A systems genetics study of swine illustrates mechanisms underlying human phenotypic traits
Zhu et al. BMC Genomics
A systems genetics study of swine illustrates mechanisms underlying human phenotypic traits
Jun Zhu 0 2
Congying Chen 0
Bin Yang 0
Yuanmei Guo 0
Huashui Ai 0
Jun Ren 0
Zhiyu Peng 1
Zhidong Tu 2
Xia Yang 4
Qingying Meng 4
Stephen Friend 3
Lusheng Huang 0
0 Jiangxi Agricultural University , Nanchang, Jiangxi , China
1 BGI , Shenzhen, Guangdong , China
2 Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai , New York, NY 10029 , USA
3 Sage Bionetworks , Seattle, WA , USA
4 Department of Integrative Biology and Physiology, University of California at Los Angeles , Los Angeles, CA , USA
Background: The pig, which shares greater similarities with human than with mouse, is important for agriculture and for studying human diseases. However, similarities in the genetic architecture and molecular regulations underlying phenotypic variations in humans and swine have not been systematically assessed. Results: We systematically surveyed ~500 F2 pigs genetically and phenotypically. By comparing candidates for anemia traits identified in swine genome-wide SNP association and human genome-wide association studies (GWAS), we showed that both sets of candidates are related to the biological process cellular lipid metabolism in liver. Human height is a complex heritable trait; by integrating genome-wide SNP data and human adipose Bayesian causal network, which closely represents bone transcriptional regulations, we identified PLAG1 as a causal gene for limb bone length. This finding is consistent with GWAS findings for human height and supports the common genetic architecture between swine and humans. By leveraging a human protein-protein interaction network, we identified two putative candidate causal genes TGFB3 and DAB2IP and the known regulators MESP1 and MESP2 as responsible for the variation in rib number and identified the potential underlying molecular mechanisms. In mice, knockout of Tgfb3 and Tgfb2 together decreases rib number. Conclusion: Our findings show that integrative network analyses reveal causal regulators underlying the genetic association of complex traits in swine and that these causal regulators have similar effects in humans. Thus, swine are a potentially good animal model for studying some complex human traits that are not under intense selection.
Systems genetics; Swine model; Complex human traits
The pig is one of the most important agriculture animals,
which has provided the largest amount of consumable red
meat protein. The pig is also a valuable model for studying
human diseases because pigs are more similar in genomic
structure to human than mice are . Pigs are used as
models in genetics analysis and gene knockout or
knock-in studies of human diseases such as cystic
fibrosis, Alzheimers disease, and BRCA1-associated
mammary carcinogenesis. Genetic studies of pig models of
human diseases led to the identification of novel
quantitative trait loci for cutaneous melanoma and a novel
mutation in the LDL receptor that contributes to
spontaneous hypercholesterolemia . Gene expression profiling
in pig models led to the identification of RACK1 as a
potential marker of malignancy for human melanocytic
proliferation . The pig has also been used to study
autoimmune, congenital, and bone diseases, as well as
cancer, diabetes, and cardiovascular diseases such as
atherosclerosis and hypertension.
However, the genetic similarity between pig and human
has not been assessed in large, systematic studies. Thus,
pigs may be underused as models for human diseases.
Here we surveyed about 500 F2 animals in a swine cross
using a high density 60 K SNP array and examined
phenotypic traits of interests to both agriculture and human
diseases, including anemia traits, limb bone length, and
number of ribs.
Integrated network or systems biology approaches,
which combine genetic, genomic, and phenotypic data
into network views, have been applied to understand
obesity [4,5], cancers , and other human diseases.
Integrated network approaches are powerful tools for
analyzing complex high-throughput data. They have
provided many new insights into diseases  and
identified many novel candidate genes that cause human
diseases , and were later validated experimentally .
By applying integrated analysis on phenotypic traits
and genotype data, we show that both swine and human
genome wide association candidates for anemia traits
are related to lipid metabolism in liver. By integrating
phenotype and genotype data with human adipose
Bayesian causal network, we identified PLAG1 for limb
bone length (corresponding to human height). We
then integrated genetic association result and human
protein-protein interaction network to identify two
novel candidate causal genes TGFB3 and DAB2IP as
well as the known regulators MESP1 and MESP2 as
responsible for the variation in rib number and
illustrated the potential underlying molecular mechanisms.
Tgfb3 knockout together with Tgfb2 in mice decreases
number of ribs, which supports TGFB3 as a regulator
for rib number in pig.
A large-scale F2 intercross comprising 1,912 pigs was
constructed by crossing the western breed White Duroc
and the Chinese breed Erhualian . These breeds
differ in growth, fat, meat quality, and other phenotypic
traits. Phenotypic traits including anemia traits, number
of ribs and limb bone length were measured at day 240
High density SNP genotypes for 497 F2 animals were
successfully generated. Of 52,183 SNPs (52,077 SNPs on
the 60 K chip  and 106 internally developed SNPs),
11,718 informative SNPs were selected for further analysis
based on their call rates, minor allele frequencies, and
Hardy-Weinberg equilibrium (HWE) tests (Methods).
The average distance between informative SNP markers
was 181 kb (median 105.6 kb). We therefore defined the
most likely regions of major loci as regions within 200
kb on each site of the most significant SNPs in
genomewide association results. To scan phenotypes against the
genome-wide SNP genotype data for association
between trait and SNP, we used the single-marker mixed
model  (Methods). At a false discovery rate (FDR) <5%
(corresponding P = 4.85 106), there were 12, 5, and 2
quantitative trait loci (QTLs) for anemia, bone length, and
rib number traits, respectively. Then, we applied systems
biology approach to identify potential causal genes
underlying QTLs of phenotype traits.
Causal genes for anemia traits
Hematopoietic disorders are associated with a variety of
human diseases such as coronary heart disease, diabetes,
and liver diseases. One founder breed of our F2 resource
population, the White Duroc pig, has the dominant coat
color and lower hemoglobin concentration, an indicator
of macrocytic anemia . Two blood parameters
related to macrocytic anemia, mean corpuscular volume
(MCV) and mean corpuscular hemoglobin (MCH) at
day 240, were recorded for this F2 cross. Since MCV and
MCH are tightly correlated (correlation coefficient = 0.89,
P = 1.76 10169), we used MCH as the representative of
anemia traits in the downstream analysis. We previously
identified a significant QTL for MCH at day 240 in a
3cM region on SSC8 using 194 microsatellite markers .
In the current study, the SNP association results of MCH
revealed 12 significant loci (Additional file 1: Table S1);
the strongest association was on SSC8 (Figure 1a), as in
our previous results. The QTL for MCH peaked at SNP
marker MARC0034580 (SSC8:43.43 Mb). The 200 kb
flanking region on each side of the marker contains only
one gene, KIT, a finding which suggests KIT is the causal
gene for MCH at locus SSC8:43.43 Mb. KIT regulatory
mutations, including a gene duplication and a splice
mutation that leads to the skipping of exon 17, are responsible
for the dominant white phenotype in pigs . These
regulatory mutations have profound pleiotropic effects on
peripheral blood cell measures in Western commercial
pigs . The second strongest association was centered
at MARC0090810 (SSC10:38.4 Mb). The 200 kb flanking
region on each side contains only one gene, ACO1, which
encodes aconitase 1. Also known as iron regulatory
element binding protein 1 (IREB1), ACO1 regulates cellular
iron homeostasis and is linked to anemia in human .
In addition, there were multiple significant QTLs for
MCH, suggesting complex genetic regulation of the MCH
trait. Multiple loci for MCH have been identified in
human GWAS [17-19] (Methods, Additional file 2:
Table S2). However, only one gene, KIT, was present in
the candidate sets from human GWAS and from our
swine cross. Bone marrow, kidney, and liver are
important tissues for red blood cell production and
homeostasis. Instead of directly comparing the two candidate sets
from swine and human, we examined how these genes
are regulated in a human liver transcriptional network
(see Methods for details). 7 swine and 28 human GWAS
candidates for MCH were included in the human liver
network. The average shortest distances were 4.95 and
5.85, corresponding to empirical p-values = 0.01 and 0.067
(Methods) for the swine and human GWAS candidates,
respectively, suggesting the swine candidate genes were
likely to be transcriptionally co-regulated in human liver.
We then sought to determine whether candidate genes
identified in swine and human GWAS involve in similar
subnetworks and similar biological processes.
Subnetworks around swine candidates for MCH were
significantly enriched in the Gene Ontology (GO) biological
process cellular lipid metabolism (fold enrichment =3.5,
Figure 1 Comparison of pig and human genetic architecture of anemia related traits such as mean corpuscular hemoglobin (MCH).
a) Pig SNP association result for MCH. There is a strong QTL at chromosome 8 along with 11 other significant QTLs. The black line represents a
p-value threshold 4.85 106, corresponding to FDR = 0.05. b). Both pig genome wide association candidates (blue nodes) and human GWAS
candidate (red nodes) for MCH are in a liver subnetwork that involves in lipid metabolism. Hepcidin (HAMP, yellow node) is a sensor for iron and
inflammation. The subnetwork provides a molecular link between anemia and lipid metabolism.
Fishers Exact Test p-value= 1.2 1011, and EASE score
 = 4.5 1011, detailed in Supplementary Results)
while subnetworks around human GWAS candidates
were enriched in the GO biological processes immune
response and lipid metabolism (fold enrichments =
1.78 and 1.55, Fishers Exact Test p-values= 3.3 1016
and 1.1 106, EASE scores = 7.8 1016 and 1.8 106,
respectively) . The enrichment of multiple GO biological
processes in the human GWAS candidates subnetwork
explains why human GWAS candidates were not
significantly co-regulated in the human liver network as a whole
(empirical p-value = 0.067 as shown above). The swine
candidate subnetwork and human candidate
subnetwork overlapped significantly (fold enrichment = 2.0,
Fishers Exact Test p-value = 6.3 109, EASE score=
1.3 108), and genes in the GO biological process
cellular lipid metabolism were even more enriched when
considering the two subnetworks together (fold
enrichment = 1.8, Fishers Exact Test p-value = 6.3 1012,
EASE score = 1.3 1011) (Figure 1b). Anemia has been
linked to lipid profiles such as cholesterol and
apolipoprotein levels, triglycerides, and lipid peroxidation in
both animal and human studies [21-28]. Many genes in the
anemia-associated subnetwork we identified participate in
diverse lipid-related functions, such as cholesterol
biosynthesis, lipid transport, and lipid oxidation, providing
mechanistic support for the phenotypic connections between
anemia and lipid metabolism. In addition, anemia,
inflammation, and obesity have been linked in many studies 
and are linked transcriptionally [4,5]. Hepcidin (encoded by
HAMP, yellow node in Figure 1b) is a body sensor for iron
and inflammation  and is increased in obese individuals
, which is a potential molecular connection between
anemia and inflammation.
In sum, our results suggest that both swine and human
candidate genes for the anemia trait MCH involve in a
similar subnetwork related to lipid metabolism, which
supports a link between anemia and obesity.
Identification of PLAG1 and HMGA1 as causal genes for
pig limb bone length and human height
Human height is a typical polygenic trait. Hundreds of
loci that affect human height have been identified in
GWAS . However, there are no good models for
studying mechanisms by which these loci affect human
height. We illustrated here that QTLs for the length of
pig limb bones are in good concordance with human
GWAS results, suggesting that the pig is such a model.
Two loci were strongly associated with limb bone
length (Figure 2a, Additional file 3: Table S3). These loci
were centered at SSC4:82.65 Mb and SSC7:35.18 Mb
with p-values of 3.37 1016 and 2.07 1045,
respectively, consistent with the result of a previous QTL study
on the length of individual bones . The flanking
regions (200 kb on each side) of chromosomes 4 and 7 loci
contain 7 genes (SDR16C5, RPS20, PLAG1, PENK, MOS,
LYN, and CHCHD7) and 5 genes (SPDEF, RPS10,
PACSIN1, HMGA1, and C6orf106), respectively. Leg length is
generally proportional to height. Our results are similar
to those of GWAS of human height. The syntenic
regions on human genome of the two loci we identified in
pigs matched perfectly with two human loci (8q12 and
6p21) associated with human height in GWAS [32,34].
The concordant results of genetic studies of pig and
human indicate that these loci have profound effects on
bone development, so it is worth identifying causal
candidate genes at these loci. HMGA1 (at SSC7:35.18 Mb
for pig and 6p21 for human) has been suggested as the
causal gene for height, possibly through a mechanism
involving modification of chromatin structure .
However, it is unclear which gene or genes at locus 8q12 are
causal for human height [32,35]. Therefore, we
compared subnetworks around swine candidate genes with
the subnetwork derived from genes known to affect
We selected 241 genes potentially related to human
height on the basis of their disease associations in the
OMIM database (http://www.ncbi.nlm.nih.gov/Omim).
Osteoblasts and adipocytes are very close in cell lineage
 and can be converted to each other by molecular
Figure 2 Genome-wide association result for the limb bone length. a) Global view of the association result shows that two major loci on
chromosomes 4 and 7 affect the limb bone length. b) Subnetworks of height-related genes based on OMIM in the human adipose transcriptional
network. c) Subnetworks of genes at the chromosome 4 locus in the human adipose network. d) Zoom-in view of the region in the height-related
OMIM gene subnetwork that overlaps with the PLAG1 subnetwork. Red nodes are height-related OMIM genes. Yellow nodes in 2c are genes mapped
to the chromosome 4 locus. Purple nodes are nodes in the OMIM gene subnetwork in 2b.
signals . Subnetworks in a human gene regulatory
network for omental fat representing a bone regulatory
network (Methods) were extracted by using the 241
OMIM genes as seeds (Figure 2b). 82 of the 241 genes
were included in the omental fat network. The largest
subnetwork in Figure 2b contained 50 of the 82 OMIM
genes, which indicates that height related genes are
coherently regulated in ometal fat. Using genes at the
human 8q12 locus as seeds, we extracted subnetworks
from the omental fat network as well (Figure 2c). There
were two subnetworks, one centered on LYN and one on
PLAG1. Only PLAG1 subnetwork overlapped with the
height-related OMIM gene subnetwork: 4 of 10 nodes
were in the OMIM subnetwork (fold enrichment = 7.8,
Fishers Exact Test p-value = 7.3 104, EASE score =
0.0063; the zoom-in view of the overlap between PLAG1
subnetwork and the OMIM height gene subnetwork is
shown in Figure 2d). This result strongly suggests that
PLAG1 is a candidate gene at the 8q12 locus. Plag1
knockout mice have reduced litter weight and retarded
embryonic and postnatal growth , which further
implicates Plag1 in regulating body size. In addition, variants
modulating the expression of a chromosome domain
encompassing PLAG1 influence stature in cattle .
Some studies suggest that PLAG1 is associated with
human height , while others suggest SDR16C5 as the
causal gene at the 8q12 locus . Our network analysis
result objectively indicates that PLAG1 is a causal gene for
Identification of TGFB3, DAP2IP, MESP1, and MESP2 as
causal genes for the number of ribs
Pigs have 13 to 16 ribs , and meat production
increases with extra ribs . In human, one extra rib can
increase cancer risk by 120-fold . Previous studies
indicate that loci on chromosomes 7 and 11 affect the
number of ribs in pigs [42-44]. The number of vertebrae
and the number of ribs are tightly correlated. Two major
QTLs for the number of vertebrae were found on
chromosomes 1 (SSC1:293.4 Mb) and 7 (SSC7:105.4 Mb), and
NR6A1 and VRTN were suggested as the causal genes
underlying the two loci, respectively [45,46]. In our F2
intercross, we recorded rib number, and tested its
association with the SNP genotypes. Our SNP association
results (Figure 3a, Additional file 4: Table S4) also revealed
two loci on chromosomes 1 and 7.
The chromosome 7 locus had the strongest association
with rib number (P= 4.7 1052) and is centered on
marker ALGA0044022 at 105.38 Mb (Additional file 5:
Figures S1a and b). Genes within the flanking region
(200 kb up- and downstream) of the SNP marker are
TGFB3, IFT43 (C14orf179), and C14orf118. The
chromosome 1 locus (association P= 3.56 1011) is centered on
the marker DRGA0002465 at 293.93 Mb. The flanking
region (200 kb on each side of the marker) contains one
To prioritize the candidate genes at these loci, we
explored the network structures around them.
Transcriptional regulatory networks of mature tissues may not
reflect regulation during early embryo development.
However, protein-protein interactions (PPI) are not
identified in a specific physiological state and thus may
capture interactions or regulations during embryonic
development. We therefore collected PPIs from
multiple sources (Methods). The PPI subnetwork around
the candidate genes (shown in Figure 3b) was
significantly enriched for genes in the KEGG Wnt signaling
pathway (fold enrichment = 20, Fishers Exact Test
p-value= 9.99 1016, EASE score = 2.1 1014). In
the early development of vertebrate embryos, the
thoracic spine forms during somitogenesis, a process
controlled by segmentation clock, whose key regulators
include Notch, Wnt, and FGF  (Figure 3c). We found
that TGFB3 and DAB2IP interact with genes in the Wnt
signaling pathway in the PPI subnetwork (Figure 3b),
suggesting that these genes are the candidate genes for the
Tgfb2 and Tgfb3 overlap in function and compensate
for each other. In Tgfb2 knockout mice, Tgfb3 regulates
rib number. Tgfb3+/ and Tgfb3/ mice have fewer ribs
than their wildtype littermates . TGFB3 plays a key
role in embryogenesis, and abnormalities in this and
other genes in the FGF pathway contribute to human
diseases such as oral cleft . DAB2IP (disabled
homolog 2 interacting protein) interacts with DAB2
. DAB2 plays an essential role in mesoderm
differentiation  and inhibits Wnt/beta-catenin signaling in
embryos . A beta-catenin (CTNNB1) gradient determines
the arrest of clock oscillation and maturation of
somitogenesis . Besides affecting the common Wnt signaling
pathway, TGFB regulates the translation of DAB2 mRNA
. We hypothesized that both loci influence rib number
by interacting with the Wnt signaling pathway and then
affecting the beta-catenin gradient and maturation of
somitogenesis (Figure 3c and d).
Somitogenesis is regulated by the Notch, Wnt and
FGF pathways . The mechanisms of both potential
regulators, TGFB3 and DAB2IP, are related to the Wnt
and FGF pathways but not to the Notch pathway. To
further explore causal regulators for the number of
ribs, we regressed the number of ribs on the genotypes
at TGFB3 and DAB2IP loci using the following model
y = + sex + batch + G + gTGFB3 + gDAB2IP + e, where
y is the number of ribs, is the mean, G is the kinship,
gTGFB3 and gDAB2IP are the genotypes at the TGFB3
and DAB2IP loci, and e is the residual. We then tested
whether other loci and their interactions with TGFB3
and DAB2IP loci explain the residual variance e using
Figure 3 Schematic diagram of the mechanism for regulating rib number. a) The genome-wide association result shows that there are two
significant loci on chromosomes 1 and 7 for rib number. b) The protein-protein interaction network around genes mapped to the chromosomes
1 and 7 loci (yellow nodes). The DAB2-DAB2IP and TGFB3 subnetworks overlap and are enriched for genes (red nodes) in the Wnt signaling
pathway (fold enrichment = 20, Fishers Exact Test p-value= 9.99 1016, EASE score = 2.1 1014). c) Schematic graph showing somite formation
(adapted from ). Vertebrae form during somitogenesis. The Wnt signaling pathway is critical for maintaining and stopping clock oscillation.
d) Hypothetically, the chromosome 1 and 7 loci affect the number of ribs through an interaction between DAB2IP-DAB2-TGFB3 and the Wnt
the following models e ~ g + g * gTGFB3 and e ~ g + g *
gDAB2IP. At SNP marker DBKK0000285 (SSC7: 60.2 Mb),
there was one locus whose genotype and interaction with
the TGFB3 locus was associated with the residual variance
(P= 9.8 106) (Figure 4). The flanking region of this
locus contains eight genes: WDR93, PLIN1, PEX11A,
MESP2, MESP1, C7H15ORF38, APN, and AP2S2. Among
them, MESP1 and MESP2 are known regulators in
somitogenesis , during which MESP2 is a key regulator of
the Notch pathway . Thus, MESP1 and MESP2 are
likely to be causal genes at this locus for rib number in
pigs and they interact with the TGFB3 locus to regulate
both the Notch and Wnt signaling pathways.
This study shows that causal genes for traits of interests
to both agriculture and human diseases can be identified
by combining high-density SNP genotyping and Systems
Biology approaches. By comparing pig and human
candidate genes from GWAS for phenotypic traits related to
anemia, bone length (equivalent to human height), and
rib number, we identified putative causal genes and
uncovered the mechanisms for these phenotypes.
Specifically, we showed that both human and pig causal genes
for anemia are related to lipid metabolism in liver. We
confirmed that HMGA1 and PLAG1 are candidate genes
for bone length, which corresponds to human height.
In genomic structure  and physiologically, pigs are
more similar to humans than are mice, one of the most
widely used models for human pathophysiology studies.
Pig organs are similar to their human counterparts in
size and shape and are a potential source for human
organ transplants. Humans and pigs also have similar
blood lipid profiles, whereas mice lack high density
Figure 4 Genome-wide association result for rib number
conditioning on the genotypes at TGFB3 and DAB2IP loci. There
is a significant locus at SSC7:60.8 Mb (p-value= 9.8 106). The
p-value for the interaction between this locus (SSC7:60.8 Mb) and
the TGFB3 locus (SSC7:107.3 Mb) is 0.02.
lipoprotein (HDL) particles. A study based on a complete
pig genome assembly  shows that selection pressure in
pigs is closer to that in humans than in mice. The dN/dS
ratio (the ratio of the rate of non-synonymous substitutes
to the rate of synonymous substitutions) is 0.144, 0.163,
and 0.116 for the pig, human, and mouse, respectively
. These lines of evidence suggest that the pig is a good
model for studying complex human diseases. Indeed, we
found that genetic complexity of human disease related
traits in the pig is similar to the complexity in human. For
example, 22 loci are significantly associated with MCH/
MCV in human GWAS [17-19]. We found 12 loci
associated with MCH at genome-wide significance in the pig. In
contrast, only one or two loci are associated with MCH in
mouse crosses [58,59]. On the other hand, the mouse is
still an important model for biomedical research and has
unique advantages, including a shorter life and hence
experimental cycle, greater availability of genetic, genomic,
and molecular resources, and ease of genetic
manipulation. The mouse is also a better model for target
validation. Thousands of knockout or transgenic mice have
been generated, and can be used to assess physiological
and molecular changes due to genetic perturbations.
Even though there are 112 naturally occurring
diseasecausing mutations common to humans and pigs , a
large overlap between most of disease causal loci in the
two species is unlikely. Humans and pigs are under
different selection pressures. Since naturally occurring
SNPs differ in the two species, GWAS candidate loci in
the two species may not significantly overlap when
compared directly. The systems genetics and integrative
network approach we used here is suitable for identifying
potential causal genes underlying loci associated with
complex human disease and, more importantly, to
uncover potential biological processes underlying these
For the rib number trait, we identified candidate genes
in the Wnt and Notch pathways, which are known to
influence rib development including TGFB3 and DAB2IP
as well as the known genes MESP1 and MESP2. Using
network approaches, we not only identified candidate
genes, but also further dissected the mechanisms that
mediate their effects on the phenotype. Abnormal rib
number is associated with at least 19 different human
diseases and syndromes (http://www.wrongdiagnosis.com/
symptoms/abnormal_rib_number/view-all.htm), such as
Herrmann-Opitz craniosynostosis, spondylocostal
dysostosis, and Campomelia Cumming type. In humans, one
extra rib in human can increase cancer risks 120-fold .
Our network analysis of the number of pig ribs not only
revealed good markers for selecting pig breeds, but also
showed that the pig is a good model for studying
embryogenesis process in general and human congenital diseases.
An SNP in NR6A1 has been implicated as the causal
gene for vertebrae number at the chromosome 1 locus
. We genotyped this SNP and compared the
genotypes of other SNPs in the chromosome 1 region. The
genotypes of the NR6A1 SNP and those around NR6A1
on the 60 K chip were similar (Additional file 5: Figure
S1c). Specifically, the genotypes of the NR6A1 SNP and
SNP marker DRGA0002465 (the center of the
chromosome 1 locus in our study) were 90% identical. In
addition, the NR6A1 SNP and the neighboring SNPs on
the 60 K SNP array had similar association results, and
the NR6A1 SNP explained less variation in rib number
than DRGA0002465 (Additional file 5: Figure S1d), for
which DAB2IP was implicated as the candidate causal
gene in our study. These results suggest that DAB2IP is
also likely to be a causal gene for rib number.
In addition to the three traits analyzed in the systems
genetics study, there are over 40 phenotypic traits
related to growth, fatness , meat quality , blood
lipid profiles , and body strength  in the swine
F2 cross. The three complex traits (anemia, leg length,
and rib number) are not well studied in other animal
models and are highly relevant to complex human
diseases. By applying systems genetics and integrative
network approaches, we were able to identify putative
causal genes and mechanisms underlying the SNPs
associated with these traits. However, systems biology
approaches have some limitations in identifying causal
genes of phenotypic traits. If gene expression data from
relevant tissues is unavailable, one must rely on
published networks and PPI networks, which have not been
linked to many novel genes. In this case, the ability to
find truly novel causal genes is limited. Alternative
approaches such as genome-wide RNAi screening are
needed to complement the systems biology approach.
In summary, our results show that humans and pigs
share similar genetic architecture for complex traits,
providing proof of concept for using swine as a model
organism for complex human diseases. Furthermore, the
coherent genetic and phenotypic data generated in the
study can provide a rich resource for future agriculture
and human disease studies.
Swine F2 animals and phenotype recording
A large-scale F2 resource population was constructed by
crossing two divergent pig breeds, White Duroc and
Chinese Erhualian, as described previously . Briefly,
two White Duroc boars from Sygen PIC China
Company and 17 Erhualian sows from three pig breeding
farms were crossed as founder animals to produce F1
animals, and 59 F1 sows were randomly mated with
9 F1 boars to produce 1,912 F2 individuals from 110
full-sib families. All F2 pigs were kept under standard
indoor conditions with natural lighting, fed formula feed
three times a day, and given ad libitum access to water
from nipple drinkers. At 46 days of age, all F2 piglets
were weaned and moved to a nursery (males were
castrated) until 120 days of age. Piglets were then
transferred to a performance test station, where growth and
feeding traits were recorded with an ACEMA 64
electronic auto-feed intake recording station, which
phenotypes feeding behavior and food intake circadianly
(ACEMO, Pontivy Cedex, France). At a mean age of
240 3 days, the pigs were sent to a commercial
slaughter facility and processed according to Chinese industry
standards. All animal work was conducted in accordance
with the guidelines for the care and use of experimental
animals established by the Ministry of Agriculture of
Phenotypic values of growth, fertility, carcass, meat
quality, immune capacity, behavior and morphological
traits were recorded in this F2 pig populations. At the
time of slaughter, blood samples were collected from the
major artery vessels near the heart and morphological
traits (including the number of ribs and the limb bone
length) and blood biochemical traits were measured. Rib
number was counted from the carcass. Both forelimb
and hind limb were removed from the right side carcass
of F2 animals and dissected from the limb. A large
caliper was used to measure the length of five limb bones:
humerus (total length from the head to the trochlea),
scapula (the maximum straight line distance from the
cavitas glenoidalis to the border of scapular cartilage),
femur (total length from the greater trochanter to the
intercondyloid fossa), ulna (length from the olecranon
process to the styloid process) and tibia (length from the
intercondylar eminence to the medial malleolus).
502 F2 animals were successfully genotyped with a 60 K
pig SNP chip (Illumina)  and an internally developed
SNP set. The position of each SNP in the pig genome
(Sscrofa10.2) was remapped with SOAP2 software. Quality
control of genotypes was performed with the GenABEL
procedure in R. SNPs with call rates < 95% or minor allele
frequency < 15%, or Hardy Weinberg equilibrium (HWE)
p-value were <5 106) and X-linked SNPs likely to be
autosomal (odds > 1000) were excluded from further
analysis. The associations between phenotypes and
genome-wide SNP genotype data were analyzed with
the single-marker association model in the GenABEL
package  based on a mixed linear model
implemented with the mmscore function of GenABEL in R
package. Sex and batch were considered as fixed effects,
and genetic co-variances among samples were considered
by fitting a kinship matrix derived from genotypes of
whole-genome SNP markers as y sex + batch + g + G ,
where y is a trait, G is kinship estimated based on
genome-wide SNP data, and g is the genotype at a query
Human liver transcriptional network
The human liver transcriptional network is the union of
molecular Bayesian causal networks constructed from
two human genetic gene expression studies [8,64]. In
one study , liver samples of 466 Caucasian subjects
were transcriptional profiled, 427 samples were
genotyped and used in genetic gene expression analysis and
Bayesian network reconstruction. Then, 8,188 genes
were selected for inclusion in the network
reconstruction process on the basis of two criteria: (1) variance of
gene expression in the top 20% of gene expression
variance; or (2) LOD scores of eQTLs of the genes were of
genome-wide significance. Similarly, in the other study
, liver tissues as well as omental adipose,
subcutaneous adipose, and stomach tissues were collected
from 1,008 patients during RYGB (Roux-en-Y gastric
bypass); 651 liver samples were gene expression
profiled and genotyped, and 7,593 genes with top gene
expression variance were selected for Bayesian network
The selected genes were input into a Bayesian network
reconstruction software package, RIMBANet-Reconstructing
Integrative Molecular Bayesian Network , based on a
previously described algorithm [65-68]. Genetics
information was used to construct structure priors as follows:
genes with cis eQTLs were allowed to be parent nodes of
genes with coincident trans eQTLs, p(cis > trans) = 1,
but genes with trans eQTLs were not allowed to be
parents of genes with cis eQTLs, p(trans > cis) = 0.
Bayesian Information Criterion (BIC) was used in the
reconstruction process. One thousand Bayesian
networks were reconstructed using different random
seeds to start the reconstruction process. From the
resulting set of 1000 networks generated by this
process, edges that appeared in more than 30% of the
networks were used to define a consensus network. In
this consensus network, an edge was removed if 1) it
was involved in a loop, and 2) it was the most weakly
supported of all edges making up the loop.
The final human liver transcription network used
in the anemia trait analysis is the union of the two
Bayesian networks constructed based on the two
data sets, which consists of 12,875 genes and 64,253
Representation of transcription network of bone tissue
No large coherent set of bone expression data was
available for building a human bone transcription
network, so we used networks of tissues that are
molecularly similar to bone. Adipocytes, osteoblasts, and
chondrocytes share common progenitors and are close
in lineage , and adipose and bone tissues share
similar gene expression profiles based on the
transcriptome body atlas . Therefore, we used a network
constructed from the adipose tissue to represent the
bone network. A human gene regulatory network for
omental fat was constructed based on 848 omental fat
samples collected in the Greenawalt et al. study 
from 1,008 patients at the time of RYGB as described
above. These omental fat samples were gene
expression profiled and genotyped. Then, 7,671 genes with
large gene expression variance across the cohort were
selected and input into Bayesian network reconstruction
package RIMBANet as described above. The constructed
omental fat network consisted of 13,979 connections and
was used for representing a bone network in our network
Human protein-protein interaction (PPI) network
The human PPI network was constructed by integrating
human PPIs from several molecular interaction databases,
both public (BIND, BioGRID, HPRD, MINT, Reactome,
DIP, and IntAct) and commercial (Ingenuity, Proteome,
MetaBase, and NetPro). Identifiers for the interacting
genes identified in these databases were mapped to Entrez
Gene IDs to obtain a unified naming system. Both
directed regulations (e.g., activates, inhibits ) and
undirected interactions (e.g., binds, covalent binding, ppi)
in these databases were mapped to undirected edges in
our PPI network, which consisted of 19,800 nodes and
Extracting a subnetwork from the Bayesian Networks and
To construct a subnetwork for a set of genes, we used
genes in the input set as seeds and selected d-step
neighbors of seeds (the shortest distance between a seed S
and a neighbor node N is equal or less than d). When
fewer than 50 seeds were used, d was set as 2; when
more than 50 seeds were used, d was set as 1. Seeds and
their d-step neighbors of seeds were nodes in the
subnetwork, and links among nodes in the subnetworks
were the same as in the whole Bayesian network.
Collecting human GWAS candidates for MCV/MCH
Human GWAS candidates for traits mean corpuscular
hemoglobin (MCH) / mean corpuscular volume (MCV)
were retrieved from the NHGRI human GWAS catalog
(http://www.genome.gov/gwastudies/), which covers 3
studies [17-19] with SNP association at genome-wide
significance p-value cutoff of 1108. Twenty-two unique
loci and genes mapped to these loci were retrieved and
listed in Additional file 2: Table S2.
Statistical analyses of co-regulation in a network and
To measure closeness or potential of co-regulation of
two genes in a network, we used the shortest distance
between the two genes. Given a set of gene, the average
shortest distance of all possible pairs was calculated. To
assess significance of the observed average shortest
distance, we randomly selected the same number of
genes in the network and calculated their average
shortest distance, and repeated the procedure 10,000 times.
The probability of the observed average shortest distance
expected by change was the number of the average
shortest distance from randomly selected sets less than
the observed shortest distance then divided by 10,000.
To identify potential functions of selected gene sets,
we compared these gene sets with each GO biological
process (GOBP)  and computed functional
enrichment using Fishers Exact test (FET) and a conservative
modification of FET as EASE scores . Total 1352
GOBPs that consist of more than 10 genes and less than
1500 genes were tested, and only GOBPs with
enrichment FET p-value <0.05/1352 were reported. To assess
significance of functional enrichment of a subnetwork
extracted from a network, we further adjusted enrichment
p-values based on an empirical p-value distribution. Given
a subnetwork in a whole network, we randomly shuffled
node labels of the network, and then tested the overlap
between the subnetwork and a biological process. The
procedure was repeated 10,000 times. The probability of
the observed p-value expected by chance is the number of
the p-values from randomly shuffled networks less than
the observed one divided by 10,000.
Additional file 1: Table S1. Loci significantly associated with MCH
(mean corpuscular hemoglobin) in the swine F2 cross at false discover
rate (FDR) <5% (corresponding P = 4.85 10 6).
Additional file 2: Table S2. Human GWAS candidates for MCH (mean
corpuscular hemoglobin)/MCV (mean corpuscular volume) retrieved from
the NHGRI GWAS catalog (www.genome.gov/gwastudies). All SNP
associations pass the genomewide p-value cutoff 1e-8.
Additional file 4: Table S4. Loci significantly associated with rib
number in the swine F2 cross at false discover rate (FDR) <5%
(corresponding P = b4.85 10 6).
Additional file 5: Figure S1. The genome-wide association result for
the number of ribs. a) The global view of the association result shows
that there are two significant loci on chromosomes 1 and 7 for the
number of ribs. b) A zoom-in view of the chromosome 7 peak shows that
there are two neighboring SNPs at the peak. c) The genotype of the SNP
DRGA0002465 at the chromosome 1 peak is similar to the genotype of
the NR6A1 SNP. d) The zoom-in view of chromosome 1 peak shows that
the SNP DRGA0002465 is more significantly associated with the number
of ribs than the NR6A1 SNP.
LH designed and organized the experiment. JZ designed and led the
integrative analyses. JR, YG, BY, CC, HA, LH carried out the animal work,
collected tissues, and scored phenotype traits. CC, YG, HA, BY, JZ performed
genotype QC. ZP contributed SNP data generation and swine and human
genome comparison. JZ, BY, ZT, XY, and QM performed integrated network
analysis. JZ wrote the initial draft. LH, JR, YG, ZP, CC, XY, and ZT helped revise
the manuscript. All authors involved in discussing the results and
commented the manuscript. All authors read and approved the final
Jun Zhu, Congying Chen are co-first authors.
This work was partially supported by Chinese grant National Basic Research
Program of China (2006CB102103), National Institutes of Health grants
R01MH090948, R21CA170722, and R01AG046170.
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