Arendonk, J.A.M., van

Bovenhuis, H.

Coster, N.B.

Heuven, H.C.M.

Madsen, O.

English

Name not available

Detection of imprinting in two commercial pig populations

Wageningen Yield 

Detection Of Imprinting In Two Commercial Pig
Populations
Albart Coster∗, Ole Madsen∗, Henri Heuven∗, Johan van Arendonk∗and Henk
Bovenhuis∗
Introduction
Genomic imprinting is an epigenetic phenomenon where the level of expression of alleles
depends on their parental origin. On the molecular level, genomic imprinting is established by
differential methylation of particular chromosomal regions which results in a parent-of-origin
dependent expression of RNA (Feil and Berger, 2007). On the phenotypic level, imprinting is
manifested through a contrast between the two heterozygote classes that exist for a genotype
(A/a and a/A) (Hager et al., 2009).
Genomic imprinting has been found in viviparous mammals and in seeded plants (Feil and
Berger, 2007). In mammals, imprinting has mainly been studied in human and mice, and to
a lower account in other species as pig, cattle, and sheep (Imprinted gene catalog: http:
//igc.otago.ac.nz/). Imprinted genes are organized in clusters and play key roles in
growth and development (Feil and Berger, 2007). In pigs, the imprinted gene Igf2 was found
associated to muscle mass and backfat thickness (de Koning et al., 2000; Van Laere et al.,
1998)
A preliminary study at our group of a F2 Meishan x commercial cross indicated imprinting
effects on reproductive traits. The objective of this research was to assess the degree of im-
printing affecting reproductive traits in two commercial pig populations.
Material and methods
Composition of the populations. In this study, data of sows from two purebred lines from
breeding companies Hypor (LW1) and Topigs (LW2) were used. Both lines belonged to the
large-white breed. To enable accurate inference of allele origin, a sow was only selected when
her father was available for genotyping and when more than nine of her paternal halfsibs were
available for genotyping. Ancestors of the selected sows were selected when available, this
included the fathers of the selected sows.
Phenotypes considered in this study were the total number of piglets born (TB) and the number
of piglets born alive (LB). Records of the first four parities were used in the analyses, records
of later parities were discarded from the analyses together with records defined as outliers.
There were records of approximately 4000 sows in the data of line LW1, of which 490 were
∗Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH,Wageningen, The Nether-
lands
genotyped. There were records of approximately 3000 sows in the data of line LW2, of which
973 were genotyped.
Composition of the marker panel. Fifteen chromosomal regions were selected which were
orthologous to regions in human and mouse in which imprinting was detected. An Illumina
384-plex golden gate assay was used for genotyping. The 384 markers were distributed over
the fifteen regions; the number of markers per region was a function of the size of the region
and of the number of imprinted genes detected in that regions in human and mouse, with a
minimum of 20 markers per region.
Mendelsoft (de Givry et al., 2005) was used to identify and correct mendelian inconsis-
tencies in the genotype data. Subsequently, cvmhaplo (Albers et al., 2006) was used to
infer allele origin. Due to the large scale of the data, cvmhaplo was run separately for each
company on a sliding window of six consecutive markers.
Statistical analyses. The markers were analyzed in an animal model that included an additive,
a dominance and an imprinting effect for each marker separately. The following model was
fitted to the data in ASReml (Gilmour et al., 2002):
y =Xb+Qq+ Za+ Zpepe+Mv + e (1)
a ∼ N(0,Aσ2a); pe ∼ N(0, Iσ2pe);
v ∼ N(0, Iσ2m); e ∼ N(0, Iσ2e),
where y is the vector of phenotypic observations, X is an incidence matrix for the fixed ef-
fects, b is an unknown vector of fixed effects, Q is the incidence matrix of a marker which is
explained in the next paragraph, q is an unknown vector of marker effects, Z is an incidence
matrix for the random animal effects, a is an unknown vector of breeding values, Zpe is an
incidence matrix of permanent environment effects, pe is an unknown vector of permanent
environment effects, M is a an incidence matrix of maternal effects, v is an unknown vector
of maternal effects, and e is the residual of the model. Maternal effects were included in the
model because these can be confounded with imprinting effects (Hager et al., 2008). Fixed
effects included in the model were a class effect accounting for the breed of the litter, the
class effect accounting for parity of the sow and the class effect accounting for the combina-
tion of farm, year and season. The model was fitted independently for each marker in each
combination of company and trait.
Incidence matrixQ relates the genotype of the sows to the corresponding elements of q , which
correspond to the additive, dominance and imprinting effect of a marker. Matrix Q was build
as Q = GS, where G is matrix denoting to which of the four genotype classes (00,01,10,11)
each genotype belongs and S the contrast matrix used by Hager et al. (2008):
S =
00
01
10
11
A D I
−1 0 0
0 1 1
0 1 −1
1 0 0
 .
Table 1: Regions in which an effect with a FDR below 0.10 was found, letters indicate whether
this was an additive (A), dominance (D), or imprinting (I) effect.
Company Trait 4 7 8 10 12 13
LW1
LB D
TB D I D
LW2
LB D A
TB A A D A A
The first column of S corresponds to the additive effect, the second column of S corresponds
to the dominance effect and the third column of S corresponds to the imprinting effect. The
four rows of S correspond to the four genotype classes.
Incremental F-ratios were calculated for the three effects corresponding to each marker, where
the marker was included as the last fixed effect in the model. Dominance was included after
additive and imprinting was included after dominance, corresponding the the order of the
columns of Q .
After fitting the models, false discovery rate (FDR) was calculated for each significance value
using R-package qvalue (Dabney et al., 2009). False discovery rate was calculated within
each combination of line, trait, and genetic effect. Tests with a false discovery rate below 0.1
were considered as significant.
Results and discussion
Analyses without marker effects were performed to estimate variance components for the
random effects in Model 1. Heritability of both traits was 0.10 in the LW1 data. In the LW2
data, h2 of TB was 0.16 and h2 of LB was 0.12. The standard error of h2 was lower than 0.03
in the four analyses.
Effects with a significant FDR were located in regions 4, 7, 8, 10, 12, and 13 (Table 1). Most
significant effects were found in the LW2 data, which is not surprising because most of the
genotyped sows belonged to this line. A significant imprinting effect was found in region 12
for line LW1.
The imprinting effect in region 12 was due to a single marker (Figure 1). The FDR of this
effect was 0.075. Estimated variance of this effect was 0.10; approximately 14% of σ2a. The
sign of this effect indicated that a maternal allele led to a greater TB than a paternal allele.
The estimated variances of the additive effects with a significant FDR, expressed as percentage
of σ2a, ranged between 10% and 1%. The estimated variance of the dominance effects with a
significant FDR, expressed as percentage of σ2a ranged between 32% and 4%.
Region
−
lo
g 1
0(P
) 0
1
2
3
4
A LW1 D LW1 I LW1
0
1
2
3
4
4 7 8 10 12 13
A LW2
4 7 8 10 12 13
D LW2
4 7 8 10 12 13
I LW2
Figure 1: Plot of -log10(P) of marker effects in genomic regions 4, 7, 8, 10, 12, and 13. The
horizontal lines are 10% FDR cutoff when this could be determined.
Acknowledgments
We acknowledge Technologiestichting STW for the financial support of this work. We ac-
knowledge Hypor and Topigs for providing the data on which this work is based.
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Detection of imprinting in two commercial pig populations

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