Published November 21, 2014
A marker-derived gene network reveals the regulatory role
of PPARGC1A, HNF4G, and FOXP3 in intramuscular fat deposition of beef cattle1
Y. Ramayo-Caldas,*†‡ M. R. S. Fortes,§ N. J. Hudson,* L. R. Porto-Neto,* S. Bolormaa,#
W. Barendse,* M. Kelly,§ S. S. Moore,§ M. E. Goddard,#║ S. A. Lehnert,* and A. Reverter*2
*CSIRO Food Futures Flagship and CSIRO Animal, Food and Health Sciences, 306 Carmody Road, St. Lucia, Brisbane, QLD
4067, Australia; †Departament de Ciencia Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona,
08193, Bellaterra, Spain; ‡INRA, UMR1313 Génétique Animale et Biologie Intégrative (GABI), Domaine de Vilvert, Bâtiment
GABI-320, 78352 Jouy-en-Josas, France; §The University of Queensland, Queensland Alliance for Agriculture and Food
Innovation, Center for Animal Science, QLD 4062, Australia; #Victorian Department of Environment and Primary Industries,
Bundoora, VIC 3083, Australia; and ║School of Land and Environment, University of Melbourne, Parkville, VIC 3010, Australia
ABSTRACT: High intramuscular fat (IMF) awards
price premiums to beef producers and is associated with
meat quality and flavor. Studying gene interactions and
pathways that affect IMF might unveil causative physiological mechanisms and inform genomic selection,
leading to increased accuracy of predictions of breeding value. To study gene interactions and pathways, a
gene network was derived from genetic markers associated with direct measures of IMF, other fat phenotypes,
feedlot performance, and a number of meat quality traits
relating to body conformation, development, and metabolism that might be plausibly expected to interact with
IMF biology. Marker associations were inferred from
genomewide association studies (GWAS) based on high
density genotypes and 29 traits measured on 10,181
beef cattle animals from 3 breed types. For the network
inference, SNP pairs were assessed according to the
strength of the correlation between their additive association effects across the 29 traits. The co-association
inferred network was formed by 2,434 genes connected
by 28,283 edges. Topological network parameters suggested a highly cohesive network, in which the genes
are strongly functionally interconnected. Pathway and
network analyses pointed towards a trio of transcription
factors (TF) as key regulators of carcass IMF: PPARGC1A, HNF4G, and FOXP3. Importantly, none of these
genes would have been deemed as significantly associated with IMF from the GWAS. Instead, a total of 313
network genes show significant co-association with the
3 TF. These genes belong to a wide variety of biological functions, canonical pathways, and genetic networks
linked to IMF-related phenotypes. In summary, our
GWAS and network predictions are supported by the
current literature and suggest a cooperative role for the 3
TF and other interacting genes including CAPN6, STC2,
MAP2K4, EYA1, COPS5, XKR4, NR2E1, TOX, ATF1,
ASPH, TGS1, and TTPA as modulators of carcass and
meat quality traits in beef cattle.
Key words: association weight matrix, beef quality, fat deposition, genomewide association study, marbling
© 2014 American Society of Animal Science. All rights reserved. J. Anim. Sci. 2014.92:2832–2845
doi:10.2527/jas2013-7484
INTRODUCTION
1The authors acknowledge the Cooperative Research Centre for
Beef Genetic Technologies for provision of individual cattle genotyping data. Y. Ramayo-Caldas was funded by an FPU PhD scholarship
from the Spanish Ministerio de Educación (AP2008–01450) and has
received the support of the European Union, in the framework of the
Marie-Curie FP7 COFUND People Programme, through the award of
an AgreenSkills’ fellowship (under grant agreement number 267196).
2Corresponding author: [email protected]
Received December 9, 2013.
Accepted April 10, 2014.
Consumer preference for high quality meat has required the identification of new breeding objectives such
as high intramuscular fat (IMF). As a complex trait, IMF
is determined by a set of interrelated genes and their interaction with environmental factors, including nutrition
(Bolormaa et al., 2011b; Moloney et al., 2013).
Our understanding of the molecular and cellular
basis of IMF is incomplete. It is known that marbling
adipocytes are usually embedded in highly vascularized
2832
A gene network for intramuscular fat
extracellular matrix seams (Harper and Pethick, 2004).
Despite the incomplete biological understanding, genetics is known to be a strong driver of IMF potential with
elite breeds sometimes possessing IMF percentage in excess of 50% in longissimus thoracis (Horii et al., 2009).
Studying gene interactions and pathways that affect IMF
might unveil causative physiological mechanisms and
inform genomic selection, leading to increased accuracy
of predictions (Snelling et al., 2013).
In recent years, systems biology—more specifically,
network-based approaches—have emerged as an alternative
and powerful tool to study complex phenotypes. Inspired by
these approaches, we adopted the association weight matrix (AWM) methodology (Fortes et al., 2010; Reverter and
Fortes, 2013a,b) to build an IMF-centered gene network
from genomewide association studies (GWAS) on 29 phenotypes related to beef cattle growth and meat quality.
MATERIALS AND METHODS
For the present study, Animal Care and Use Committee approval was not required because no new animals
were handled in this study. This study was performed on
phenotype and genotype collected previously and maintained in databases from the Cooperative Research Centre for Beef Genetic Technologies (Armidale, New South
Wales, Australia), as described in the following section.
Animals, Phenotypes, and
Genomewide Association Studies
We used animal resources described in previous studies (Bolormaa et al., 2011a,b, 2013). In brief, the cattle
were sourced from 9 different populations of 3 breed
types: Bos taurus, Bos indicus, and tropical composites
(B. taurus × B. indicus). The experimental design and
quantitative analysis of the data have been reported previously (Barwick et al., 2009a,b; Johnston et al., 2003a,b;
Perry et al., 2001; Reverter et al., 2003a,b; Upton et al.,
2001). For the present study we used GWAS results
from 29 phenotypes. These phenotypes were based on
measurements taken on the farm, at the beginning and
at the end of the feedlot period, and on the carcass after
slaughtering. Standard pre- and postslaughter procedures
were used throughout the project. For the feedlot period, cattle were assigned to 1 of 3 target market carcass
weight groups (Domestic [220 kg], Korean [280 kg], and
Japanese [340 kg]) and slaughtered when the mean of the
slaughter group reached their assigned market weight.
Carcasses were hung in chillers within 1 h of stunning.
The left side of each carcass was quartered between 20
and 24 h after slaughter. Not all cattle were measured
for all phenotypes. In particular, IMF was measured via
ultrasound scans on a small cohort of animals (n = 726),
2833
while carcass IMF (CIMF; n = 6,267) was measured either through near-infrared spectroscopy or by the etherextracted fat method in a sample of the longissimus thoracis that was taken caudal to the quartering site. A total
of 10,181 animals of the 3 breed types (3,384 B. indicus,
3,296 B. taurus, and 3,501 B. taurus × B. indicus) with
SNP genotypes and measured for at least 1 trait were
used in this study. Summary statistics, number of records
available for each trait, and a brief description of the 29
phenotypes are presented in Table 1.
We used GWAS results reported previously in separate studies for residual feed intake and carcass and meat
quality traits (Bolormaa et al., 2011a,b, 2013, 2014). In
total, 729,068 SNP data were used in this study. Those
SNP were obtained from 5 different SNP panels: the Illumina (San Diego, CA) platforms BovineHD comprising 777,963 SNP; the BovineSNP50 version 1 and version 2 comprising 54,001 and 54,609 SNP, respectively;
the BovineLD panel comprising 6,909 SNP; and finally
the ParalleleSNP10K chip (Affymetrix, Santa Clara,
CA) comprising 11,932 SNP. All SNP were mapped to
the UMD 3.1 build of the bovine genome sequence assembled by the Centre for Bioinformatics and Computational Biology at the University of Maryland (www.
cbcb.umd.edu/research/bos_taurus_assembly.shtml).
High-density SNP genotypes were imputed for all animals using Beagle (Browning and Browning, 2011). The
number of genotypes for each platform and by each breed
as well as the accuracy of the imputation process was given in Bolormaa et al. (2013). Briefly, the imputations of
the 7K, 10K, and 50K SNP genotype data to 729,068 SNP
were performed in 2 sequential stages: from 7K or 10K or
50K data to a common 50K data and then from the common 50K data to 800K data. Imputation was done within
breed and using 30 iterations of Beagle. The high density
(HD) genotypes of each breed type were used as a reference set to impute the 50K genotypes of each pure breed
within the corresponding breed type. For the 4 composite
breeds, all the HD genotypes were used as a reference set
to impute the 50K genotypes of each breed.
Genomic relationship matrices (GRM) were calculated including all animals, and fixed effects were
fitted in the models to define cohorts and account for
phenotype measurement procedures (e.g., assay plate
for IGF-I phenotypes). The GRM based on all data was
calculated using the methodology of Yang et al. (2010).
Details regarding models and fixed effects fitted for each
trait were reported within publications of quantitative
analysis based on pedigree information, performed before genotyping (Johnston et al., 2003a,b; Reverter et al.,
2003a,b). Associations between trait and SNP genotypes
were analyzed 1 SNP at a time, fitting animal models
that consisted of GRM, fixed effects, SNP, and error in
the ASReml software (Gilmour et al., 2002).
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Ramapo-Caldas et al.
Table 1. Description and summary statistics for the 29 traits analyzed in this study: number of animals genotyped (n),
mean, SD, and heritability estimate (h2)
Trait
Definition and units
Body weights
BWT
BW measured at birth, kg
WT6
BW measured at 6 mo of age, kg
WTE
BW measured at feedlot entry, kg
RFIWT
Metabolic midtest BW in the residual feed intake (RFI) test period, kg0.73
WTX
BW measured at feedlot exit, kg
CWT
Carcass weight, kg
Body heights
HH6
Hip height at 6 mo of age, cm
HHE
Hip height feedlot entry, cm
HHX
Hip height feedlot exit, cm
HUMP
Hump height, assessed by Meat Standards Australia grader, mm
Fat deposition
P8X
Scanned P81 fat depth at feedlot exit, mm
RIBX
Scanned rib fat depth at feedlot exit, mm
CP8
Carcass P8 fat depth, mm
CRIB
Carcass rib fat depth, mm
IMF
Intramuscular fat, %
CIMF
Carcass intramuscular fat, %
MARB
Meat Standards Australia2 marbling score, scale 1 to 11.
MS
AUS-MEAT3 marbling score, scale 0 to 9
Meat quality and yield
SF
Shear force in longissimus thoracis, kg
CRBY
Carcass retail beef yield, %
EMAX
Eye muscle area at feedlot exit, cm2
CEMA
Carcass eye muscle area, cm2
Insulin-like growth factor I (IGF-I)
IGF6
Serum IGF-I at 6 mo of age, ng/mL
IGFE
Serum IGF-I at feedlot entry, ng/mL
IGFX
Serum IGF-I at feedlot exit, ng/mL
Feed intake
RFI
Residual feed intake, kg/d
DFI
Daily feed intake, kg/d
ADG
Average daily gain, kg/d
Growth and reproduction
GLD
Gestation length, d
n
Mean
SD
h2
1,778
10,330
7,270
1,751
5,992
6,460
36.2
238.2
357.4
89.6
504.2
282.7
6.1
55.4
71.8
14.5
95.8
56.8
0.30
0.39
0.42
0.61
0.44
0.39
6,668
1,972
2,158
1,140
120.3
131.6
139.2
139.7
8.1
8.3
8.3
38.0
0.55
0.42
0.32
0.32
5,162
5,162
6,149
5,880
726
6,267
2,425
4,515
11.2
8.0
11.2
7.6
5.0
3.6
0.9
0.8
4.5
3.6
4.7
4.1
1.0
1.9
0.6
0.8
0.49
0.47
0.32
0.21
0.38
0.31
0.41
0.22
5,801
2,929
4,905
1,578
4.5
67.0
68.0
75.0
1.1
3.4
10.9
8.6
0.22
0.38
0.14
0.39
929
1,254
954
261.9
484.0
620.6
151.4
191.7
134
0.30
0.20
0.22
4,241
3,312
2,123
–1.4
12.1
1.4
2.1
2.2
0.4
0.35
0.51
0.45
528
282.0
4.9
0.56
1P8 = a point located at the intersection of a line drawn anterior to the tuber ischii and another line drawn ventrally from the spinus process of the third sacral vertebra.
2Meat
Standards Australia: www.beefandlamb.com.au/How_to/Meat_Standards_Australia.
Murarrie, QLD, Australia.
3AUS-MEAT Ltd.,
Association Weight Matrix
The AWM methodology (Fortes et al., 2010) was used
to build a gene network from the GWAS data. Accordingly,
allele substitution effects of each SNP on each trait were
z-score standardized and used to construct a matrix having
as many columns as traits and as many rows as selected
SNP. The selection of SNP considers the P-value of their
association to traits and the proximity to known genes. An
R (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org/foundation/) script was written to
automate the process of building the AWM (Reverter and
Fortes, 2013a). We used CIMF as the key trait for this
AWM and followed the procedures previously described
(Fortes et al., 2010), with 2 modifications to account for the
higher number of SNP available. Initially, the AWM was
developed using data from 50K SNP and for the current
study we are using over 700K data. The 2 modifications
were 1) a nominal cutoff P-value of 0.01 (instead of the
original 0.05) and 2) only selecting SNP mapped close to a
gene or within a gene. Those SNP located in coding regions
or within 2,500 bp of the coding region of a gene were captured (UMD3 bovine genome assembly). The R script was
used to visualize the hierarchical clustering of traits (AWM
columns) and genes (AWM rows).
To identify significant gene–gene interactions we
used the partial correlation and information theory
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A gene network for intramuscular fat
(PCIT) algorithm (Reverter and Chan, 2008). Gene interactions were predicted using correlation analysis of the
SNP effects across pairwise rows of the AWM. Hence,
the AWM predicted gene interactions based on significant
co-association between SNP. In the network, every node
represents a gene (or SNP), whereas every edge connecting 2 nodes represents a significant gene–gene interaction
(based on SNP–SNP co-association). Finally, the Cytoscape software (Shannon et al., 2003) was used to visualize the gene network and the CentiScaPe plugin (Scardoni
et al., 2009) was used to calculate specific node centrality
values and network topology parameters.
Gene Functional Classification,
Network, and Pathways Analyses
Gene functional classification, pathways analyses,
and biological network generation were performed using Ingenuity Pathways Analysis software (IPA; Ingenuity Systems, Redwood City, CA; www.ingenuity.
com). The list of human homologs that correspond to
the protein-coding bovine genes was uploaded into IPA.
Subsequently, each gene identifier was mapped to its
corresponding gene object in the Ingenuity Pathways
Knowledge Base. Functional Analysis of IPA was use to
identify the most represented biological functions. A canonical pathways analysis was generated to identify the
most significant pathways from the IPA library. Fischer’s
exact test was used to calculate a P-value, which determines the probability that each biological function and/
or canonical pathway enrichment is due to chance alone.
The cutoff for considering a significant association was
established by Benjamini and Hochberg multiple testing
correction of the P-value (false discovery rate < 0.05;
Benjamini and Hochberg, 1995). Networks were generated using IPA based on gene connectivity. Network
analysis returns a score that ranks networks according
to their degree of relevance to the network eligible molecules in the dataset (Calvano et al., 2005). The network
score is based on the hypergeometric distribution and is
calculated with the right-tailed Fisher’s exact test. Only
those genes that demonstrate direct (through a single
link) and indirect (through a link to a neighboring gene)
relationships to other genes or proteins were integrated
into the analysis.
To identify potential regulators of the predicted network, we focused on the transcription factors (TF) contained in the network. We applied an information lossless
approach (Reverter and Fortes, 2013b) that explored all
the possible trios of 153 TF in the network and identified the TF trio that spanned most of the network topology
with minimum redundancy. We also identified genes that
were co-associated to these key TF and used these as a target gene list to perform core analysis function using IPA.
RESULTS
Hierarchical cluster analysis (Fig. 1) and correlations
estimated from additive SNP effects (Fig. 2) showed the
level of proximity between the meat quality and the growth
phenotypes studied. A summary of the GWAS results is
provided, reported as the number of SNP associated with
each trait at 5 different significance levels (Table 2).
The network built via the AWM analysis (Fig. 3) was
formed by 2,603 genes (nodes) connected by 396,081
predicted gene interactions (edges). The SNP underpinning these network genes are listed (Supplementary Table S1; see online version of the article at http://journalofanimalscience.org). These predicted interactions were
based on direct correlations between SNP effects deemed
significant by the PCIT algorithm. Significant correlations were 0.56 ± 0.089 (average ± standard deviation)
in absolute value. Considering only correlations ≥0.7 in
absolute value (among those that were significant), the
number of network edges was reduced to 28,283 for visualization and analysis. Network nodes, or genes connected by these strongest correlations, were 2,434. Network
parameters such as average centrality degree (23.23) and
average distance (3.98) suggested a highly connected
network, in which the nodes (genes) are strongly functionally interconnected. The number of genes and interactions presented in this network associated with IMF
and related to meat production traits is evidence for the
complex nature of the studied phenotypes.
To identify potential regulators of the predicted network, we focused on the 153 TF contained in the network
(Supplementary Table S2; see online version of the article
at http://journalofanimalscience.org). We applied an information lossless approach that explored all the possible
trios among the available 153 TF and identified the TF trio
that spanned most of the network topology with minimum
redundancy. These 3 TF were peroxisome proliferatoractivated receptor gamma coactivator 1 alpha (PPARGC1A), hepatocyte nuclear factor 4 gamma (HNF4G, alias
NR2A3) and forkhead box P3 (FOXP3). Table 3 gives the
P-values from the association of the 3 SNP corresponding to these 3 TF across the 29 phenotypes explored in
this study. Supporting their functional relevance, the 3 TF
showed higher than average centrality values (entire network = 23.23, HNF4G = 153, PPARGC1A = 108, and
FOXP3 = 102). In the network, a total of 313 genes show
significant co-association with the 3 TF (Fig. 3).
To gain insight into the canonical pathways and
biological processes that were overrepresented, the list
of these 313 predicted target genes was explored using
the core analysis function included in IPA. The top 9
biological functions identified by IPA included categories related to a wide variety of physiological and biological events (Table 4). Among the most representative
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Ramapo-Caldas et al.
Figure 1. Hierarchical cluster analysis of the SNP effects included in the association weight matrix (AWM) across the 29 phenotypes analyzed in this study.
Phenotype abbreviations: DFI = daily feed intake; WT6 = BW measured at 6 mo of age; EMAX = eye muscle area at feedlot exit; BWT = BW measured at birth;
CWT = carcass weight; SF = shear force in longissimus thoracis; HH6 = hip height at 6 mo of age; HHE = hip height feedlot entry; WTE = BW measured at feedlot entry; HHX = hip height feedlot exit; PX8 = scanned P8 (a point located at the intersection of a line drawn anterior to the tuber ischii and another line drawn
ventrally from the spinus process of the third sacral vertebra) fat depth at feedlot exit; RIBX = scanned rib fat depth at feedlot exit; CRIB = carcass rib fat depth;
CP8 = carcass P8 fat depth; RFI = residual feed intake; IGF6 = serum IGF-I at 6 mo of age; IGFE = serum IGF-I at feedlot entry; IGFX = serum IGF-I at feedlot
exit; IMF = intramuscular fat; RFIWT = metabolic midtest BW in the RFI test period; WTX = BW measured at feedlot exit; CRBY = carcass retail beef yield;
CEMA = carcass eye muscle area; HUMP = hump height assessed by Meat Standards Australia grader; GLD = gestation length; CIMF = carcass intramuscular
fat; MS = AUS-MEAT (AUS-MEAT Ltd., Murarrie, QLD, Australia) marbling score; MARB = Meat Standards Australia (www.beefandlamb.com.au/How_to/
Meat_Standards_Australia) marbling score; IGFX = serum IGF-I at feedlot exit. See online version for figure in color.
A gene network for intramuscular fat
2837
Figure 2. Heat map of the Pearson correlations between phenotypes estimated from additive SNP effects. BWT = BW measured at birth; WT6 = BW measured
at 6 mo of age; WTE = BW measured at feedlot entry; RFIWT = metabolic midtest BW in the RFI test period; WTX = BW measured at feedlot exit; CWT = carcass
weight; HH6 = hip height at 6 mo of age; HHE = hip height feedlot entry; HHX = hip height feedlot exit; HUMP = hump height assessed by Meat Standards Australia
grader; P8X = scanned P8 (a point located at the intersection of a line drawn anterior to the tuber ischii and another line drawn ventrally from the spinus process of the
third sacral vertebra) fat depth at feedlot exit; RIBX = scanned rib fat depth at feedlot exit; CP8 = carcass P8 fat depth; CRIB = carcass rib fat depth; IMF = intramuscular
fat; CIMF = carcass intramuscular fat; MARB = Meat Standards Australia (www.beefandlamb.com.au/How_to/Meat_Standards_Australia) marbling score; MS = AUSMEAT (AUS-MEAT Ltd., Murarrie, QLD, Australia) marbling score; SF = shear force in longissimus thoracis; CRBY = carcass retail beef yield; EMAX = eye muscle
area at feedlot exit; CEMA = carcass eye muscle area; IGF6 = serum IGF-I at 6 mo of age; IGFE = serum IGF-I at feedlot entry; IGFX = serum IGF-I at feedlot exit;
RFI = residual feed intake; DFI = daily feed intake; GLD = gestation length. See online version for figure in color.
canonical pathways known to influence meat quality
there were protein ubiquitination pathway, calcium signaling, PPARα/RXRα (retinoid X receptor α) activation,
and actin cytoskeleton signaling pathways (Table 5).
Finally, according to IPA the identified genes were
mapped to 9 genetic networks (Table 6). Seven of the nine
networks included biological functions related to developmental and cellular processes such as Organ Morphology,
Embryonic Development, Developmental Disorder, Cell
Signaling, Cellular Assembly and Organization, Cellular
Function and Maintenance, Cell Morphology, and CellTo-Cell Signaling and Interaction (Table 6). For instance,
the first genetic network, having an IPA network score of
45 and 25 focus genes, presented functions related to Cell
Signaling, Molecular Transport, and Vitamin and Mineral
Metabolism (Fig. 4). The second 1, with a score of 44 and
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Ramapo-Caldas et al.
25 focus genes was centered on Cellular Assembly and Organization, Cancer, and Cellular Function and Maintenance
(Supplementary Fig. S1; see online version of the article at
http://journalofanimalscience.org). It should be noted that
according to IPA and in agreement with the AWM-derived
network, a connection was predicted among these 7 networks (Supplementary Fig. S2; see online version of the
article at http://journalofanimalscience.org). It is tempting
to speculate that the genes within these networks play an
important role in the genetic determination of IMF.
DISCUSSION
To date and in spite of its importance for beef quality,
our understanding of the molecular and cellular basis of
IMF is incomplete. There are no proven causal mutations
and the adipocyte cell population that drives the development of IMF and thereby marbling has not been well
characterized (Harper and Pethick, 2004; Yamada et al.,
2009). Previous GWAS have explained only a fraction of
the phenotypic variance (Bolormaa et al., 2011b; Liu et
al., 2013; Peters et al., 2012; Ramayo-Caldas et al., 2012).
Gene expression analyses have merely tended to identify
end-point fat metabolism genes such as members of the
fatty-acid-binding protein (FABP) family (Wang et al.,
2005), as opposed to potent regulatory molecules higher
up in the transcriptional hierarchy. Our network predictions may contribute to an enhanced understanding of the
molecular and cellular basis of IMF. Indeed some of the
biological process and pathways identified could have
been expected from a network predicted from GWAS of
IMF-related phenotypes and this gives confidence in the
reliability of the results. Others, however, were unexpected and might lead to new insights on IMF physiology.
Molecular processes controlling IMF percentage are
complex and not fully understood. Due to its economical
relevance for the beef industry, this complex phenotype
has been extensively studied (De Smet et al., 2004; Garnier et al., 2003; Parnell, 2004; Sevane et al., 2013; Tizioto et al., 2012; Warner et al., 2010). However, previous
studies had narrow success in the identification of genetic
regulators of IMF. This narrow success can be attributed
to GWAS examining the association of a chromosomal
region (QTL scan) and/or single genetic variants (SNP)
but ignoring the functional interactions between genes.
Regarding our best trio of TF (PPARGC1A, HNF4G,
and FOXP3), 2 findings are noteworthy: First, a GWAS
would have failed to capture their relevance because none
of the SNP located in their coding regions would have been
found to be significant in the GWAS for IMF (Table 3).
Second, they show co-association with a large number of
genes and other TF, some of which belong to relevant biological functions and pathways plausibly associated with
IMF (Fig. 3). However, it is equally feasible that these TF
Table 2. Number of SNP associated with 29 traits at
varying P-value thresholds
P-value thresholds
0.05
0.01
0.001
0.0001
Trait1
Body weights
BWT
53,079
16,130
3,731
748
WT6
74,801
25,410
6,179
2,325
WTE
64,267
18,840
4,174
1,398
WTX
77,793
30,025
11,151
5,328
RFIWT
53,102
12,894
1,824
297
CWT
77,278
29,900
11,828
6,331
Body heights
HH6
69,145
24,113
7,453
3,047
HHE
52,277
13,979
2,054
451
HHX
49,493
12,072
2,117
543
HUMP
43,712
10,270
1,622
412
Fat deposition
P8X
57,977
15,090
2,619
610
RIBX
50,220
12,442
1,743
237
CP8
51,699
13,958
2,572
775
CRIB
50,241
12,402
1,210
146
IMF
36,636
8,125
987
137
CIMF
50,840
13,190
1,975
362
MARB
49,610
12,452
1,803
278
MS
50,298
12,107
1,817
252
Meat quality and yield
SF
57,942
17,792
3,688
1,029
CRBY
40,876
8,656
1,017
124
EMAX
44,737
10,198
1,200
170
CEMA
47,122
11,561
1,374
127
Insulin-like growth factor I (IGF-I)
IGF6
42,084
10,053
1,816
712
IGFE
46,902
11,952
2,432
908
IGFX
44,497
10,610
1,404
136
Feed intake
ADG
63,317
19,934
3,526
925
DFI
76,364
32,019
15,332
10,334
RFI
70,243
24,464
6,351
1,171
Growth and reproduction
GLD
39,997
9,328
1,236
166
0.00001
70
1,170
710
1,744
46
2,549
1,606
162
213
144
165
31
382
9
7
37
20
40
566
23
28
14
413
500
28
246
6,967
225
9
1BWT = BW measured at birth; WT6 = BW measured at 6 mo of age;
WTE = BW measured at feedlot entry; RFIWT = metabolic midtest BW in
the residual feed intake (RFI) test period; WTX = BW measured at feedlot
exit; CWT = carcass weight; HH6 = hip height at 6 mo of age; HHE =
hip height feedlot entry; HHX = hip height feedlot exit; HUMP = hump
height assessed by Meat Standards Australia grader; P8X = scanned P8 (a
point located at the intersection of a line drawn anterior to the tuber ischii
and another line drawn ventrally from the spinus process of the third sacral
vertebra) fat depth at feedlot exit; RIBX = scanned rib fat depth at feedlot
exit; CP8 = carcass P8 fat depth; CRIB = carcass rib fat depth; IMF = intramuscular fat; CIMF = carcass intramuscular fat; MARB = Meat Standards
Australia (www.beefandlamb.com.au/How_to/Meat_Standards_Australia)
marbling score; MS = AUS-MEAT (AUS-MEAT Ltd., Murarrie, QLD, Australia) marbling score; SF = shear force in longissimus thoracis; CRBY =
carcass retail beef yield; EMAX = eye muscle area at feedlot exit; CEMA =
carcass eye muscle area; IGF6 = serum IGF-I at 6 mo of age; IGFE = serum
IGF-I at feedlot entry; IGFX = serum IGF-I at feedlot exit; DFI = daily feed
intake; GLD = gestation length.
A gene network for intramuscular fat
2839
Figure 3. Association weight matrix gene network. A) Entire gene network formed by 2,603 genes connected by 396,081 predicted interactions (edges).
The color spectrum ranges from green to red for low and high network density, respectively. B) Subset of the co-association network showing the best trio of
transcription factors: PPARGC1A, HNF4G, and FOXP3. Node color corresponding with the functional classification of the in silico predicted target gene as
follows: transcription factor (red), lipid and carbohydrate metabolism process (blue), cellular and development process (green), mineral and vitamin metabolism
(gray), and other (while). Node shape indicates classification as diamond (transcription factor) and ellipse (other genes). The size of the nodes corresponding to
the best trio of transcription factors (PPARGC1A, HNF4G, and FOXP3) has been enlarged to facilitate their location. See online version for figure in color.
might influence the expression of other genes important
for IMF but not represented in this network.
In the predicted network, PPARGC1A, a TF that
activates a variety of hormone receptors and other TF
involved in the regulation of white adipocyte differentiation and is a master regulator of muscle fiber composition, mitochondrial content, and energy homeostasis
(Ichida et al., 2002; Summermatter et al., 2013), showed
co-association with a total of 108 genes. According to
their functional classification, 18% of these genes are
related to cellular assembly and organization processes
(LYN, CHD7, WIF1, RAB2A, EYA1, PTCH1, ARFGEF1,
ERCC8, SNCA, AKAP7, BNIP1, NTNG2, HAS2, RGS20,
TERF1, STAU2, CHRNA7, NR2E1, RABEP1, and
CKAP5). In addition, some of these genes are involved
with tissue morphology and skeletal and muscle development (TOX, SDCBP, CHD7, PTCH1, PDLIM5, TCF7L1,
RLBP1, and ASPH; Supplementary Table S3; see online
version of the article at http://journalofanimalscience.
org). Genetic variants in the human PPARGC1A have
been associated with the occurrence of nonalcoholic fatty liver disease, susceptibility to type II diabetes, insulin
resistance, and obesity (Esterbauer et al., 2002; Hara et
al., 2002; Yoneda et al., 2008). In livestock species such
as pigs and beef cattle, several studies have suggested
the role of PPARGC1A as a potential candidate gene
for both productive and reproductive traits (Komisarek
and Walendowska, 2012; Pena et al., 2013; Sevane et al.,
2013; Shin and Chung, 2013; Tizioto et al., 2012).
Another TF predicted as critical for IMF regulation
was HNF4G, which is a nuclear hormone receptor expressed in various tissues including pancreas, testis, kidneys, and both small and large intestine in mice (Taraviras et al., 2000). Compared to wild-type mice, HNF4G
knockout mice have lower energy expenditure and
higher body weight (Gerdin et al., 2006) and recently
genetic variants of human HNF4G have been associated
with height and obesity (Berndt et al., 2013). In addition, HNF4G constitutively binds to fatty acids (Wisely
et al., 2002). Among the 153 AWM-predicted target
genes for HNF4G (Supplementary Table S4; see online
version of the article at http://journalofanimalscience.
org), 15.6% are related with tissue morphology and development (LYN, TOX, SDCBP, CHD7, RAK3, EYA1,
GTPBP4, MAGI2, PEX2, COL12A1, NTRK3, GRIK1,
CACNB4, LGALS1, MERTK, HCK, HAS2, STAU2, SLC4A7, NR2E1, TRIM55, MAP2K4, FHIT, and ZFHX4)
and 6.5% were involved in lipid and carbohydrate metabolism (LYN, TTPA, OCRL, PLEKHA8, TGS1, GRIK1,
CACNB4, LGALS1, HAS2, and PIP4K2A). These last 2
2840
Ramapo-Caldas et al.
Table 3. Corresponding P-values from the association of
the 3 SNP corresponding to the best trio of transcription
factors (PPAARGC1A, FOXP3, and HNF4G)
Gene:
SNP:
PPARGC1A
BovineHD
4100004861
FOXP3
BovineHD
3000025421
HNF4G
BovineHD
1400011719
1.03 × 10–3
1.10 × 10–2
2.47 × 10–1
5.97 × 10–1
6.06 × 10–4
1.54 × 10–3
7.40 × 10–1
5.15 × 10–3
4.02 × 10–3
6.27 × 10–3
4.32 × 10–3
7.81 × 10–3
4.10 × 10–1
1.06 × 10–1
8.63 × 10–1
1.80 × 10–1
2.48 × 10–3
2.24 × 10–1
1.18 × 10–1
8.07 × 10–1
4.80 × 10–1
3.13 × 10–1
5.54 × 10–1
2.19 × 10–1
4.98 × 10–1
8.07 × 10–1
7.18 × 10–1
6.55 × 10–1
5.07 × 10–1
4.58 × 10–1
5.54 × 10–1
3.93 × 10–1
7.08 × 10–1
2.82 × 10–1
7.77 × 10–1
6.63 × 10–1
1.61 × 10–1
1.04 × 10–1
1.01 × 10–1
1.80 × 10–1
4.54 × 10–1
3.10 × 10–1
8.88 × 10–1
6.01 × 10–2
2.95 × 10–1
5.49 × 10–1
1.84 × 10–1
3.43 × 10–1
4.54 × 10–1
7.91 × 10–1
1.38 × 10–3
2.27 × 10–6
9.08 × 10–3
8.88 × 10–1
4.78 × 10–3
1.56 × 10–1
7.77 × 10–1
8.23 × 10–1
Trait1
P-values for body weights
BWT
1.14 × 10–1
WT6
WTE
RFIWT
WTX
CWT
10–4
5.85 ×
4.07 × 10–4
3.48 × 10–1
2.50 × 10–3
1.44 × 10–3
P-values for body heights
HH6
1.62 × 10–2
HHE
HHX
HUMP
10–3
6.76 ×
4.43 × 10–1
4.89 × 10–1
P-values for fat deposition
P8X
6.03 × 10–1
RIBX
CP8
CRIB
IMF
CIMF
MARB
MS
10–1
1.00 ×
4.03 × 10–1
1.82 × 10–1
3.90 × 10–1
8.38 × 10–2
8.88 × 10–1
8.62 × 10–1
P-values for meat quality and yield
SF
8.07 × 10–1
CRBY
EMAX
CEMA
P-values for IGF-I
IGF6
IGFE
IGFX
10–2
4.10 ×
8.60 × 10–3
1.06 × 10–1
7.29 × 10–1
10–1
7.52 ×
6.79 × 10–2
P-values for feed intake
RFI
5.35 × 10–2
DFI
ADG
10–1
3.80 ×
6.86 × 10–2
P-values for growth and reproduction
GLD
7.40 × 10–1
ABWT
= BW measured at birth; WT6 = BW measured at 6 mo of age;
WTE = BW measured at feedlot entry; RFIWT = metabolic midtest BW in the
residual feed intake (RFI) test period; WTX = BW measured at feedlot exit;
CWT = carcass weight; HH6 = hip height at 6 mo of age; HHE = hip height
feedlot entry; HHX = hip height feedlot exit; HUMP = hump height assessed
by Meat Standards Australia grader; P8X = scanned P8 (a point located at the
intersection of a line drawn anterior to the tuber ischii and another line drawn
ventrally from the spinus process of the third sacral vertebra) fat depth at feedlot
exit; RIBX = scanned rib fat depth at feedlot exit; CP8 = carcass P8 fat depth;
CRIB = carcass rib fat depth; IMF = intramuscular fat; CIMF = carcass intramuscular fat; MARB = Meat Standards Australia (www.beefandlamb.com.au/
How_to/Meat_Standards_Australia) marbling score; MS = AUS-MEAT (AUSMEAT Ltd., Murarrie, QLD, Australia) marbling score; SF = shear force in
longissimus thoracis; CRBY = carcass retail beef yield; EMAX = eye muscle
area at feedlot exit; CEMA = carcass eye muscle area; IGF6 = serum IGF-I at 6
mo of age; IGFE = serum IGF-I at feedlot entry; IGFX = serum IGF-I at feedlot
exit; DFI = daily feed intake; GLD = gestation length.
Table 4. Description of the significant (P-value < 0.01)
molecular and cellular biological functions identified by
Ingenuity Pathways Analysis software (Ingenuity Systems, Redwood City, CA)
Category
Cellular Assembly and Organization
Molecular Transport
Cell Morphology
Cell-To-Cell Signaling and Interaction
Organ Morphology
Hereditary Disorder
Cellular Growth and Proliferation
Developmental Disorder
P-value
6.15 × 10–4
1.56 × 10–3
1.42 × 10–4
1.42 × 10–4
1.82 × 10–3
1.36 × 10–11
1.00 × 10–2
3.94 × 10–7
Genes
52
38
33
29
25
20
11
9
functional categories agree with the described association of HNF4G and fatty acids (Wisely et al., 2002).
The third key TF identified in this study was FOXP3.
The protein encoded by this gene is a member of the forkhead/winged-helix family of transcriptional regulators
and plays a critical role in the control of immune response
(Kim, 2009). For example, FOXP3 takes part in the regulation of immune pathways such as T helper cell differentiation, lymphocyte signaling, and cytokine production by
Th17 cells. T cells are critical mediators of the adaptive
immune response but also act in the regulation of bone
homeostasis via direct interaction with bone marrow, stromal cells, and osteoblasts and by releasing cytokines and
Wnt ligands (Pacifici, 2010). FOXP3 is a critical TF in the
T cell regulation (Rudensky, 2011). The role of FOXP3 as
regulator of genes that were activated during adipogenesis
of human bone marrow-derived mesenchymal stem cells
has been demonstrated (Menssen et al., 2011). Decrease
of FOXP3+ T cells has been associated with inflammation
of visceral adipose tissue and related metabolic syndrome
(Cipolletta et al., 2012; Feuerer et al., 2009). FOXP3
also participates in pathways related to developmental
processes such as the Wnt/β-catenin and notch signaling
pathways. According the functional annotation performed
with IPA, among the 103 AWM-target genes of FOXP3
(Supplementary Table S5; see online version of the article
at http://journalofanimalscience.org), 11.6% were related
to cellular assembly and organization (HDAC8, GJB1,
PAK3, SYTL4, PLS3, FGD1, NLGN3, SYN1, GNL3L,
MSN, ARHGEF9, and CAPN6). In addition, genes involved with tissue morphology such as connective tissue
and development (GUCY2F, GJB1, PAK3, OGT, EDNRB,
NLGN3, SYN1, ARHGEF9, COL4A6, PRPS1, UBA1, and
CLCN5) were also predicted as FOXP3 target genes. It is
worth noting that CAPN6 was an AWM-predicted target
genes of FOXP3 and CAPN6 is a member of the calpain
gene family. Members of this family play an essential
role in development processes and are Ca2+–dependent
modulator cysteine proteases. CAPN6 is predominantly
expressed in embryonic muscles and placenta (Dear and
2841
A gene network for intramuscular fat
Table 5. Description of the canonical pathway significantly (P-value < 0.05) overrepresented in the association
weight matrix-derived network
Pathways
Protein Ubiquitination
Calcium Signaling
Reelin Signaling in Neurons
TNFR1 Signaling
Tryptophan Degradation
PPARα/RXRα Activation
TNFR2 Signaling
Actin Cytoskeleton Signaling
P-value
4.17 × 10–3
6.61 × 10–3
1.58 × 10–2
1.95 × 10–2
1.99 × 10–2
2.09 × 10–2
4.57 × 10–2
4.79 × 10–2
Genes
PSMD10, USP20, USP11, ANAPC5, DNAJC5B, UBA1, NEDD4L, HSPA1L, and XIAP
HDAC8, CHRNA7, ATP2A3, TRPC5, ASPH, TRPC7, and GRIK1
MAP2K4, HCK, LYN, and ARHGEF9
MAP2K4, PAK3, and XIAP
DDC and ALDH7A1
MAP2K4, CAND1, IL1RAPL2, CKAP5, TGS1, and PPARGC1A
MAP2K4 and XIAP
FGF16, PAK3, FGF12, FGD1, PIP4K2A, and MSN
Boehm, 1999). Recent in vivo studies have shown that
CAPN6 promotes skeletal muscle differentiation during
mouse development and regeneration, suggesting a novel
physiological function for CAPN6 in the differentiation
of skeletal muscle (Tonami et al., 2013). Yet the role of
the calpain gene family in determining meat tenderness
and IMF percentage in beef cattle has been extensively
recognized (Barendse, 2011; Barendse et al., 2008; Kerry
and Ledward, 2009; Tizioto et al., 2013).
Among the overrepresented pathways in the AWMderived network was PPARα/RXRα activation (Table 5).
This pathway is modulated by the coordinated actions
of PPARGC1A and other TF. Members of these pathways presented in the predicted network were MAP2K4,
CAND1, IL1RAPL2, CKAP5, TGS1, and PPARGC1A. In
addition to the PPARα/RXRα pathway, MAP2K4 also is
on the transforming growth factor beta (TGF-β) signaling pathway. Recent results have underscored the role of
PPARGC1A and TGF-β signaling pathways on bovine
muscle development (Hudson et al., 2013). PPARGC1 is a
co-regulator of PPARG and interacts with several nuclear
receptors to bind to the promoter region of gluconeogenic
molecules including the glucocorticoid receptor and hepatocyte nuclear factor (HNF4). HNF4 regulates the expression of several genes involved in the different metabolism processes including cholesterol, fatty acids, and
glucose metabolism (Eloranta and Kullak-Ublick, 2005;
Stoffel and Duncan, 1997). In mammals, HNF4 isoforms
comprise 2 genes: HNF4A and HNF4G (Drewes et al.,
1996). Despite the functional differences between both
isoforms, a transcriptional dependence among them was
documented (Boj et al., 2001; Drewes et al., 1996). Moreover, a comparison of the DNA binding domain of human
HNF4G shows a high homology with a 95% identity to
HNF4A (Drewes et al., 1996). This remarkable homology suggests that HNF4G may have functions similar to
HNF4A in the transcriptional regulation of hepatic genes.
A functional cooperation in the regulation of hepatic
gluconeogenesis between PPARGC1 and HNF4A was reported (Rhee et al., 2003). In the network, PPARGC1 and
HNF4G shared a total of 48 AWM-predicted target genes.
A detail examination of these genes show that some of
them are involved in relevant biological processes related
with IMF such as lipid and carbohydrate metabolism (including TMEM68, TTPA, TGS1, and SLC44A1) as well
as vitamin metabolism (ATP2A3, ASPH, CHRNA7, LYN,
GGH, TTPA, and STC2). Also, constitutive overexpression of human STC2 in mice resulted in growth restriction,
reducing bone and skeletal muscle growth (Johnston et al.,
2010). Furthermore, among the 48 common target genes,
3 were associated to economically relevant traits in cattle:
XKR4, COPS, and EYA1. Genetic variants of the XKR4
gene were associated with subcutaneous rump fat thickness (Bolormaa et al., 2011b; Porto Neto et al., 2012). The
role of COPS (a gene member of hypoxia-inducible factor
1-alpha [HIF1-A] and TGF-β receptor signaling pathway)
Table 6. Description of the 9 significant gene networks identified by Ingenuity Pathways Analysis software (Ingenuity Systems, Redwood City, CA).
Network ID1
1
2
3
4
5
6
7
8
9
1ID
Score
45
44
41
33
25
24
22
20
20
= network identifier.
Genes
25
25
24
20
16
16
15
14
14
Top biological functions
Cell Signaling, Molecular Transport, and Vitamin and Mineral Metabolism
Cellular Assembly and Organization, Cellular Function and Maintenance, and Cancer
Developmental Disorder, Hereditary Disorder, and Neurological Disease
Nucleic Acid Metabolism, Small Molecule Biochemistry, and Hereditary Disorder
Embryonic Development, Cancer, and Reproductive System Disease
Hereditary Disorder, Metabolic Disease, and Renal and Urological Disease
Carbohydrate Metabolism, Small Molecule Biochemistry, and Developmental Disorder
Molecular Transport, Cell Morphology, and Organ Morphology
Cell Morphology, Cell-To-Cell Signaling and Interaction, and Cellular Assembly and Organization
2842
Ramapo-Caldas et al.
Figure 4. Network 1 as generated by Ingenuity Pathways Analysis software (Ingenuity Systems, Redwood City, CA). The significant biological functions the
network comprises are Cell Signaling, Molecular Transport, and Vitamin and Mineral Metabolism. The network is displayed graphically with nodes representing
genes or gene products and edges representing known biological relationships between nodes. Only those genes with known direct (continues lines) and indirect
(discontinues lines) interactions to other genes are represented.
in bovine muscle development was suggested (Sun et al.,
2012). Finally, evidence for the role of EYA1 gene and its
interaction with PPARG modulating was predicted by an
AWM gene network associated to cattle puberty, which
included fat deposition measurements (Fortes et al., 2011).
A recent gene expression study suggested that EYA1 may
have important roles in differentiated as well as undifferentiated muscle in bovine (Hudson et al., 2013).
Other overrepresented pathways in the IMF network were protein ubiquitination pathway, actin cytoskeleton, and calcium signaling pathway (Table 4). Actin cytoskeleton and chemokine signaling are pathways
recently reported as overrepresented in an AWM-derived network for intramuscular fatty acid composition
in porcine (Ramayo-Caldas et al., 2014). We hypothesize that PPARGC1A, HNF4G, and FOXP3 mediate
a highly interconnected regulatory cascade including
pathways such as PPARα/RXRα, protein ubiquitination pathway, calcium signaling pathway, actin cytoskeleton, immune response, and Wnt/β-catenin that
seem relevant for muscle development and IMF con-
tent. In fact, the role of these TF in the transcriptional
regulation of these pathways and biological processes
remains the subject of many studies (Berridge et al.,
2000; Cao and Chen, 2009; Sun et al., 2012; van Amerongen and Nusse, 2009; Zhao et al., 2012).
In summary, our GWAS and network predictions are
supported by literature and suggest a cooperative role of
3 TF, PPARGC1A, HNF4G, and FOXP3, as well as other
relevant genes such as CAPN6, STC2, MAP2K4, EYA1,
COPS5, XKR4, NR2E1, TOX, ATF1, ASPH, TGS1, and
TTPA in modulating the IMF-related phenotypes. Further studies will be required to elucidate specific cellular
and molecular processes of interaction among the 3 TF
and their target genes predicted to determine IMF composition and related meat quality traits in beef cattle.
LITERATURE CITED
Barendse, W. 2011. Haplotype analysis improved evidence for candidate genes for intramuscular fat percentage from a genome wide
association study of cattle. PLoS ONE 6:E29601.
A gene network for intramuscular fat
Barendse, W., B. E. Harrison, R. J. Bunch, and M. B. Thomas. 2008.
Variation at the Calpain 3 gene is associated with meat tenderness in zebu and composite breeds of cattle. BMC Genet. 9:41
Barwick, S. A., D. J. Johnston, H. M. Burrow, R. G. Holroyd, G.
Fordyce, M. L. Wolcott, W. D. Sim, and M. T. Sullivan. 2009a.
Genetics of heifer performance in ‘wet’ and ‘dry’ seasons and
their relationships with steer performance in two tropical beef
genotypes. Anim. Prod. Sci. 49:367–382.
Barwick, S. A., M. L. Wolcott, D. J. Johnston, H. M. Burrow, and
M. T. Sullivan. 2009b. Genetics of steer daily and residual feed
intake in two tropical beef genotypes, and relationships among
intake, body composition, growth and other post-weaning measures. Anim. Prod. Sci. 49:351–366.
Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery rate – A practical and powerful approach to multiple testing.
J. R. Stat. Soc. Series B Stat. Methodol. 57:289–300.
Berndt, S. I., S. Gustafsson, R. Maegi, A. Ganna, E. Wheeler, M. F.
Feitosa, A. E. Justice, K. L. Monda, D. C. Croteau-Chonka, F.
R. Day, T. Esko, T. Fall, T. Ferreira, D. Gentilini, A. U. Jackson, J. A. Luan, J. C. Randall, S. Vedantam, C. J. Willer, T. W.
Winkler, A. R. Wood, T. Workalemahu, Y.-J. Hu, S. H. Lee, L.
Liang, D.-Y. Lin, J. L. Min, B. M. Neale, G. Thorleifsson, J.
Yang, E. Albrecht, N. Amin, J. L. Bragg-Gresham, G. Cadby, M.
den Heijer, N. Eklund, K. Fischer, A. Goel, J.-J. Hottenga, J. E.
Huffman, I. Jarick, A. Johansson, T. Johnson, S. Kanoni, M. E.
Kleber, I. R. Koenig, K. Kristiansson, Z. Kutalik, C. Lamina, C.
Lecoeur, G. Li, M. Mangino, W. L. McArdle, C. Medina-Gomez,
M. Mueller-Nurasyid, J. S. Ngwa, I. M. Nolte, L. Paternoster,
S. Pechlivanis, M. Perola, M. J. Peters, M. Preuss, L. M. Rose,
J. Shi, D. Shungin, A. V. Smith, R. J. Strawbridge, I. Surakka,
A. Teumer, M. D. Trip, J. Tyrer, J. V. Van Vliet-Ostaptchouk, L.
Vandenput, L. L. Waite, J. H. Zhao, D. Absher, F. W. Asselbergs,
M. Atalay, A. P. Attwood, A. J. Balmforth, H. Basart, J. Beilby,
L. L. Bonnycastle, P. Brambilla, M. Bruinenberg, H. Campbell,
D. I. Chasman, P. S. Chines, F. S. Collins, J. M. Connell, W.
O. Cookson, U. de Faire, F. de Vegt, M. Dei, M. Dimitriou, S.
Edkins, K. Estrada, D. M. Evans, M. Farrall, M. M. Ferrario,
J. Ferrieres, L. Franke, F. Frau, P. V. Gejman, H. Grallert, H.
Groenberg, V. Gudnason, A. S. Hall, P. Hall, A.-L. Hartikainen,
C. Hayward, N. L. Heard-Costa, A. C. Heath, J. Hebebrand, G.
Homuth, F. B. Hu, S. E. Hunt, E. Hyppoenen, C. Iribarren, K.
B. Jacobs, J.-O. Jansson, A. Jula, M. Kahonen, S. Kathiresan,
F. Kee, K.-T. Khaw, M. Kivimaki, W. Koenig, A. T. Kraja, M.
Kumari, K. Kuulasmaa, J. Kuusisto, J. H. Laitinen, T. A. Lakka,
C. Langenberg, L. J. Launer, L. Lind, J. Lindstrom, J. Liu, A. Liuzzi, M.-L. Lokki, M. Lorentzon, P. A. Madden, P. K. Magnusson, P. Manunta, D. Marek, W. Maerz, I. M. Leach, B. McKnight,
S. E. Medland, E. Mihailov, L. Milani, G. W. Montgomery, V.
Mooser, T. W. Muehleisen, P. B. Munroe, A. W. Musk, N. Narisu,
G. Navis, G. Nicholson, E. A. Nohr, K. K. Ong, B. A. Oostra, C.
N. A. Palmer, A. Palotie, J. F. Peden, N. Pedersen, A. Peters, O.
Polasek, A. Pouta, P. P. Pramstaller, I. Prokopenko, C. Puetter,
A. Radhakrishnan, O. Raitakari, A. Rendon, F. Rivadeneira, I.
Rudan, T. E. Saaristo, J. G. Sambrook, A. R. Sanders, S. Sanna,
J. Saramies, S. Schipf, S. Schreiber, H. Schunkert, S.-Y. Shin, S.
Signorini, J. Sinisalo, B. Skrobek, N. Soranzo, A. Stancakova,
K. Stark, J. C. Stephens, K. Stirrups, R. P. Stolk, M. Stumvoll,
A. J. Swift, E. V. Theodoraki, B. Thorand, D.-A. Tregouet, E.
Tremoli, M. M. Van der Klauw, J. B. J. van Meurs, S. H. Vermeulen, J. Viikari, J. Virtamo, V. Vitart, G. Waeber, Z. Wang,
E. Widen, S. H. Wild, G. Willemsen, B. R. Winkelmann, J. C.
M. Witteman, B. H. R. Wolffenbuttel, A. Wong, A. F. Wright,
M. C. Zillikens, P. Amouyel, B. O. Boehm, E. Boerwinkle, D. I.
Boomsma, M. J. Caulfield, S. J. Chanock, L. A. Cupples, D. Cusi,
2843
G. V. Dedoussis, J. Erdmann, J. G. Eriksson, P. W. Franks, P.
Froguel, C. Gieger, U. Gyllensten, A. Hamsten, T. B. Harris, C.
Hengstenberg, A. A. Hicks, A. Hingorani, A. Hinney, A. Hofman, K. G. Hovingh, K. Hveem, T. Illig, M.-R. Jarvelin, K.H. Joeckel, S. M. Keinanen-Kiukaanniemi, L. A. Kiemeney,
D. Kuh, M. Laakso, T. Lehtimaki, D. F. Levinson, N. G. Martin, A. Metspalu, A. D. Morris, M. S. Nieminen, I. Njolstad,
C. Ohlsson, A. J. Oldehinkel, W. H. Ouwehand, L. J. Palmer,
B. Penninx, C. Power, M. A. Province, B. M. Psaty, L. Qi, R.
Rauramaa, P. M. Ridker, S. Ripatti, V. Salomaa, N. J. Samani,
H. Snieder, T. I. A. Sorensen, T. D. Spector, K. Stefansson, A.
Tonjes, J. Tuomilehto, A. G. Uitterlinden, M. Uusitupa, P. van
der Harst, P. Vollenweider, H. Wallaschofski, N. J. Wareham,
H. Watkins, H. E. Wichmann, J. F. Wilson, G. R. Abecasis, T.
L. Assimes, I. Barroso, M. Boehnke, I. B. Borecki, P. Deloukas,
C. S. Fox, T. Frayling, L. C. Groop, T. Haritunian, I. M. Heid,
D. Hunter, R. C. Kaplan, F. Karpe, M. F. Moffatt, K. L. Mohlke,
J. R. O’Connell, Y. Pawitan, E. E. Schadt, D. Schlessinger, V.
Steinthorsdottir, D. P. Strachan, U. Thorsteinsdottir, C. M. van
Duijn, P. M. Visscher, A. M. Di Blasio, J. N. Hirschhorn, C. M.
Lindgren, A. P. Morris, D. Meyre, A. Scherag, M. I. McCarthy,
E. K. Speliotes, K. E. North, R. J. F. Loos, and E. Ingelsson.
2013. Genome-wide meta-analysis identifies 11 new loci for
anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45:501–512.
Berridge, M. J., P. Lipp, and M. D. Bootman. 2000. The versatility and
universality of calcium signalling. Nat. Rev. Mol. Cell Biol. 1:11–21.
Boj, S. F., M. Parrizas, M. A. Maestro, and J. Ferrer. 2001. A transcription factor regulatory circuit in differentiated pancreatic
cells. Proc. Natl. Acad. Sci. USA 98:14481–14486.
Bolormaa, S., B. J. Hayes, K. Savin, R. Hawken, W. Barendse, P.
F. Arthur, R. M. Herd, and M. E. Goddard. 2011a. Genomewide association studies for feedlot and growth traits in cattle. J.
Anim. Sci. 89:1684–1697.
Bolormaa, S., L. R. Neto, Y. D. Zhang, R. J. Bunch, B. E. Harrison,
M. E. Goddard, and W. Barendse. 2011b. A genome-wide association study of meat and carcass traits in Australian cattle. J.
Anim. Sci. 89:2297–2309.
Bolormaa, S., J. E. Pryce, K. Kemper, K. Savin, B. J. Hayes, W. Barendse, Y. Zhang, C. M. Reich, B. A. Mason, R. J. Bunch, B. E.
Harrison, A. Reverter, R. M. Herd, B. Tier, H. U. Graser, and M.
E. Goddard. 2013. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality
traits in Bos taurus, Bos indicus, and composite beef cattle. J.
Anim. Sci. 91:3088–3104.
Bolormaa, S., J. E. Pryce, A. Reverter, Y. Zhang, W. Barendse, K.
Kemper, B. Tier, K. Savin, B. J. Hayes, and M. E. Goddard.
2014. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle.
PLoS Genet. 10:E1004198.
Browning, B. L., and S. R. Browning. 2011. A fast, powerful method
for detecting identity by descent. Am. J. Hum. Genet. 88:173–182.
Calvano, S. E., W. Z. Xiao, D. R. Richards, R. M. Felciano, H. V.
Baker, R. J. Cho, R. O. Chen, B. H. Brownstein, J. P. Cobb, S.
K. Tschoeke, C. Miller-Graziano, L. L. Moldawer, M. N. Mindrinos, R. W. Davis, R. G. Tompkins, S. F. Lowry, and I. H. R.
Injury. 2005. A network-based analysis of systemic inflammation in humans. Nature 437:1032–1037.
Cao, X., and Y. Chen. 2009. Mitochondria and calcium signaling in
embryonic development. Semin. Cell Dev. Biol. 20:337–345.
Cipolletta, D., M. Feuerer, A. Li, N. Kamei, J. Lee, S. E. Shoelson, C.
Benoist, and D. Mathis. 2012. PPAR-gamma is a major driver
of the accumulation and phenotype of adipose tissue Treg cells.
Nature 486:549–553.
2844
Ramapo-Caldas et al.
De Smet, S., K. Raes, and D. Demeyer. 2004. Meat fatty acid composition as affected by fatness and genetic factors: A review. Anim.
Res. 53:81–98.
Dear, T. N., and T. Boehm. 1999. Diverse mRNA expression patterns
of the mouse calpain genes Capn5, Capn6 and Capn11 during
development. Mech. Dev. 89:201–209.
Drewes, T., S. Senkel, B. Holewa, and G. U. Ryffel. 1996. Human
hepatocyte nuclear factor 4 isoforms are encoded by distinct
and differentially expressed genes. Mol. Cell. Biol. 16:925–931.
Eloranta, J. J., and G. A. Kullak-Ublick. 2005. Coordinate transcriptional regulation of bile acid homeostasis and drug metabolism.
Arch. Biochem. Biophys. 433:397–412.
Esterbauer, H., H. Oberkofler, V. Linnemayr, B. Iglseder, M. Hedegger, P. Wolfsgruber, B. Paulweber, G. Fastner, F. Krempler,
and W. Patsch. 2002. Peroxisome proliferator-activated receptor-gamma coactivator-1 gene locus: Associations with obesity
indices in middle-aged women. Diabetes 51:1281–1286.
Feuerer, M., L. Herrero, D. Cipolletta, A. Naaz, J. Wong, A. Nayer, J. Lee,
A. B. Goldfine, C. Benoist, S. Shoelson, and D. Mathis. 2009. Lean,
but not obese, fat is enriched for a unique population of regulatory
T cells that affect metabolic parameters. Nat. Med. 15:930–939.
Fortes, M. R. S., A. Reverter, S. H. Nagaraj, Y. Zhang, N. N. Jonsson,
W. Barris, S. Lehnert, G. B. Boe-Hansen, and R. J. Hawken.
2011. A single nucleotide polymorphism-derived regulatory
gene network underlying puberty in 2 tropical breeds of beef
cattle. J. Anim. Sci. 89:1669–1683.
Fortes, M. R. S., A. Reverter, Y. Zhang, E. Collis, S. H. Nagaraj, N.
N. Jonsson, K. C. Prayaga, W. Barris, and R. J. Hawken. 2010.
Association weight matrix for the genetic dissection of puberty
in beef cattle. Proc. Natl. Acad. Sci. USA 107:13642–13647.
Garnier, J. P., R. Klont, and G. Plastow. 2003. The potential impact
of current animal research on the meat industry and consumer
attitudes towards meat. Meat Sci. 63:79–88.
Gerdin, A. K., V. V. Surve, M. Jonsson, M. Bjursell, M. Bjorkman,
A. Edenro, M. Schuelke, A. Saad, S. Bjurstrom, E. J. Lundgren,
M. Snaith, R. Fransson-Steen, J. Tornell, A.-L. Berg, and M.
Bohlooly-Y. 2006. Phenotypic screening of hepatocyte nuclear
factor (HNF) 4-gamma receptor knockout mice. Biochem. Biophys. Res. Commun. 349:825–832.
Gilmour, A., B. Gogel, B. Cullis, S. Welham, and R. Thompson.
2002. ASReml user guide release 1.0. VSN International Ltd.,
Hemel Hempstead, UK.
Hara, K., K. Tobe, T. Okada, H. Kadowaki, Y. Akanuma, C. Ito, S.
Kimura, and T. Kadowaki. 2002. A genetic variation in the
PGC-1 gene could confer insulin resistance and susceptibility
to Type II diabetes. Diabetologia 45:740–743.
Harper, G. S., and D. W. Pethick. 2004. How might marbling begin?
Aust. J. Exp. Agric. 44:653–662.
Horii, M., Y. Sakurai, Y. Kanbe, K. Kasai, T. Asada, M. Kobayashi,
M. Yamada, M. Hayashi, and K. Hodate. 2009. Relationship between Japanese Beef Marbling Standard numbers and intramuscular lipid in M. longissimus thoracis of Japanese Black steers
from 1996 to 2004. Anim. Sci. J. 80:55–61.
Hudson, N. J., R. E. Lyons, A. Reverter, P. L. Greenwood, and B.
P. Dalrymple. 2013. Inferring the in vivo cellular program of
developing bovine skeletal muscle from expression data. Gene
Expr. Patterns 13:109–125.
Ichida, M., S. Nemoto, and T. Finkel. 2002. Identification of a specific molecular repressor of the peroxisome proliferator-activated receptor gamma Coactivator-1 alpha (PGC-1alpha). J. Biol.
Chem. 277:50991–50995.
Johnston, D. J., A. Reverter, H. M. Burrow, V. H. Oddy, and D. L.
Robinson. 2003a. Genetic and phenotypic characterisation of
animal, carcass, and meat quality traits from temperate and
tropically adapted beef breeds. 1. Animal measures. Aust. J.
Agric. Res. 54:107–118.
Johnston, D. J., A. Reverter, D. M. Ferguson, J. M. Thompson, and
H. M. Burrow. 2003b. Genetic and phenotypic characterisation
of animal, carcass, and meat quality traits from temperate and
tropically adapted beef breeds. 3. Meat quality traits. Aust. J.
Agric. Res. 54:135–147.
Johnston, J., Y. Ramos-Valdes, L.-A. Stanton, S. Ladhani, F. Beier,
and G. E. DiMattia. 2010. Human stanniocalcin-1 or-2 expressed in mice reduces bone size and severely inhibits cranial
intramembranous bone growth. Transgenic Res. 19:1017–1039.
Kerry, J. P., and D. Ledward. 2009. Improving the sensory and nutritional quality of fresh meat. Woodhead Publishing, Sawston,
Cambridge, UK.
Kim, C. H. 2009. FOXP3 and its role in the immune system. Adv.
Exp. Med. Biol. 665:17–29.
Komisarek, J., and A. Walendowska. 2012. Analysis of the PPARGC1A gene as a potential marker for productive and reproductive
traits in cattle. Folia Biol. (Krakow) 60:171–174.
Liu, R. R., Y. F. Sun, G. P. Zhao, F. J. Wang, D. Wu, M. Q. Zheng, J.
L. Chen, L. Zhang, Y. D. Hu, and J. Wen. 2013. Genome-wide
association study identifies loci and candidate genes for body
composition and meat quality traits in Beijing-You chickens.
PLoS ONE 8:E61172.
Menssen, A., T. Haeupl, M. Sittinger, B. Delorme, P. Charbord, and
J. Ringe. 2011. Differential gene expression profiling of human
bone marrow-derived mesenchymal stem cells during adipogenic development. BMC Genomics 12:461.
Moloney, A. P., M. T. Mooney, J. P. Kerry, C. Stanton, and P. O’Kiely.
2013. Colour of fat, and colour, fatty acid composition and sensory characteristics of muscle from heifers offered alternative forages to grass silage in a finishing ration. Meat Sci. 95:608–615.
Pacifici, R. 2010. T cells: Critical bone regulators in health and disease. Bone 47:461–471.
Parnell, P. F. 2004. Industry application of marbling genetics: A brief
review. Aust. J. Exp. Agric. 44:697–703.
Pena, R. N., J. L. Noguera, J. Casellas, I. Diaz, A. I. Fernandez, J. M. Folch,
and N. Ibanez-Escriche. 2013. Transcriptional analysis of intramuscular fatty acid composition in the longissimus thoracis muscle of
Iberian × Landrace back-crossed pigs. Anim. Genet. 44:648–660.
Perry, D., W. R. Shorthose, D. M. Ferguson, and J. M. Thompson.
2001. Methods used in the CRC program for the determination
of carcass yield and beef quality. Aust. J. Exp. Agric. 41:953–957.
Peters, S. O., K. Kizilkaya, D. J. Garrick, R. L. Fernando, J. M.
Reecy, R. L. Weaber, G. A. Silver, and M. G. Thomas. 2012.
Bayesian genome-wide association analysis of growth and yearling ultrasound measures of carcass traits in Brangus heifers. J.
Anim. Sci. 90:3398–3409.
Porto Neto, L. R., R. J. Bunch, B. E. Harrison, and W. Barendse.
2012. Variation in the XKR4 gene was significantly associated
with subcutaneous rump fat thickness in indicine and composite
cattle. Anim. Genet. 43:785–789.
Ramayo-Caldas, Y., M. Ballester, M. R. Fortes, A. Esteve-Codina, A.
Castello, J. L. Noguera, A. I. Fernandez, M. Perez-Enciso, A. Reverter, and J. M. Folch. 2014. From SNP co-association to RNA
co-expression: Novel insights into gene networks for intramuscular fatty acid composition in porcine. BMC Genomics 15:232.
A gene network for intramuscular fat
Ramayo-Caldas, Y., A. Mercade, A. Castello, B. Yang, C. Rodriguez,
E. Alves, I. Diaz, N. Ibanez-Escriche, J. L. Noguera, M. PerezEnciso, A. I. Fernandez, and J. M. Folch. 2012. Genome-wide
association study for intramuscular fatty acid composition in an
Iberian × Landrace cross. J. Anim. Sci. 90:2883–2893.
Reverter, A., and E. K. F. Chan. 2008. Combining partial correlation
and an information theory approach to the reversed engineering
of gene co-expression networks. Bioinformatics 24:2491–2497.
Reverter, A., and M. R. Fortes. 2013a. Association weight matrix: A
network-based approach towards functional genome-wide association studies. Methods Mol. Biol. 1019:437–447.
Reverter, A., and M. R. S. Fortes. 2013b. Breeding and genetics
symposium: Building single nucleotide polymorphism-derived
gene regulatory networks: Towards functional genomewide association studies. J. Anim. Sci. 91:530–536.
Reverter, A., D. J. Johnston, D. M. Ferguson, D. Perry, M. E. Goddard, H. M. Burrow, V. H. Oddy, J. M. Thompson, and B. M.
Bindon. 2003a. Genetic and phenotypic characterisation of
animal, carcass, and meat quality traits from temperate and
tropically adapted beef breeds. 4. Correlations among animal,
carcass, and meat quality traits. Aust. J. Agric. Res. 54:149–158.
Reverter, A., D. J. Johnston, D. Perry, M. E. Goddard, and H. M. Burrow.
2003b. Genetic and phenotypic characterisation of animal, carcass,
and meat quality traits from temperate and tropically adapted beef
breeds. 2. Abattoir carcass traits. Aust. J. Agric. Res. 54:119–134.
Rhee, J., Y. Inoue, J. C. Yoon, P. Puigserver, M. L. Fan, F. J. Gonzalez, and B. M. Spiegelman. 2003. Regulation of hepatic fasting
response by PPAR gamma coactivator-1 alpha (PGC-1): Requirement for hepatocyte nuclear factor 4 alpha in gluconeogenesis. Proc. Natl. Acad. Sci. USA 100:4012–4017.
Rudensky, A. Y. 2011. Regulatory T cells and Foxp3. Immunol. Rev.
241:260–268.
Scardoni, G., M. Petterlini, and C. Laudanna. 2009. Analyzing biological
network parameters with CentiScaPe. Bioinformatics 25:2857–2859.
Sevane, N., E. Armstrong, O. Cortes, P. Wiener, R. P. Wong, and S. Dunner.
2013. Association of bovine meat quality traits with genes included
in the PPARG and PPARGC1A networks. Meat Sci. 94:328–335.
Shannon, P., A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker. 2003. Cytoscape:
A software environment for integrated models of biomolecular
interaction networks. Genome Res. 13:2498–2504.
Shin, S., and E. Chung. 2013. Novel SNPs in the bovine ADIPOQ
and PPARGC1A genes are associated with carcass traits in
Hanwoo (Korean cattle). Mol. Biol. Rep. 40:4651–4660.
Snelling, W. M., R. A. Cushman, J. W. Keele, C. Maltecca, M. G.
Thomas, M. R. S. Fortes, and A. Reverter. 2013. Breeding and
genetics symposium: Networks and pathways to guide genomic
selection. J. Anim. Sci. 91:537–552.
Stoffel, M., and S. A. Duncan. 1997. The maturity-onset diabetes of
the young (MODY1) transcription factor HNF4 alpha regulates
expression of genes required for glucose transport and metabolism. Proc. Natl. Acad. Sci. USA 94:13209–13214.
Summermatter, S., G. Shui, D. Maag, G. Santos, M. R. Wenk, and C.
Handschin. 2013. PGC-1alpha improves glucose homeostasis in
skeletal muscle in an activity-dependent manner. Diabetes 62:85–95.
Sun, W., N. J. Hudson, A. Reverter, A. J. Waardenberg, R. L. Tellam,
T. Vuocolo, K. Byrne, and B. P. Dalrymple. 2012. An Always
Correlated gene expression landscape for ovine skeletal muscle,
lessons learnt from comparison with an “equivalent” bovine
landscape. BMC Res. Notes 5:632.
2845
Taraviras, S., T. Mantamadiotis, T. Dong-Si, A. Mincheva, P. Lichter, T. Drewes, G. U. Ryffel, A. P. Monaghan, and G. Schutz.
2000. Primary structure, chromosomal mapping, expression
and transcriptional activity of murine hepatocyte nuclear factor
4 gamma. Biochim. Biophys. Acta 1490:21–32.
Tizioto, P. C., J. E. Decker, J. F. Taylor, R. D. Schnabel, M. A. Mudadu,
F. L. Silva, G. B. Mourao, L. L. Coutinho, P. Tholon, T. S. Sonstegard, A. N. Rosa, M. M. Alencar, R. R. Tullio, S. R. Medeiros,
R. T. Nassu, G. L. Feijo, L. O. Silva, R. A. Torres, F. Siqueira, R.
H. Higa, and L. C. Regitano. 2013. Genome scan for meat quality traits in Nelore beef cattle. Physiol. Genomics 45:1012–1020.
Tizioto, P. C., S. L. Meirelles, R. R. Tulio, A. N. Rosa, M. M. Alencar,
S. R. Medeiros, F. Siqueira, G. L. D. Feijo, L. O. C. Silva, R. A. A.
Torres, and L. C. A. Regitano. 2012. Candidate genes for production traits in Nelore beef cattle. Genet. Mol. Res. 11:4138–4144.
Tonami, K., S. Hata, K. Ojima, Y. Ono, Y. Kurihara, T. Amano, T.
Sato, Y. Kawamura, H. Kurihara, and H. Sorimachi. 2013. Calpain-6 deficiency promotes skeletal muscle development and
regeneration. PLoS Genet. 9:E1003668.
Upton, W., H. M. Burrow, A. Dundon, D. L. Robinson, and E. B. Farrell. 2001. CRC breeding program design, measurements and
database: Methods that underpin CRC research results. Aust. J.
Exp. Agric. 41:943–952.
van Amerongen, R., and R. Nusse. 2009. Towards an integrated view
of Wnt signaling in development. Development 136:3205–3214.
Wang, Y. H., K. A. Byrne, A. Reverter, G. S. Harper, M. Taniguchi,
S. M. McWilliam, H. Mannen, K. Oyama, and S. A. Lehnert.
2005. Transcriptional profiling of skeletal muscle tissue from
two breeds of cattle. Mamm. Genome 16:201–210.
Warner, R. D., P. L. Greenwood, D. W. Pethick, and D. M. Ferguson.
2010. Genetic and environmental effects on meat quality. Meat
Sci. 86:171–183.
Wisely, G. B., A. B. Miller, R. G. Davis, A. D. Thornquest Jr., R.
Johnson, T. Spitzer, A. Sefler, B. Shearer, J. T. Moore, A. B.
Miller, T. M. Willson, and S. P. Williams. 2002. Hepatocyte nuclear factor 4 is a transcription factor that constitutively binds
fatty acids. Structure 10:1225–1234.
Yamada, T., S. Sasaki, S. Sukegawa, T. Miyake, T. Fujita, H. Kose, M.
Morita, Y. Takahagi, H. Murakami, F. Morimatsu, and Y. Sasaki.
2009. Novel SNP in 5’ flanking region of EDG1 associated with
marbling in Japanese Black beef cattle. Anim. Sci. J. 80:486–489.
Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders,
D. R. Nyholt, P. A. Madden, A. C. Heath, N. G. Martin, G. W.
Montgomery, M. E. Goddard, and P. M. Visscher. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:565–569.
Yoneda, M., K. Hotta, Y. Nozaki, H. Endo, T. Uchiyama, H. Mawatari,
H. Iida, S. Kato, K. Hosono, K. Fujita, K. Yoneda, H. Takahashi,
H. Kirikoshi, N. Kobayashi, M. Inamori, Y. Abe, K. Kubota, S.
Saito, S. Maeyama, K. Wada, and A. Nakajima. 2008. Association
between PPARGC1A polymorphisms and the occurrence of nonalcoholic fatty liver disease (NAFLD). BMC Gastroenterol. 8:27.
Zhao, C., F. Tian, Y. Yu, J. Luo, A. Mitra, F. Zhan, Y. Hou, G. Liu,
L. Zan, M. S. Updike, and J. Song. 2012. Functional genomic
analysis of variation on beef tenderness induced by acute stress
in Angus cattle. Comp. Funct. Genomics 2012:1.