Download A QTL affecting daily feed intake maps to Chromosome 2 in pigs

A QTL affecting daily feed intake maps to Chromosome 2 in pigs
Ross D. Houston,1,2* Chris S. Haley,3 Alan L. Archibald,3 Kellie A. Rance1
1
Aberdeen Centre for Energy Regulation and Obesity (ACERO), Energy Balance and Obesity Division, Rowett Research Institute,
Bucksburn, Aberdeen AB21 9SB, Scotland, UK
2
Division of Agriculture and Forestry, School of Biological Sciences, University of Aberdeen, AB24 4FA, Scotland, UK
3
Department of Genetics and Genomics, Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, Scotland, UK
Received: 29 November 2004 / Accepted: 3 March 2005
Abstract
Our understanding of the molecular genetic basis of
several key performance traits in pigs has been significantly advanced through the quantitative trait
loci (QTL) mapping approach. However, in contrast
to growth and fatness traits, the genetic basis of feed
intake traits has rarely been investigated through
QTL mapping. Since feed intake is an important
component of efficient pig production, the identification of QTL affecting feed intake may lead to the
identification of genetic markers that can be used in
selection programs. In this study a QTL analysis for
feed intake, feeding behavior, and growth traits was
performed in an F2 population derived from a cross
between Chinese Meishan and European Large
White pigs. A QTL with a significant effect on daily
feed intake (DFI) was identified on Sus scrofa Chromosome 2 (SSC2). A number of suggestive QTL with
effects on daily gain, feed conversion, and feeding
behavior traits were also located. The significant
QTL lies close to a previously identified mutation in
the insulin-like growth factor 2 gene (IGF2) that affects carcass composition traits, although the IGF2
mutation is not segregating in the populations analyzed in the current study. Therefore, a distinct
causal variant may exist on the P arm of SSC2 with
an effect on feed intake.
Introduction
The advent of porcine molecular genetic marker
maps and advances in statistical methodology have
*Present address: Division of Genetics and Genomics, Roslin
Institute (Edinburgh), Roslin, Midlothian EH25 9PS, Scotland, UK
Correspondence to: Ross D. Houston; E-mail: ross.houston@bbsrc.
ac.uk
464
facilitated the mapping of quantitative trait loci
(QTL) for economically important traits in livestock.
The genetic basis of performance traits, such as
growth and fatness in pigs, has been well studied
through QTL mapping in experimental crosses between various phenotypically divergent founder
breeds such as Chinese Meishan or Wild Boar and
European Large White (reviewed in Bidanel and
Rothschild 2002). These QTL mapping studies have
led to the identification of several genomic regions
with significant effects on these performance traits.
The identified QTL can be seen as a starting point for
identifying the underlying causal polymorphisms, or
closely linked markers, which can be used in marker-assisted selection programs to improve performance (Georges 1999).
While feed intake is clearly an economically
important trait in pigs and accounts for a significant
proportion of the costs involved in pig production
(Webb 1998), the molecular genetic basis of feed intake traits has received less attention than that of
other performance traits. This may be partly because
of the difficulty of measuring and analyzing accurate
feed intake data in a group-housed system compared
with traits such as daily gain and backfat (Hyun et al.
1997). However, with the development of electronic
feed intake equipment tailored for use in such environments (Hyun et al. 1997), the opportunity exists to
obtain accurate measures of feed intake, feed efficiency, and feeding behavior and, therefore, to investigate the molecular genetic basis of these traits. The
relative difficulty in measuring accurate feed intake
and feed efficiency indicates that marker-assisted
selection may have advantages over traditional
selection strategies for these traits, provided markers
associated with suitably large and consistent phenotypic effects can be identified. The QTL mapping approach is often the starting point for locating such
markers.
DOI: 10.1007/s00335-004-4026-0 Volume 16, 464–470 (2005) Springer Science+Business Media, Inc. 2005
R.D. HOUSTON
ET AL.:
DAILY FEED INTAKE QTL
IN
PIGS
A number of candidate genes have been associated with feed intake, growth, and fatness in pigs,
including leptin (LEP), leptin receptor (LEPR), and
melanocortin-4 receptor (MC4R) (reviewed in Roehe
et al. 2003). It is interesting to examine whether any
QTL identified for feed intake or growth traits
coincide with the regions of the genome to which
these genes map. Also, since adiposity stores are a
key variable in regulating the feed intake of an animal (Woods and Seeley 2000), it is worthwhile
assessing whether any of the previously identified
major QTL affecting fatness are associated with a
pleiotropic effect on feed intake. Furthermore, identification of genomic regions associated with feed
intake may be of value to human obesity research
through the use of comparative maps. Therefore, the
objective of this study was to analyze electronically
recorded feed intake data for a Meishan · Large
White F2 population in a genome scan to identify
QTL with effects on feed intake, growth, and feeding
behavior traits.
Materials and methods
Animals. The animals used in this study came from
an F2 population derived from a cross between Meishan and Large White pigs at the Roslin Institute.
The subset of animals that had feed intake data recorded came from two separate batches—one born in
1995 and the other born in 1996. For both of these
populations, reciprocal F1 crosses were produced
from unrelated Meishan and Large White F0 animals.
The F0 Meishan pigs were derived from the importation of 11 males and 21 females from the Jiadan
county pedigree on the Lou Tang research farm in
China in 1987 (Haley et al. 1992). The F0 Large White
pigs were from a British control population, derived
from a broad sample of genotypes in 1982 (Walling et
al. 1998). For the animals used in the current study, a
total of 9 F0 males and 18 F0 females were used to
produce 4 F1 males and 58 F1 females, which were
intercrossed to produce 88 F2 males and 67 F2 females (total n = 155)
Performance testing. The F2 population was
performance tested using single-spaced FIRE feeders
(Feed Intake Recording Equipment; Osborne Europe
Ltd, Newcastle-Upon-Tyne). The feeder animals
were approximately three months old (or between 30
and 35 kg) at the start of the trial and were tested
until they reached approximately 80 kg, although
some animals remained in the feeders for longer.
During the trial animals were group-housed in pens
with solid floors and bedded with straw, with each
pen containing 13 single-sex animals and one feeder
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station, to which the animals were given 24-hour
access. They were fed a dry pelleted proprietary ration containing 18% crude protein, 5.09% oil, 6.38%
fiber 5.78% ash, and 1.05% lysine ad libitum. Each
animal was assigned an individual identity number
(ID) and a tag number, which were used by the
electronic recording system for animal identification.
Data summary. Every time an animal visited
the feeder, the ID and weight of the animal, the total
feed intake during the visit, and the time spent in
the feeder were electronically recorded and used to
give daily measurements of median weight (of all
measurements taken during the day), total feed intake, number of visits to the feeder, and the total
time in the feeder for each animal. Through individual scrutiny of each animalÕs data records, the
suitability of each animal for inclusion in the analysis was determined based on the quality of the
growth curve and the quantity of measurement errors. The daily data records for the included animals
were summarized to give measurements of average
daily gain (ADG, g), daily feed intake (DFI, g), feed
conversion ratio (FCR), number of visits to the feeder
per day (NVD), time in feeder per day (DFT, min),
average feed per visit (AFV, g), and average feeding
rate (AFR, g/sec). These traits were calculated for the
early (35–55 kg), late (55–80 kg), and entire (35–
80 kg) test periods.
Map construction. Genotype data for 114
markers across the 18 autosomes and the sex chromosome for the QTL pedigree were available on the
ResSpecies database hosted at the Roslin Institute
(www.resspecies.org). Marker order and sex-averaged
marker positions were estimated using the BUILD
option in Cri-Map 2.4 (Green et al. 1990).
QTL Analysis. The fixed effects of litter, gender, batch (year of birth), and feeder number were
assessed using a restricted maximum likelihood
(REML) approach in Genstat 6.1 (Genstat Committee 2002) to determine which effects were
explaining a significant proportion of the phenotypic variance in the feeder traits. Because of the
relatively small sample size (n = 155) and the large
number of full-sib families in the F2 population
(n = 58), it was decided not to include litter as a
fixed effect in the QTL analyses because of the
substantial loss of degrees of freedom. Feeders were
gender-specific and, therefore, fitting feeder number
in the analyses accounts for variance resulting from
gender. Since the same feeder may have functioned
differently in the different years, a composite vari-
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able of feeder number · batch was included as a
fixed effect in the QTL analysis. The covariates of
age at 35 kg or age at 55 kg were also included in
the model, depending on which growth period was
under assessment. The halothane gene was not
segregating in the Large White founder animals
(Cameron et al. 1988). To account for genetic effects from other chromosomes, QTL detected at the
suggestive level were included as cofactors in any
further analysis of that particular trait.
The web-mounted QTL Express software (http://
qtl.cap.ed.ac.uk; Seaton et al. 2002) was used to
calculate the information content across the genome
and the proportion of animals with missing genotype
information. The statistical principles of Haley et al.
(1994) were then applied to perform the QTL analysis in which the genotype information is used to
calculate the probabilities of F2 individuals having a
particular putative QTL genotype, based conditionally on marker genotypes and accounting for the
parental origin of the alleles. The probabilities are
then used in a least-squares framework to investigate the genetic model underlying the trait (Knott et
al. 1998). To test for evidence of an imprinting effect
associated with the QTL, an imprinting term was
included as an addition to the additive and dominance model. The imprinting model was compared
with a model assuming no QTL (F statistic, 3 degrees
of freedom) and the best-single-QTL model (F statistic, 1 degree of freedom).
Thresholds. Suggestive and significant thresholds were calculated following the guidelines of
Lander and Kruglyak (1995) using a permutation test
(Churchill and Doerge 1994) with the map and
genotype data described above and randomly created
phenotype data with a mean of 0 and a standard
deviation of 1 (these data were used because of the
large number of traits and growth periods analyzed).
Five thousand permutations were performed in the
QTL Express program, which calculates the F ratio
that is equivalent to the 0.05-level that chromosomewide thresholds automatically. Following the principles of Knott et al. (1998), the suggestive thresholds
were determined from the 0.05 chromosome-wide
level for each chromosome separately. For the genome-wide significance threshold, the permutation
process was repeated using the actual phenotype data
for the trait that gave the highest F-ratio in the QTL
analysis (DFI 2). The thresholds for determining the
genome-wide significance of the imprinting model
were calculated as described in Knott et al. (1998). For
significant QTL an approximation of the confidence
interval was calculated by the 1-LOD drop support
method (Lander and Botstein 1989).
R.D. HOUSTON
ET AL.:
DAILY FEED INTAKE QTL
IN
PIGS
Results
Linkage map. In order to build a linkage map based
on the genotype data for the Meishan · Large White
F2 population, the Cri-Map BUILD option was used.
The map details are presented in Fig.1. The gender
averaged map length was 2131 cM, which gives an
average distance between markers of 18.7 cM. The
information content was generally maintained above
0.5 throughout the genome. The animals used in the
current study were a subset of those used in Lee et al.
(2003), and the estimated gender-averaged order and
positions for the markers common to both studies
were similar. The marker order was consistent with
other published linkage maps (Archibald et al. 1995;
Rohrer et al. 1996), while estimated marker positions were similar.
Thresholds. A permutation analysis in QTL
Express was used to calculate the genome-wide significant and suggestive thresholds. The genomewide significance threshold was calculated to be
8.38 at the 5% level, while the suggestive threshold
varied between 4.19 and 5.42, depending on the
chromosome under analysis. The 5% genome-wide
threshold calculated using permutation of the trait
DFI 2 was calculated to be 8.50. These thresholds are
comparable to previously published studies (e.g.,
Knott et al. 1998; Walling et al. 1998; de Koning et al.
1999). The genome-wide significance threshold for
the imprinting model was estimated to be 6.5, while
the suggestive thresholds for SSC2 was estimated to
be 4.2. The F ratio corresponding to the nominal
significance level when comparing the imprinting
model to the best single-QTL model was 3.9.
QTL analysis. A genome-wide scan for QTL
affecting the calculated feed intake, feeding behavior, and growth traits was performed. The abbreviations and descriptive statistics for the measured
traits are presented in Table 1. The only QTL that
reached genome-wide significance was located on
SSC2 at 28 cM, and it affects daily feed intake during
the 55–80 kg growth period (Table 2, Fig. 2). There
was also suggestive evidence of an effect on daily
feed intake during the entire growth period with a
similar additive effect, but the best-estimated position was 3 cM (Table 2, Fig. 2). There was suggestive
evidence for QTL that affect average daily gain
(Chromosomes 8, 11, and 17), daily feed intake
(Chromosomes 11, 13, and 17), feed conversion ratio
(chromosomes 11, 12, and 14), daily feeding time
(Chromosome 6), average feed per visit (chromosomes 11 and 15), and average feeding rate (chromosomes 3 and 14) (Table 2).
R.D. HOUSTON
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467
Fig. 1. Names of the markers used in the study, with their calculated order and linkage map positions (given in centimorgans).
While the best-estimated position on SSC2 of the
significant QTL affecting DFI was 28 cM, the test
statistics were high over much of the chromosome
(Fig. 2). A calculation of the 1-LOD drop support
confidence intervals suggests that the QTL was located between 1 and 63 cM. The QTL that affect DFI
(55–80 kg and 35–80 kg) were additive in nature with
no evidence of a dominance effect. The additive effects associated with the QTL were large at 244 g
(±58) and 179 g (±46) for the 55–80 kg and 35–80 kg
growth periods, respectively (Table 2). The positive
estimation of the additive effect indicates that the
feed intake–increasing allele originates from the
Large White line. There was no evidence that
including an imprinting term significantly improved
the model compared with the additive and dominance model. Therefore, it can be concluded that
there is no parent-of-origin effect associated with the
QTL.
Discussion
To locate and characterize QTL with effects on feed
intake, feeding behavior, and growth traits, a genome-wide QTL analysis was performed in a Meishan
· Large White F2 population which had been per-
formance-tested with electronic feeding equipment.
Potentially, the most interesting QTL was located
on SSC2 and had an additive effect on daily feed intake during the late test period, which was significant at the genome-wide level, and an additive effect
on daily feed intake during the entire test period,
which reached the suggestive level. A number of
other QTL that reached the suggestive threshold
were located, including QTL affecting average daily
gain, feed conversion ratio, and various feeding
behavior traits.
The appropriate significance thresholds for
determining suggestive and significant QTL were
determined by permutation tests in the current
study. While these account for the testing of multiple positions across the genome, they do not account
for the testing of multiple traits. For example, with
21 different traits analyzed, one expects approximately 21 QTL to surpass the suggestive threshold
by chance alone. Therefore, as with many QTL
genome scans, it is important to be alert to the
possibility that a number of the putative QTL detected may be false-positive results. Nonetheless, it
is important to note these putative QTL since
comparisons across multiple studies and populations
can provide much stronger evidence for a QTL
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Table 1. Summary of the feeder traits analyzed in each growth period (means and standard deviations)
35–55 kg (1)
55–80 kg (2)
35–80 kg (3)
Trait
Abbrev.
Mean
SD
Mean
SD
Mean
SD
Average daily gain (g)
Daily feed intake (g)
Feed conversion ratio
Number of visits per day
Time in feeder per day (min)
Average Feed per visit (g)
Feed rate (g/min)
ADG
DFI
FCR
NVD
DFT
AFV
AFR
619
1795
2.95
6.87
58.3
287
31.6
122
275
0.45
2.24
12.5
93
6.66
635
2207
3.52
6.54
55.6
370
40.5
123
397
0.53
2.25
14.8
122
9.18
620
2005
3.26
6.66
56.6
329
36.2
105
306
0.41
2.08
13.2
104
7.44
SD = standard deviation
composition traits has been discovered (Van Laere
et al. 2003). The causal polymorphism is a guanineto-adenine polymorphism in intron 3 of the imprinted IGF2 gene (IGF2-intron3-G3072A) and has
been shown to affect gene expression through the
disruption of a transcription factor binding site (Van
Laere et al. 2003). IGF2-intron3-G3072A lies close to
the best-estimated positions of the daily feed intake
QTL identified in the current study, which raises the
possibility that the mutation has a pleiotropic effect
on DFI. The animals analyzed in the current study
came from a larger Meishan · Large White resource
population, in which all founders and F1 sires were
within a particular region. A previous QTL analysis
in pigs has examined daily feed intake, although no
significant or suggestive QTL were located (Rohrer
2000), which may be partly because of the low
number of animals with feed intake records in the
analysis (n = 92). For this reason, further QTL studies for feeding traits in pigs are recommended, particularly to determine whether the finding of a
significant QTL for daily feed intake on SSC2 can be
replicated in other populations.
The P arm of SSC2 has received much attention
recently since the causal polymorphism underlying a
QTL with a large effect on fatness and carcass
Table 2. Details of all QTL detected at the suggestive or significant level, their estimated position on the chromosome, and
additive and dominance effectsa
SSC
Traitb
Pos.(cM)c
F ratio
Additive effecte(SE)
Dominance effectf(SE)
2
2
3
3
6
7
7
7
8
11
11
11
11
12
12
12
13
14
14
14
15
15
17
17
Daily feed intake 2
Daily feed intake 3
Average feeding rate 2
Average feeding rate 3
Time in feeder per day 1
Average feeding rate 1
Average feeding rate 2
Average feeding rate 3
Average daily gain 2
Average daily gain 1
Daily feed intake 1
Feed conversion ratio 2
Average feed per visit 2
Feed conversion ratio 2
Feed conversion ratio 3
Average feeding rate 2
Daily feed intake 1
Average feeding rate 1
Average feeding rate 3
Feed conversion ratio 2
Average feed per visit 1
Average feed per visit 3
Average daily gain 3
Daily feed intake 3
28
3
49
46
48
150
150
150
43
76
76
30
0
4
0
73
0
81
81
77
71
61
16
21
9.3
7.9
8.2
5.2
6.5
6.1
5.7
6.8
6.7
5.1
5.2
7.8
5.7
5.6
7.1
6.4
6.7
5.9
7.1
5.4
6.9
4.8
4.8
4.9
244
179
5.72
3.65
73.5
0.28
2.78
0.55
)73.6
)40.3
)104
)0.30
)8.70
0.11
0.09
3.52
24.9
)1.95
)1.95
)0.22
)52.8
)54.1
19.6
28.4
)121
)39.4
2.68
1.29
)709
)3.86
)4.97
)5.77
43.4
)44.7
)46.4
0.30
)124
0.34
0.25
5.11
174
2.29
3.62
)0.08
27.6
30.7
)67.9
)272
a
(58.0)
(45.8)
(1.54)
(1.19)
(126)
(0.67)
(1.23)
(0.96)
(20.6)
(16.2)
(33.6)
(0.08)
(22.7)
(0.07)
(0.05)
(1.47)
(37.7)
(0.72)
(1.02)
(0.07)
(15.4)
(18.3)
(14.2)
(50.8)
(89.9)
(66.1)
(2.64)
(1.94)
(199)
(1.1)
(2.06)
(1.60)
(48.8)
(22.0)
(45.5)
(0.15)
(36.6)
(0.11)
(0.07)
(2.15)
(58.0)
(1.04)
(1.48)
(0.11)
(28.7)
(37.8)
(25.6)
(90.2)
SSC = Sus crofa chromosome, cM = centimorgan, SE = standard error, Pos. = position.
1, 2, 3 refer to growth periods 35–55 kg, 55–80 kg, and 35–80 kg respectively.
Sex-averaged map positions from the marker closest to the distal tip of the P arm.
d
Significance threshold F = 8.50, suggestive threshold F = 4.19–5.42 depending on chromosome under study.
e
Positive value indicates increasing allele originates from Large White, negative value indicates increasing allele originates from Meishan.
f
Positive value indicates heterozygotes have a higher mean trait value than the midparent.
b
c
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PIGS
Acknowledgments
The authors are indebted to the staff at Dryden Farm
(Roslin) for care of the animals and data collection.
They thank Rosalie Waldron, Heather Finlayson,
Alison Downing, and Jen Anderson for technical
assistance. This project was supported through the
LINK SLP program, with funding from DEFRA,
MLC, Cotswold Pig Development Company Ltd.,
JSR Healthbred Ltd., Newsham Hybrid Pigs Ltd.,
Rattlerow Farms, and PIC.
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Fig. 2. A plot of the likelihood profile for daily feed intake
across Chromosome 2.
fixed for guanine at the mutation, except one heterozygous founder Large White and one heterozygous F1 sire (Van Laere et al. 2003). However, the F2
offspring derived from this heterozygous sire were
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intake data, thus indicating that IGF2-intron3G3072A is not responsible for the feed intake effects
shown in the current study. Therefore, a distinct
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· Large White population. Since the estimated confidence interval for the DFI QTL is very large, there
are likely to be dozens of other positional candidate
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