetic consequences of selection for reproductive 
performance. In total, the pathway categories of 
steroid/lipid metabolism, immune response, signaling 
pathways and other metabolic processes were 
significantly different in the selected high fertility mouse 
line FL1 compared to a non selected control line. On the 
other hand, numerous of the differently expressed genes 
in each significant pathway had also been identified by 
GSEA which were known to be involved in female 
reproductive processes as folliculogenesis, ovulation, 
atresia, or luteinization and thus might play a role in 
increasing the ovulation number (in brown color in 
Appendix 2). For example, we identified Casp3 in the 
pathway of natural killer cell mediated cytotoxicity or 
Huntington's disease or Parkinson's disease, Casp7 in 
Alzheimer's disease, Casp8 in RIG-I-like receptor 
signaling pathway or Toll-like receptor signaling pathway 
and Casp9 in VEGF signaling pathway. Those members 
of Casp family are well known to play a fundamental role 
during follicular atresia (Valdez et al., 2005). Members of 
Gpx family including from Gpx1 to Gpx7 were mostly 
identified in the pathway of glutathione metabolism, which 
may be important factors in protecting cells of the 
cumulus-oocyte complex and luteal cells from oxidative 
stress inducing cell death (Al-Gubory et al., 2005; 
Luciano et al., 2006). Otherwise, H2-Q7 was identified in 
the pathway of antigen processing and presentation 
whose functional role for reproductive performance is not 
yet clear. 

 

Identification of pig reproductive QTL genes 

 

Based on the previous identified mouse genes in each 
significant pathway, we then applied comparative 
genomic analysis to the pig genome with PigQTLdb in 
order to locate homology genes to the interval of the QTL 
for pigs’ traits of reproduction. The results are shown in 
Appendices 3 and 4, respectively for up or down 
regulated pathway genes to pig QTLs. The information 



  
 
 

 
Table 1. The number of pig down or up QTL genes related to traits of pig reproduction in PigQTLdb.  

 
Trait type Trait name Number of pig up QTL genes Number of pig down QTL genes 

 

 Age at puberty 8 101 
 

 Ejaculation duration 0 5 
 

 Ejaculation times 0 3 
 

Reproduction traits 
Gestation length 0 29 

 

Semen pH 0 1  

 
 

 Sperm abnormality rate 0 2 
 

 Sperm concentration 0 2 
 

 Sperm per ejaculate 0 1 
 

 Embryo survival 0 1 
 

 Mummified pigs 0 4 
 

Litter size 
Number of viable embryos 0 1 

 

Ovulation rate 6 50 
 

 
 

 Total number born 1 8 
 

 Total number born alive 0 7 
 

 Epididymis weight 0 4 
 

 Left teat number 0 10 
 

 Nonfunctional nipples 10 115 
 

 Number of corpora lutea 0 14 
 

Reproductive organ Right teat number 0 5 
 

 Seminiferous tubule diameter 0 2 
 

 Teat number 3 192 
 

 Testicular weight 2 20 
 

 Uterine horn length 7 31 
 

Endocrine 
Plasma FSH concentration 0 2 

 

Testosterone level 0 11  

 
 

 
In PigQTLdb, the traits of pig reproduction were classified into four types including reproduction traits, litter size, reproductive organ and 
endocrine. In each type, different traits were named. The numbers of pig up or down QTL genes were based on Additional file 3 and 4. Up or 
down QTL genes here represent that the genes located in reported pig reproduction QTL regions within identified up or down pathways. 

 

 

about the number of pig down or up QTL genes related to 
traits of pig reproduction is shown in Table 1.  

In PigQTLdb, the traits of pig reproduction were 
classified into four types including reproduction traits, 
litter size, reproductive organ and endocrine. In each 
type, different traits were named. Obviously, more pig 
down QTL genes were found than up QTL genes as more 
down-regulated pathways were identified. For up QTL 
genes, most of them were related to age at puberty (one 
of the reproduction traits in AnimalQTLdb), ovulation rate 
(litter size), nonfunctional nipples and uterine horn length 
(reproductive organ). By contrast, most of down QTL 
genes were related to teat number and nonfunctional 
nipples (reproductive organ), age at puberty (reproduction 
traits) and ovulation rate (litter size), each number was 
over 50. On the other hand, 13 down QTL genes were 
identified to be related to pig trait type of endocrine, 
including 11 genes for testosterone level and 2 genes for 
plasma FSH concentration (in 

 
 

 

green color in Appendix 4), these related traits were not 
found in up QTL genes. For examples, UQCRC1 
(ubiquinol-cytochrome c reductase core protein I) in 
pathways of Alzheimer's disease and Parkinson's disease 
and Huntington's disease and oxidative phosphorylation, 
UBA7 (ubiquitin-like modifier activating enzyme 7) in the 
pathway of Parkinson's disease, GMPPB (GDP-mannose 
pyrophosphorylase B) in the pathway of fructose and 
mannose metabolism, they were located in chromosome 
13 and identified to be related to testosterone level (Ren 
et al., 2009). OAT (ornithine aminotransferase) in the 
pathway of arginine and proline metabolism and GK 
(glycerol kinase) in the pathway of glycerolipid 
metabolism were both located in chromosome X and 
identified to be related to plasma FSH concentration. 
 

Totally, less pig genes were located in reported QTL 
regions than mouse identified pathway genes, which can 
ensure the accuracy of targets for pig reproduction. 



 
 
 

 

Interestingly, there were more homology genes in latterly 
ranked pathway located to the interval of pig reproductive 
QTLs especially for five up-regulated pathways (Appendix 
3).  

For example, there was only one pig QTL gene MPST 
(mercaptopyruvate sulfurtransferase) identified in the 
pathway of cysteine and methionine metabolism, which 
was located in chromosome 5 related to reproductive 
organ of teat number.  

METTL6 (methyltransferase like 6) might play a 
functional role in transferase activity, which was found in 
the pathway of selenoamino acid metabolism and located 
in chromosome 13 and with relationship of litter size of 
ovulation rate and reproductive trait of age at puberty and 
reproductive organ of nonfunctional nipples (Rathje et al., 
1997; Jonas et al., 2008). Another gene found in the 
same pathway was SEPHS1 (selenophosphate 
synthetase 1), located in chromosome 15 and related to 
both ovulation rate and nonfunctional nipples, which was 
well known in function of ATP binding (Rohrer et al., 
1999).  

In the pathway of heparan sulfate biosynthesis, GLCE 
(glucuronic acid epimerase) in chromosome 1 and EXT1 
(exostoses 1) in chromosome 4 were identified to be 
related to age at puberty and ovulation rate respectively 
(Jonas et al., 2008).  

In the last two up-regulated pathways including 
hedgehog signaling pathway and the pathway of 
arrhythmogenic right ventricular cardiomyopathy (ARVC), 
there were almost the same pig QTL genes identified. For 
examples, CSNK1E (casein kinase 1, epsilon) in 
chromosome 5 was found to be related to reproductive 
organs of both teat number and nonfunctional nipples. 
Members of wingless-type MMTV integration site family 
including WNT1 and WNT10B and DHH in chromosome 
5 were all found to be related to reproductive organ of 
uterine horn length (Wilkie et al., 1999). BMP5 (bone 
morphogenetic protein 5) and a member of RAS 
oncogene family RAB23 in chromosome 7 were both 
identified to be related to age at puberty (Cassady et al., 
2001). BTRC (beta-transducin repeat containing) and 
SUFU (suppressor of fused homolog, drosophila) in 
chromosome 14 were found to be related to nonfunctional 
nipples (Jonas et al., 2008). GLI2 (GLI family zinc finger 
2) in chromosome 15 was identified to be related to both 
ovulation rate and nonfunctional nipples (Jonas et al., 
2008; Rohrer et al., 1999). Besides there were some 
novel genes in chromosome 1 and 5 related to age at 
puberty or uterine horn length.  

By contrast, there were more important pig QTL genes 
found in down-regulated pathways (the brown colors in 
Appendix 4). For examples, in the pathway of natural 
killer cell mediated cytotoxicity or Huntington's disease or 
Parkinson's disease, Casp3 (caspase 3, apoptosis-
related cysteine peptidase) was also identified as one of 
pig QTL genes, which was located in chromosome 15 
and related to the traits of nonfunctional nipples, number 

 
 
 
 

 

of corpora lutea, and gestation length (Jonas et al., 2008; 
Wilkie et al., 1999). In some signaling pathways such as 
VEGF signaling pathway, Toll-like receptor signaling 
pathway, T cell receptor signaling pathway, B cell 
receptor signaling pathway and Fc epsilon RI signaling 
pathway, the gene of MAP2K1 (mitogen-activated protein 
kinase kinase 1) in chromosome 1 was identified to be 
almost related to the traits of teat number, testicular 
weight and age at puberty (Ren et al., 2009; Rohrer, 
2000; Guo et al., 2008). Besides there were also some 
novel genes identified to be related to pig traits of 
reproduction. For examples, in the pathway of fatty acid 
metabolism, a novel gene described as acyl coenzyme A 
synthetase long-chain 1 fragment (EC 6.2.1.3) in 
chromosome 15 was identified to be related to 
nonfunctional nipples, number of corpora lutea and 
gestation length (Jonas et al., 2008; Wilkie et al., 1999).  

Nowadays, mice have been used as models for swine 
selection systems, the genetic parameters as the number 
of ovulation could be useful in determining alternative 
selection criteria for increasing the number born in mice, 
and potentially in swine (Christenson et al., 1987; Long et 
al., 1991). Combining microarray analysis of mouse high 
fertility dataset by GSEA with pigQTLdb will substantially 
help us to identify candidate genes in reported QTL 
regions related to pig reproduction that are eventually 
responsible for increased fertility performance in pig and 
are also helpful for understanding the genetic information 
of pig reproduction in genomics. Furthermore, we can 
also provide some novel pig genes in identified pathways 
with relationship of reproduction. 
 

 

ACKNOWLEDGEMENTS 

 
Our work is funded by National Natural Science 
Foundation of China (grant No. 31072003 and 30871782) 
and the National High Technology Research and 
Development Program of China (863 project) (grant No. 
2008AA101002 and 2006AA10Z1E3). 
 

 
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APPENDICES 

 
Appendix 1 

 

The list of significantly up or down regulated pathways by GSEA of mouse dataset 

 

The data provided represent ranked list of significantly up or down regulated pathways by GSEA of dataset GSE11113. 
The numbers in the bracket after each pathway name represent the numbers of genes in each identified pathway. 
 

 

Appendix 2 

 

The list of significant genes in each identified pathway in mouse 

 
The data provided represent the list of significant genes in each identified pathway, including the information of probe 
set IDs and gene symbols. Up-regulated pathways are in red color and down-regulated pathways are in green color. 
 

 

Appendix 3 

 

Pig QTL genes in up-regulated pathways 

 
The data provided represent the list of pig QTL genes in up-regulated pathways based on mouse data analysis and 
PigQTLdb. 
 

 

Appendix 4 

 

Pig QTL genes in down-regulated pathways 

 
The data provided represent the list of pig QTL genes in down-regulated pathways based on mouse data analysis and 
PigQTLdb. 


