Transferring QTL technology
to the pig breeding industry (PigQTech) - a demonstration project
(Contract no. BIO4-CT97-2243)
Operative
commencement date: October
1, 1997
Completion
date: September 30,
2000
EC
Contact: DGXII, Scientific
Officer: Irene Norstedt, Freek Heidekamp
Costs: Total
cost=2,024,235 ECU; EC contribution=1,220,000 ECU (60% of total
cost)
Date of actual
receipt of Community funds:
October 1997
Project
Coordinator: Prof. Leif
Andersson, Dept of Animal Breeding and Genetics, Swedish University of
Agricultural Sciences - SLU, Department of Animal Breeding and Genetics, Box
597, S-751 24 Uppsala, Sweden. Phone +46 18 471 4904; Fax:
+46 18 471 4833; Email: leif.andersson@hgen.slu.se.
Partners:
Participant Nr
1:
Swedish University of Agricultural Sciences – SLU, Department of Animal Breeding and Genetics, Box 597, S-751 24 Uppsala, Sweden
Contractual status: Contractor, Coordinator
Participant Nr 2: Roslin Institute (Edinburgh) – RIO, Division of Genetics and Biometry, Roslin, EH25 9PS Midlothian, United Kingdom Contractual status: Associated Contractor
Participant Nr
3:
Pig Improvement Company – PIC, Fyfield Wick, Abingdon, OX13 5NA Oxfordshire, United Kingdom
Contractual status: Contractor
Participant Nr
4:
Scan Avel HB – SCAN, Box 505, S-244 24 Kaevlinge, Sweden
Contractual status: Associated Contractor
Participant Nr 5:
Cooperativa Agrícola y Ganadera de Lleida –
COPAGA, Departamento de Producciòn Porcina, Polígono Industrial ”El Segre”,
Avenida de la Indústria, P-203, Lleida 25080, Catalonia,
Spain
Contractual status: Associated Contractor
Participant Nr 6:
Institut de Recerca i Tecnologia Agroalimentàries
–IRTA, Area de Producción Animal. Centro UdL-IRTA, Alcalde Rovira Roure, 177,
Lleida 25198, Catalonia, Spain
Contractual status: Associated Contractor
Participant Nr
7:
Universitat Autònoma de Barcelona – UAB, Dept. Patologia i Produccions Animals, Facultat de Veterinària, Bellaterra, 08193 Catalunya, Spain
Contractual status: Associated Contractor
A. Scientific achievements
Introduction
The aim of the project has been to demonstrate how the farm animal breeding industry can utilise gene mapping technology to accelerate genetic improvement. Previous theoretical studies had suggested that the use of marker assisted selection could potentially increase the annual improvement for quantitative traits like backfat with about 10% and for more difficult traits such as meat quality and reproduction by as much as 40-60% compared with existing technology.
The work has comprised two major tasks:
1. Commercially relevant populations have been screened for segregation at QTLs identified in experimental populations. The aim has been to establish optimal strategies for QTL detection in commercial pig populations and the extent to which QTLs explaining major phenotypic differences between divergent lines used in experimental studies also explain quantitative variation within commercial lines. The results are important for specifying future strategies for finding economically valuable QTLs.
2. Marker assisted backcrossing has been
used to demonstrate how a QTL allele can be introgressed from one breed to
another. The work has focused on the major fatness QTL on pig chromosome 4
previously identified in a wild pig/Large White intercross. The end result was
not designed to be a commercially viable product in its own right, but the
process has validated a number of points of major importance for the
exploitation of QTLs in livestock.
Task 1: Establishment of database and network.
Participants: RIO (no. 2)
A project database was established which allowed
storage, management and retrieval of project data. The project database was
developed along with web based tools to access data. Data submission for the
pedigrees and phenotypes was in the form of Excel formatted or text files sent
by all participants to Participant 2 by email. Once these were loaded,
participants defined markers and loaded marker genotypes into the database via
their own web browser. Comprehensive error checking routines were built into the
database and if errors were detected on submission of genotype data these were
reported directly back to the user. This allowed individual users to check and
correct their own data. The database also has a variety of tools allowing export
of the data in different formats suitable for use with programs for linkage
analysis, statistical analysis of phenotypes and QTL analysis. All phenotype and
genotype data from pedigrees was loaded into the database.
A web site has been developed for the
project outlining the purpose, participants, research, etc. At present this is
private to participants with access via username and password, but once results
are passed for general dissemination, these can be mounted on the site and the
site made publicly accessible.
Task 2: Determine optimal design for detecting QTLs in commercial populations.
Participants: RIO (no. 2), IRTA (no.
6)
The objective was to optimise the sampling of
animals to be genotyped so as to obtain the maximum information from a limited
sample of all animals available. There are three major factors involved in
optimising the sampling of a population in order to maximise the chance of
detecting a QTL. These are the informativeness of the markers, the
informativeness of the QTL and the pedigree structure. To increase the
informativeness of the QTL, selection on the family variance proved effective,
in that it reduced the number of individuals to be genotyped to detect a QTL of
a given effect. The second major factor influencing power of QTL detection is
the population structure. This boils down to the number of informative
comparisons that can be made within a family, which is related to family size
(number of full or half-sibs in a family). For example, a family of 8 full sibs
gives 11 times the amount of information per genotype than a family of 2
full-sibs. Thus, large families are much preferred to small families. Similar
arguments apply to half-sibs, i.e. large half-sib families are much preferred
Our studies show that each half-sib pair of individuals is worth between 0.25
and 0.5 of a full sib pair. Thus, it is possible to get an approximate idea of
the comparative value of pedigrees of different structure using these figures.
We should also note that family size is a relatively robust measure of
informativeness in a particular sample, i.e. it is valuable for both large and
small QTL effects and is not biased in situations where selection based on the
variance might be.
These considerations together with
further calculations were used to select families from the target populations
once Particpants 3 and 4 had summarised the families available for selection in
terms of structure and phenotypic variation. In essence we focussed on large
families (half-sibships) and within these, on families with the highest
variance.
Task 3: Identification of candidate chromosome regions harbouring QTLs for growth and carcass traits.
Participants:
All (1-7)
The QTL analysis focused on ten chromosomal regions
(Table 1). Seven of these were candidate regions based on previous reports of
QTL segregating in experimental populations while three were control regions for
which no QTL had been reported at the start of the project. This design was
chosen in order to evaluate to which extent QTLs segregating in experimental
populations also is of major importance in commercial
populations.
Table 1. Chromosomal regions selected for
the PigQTech project.
Chromo-
some
Interval
Motivation
Reference
1
CGA-Sw2185
Control
-
2
IGF2-region
QTL for % lean meat and back-fat
thickness
Jeon
et al., 1999;
in WBxLW and LWxPietrain intercrosses
Nezier
et al., 1999
3
Sw72-Sw349
QTL
for post-weaning Average Daily Gain
Casas-Carrillo
et
al.,
1997
4
Sw35-Sw839
Major QTL for fatness and growth confirmed Andersson et al.,
1994
in several populations
6
S0003-Sw316
Control
-
7
MHC-region
Major QTL for fatness in crosses between
Rohrer et al. 1998
Meishan and European pigs
8
Sw905-Sw1029
QTL for carcass traits in a WBxLW
Andersson-Eklund et
al.,
intercrosses
1998
9
Sw983-Sw21
Control
-
10
S0070-Sw1041 QTL for growth rate in a WBxLW cross
Knott et al, 1998
13
S0068-Sw398 QTL affecting early growth rate (from birth Andersson et al.,
1994
to
30 kg) in two independent studies
Yu et al., 1995
LW=Large White; WB=Wild Boar
We decided during the project to include mitochondrial DNA (mtDNA) as an
additional marker in the project. The reason for this was the finding by
Participant 1 that pigs have been domesticated independently in Europe and Asia
and that some major European breeds (Large White and Landrace) have a hybrid
origin (Giuffra et al., 2000, Genetics 154:1785). The geographic origin of
mitochondrial DNA is easily recognised by a DNA test. The animal material
collected within the project was an excellent resource for testing the
possibility that genetic variation in mtDNA affects phenotypic traits in
pigs.
Task 4: Collecting
phenotypic data and genomic DNA from target
populations.
Participants: PIC (no. 3), SCAN (no. 4), Copaga
(no.5), IRTA (no. 6)
Participants 3, 4, 5 and 6 collected about tissue
samples and phenotypic data on about 5,000 pigs in total representing 10
different populations as outlined in Table 2. The selection of the material
collected by Participants 3 and 4 followed the strategy established in Task 2.
Pedigree records and phenotypic data were submitted to the database established
by Participant 2 under Task 1. This strategy could not be used for the pedigree
material collected by Participants 5 and 6 since the material were established
already at the start of the project.
Tissue samples were transferred to
genotyping laboratories for preparation of genomic DNA and marker
analysis.
Table 2. Populations included in the
project.
Breed
Traits
recorded
Contributed by Participant 3
Pietrain
Growth, back-fat
Hampshire
Growth, back-fat
Large White
Growth, back-fat
Meishan X Large White Growth,
back-fat
Contributed by Participant 4
SW Large White
Growth, back-fat
SW Landrace
Growth, back-fat
SW Hampshire
Growth, back-fat
Contributed by Participant 5
Large White
Growth, back-fat, meat quality
Pietrain
Growth, back-fat, meat quality
Contributed by Participant 6
Landrace
Growth, back-fat, meat quality
Task 5: Genotyping of target populations
Participants: SLU (no. 1) PIC (no. 3), UAB (no.
7)
Participants 1, 3 and 7 were responsible for
genotyping the material as outlined in Table 3. This task included the
preparation of genomic DNA and the analysis of at least one highly informative
marker per selected chromosome region. Thus more than 50,000 genotypes have been
generated during the course of this project. To facilitate this work some time
were spent to select robust markers and to optimise conditions for multiplexing
markers.
According to the
original plan the genotyping task should be divided into two parts. The first
one comprised screening one marker per chromosome region to be used for the
detection of QTLs. This was accomplished according to the project plan. This
activity was then planned to be followed by a second round of genotyping adding
six markers to the most significant QTLs in order to provide better estimates of
their location and effect. However, the delays encountered in collecting
pedigree material as well as in generating genotype data did not allow the
further screening of microsatellite markers (see further discussion below).
However, this was compensated by typing mtDNA in all populations. This allowed
us to make a large scale screening of the presence of Asian mtDNA types in
European pigs as well as testing the possibility that variation in mtDNA
influence production traits in pigs.
All genotype data were
entered in the database established in Task 1 and were used for QTL analysis in
Task 6.
Table 3. Summary of genotyping
responsibilities
Material collected by Material
genotyped by
Participant 3
Participant 3
Participant 4
Participant 1
Participant 5
Participant 7
Participant 6
Participant 7
Task 6: Statistical analysis
Participants: RIO (no. 2), IRTA (no. 6)
The objective of this task was to analyse
the data generated within the project to demonstrate the presence of segregating
QTLs in the populations sampled. We have currently analysed the data looking for
QTL using the least-squares half-sib approach. We have also performed analyses
of the effect of mitochondrial haplotype in all populations where there were
sufficient numbers of each of the two haplotypes. The simpler, least squares
based methods are rapid and robust and can be used to give a preliminary picture
of the candidate QTL. Methods for performing this analysis simply require both
phenotypic and genotypic data. During the course of the project we have
developed a web-based software capable of performing least-squares half-sib
analyses which was used for the analyses of the project data. This software,
currently available at http://latte.cap.ed.ac.uk/ will allow
other researcher or industry groups to readily perform similar analyses of their
own data.
The results of the QTL
analysis as regards growth and backfat thickness are compiled in Table 4 for the
ten populations. We have carried out a total of 200 individual QTL tests (10
chromosomal regions x 10 populations x 2 traits). We thus expected to observe
about 10 “significancies” at the nominal level (P<0.05) by chance only.
However, we observed 22 significant effects or 26 if we take into account that
four regions showed significant effects for both growth and fatness. We can thus
safely conclude that we have observed more significant QTL than expected by
chance. Some of the QTLs indicated in Table 4 are due to spurious effects, but
many represent true effects of potential commercial significance. The results
presented in Table 4 represent significant effects across all families for a
particular chromosome and population. In addition, we observed individual sire
families with suggestive QTL effects.
Table 4. Summary of QTL for backfat thickness (F) or
growth/daily gain (G) detected in the PigQTech project. A significant QTL effect
for the two traits in a particular population is indicated by an F or G,
respectively.
Candidate regions
Control regions
Populationa
1qb
2b 3
4
7
8
10
13
1p
6
9
Hampshire (3)
-
G
G
Hampshire (4)
-
F
F
Landrace
(4)
-
F,G
F
Landrace
(6)
-
F
F
Large White (3)
G
-
F
F
Large White (4)
-
F
Large White (5)
-
F,G
F,G
F
Pietrain
(3)
-
F
Pietrain
(5)
-
F,G
G
F
Meishan (3)
F
-
G
G
a3, 4, 5, and 6 refer to which Participant provided
the particular population
bParticipant 3 decided to use a region on chromosome
1q instead of the region on chromosome 2 for commercial reasons. A dash in these
two columns indicates that this region has not been tested in this particular
population.
Several important
conclusions can be drawn from these data:
1.
A striking
observation was the consistent QTL effect associated with chromosome 4. We
observed significant effects in six out of ten populations. This result strongly
suggests that Marker Assisted Selection using markers on chromosome 4 could
improve commercial breeding programs.
2.
Besides the
consistent effect of chromosome 4, there were not significantly more positive
test results for candidate regions than for control regions (Table 4). The
results imply that it is recommended to rather investigate all parts of the
genome in future QTL analysis of commercial populations. It is worth noticing
that several recent reports have indicated the presence of a QTL for fat
deposition traits in the region of chromosome 6 chosen as a control region on
the basis of the information available when this project started. In fact,
chromosome 6 was the region with the second largest number of positive test
results in our study suggesting that this QTL is segregating at least in some
commercial populations (Table 4).
3.
The Meishan
synthetic line showed positive QTL effects for growth or fat deposition on
chromosomes 1q, 4 and 7. This result is entirely consistent with a number of
reports describing the segregation of major QTLs in these regions in crosses
between Meishan and European breeds. The results imply that Marker Assisted
Selection could improve the efficiency in the construction of such synthetic
lines.
4.
There was
no correlation between the presence of a particular QTL among different
populations of the same breed. It is possible that the frequency of QTL alleles
differs considerably between populations of the same breed but the result may at
least partly be explained by a limited statistical power in the current study
leading to a failure to detect segregating QTLs in some
populations.
We have also analysed possible effects of
the mtDNA type in those populations which contained sufficient numbers of the
European and Asian type. The results are compiled in Table 5. The results show
that there is no consistent effect of the mtDNA type on these two important
production traits in pigs. The significant effect on growth observed for the
Swedish Large White population may very well be a spurious effect due to chance
or due to confounding with family effects.
Table 5. Results of association analysis between the
mtDNA type and growth and fatness traits in PigQTech
populations.
mtDNA type
Populationa
Trait
n
European
Asian
Significance
Large White (4)
Growth/day (gr)
444 559.4
595.3
0.01-0.001
Large White (4)
Backfat (mm)
444 10.75
11.00
n.s.
Large White (5)
Growth/day (gr)
188 571.0
562.3
n.s
Large White (5)
Backfat (mm)
188 11.14
11.29
n.s
Landrace
(4)
Growth/day (gr)
478 579.8
540.5
>0.1
Landrace
(4)
Backfat (mm)
478 10.49
10.44
n.s.
Landrace
(6)
Growth/day (gr)
544 580.8
584.4
n.s.
Landrace
(6)
Backfat (mm)
544 13.30
13.51
n.s.
Pietrain
(5)
Growth/day (gr)
507 510.4
498.5
n.s.
Pietrain
(5)
Backfat (mm)
507 8.62
8.55
n.s.
a3, 4, 5, and 6 refer to which Participant provided
the particular population
Task 7: Introgression of a major QTL into a purebred line
Participants: SLU (no. 1)
This task involved the analysis of a
major QTL on pig chromosome 4, first identified in a Wild Boar/Large White
intercross. The QTL is located on pig chromosome 4 and has a large effect on
fatness and growth. Interestingly, the results of Task 6 show that this is also
a major QTL for fatness and growth in commercial populations. The aim of this
task was to introgress the QTL allele from the Wild Boar associated with high
fat deposition to a Large White population harbouring QTL alleles associated
with leanness. A backcross pedigree (BC4) involving three recombinant sires were
established with a total of 181 offspring, evenly distributed among the three
sires. At least one QTL for fatness/meat quality was confirmed and the region
was narrowed down to about 7 cM on the q arm of chromosome 4. One recombinant
offspring from BC4 was selected and backcrossed to Large White animals to
generate the 2nd generation of the introgression experiment. The
analysis of 50 progeny from this generation allowed us to formally exclude a
part of the QTL region. The results of Task 7 demonstrate that it is feasible to
introgress a QTL from one population to another and that the selective
back-crossing can be used to improve the map localisation of a QTL. This is an
important step towards the molecular characterisation of a major QTL and will
facilitate the commercial exploitation of such a QTL since it is possible to use
more closely linked markers for Marker Assisted Selection.
Task 8: Dissemination
Participants: All (no. 1-7)
As described in detail in the individual reports
below, the scope of the PigQTech project has been widely disseminated to
researchers and potential industrial partners within and outside EU. The
PigQTech project is the most extensive project carried out so far to explore the
usefulness of QTL technology applied to the pig breeding industry. Our
presentations at international and national meetings have therefore raised
considerable interest from the audience.
Concluding remarks
The PigQTech project has generated a considerable
amount of important information as regards the application of QTL technology in
animal production. We experienced some unexpected difficulties related to QTL
analyses using material derived from commercial populations. One of these was
that the project took place during a severe recession in the pig industry with
falling prices. A consequence was that several pig producers rapidly decreased
production, which made it much more difficult to collect the samples for the
PigQTech project. This was the main reason why Participant 3 could not deliver
samples from the target population on time. This in turn delayed the start of
genotyping to the extent that Milestone 22 could not be completed during the
project. Another problem with commercial populations was that we observed a very
high rate of parentage errors in some populations. This also delayed the
collection of the number of animals needed for the project. The observation
implies that another benefit from a QTL program would be that parentage errors
will be detected and thereby increase the precision in the breeding
program.
The current project
involved a large amount of marker analysis, more than 50,000 records. An
important experience is that it is absolutely necessary to invest in good
laboratory routines and bioinformatic resources in order to handle such large
amount of information.
An additional
achievement of the PigQTech project is that we have collected an excellent
material for evaluating the importance of QTLs and candidate genes in commercial
pig populations. This allowed us to test the possibility that variation in
mitochondrial DNA may influence production traits in pigs. Our results did not
indicate that this is the case. Similarly, participant 1 is currently using the
PigQTech material to screen for genetic diversity in the KIT gene controlling
dominant white colour in Large White and Landrace populations. We also think
that the material now collected constitute a unique resource for testing the
power of Linkage Disequilibrium mapping in commercial pig populations. The plan
is to develop a high density marker map for some of the regions with confirmed
QTLs and use these markers for Linkage Disequilibrium mapping in the PigQTech
populations.
Individual
reports
Participant no. 1:
Swedish University of Agricultural Sciences – SLU, Department of Animal Breeding and Genetics, Box 597, S-751 24 Uppsala, Sweden
Scientific
Team: Dr. L. Andersson, Dr.
V. Amarger, Dr. E. Giuffra, Dr. M. Moller, Mrs. U. Gustafsson, Mrs. S.
Johansson
Task 3: Identification of candidate chromosome regions harbouring QTLs for growth and carcass traits.
The set of markers to be used in this project was
established during the first year of the project. However, we decided during the
project to include mitochondrial DNA (mtDNA) as an additional marker in the
project. The reason for this was our observation that pigs have been
domesticated independently in Europe and Asia and that some major European
breeds (Large White and Landrace) have a hybrid origin (Giuffra et al., 2000,
Genetics 154:1785). The geographic origin of mitochondrial DNA is easily
recognised by a simple DNA test. The animal material collected within the
project was an excellent resource for testing the possibility that genetic
variation in mtDNA affects phenotypic traits in pigs.
Milestone 1: Candidate chromosome regions identified,
complete.
Task 5: Genotyping of target populations
Tissue samples from the Hampshire, Large White and
Landrace populations were received from Participant 4. Genomic DNA was prepared
from all animals. Genetic markers representing the 10 selected chromosomal
regions have been analysed. All genotypes have been interpreted, checked for
pedigree errors and then submitted to the ResSpecies database (Participant 2)
for QTL analysis. Since the sampling of the Large White and Landrace populations
was so delayed it was not possible to genotype six additional microsatellites
from QTL regions, so this activity was replaced by analysing the mtDNA type of
all animals.
Milestone 10.
Genomic DNA prepared for all animals, complete.
Milestone 17. Genotyping of two microsatellites per candidate region, complete.
Milestone 22. Genotyping of six additional microsatellites per
population, not complete.
Task 7: Introgression of a major QTL into a purebred line
This task involved the introgression of a
major QTL, first identified in a Wild Boar/Large White intercross, into a Large
White population. The QTL is located on pig chromosome 4 and has a large effect
on fatness and growth. A backcross pedigree (BC4) involving three recombinant
sires were established with a total of 181 offspring, evenly distributed among
the three sires. Fourteen genetic markers were used to determine the amount of
the recombinant chromosome present and six markers were then selected for typing
all animals for the QTL analyses. At least one QTL for fatness/meat quality was
confirmed and the region was narrowed down to about 7 cM on the q arm of
chromosome 4. One recombinant offspring from BC4 was selected and backcrossed to
Large White animals to generate the 2nd generation of the
introgression experiment. The analysis of 50 progeny from this generation
allowed us to formally exclude a part of the QTL region. The results of Task 7
demonstrate that it is feasible to introgress a QTL from one population to
another and that the selective back-crossing can be used to narrow down the size
of a QTL region considerably.
Milestone
3: Breeding
animals for introgression experiment identified, 1st generation,
complete
Milestone 11: Records on fatness traits collected, 1st generation, complete
Milestone
12: Analysis of chromosome 4 markers, 1st
generation,
complete
Milestone
14: Breeding animals for introgression experiment
identified, 2nd generation,
complete
Milestone 15: QTL analysis, 1st
generation, complete
Milestone 19: Records on fatness traits collected, 2nd generation, complete
Milestone
20: Analysis of chromosome 4 markers, 2nd
generation,
complete
Milestone 21: QTL analysis, 2nd generation,
complete
Task 8: Dissemination
The project was presented as an invited
lecture “Marker assisted selection:
issues and applications” during XXVI International conference on Animal
Genetics, Auckland August 9-14, 1998
The coordinator (LA) was the local host
for the EC Life Sciences Demonstration Conference held in Uppsala, October
11-14, 1998. The PigQTech project was presented as a poster and LA participated
in a press conference in which some of the ongoing demonstration projects were
presented.
Participant
no. 2:
Roslin Institute (Edinburgh) -
RIO
Roslin, Midlothian, EH25 9PS, United
Kingdom
Scientific
Team: Dr. A. Law, Mr. D.
Nicholson, Dr. S. Blott, Dr. D. Chatziplis, Dr. G. Goosen, Mr. P. Shaw, Mr. G.
Lee, Professor C.S. Haley
Task 1: Establishment of database and network.
A project database has been established which allows
storage, management and retrieval of project data and which is accessible via
the world wide web. The project database has been developed along with web based
tools to access data. Data submission for the pedigrees and phenotypes is in the
form of Excel formatted or text files sent to us by email. Once these are
loaded, participants define markers and load marker genotypes into the database
via their own web browser. There are comprehensive error checking routines built
into the database and if errors are detected on submission or genotype data
these are reported directly back to the user. This allows individual users to
check and correct their own data. The database also has a variety of tools
allowing export of the data in different formats. Thus data can be exported to
check on the pedigree, genotypes or phenotypes. In addition data can be exported
in formats suitable for use with programs for linkage analysis, statistical
analysis of phenotypes and QTL analysis. All data from pedigrees and on
phenotypes and genotypes from the project was loaded into the database.
The database and tools for loading and
extracting data have in part been supported by the project and are now available
and being used by other projects involving Roslin Institute and its
collaborators. The development of the database has provided a striking
demonstration of how critical good database/bioinformatics resources are to the
success of a project such as this one.
A web site has been developed for the
project outlining the purpose, participants, research, etc. At present this is
private to participants with access via username and password, but once results
are passed for general dissemination, these can be mounted on the site and the
site made publicly accessible.
Deliverables:
Milestone
4 :
Achieved
Task 2: Determine optimal design for detecting QTLs.
The objective was to optimise the sampling of
animals to be genotyped so as to obtain the maximum information from a limited
sample of all animals available. We have spent some time exploring alternative
sampling strategies using relatively simple pedigrees consisting of full-sib
families nested within a half-sib structure. There are three major factors
involved in optimising the sampling of a population in order to maximise the
chance of detecting a QTL. These are: the informativeness of the markers, the
informativeness of the QTL and the pedigree structure. To increase the
informativeness of the QTL, selection on the family variance proved effective,
in that it reduced the number of individuals to be genotyped to detect a QTL of
a given effect.
The second major factor
influencing power of QTL detection is the population structure. This boils down
to the number of informative comparisons that can be made within a family which
is related to family size (number of full or half-sibs in a family). For
example, a family of 8 full sibs gives 11 times the amount of information per
genotype than a family of 2 full-sibs. Thus large families are much preferred to
small families. Similar arguments apply to half-sibs, i.e. large half-sib
families are much preferred Our studies show that each half-sib pair of
individuals is worth between 0.25 and 0.5 of a full sib pair. Thus it is
possible to get an approximate idea of the comparative value of pedigrees of
different structure using these figures. We should also note that family size is
a relatively robust measure of informativeness in a particular sample, i.e. it
is valuable for both large and small QTL effects and is not biased in situations
where selection based on the variance might be.
These considerations together with
further calculations were used to select families from the target populations
once the companies had summarised the families available for selection in terms
of structure and phenotypic variation. In essence we focussed on large families
(half-sibships) and within these, on families with the highest
variance.
Deliverables:
Milestone
2,
5
:Achieved
Deliverable
1 : Achieved
Task 6: Statistical analysis
The objective of this task is the
analysis of data generated within the project to demonstrate the presence of
segregating QTLs in the populations sampled. The simpler, least squares based
methods are rapid and robust and can be used to give a preliminary picture of
the candidate QTL. Methods for performing this analysis are in place and simply
require both phenotypic and genotypic data. During the course of the project we
have developed a web-based software capable of performing least-squares half-sib
analyses which was used for the analyses of the project data. This software,
currently available at http://latte.cap.ed.ac.uk/ will allow
other researcher or industry groups to readily perform similar analyses of their
own data.
The more complex methods can be very slow
to compute and susceptible to non-normality or other problems in the data, but
may give a more complete picture. We have developed a two step procedure to
estimate QTL size and effect. Step one is where the probabilities of inheriting
the same allele identical by decent (IBD) are calculated. Step two uses these
probabilities, phenotypic and genotypic information in a maximum likelihood
routine to determine putative QTL size and effect.
Additional data were generated in the
project on mitochondrial haplotypes. These were classified into two main
classes, European and Asian, and the effect of alternative haplotypes on
performance was assessed. These analyses were performed in a mixed model
context.
We have currently analysed the data looking for QTL
using the least-squares half-sib approach. We have also analysed data from
regions with an apparent significant effect in one population. We have also
performed analyses of the effect of mitochondrial haplotype in all populations
where there were sufficient numbers of each of the two haplotypes. Results of
these analyses are given in the main body of the report.
Deliverables:
Milestones 13,
15, 18
Achieved
Milestone
23
Ongoing
Deliverable 5,
6: This
report
Task 8: Dissemination
Haley, C., Kearsey, M., Knott, S., Seaton, G., George, A. & Visscher, P. (2000) User-friendly software to map QTLs in outbred populations. GAIT Workshop, Nottingham University, Nottingham, U.K., 12th-14th April, 2000.
Haley, C., George, A. & Knott, S. (2001) QTL mapping in livestock: progress and prospects. European Biotechnology Node for Interaction with China, (EBNIC), China/European Union Workshop, Shenzhen, People's Republic of China, 11th-13th October, 2000.
Participant no.
3:
Pig Improvement Company - PIC
Fyfield Wick, Abingdon, OX13 5NA Oxfordshire, United Kingdom
Scientific
Team:
Dr. G.J. Evans, Dr. O.I. Southwood, Dr. T. Short, Dr. G.S. Plastow
Task 3: Identification of candidate chromosome regions harbouring QTLs for growth and carcass traits.
The objective of this task was to choose candidate chromosomal regions for screening of QTLs. Ten regions were chosen which comprised seven candidate QTL regions that had previously been reported in the literature and three control regions where no QTL had been previously reported. Dr Evans spent two and a half months from September-November 1997 in Uppsala, Sweden working with partner 1 on this task (see also task 5).
Milestones:
1. Identification of candidate chromosomal regions complete.
Task 4: Collecting
phenotypic data and genomic DNA from target
populations.
The objective of this task was to collect DNA and phenotypic information from sufficiently large numbers of animals from each pig population to allow a selection of the most informative 500 samples per population to be chosen. Four PIC populations were identified for use in the project, three pure breeds (Large White, Hampshire, Pietrain) and one recent Meishan synthetic line. The phenotypic information collected comprised growth rate and back-fat measurements. All phenotypic information was collected from the four populations and the records were submitted to the database established from task 1. This phenotypic information was used as model data by partner 2 to help to refine the optimal sampling strategy (task 2).
Using the optimal sampling strategy determined in task 2, the phenotypic variability within each litter was calculated. For each population the 10 sires with the most variable offspring were chosen. Then the largest, most variable litters were chosen from each of these sires until 50 offspring from each sire had been selected. This resulted in approximately 500 animals selected from each population for genotyping and should be the optimal selection of animals for detection of a QTL in the population.
Some initial delays were encountered in obtaining the phenotypic data which the delayed the use of the sampling regime to select the most informative samples for genotyping. Although this delayed the start of the genotyping, the collection of the data was timely enough for submission to the database (milestone 16).
Milestones:
6. Identification of sires to be sampled form target populations complete
7. Blood/Tissue
samples collected from identified sires complete
8. Identification of animals to be sampled from the target populations
complete
9. Blood or
tissue samples collected from all animals complete
16. All
phenotypic records submitted to database complete
Task 5: Genotyping of
target populations
The objective of this task was to genotype each animal using two microsatellite markers per chromosomal region. Thus in each population, 2 markers per region x 10 regions x 500 animals = 10000 data points per population.
As mentioned Dr Evans spent a secondment period in Uppsala, Sweden in 1997 working with partner 1, investigating microsatellite markers from each of the candidate chromosomal regions (see also task 3). The markers were investigated for reaction conditions, robustness and ability to multiplex with other markers chosen for the project. Information on approximately five markers per region was initially gathered from which two per region were used to genotype the populations.
After selection of the animals to be genotyped from the target populations, DNA was prepared for all the sires dams and offspring. All sires were screened with the list of potential markers and multiplexes that were determined as mentioned above. The 20 most informative markers for each population were determined and routine genotyping was performed on the dams and offspring.
Routine genotyping began behind schedule due to the late data collection mentioned above. This was the first time that a project of such size and scope had been undertaken in this laboratory and delays were also encountered with generation and manipulation of genotypes. An upgrade to the PIC DNA sequencer was purchased in April 1999, helping to increase the generation of genotype data by 3 fold. However, manipulation of the generated data also became an issue. It is necessary to eliminate inheritance errors before genotypes can be accepted by the database developed by partner 2. Identification of these errors was extremely time consuming without an in house laboratory management database. Submission of genotypes to partner 2 was delayed due to these difficulties. It had been anticipated that there would be a need for a laboratory management database to manipulate the data. However, the only tool available specifically for this type of work was from an academic laboratory at University of Liege, Belgium. Despite working with this group in Cambridge and Liege it was not possible to obtain working version in time to be useful for the project. This system is still under development and no product support is available since it is not a commercial product. As this effort ultimately failed an alternative system was developed locally using a Microsoft Access database. This provided an immediate short-term solution that allowed the genotypes to be collected and successfully submitted to the database of Partner 2 for analysis. However, the delays encountered did not allow time for further screening of interesting QTL regions with extra microsatellites after the first analysis.
Deliverables
10. DNA
prepared from all sires and offspring from four populations complete
17. Genotyping of two microsatellites per candidate region complete
22. Genotyping of six additional microsatellite per population incomplete
Task 8:
Dissemination
Project introduced in “The role of major genes and DNA technology in selection for meat quality in pigs”, AG de Vries, A Sosnicki, JP Garnier & GS Plastow, Meat Science 49 Suppl 1 S245-S255 (1998).
Project described in “Advances in pig genomics and industry applications.”, Rothschild, M F and Plastow, G S. 1999 AgBiotechNet 10 p1-8.
7th Agrogene Seminar "Genomics and New Molecular Tools", Paris 25/26
Feb 1999.
British Pig Breeders Round Table (24-26 March 1999) Presentation - “Transferring QTL Technology to the Pig Breeding Industry - An EC demonstration project.
Project described in “The use of gene
technology for optimal development of
pork meat quality”. AG de
Vries, L Faucitano, A Sosnicki, GS Plastow, Food Chemistry 69 397-405,
2000.
“Molecular genetics in the swine
industry” (2000). Plastow G.S. Proceedings
Anais Do III Simposio Nacional, Sociedade Brasileira de Melhoramento
Animal. p21-30. Eds I J Nunes, F E
Madalena & M A E Silva
Included in PIC Pork Chain
Conference (Barcelona, Spain, March 2000):
“Aplicacion de neuvas tecnologias para la selecion de carne de cerdo de
calidad”
International Society of Animal Genetics, Minneapolis July 2000. PigQTech project discussed during a talk as an invited speaker.
Paper presented at China-EU Animal Biotechnology Workshop, Shenzhen, October 2000 (EBNIC). “Needs for Biotechnology in Animal Production”, G S Plastow, Conference Proceedings p131-140.
Participant no
4:
Scientific team:
M.Sc.Agric Ingela Hedebro Velander
Task 4: Collecting phenotypic data and genomic DNA from target populations.
Data structure and pedigree information of pure-bred pigs were investigated. In the data base of Scan all females and males have at least 3 generations of pedigree. That was enough for the project. An internal database for collecting data to PIGQTECH was evaluated.
Collecting genomic DNA from Scan Genetics three populations were done by sampling ear-notches from each pig in whole litters. About 25 herds were included from the start and ear notched were taken when technicians or the breeder himself were id-marking the piglets. The plan was to collect DNA from 20 litters of 20 AI-sires of Landrace, Yorkshire and Hampshire. The total aim of samples for the project was 3500 - 4000 pigs per breed. After collection period of ear notches and phenotypic data, 5 sires per breed with in total 500 offspring per breed were selected.
The ear notches was frozen and stored at Scan Genetics in Kävlinge. During this period technicians as well as breeders were involved. Visits to farms were made to check security in collecting DNA by ear-notches. The traits in question to be investigated were growth rate and back fat, measured at field testing. These data started to be available during february 1999. After ultrasonic testing a decision was made which of the sires that were going to be selected for the project. During this first collection part of the project there were some difficulties in collection of DNA from so many herds. The time it took to implement the new routine to all technicians, how to take samples and how to send the ear-notches from almost all litters in the herd, was longer than expected. The sampling itself was also very time-consuming at the farms.
All together during this project technicians have been sampling 8,200 ear notches (3080 H, 3100 L and 2020 Y) from offspring of selected AI-sires and pure bred dams in nucleus herds connected to the breeding program within Scan Genetics. A lot of people were involved in the collecting period and, at the same time, a decreasing pig market affected the nucleus herds. Two of the herds involved in the project from the beginning finished their production during this period. This meant that samples collected from these farms were not possible to use later on in the project due to missing phenotypic data. Also, for some sires just a few samples have been sent in due to the fact that some sires were not popular to use among the nucleus herds. The database for all collected samples, which was established the first period of the project, connected all phenotypic data with the samples taken. This lead to the decision of which H-, L- and Y -sires to use further on in the QTL-analysis phase.
During 1999-2000 phenotypic data were collected from each pig at about 100 kg live weight (range 80-130 kg). Back fat was measured by ultrasonic testing and at the same time the pigs were weighed by our technicians. Out of all collected data from the three populations 5 - 6 sires per breed and trait were selected for genotyping. This was done by analysing the variation of daily gain and backfat, between sires and the variation within litter for each sire. The aim was to select the sires with the largest variation due to these two traits to be further used for genotyping. Also, breeding values for each pig were evaluated. After analysis, data concerning 966 Hampshires, out of 8 sires and 173 dams, were delivered to the PIGQTECH database at Roslin institute. For Landrace the delivered data were 760 pigs out of 7 sires and 128 dams, and for Yorkshire 633 pigs, out of 6 sires and 115 dams.
The difficulties in this task were to find good routines for collecting DNA samples. This has not been done before in a larger scale. In this project we had to convince and teach farmers as well as our technicians to take the right samples among all other ordinary work in the herd. Therefor, the start up time was much longer than expected. There were a lot of samples collected that were not used in the analysis. This could have been avoided by a more precise plan from the beginning and number of sires or number of herds could have been minimised. On the other hand, collecting of DNA by ear notches is in the long run a good way of sampling in a normal nucleus production herd. There is a big difference in collecting in ordinary commercial herds than to collect from a research herd. Another unexpected problem was that not all animals were ultrasonic tested. This caused some problems and delay of the genome analysis as we had to wait for all the phenotypic values before we could select the animals for the project as only litters with at least 4 tested animals could be used. The project has demonstrated that it is possible and sometimes even recommendable to use common nucleus herds for collection of DNA on a larger scale as long as the choice of traits, herds, sires and animals are carefully specified and people involved get resources and proper instructions to do it. The accuracy in sampling has in total shown to be very high.
Deliverables:
Milestone 6: Identification of sires to be sampled from target populations, completed
Milestone 7: Blood or tissue samples collected from identified sires, completed
Milestone 8: Identification of animals to be sampled from target populations, completed
Milestone 9: Blood or tissue samples collected from all animals, completed
Milestone 16: All phenotypic records submitted to the database, completed
Task 8:
Dissemination
Dissemination of the project has been made within the Scan company by one article in the internal monthly paper. At meetings with farmers and owners of the Scan company the project has been presented. Dissemination of the project has also been made at Pig breeders and producers conference in Sweden. The project has also been presented to the agricultural students in Alnarp.
Partcipant no. 5:
Cooperativa Agricola y Ganadera de Lleida
(COPAGA)
Polígono Industrial "El Segre", 25191 Lleida,
Spain
Scientific Team: A. Izquierdo, E.
Gras, E. Ramells, L.Garcia, P. Borrás
TASK
4: Collecting phenotypica data and genomic DNA
Sires from Large White and Pietrain breeds and batches with
offspring were chosen in collaboration with Partner 6. Copaga participated in
the planning of appropriate matings and was responsible for animal management.
All animals controlled descend from 5 sires in each breed. Blood was collected
from all animals, sires, and dams and sent to Partner 7 for DNA extraction.
Copaga provided the abattoir for slaughtering the animals. The traits recorded
were:
-
Weight at 120,
150,165 and 175 days of age
-
Backfat thickness
at 120, 150, 165 and 175 days of age
-
Carcass
weight
-
Length of
carcass
-
Backfat thickness
on carcass (two measurements)
-
pH 45' and 24 h on longissimus dorsi and
semimembranousus.
-
Conductivity 45'
and 24h on longissimus dorsi and semimembranousus
Meat quality analyses (fatty acid profiles and chemical
composition) have been carried out in a subsample.
The total number of animals slaughtered and the
family structure for the breeds analysed is in the Tables
below.
PIETRAIN
Males
1
2
3
4
5
Total
Nº Sows
66
Nº Litters
20 19
12
12
11
74
Nº Offsprings
141
165
58
96
81
541
LARGE WHITE
Males
1
2
3
4
5
Total
Nº Sows
60
Nº Litters
20 14 9 13 19 75
Nº Offsprings
129 85
69
84
116
483
Task
8: Dissemination.
No scientific publications has been written yet, but two
reports have appeared in two newspapers: El Segre (21st january 1998,
and Diario Cinco Días 13th february 1998). The latter is a national
information newspaper, and the former a local newspaper.
Participant no. 6:
Institut de Recerca i Tecnologia
Agroalimentàries –IRTA
Area de Producción Animal, Centro
UdL-IRTA, Av. Alcalde Rovira Roure, 177, 25198 Lleida,
Spain
Scientific Team: J.L. Noguera, L
Varona, J. Estany, D. Babot, M. Tor, D. Cubiló, M.
Pérez-Enciso.
Task
2: Determine optimal design for detecting QTLs in commercial
populations
The optimization of an experimental design in the Spanish
populations accounted for multiple traits (meat and growth traits). The main
parameter that can be changed is the number of sires. Based on simulations and
on theoretical studies, we chosed five sires in order to reduce risk that all
sires were homozygous for the QTL, and to increase power.
Task
4: Collecting phenotypic data and genomic DNA from target populations.
Five Landrace sires have been chosen and batches have been
selected in a similar scheme as for Pietrain and Large White from Copaga. Blood
has been extracted and sent to Partner 7. Copaga provided the abattoir for
slaughtering the animals. The traits recorded were:
-
Weight at 120,
150,165 and 175 days of age
-
Backfat thickness
at 120, 150, 165 and 175 days of age
-
Carcass
weight
-
Length of
carcass
-
Backfat thickness
on carcass (two measurements)
-
pH 45' and 24 h on
longissimus dorsi and semimembranousus.
-
Conductivity 45'
and 24h on longissimus dorsi and semimembranousus
Meat quality analyses (fatty acid profiles and chemical
composition) have been carried out in a subsample. The total number of animals
slaughtered and the family structure for the breeds analysed is given in the
Table below.
LANDRACE
Males
1
2
3
4
5
Total
Nº Sows
80
Nº Litters
17 19 17 15 14 86
Nº Offsprings
116 109 119 102 105 551
Task
6: Statistical analysis.
We have developing a series of Statistical tools to analyse
the data from this project using both Bayesian and classical approaches. We have
focused our research on three main topics.
1.
Computation of
genetic relationships with DNA markers. The accurate estimation of the
probability of identity by descent (IBD) at loci or genome positions of interest
is paramount to the genetic study of quantitative traits. We present a Monte
Carlo Markov Chain method to compute IBD probabilities between individuals
conditional on DNA markers and on pedigree information. The IBDs can be obtained
in a completely general pedigree at any genome position of interest, and all
marker and pedigree information available is used. The method can be split into
two steps at each iteration. First, phases are sampled using current genotypic
configurations of relatives and second, crossover events are simulated
conditional on phases. Internal track is kept of all founder origins and
crossovers such that the IBD probabilities averaged over replicates are rapidly
obtained. We illustrate the method with some examples. First, we show that all
pedigree information should be used to obtain line origin probabilities in F2
crosses. Second, the distribution of genetic relationships between half and full
sibs is analysed in both simulated data and in real data from a F2 cross in
pigs.
2.
Quantitative Trait Loci Mapping in F2
crosses between outbred lines. We develop a mixed-model approach for QTL
analysis in crosses between outbred lines that allows for QTL segregation within
lines as well as for differences in mean QTL effects between lines. We also
propose a method called “segment
mapping” that is based in partitioning the genome in a series of segments. The
expected change in mean according to percentage of breed origin, together with
the genetic variance associated with each segment, is estimated using maximum
likelihood. The method also allows the estimation of differences in additive
variances between the parental lines. Completely fixed random and mixed models
together with segment mapping are compared via simulation. The segment mapping
and mixed-model behaviours are similar to those in classical methods, either the
fixed or random models, under simple genetic models (a single QTL with
alternative alleles fixed in each line), whereas they provide less biased
estimates and have higher power than fixed or random models in more complex
situations, i.e. when the QTL are segregating within the parental lines. The
segment mapping approach is particularly useful to determining which chromosome
regions are likely to contain QTL when these are linked.
3.
Computation of Bayes factors in QTL
analysis. A fundamental issue in
quantitative trait locus (QTL) mapping is to determine the plausibility of a QTL
at a given genome location. The Bayesian analysis offers an attractive way of
testing alternative models (here, QTL vs no QTL) via the Bayes factor. There
have been several numerical approaches to compute the Bayes Factor, mostly based
on Markov Chain Monte Carlo (MCMC), but these strategies are subject to
numerical or stability problems. We propose a simple and stable approach to
calculate the Bayes Factor between nested models. The procedure is based on a
reparameterization of a variance component model in terms of intra-class
correlation. The Bayes Factor can then be easily calculated from the output of a
MCMC scheme by averaging conditional densities at the null intra-class
correlation. We have studied the performance of the method using simulation. We
have applied this approach to QTL analysis in an outbred population. We have
also compared it with the Likelihood Ratio Test and we have analyzed its
stability. Simulation results were very similar to the simulated parameters. The
posterior probability of QTL model increases as the QTL effect does. The
location of the QTL is also correctly obtained. The use of meta-analysis is
suggested from the properties of the Bayes Factor.
4.
We have reported
the first preliminary results from the Spanish Pietrain population. Clear
indications of the segregation of a couple of QTLs affecting several traits were
obtained.
Task
8: Dissemination.
Communications of results of the project in several
meetings has been presented :
Spanish Meeting of Quantitative and Applied
Genetics (Caldes, Spain, June 2000). L. Varona, G. Davalos,
M. Pérez-Enciso, J.M. Folch, N. Jiménez, A. Sanchez and J. L. Noguera.
Análisis Bayesiano multivariante de componentes de varianza de caracteres de
producción en un población Pietrain usando marcadores
moleculares.
International Society of Animal Genetics (Minneapolis, USA, July, 2000). Varona, G. Davalos, M. Pérez-Enciso, J.M. Folch, N. Jiménez, A. Sánchez and J. L. Noguera. A Bayesian multivariate analysis with genome segment mapping: an application for production traits in a Pietrain population.
Participant no. 7:
Universitat Autònoma de Barcelona -
UAB
Dept. Patologia i Produccions Animals, Facultat de Veterinària, 08193 Bellaterra, Spain
Scientific Team: Dr. A. Sánchez, Dr. J.M. Folch, Mr. G. Davalos, Mr. A. Clop, Mrs. N. Bello, Mrs. N. Jimenez.
Task 4: Collecting
phenotypic data and genomic DNA from target
populations.
The participation in this task has been to collect DNA from sufficiently large numbers of animals from three populations (Pietrain, Large White and Landrace) contributed by participants 5 and 6 to allow 500 samples per population to be analysed. The selection of samples has been carried out according to the optimal sampling strategy as defined from task 2. All DNA extractions have been performed according to data from animals slaughtered. A total of 1812 DNA samples have been extracted (Table 1). A duplicate of blood from each sample has been stored.
Deliverables:
Milestone 7. Collection of blood or tissue from sires, completed.
Milestone 9. Collection of blood or tissue samples in the three populations and DNA extractions, completed.
Milestone 10. Genomic DNA prepared for all animals, completed.
Task 5: Genotyping of
target populations
The objective of this task was to genotype each animal in the three populations using informative microsatellite markers per chromosomal region. Microsatellite markers from each of the candidate chromosomal regions were analysed for optimal reaction conditions, robustness and ability to multiplex with other markers chosen for the project. Information on about five markers per region was initially provided. From these five markers, a minimum of two per region has been selected for genotyping the populations according their heterozygosity in the sires. Polymorphism of informative markers in sires was also analysed in dams for the three populations. The existence of at least one informative marker per each sire and chromosomal region was the basic selection criterion of markers. This goal was covered for most of the cases.
The total number of markers selected for analysis was 26 in the Pietrain population and 23 in the Landrace and Large White populations. After preliminary analysis of data, some evidence of a QTL segregating for several traits in chromosome 6 for the Pietrain population was obtained and 4 additional markers were analysed in this population. Genotyping of offspring for selected markers was completed for all 1812 samples received from the three populations. Four inter-laboratory control samples were genotyped for the 36 different microsatellites.
Microsatellite analysis of samples revealed that pedigree errors in the populations ranged between 2.7% in Landrace to 21.1% in Pietrain (Table 2). All animals of uncertain genealogy were discarded for statistical analysis.
Results were recorded and stored using the GEMMA software (D. Milan, INRA, France) and submitted to ResSpecies database (Roslin institute).
Deliverables:
Milestone 17. List of markers, PCR optimisation and multiplex conditions for each of the chromosomal regions completed. Genotyping of sires and dams for the three populations completed. Genotyping of markers for offspring in the three populations completed. Genotyping of additional markers for chromosome 6 in the Pietrain population completed.
Task 8: Dissemination
No scientific publications bearing specifically on this project has been written yet.
Reports and communications of the project during several
meetings has been presented:
-
Spanish Meeting of
Animal Production (Zaragoza, Spain, May 1999).
-
Spanish Meeting of
Quantitative and Applied Genetics (Caldes, Spain, June
2000).
-
International
Society of Animal Genetics (Minneapolis, USA, July 2000).
Table 1. Samples
received.
|
Sires |
Dams |
Offspring |
Total |
PIETRAIN |
5 |
66 |
549 |
620 |
LARGE WHITE |
5 |
60 |
491 |
556 |
LANDRACE |
5 |
81 |
550 |
636 |
TOTAL |
15 |
207 |
1590 |
1812 |
Table 2. Samples of confirmed genealogy by microsatellite markers and used for statistical analysis.
|
Sires |
Dams Confirmed (total) |
Offspring confirmed (total) |
% pedigree errors |
PIETRAIN |
5 |
59 (66) |
433 (549) |
21.1 |
LARGE WHITE |
5 |
54 (60) |
388 (491) |
20.9 |
LANDRACE |
5 |
79 (81) |
535 (550) |
2.7 |
TOTAL |
15 |
192 (207) |
1356 (1590) |
14.7 |
B. Table on objective
fulfilment
Milestone (M)/Deliverable (D) |
Task |
Status |
Partners |
M1: Candidate chromosome regions identified |
3 |
Complete |
All |
M2: Optimal sampling regime for two-generation families established |
2 |
Complete |
2,6 |
M3: Breeding animals for introgression experiment identified, 1st generation |
7 |
Complete |
1 |
M4: Database and network established |
1 |
Complete |
2 |
M5: Optimal sampling regime for three-generation families established |
2 |
Complete |
2,6 |
M6: Identification of sires to be sampled from target populations |
4 |
Complete |
3,4,5,6 |
M7: Blood or tissue samples collected from identified sires |
4 |
Complete |
3,4,5,6 |
M8: Identification of animals to be sampled from target populations |
4 |
Complete |
3,4,5,6 |
M9: Blood or tissue samples collected from all animals |
4 |
Complete |
3,4,5,6 |
M10: Genomic DNA prepared for all animals |
5 |
Complete |
1,3,7 |
M11: Records on fatness traits collected, 1st generation |
7 |
Complete |
1 |
M12: Analysis of chromosome 4 markers completed, 1st generation |
7 |
Complete |
1 |
M13: Methods for QTL detection in the target populations optimised |
6 |
Complete |
2,6 |
M14: Breeding animals for introgression experiment identified, 2nd generation |
7 |
Complete |
1 |
M15: QTL analysis completed, 1st generation |
7 |
Complete |
1 |
M16: All phenotypic records submitted to database |
4 |
Complete |
3,4,5,6 |
M17: Genotyping of two microsatellites per candidate region completed |
5 |
Complete |
1,3,7 |
M18: Preliminary QTL analyses of target populations completed |
6 |
Complete |
2,6 |
M19: Records on fatness traits collected, 2nd generation |
7 |
Complete |
1 |
M20: Analysis of chromosome 4 markers completed, 2nd generation |
7 |
Complete |
1 |
M21: QTL analysis completed, 2nd generation |
7 |
Complete |
1 |
M22: Genotyping of six additional microsatellites per population |
5 |
Not complete |
1,3,7 |
M23: Final QTL analysis |
6 |
Complete |
2,6 |
D1: Report on evaluation of optimal sampling regime for QTL detection |
2 |
Complete |
2,6 |
D2: Annual report. |
- |
Complete |
All |
D3: Evaluation of statistical methodologies for QTL detection |
6 |
Complete |
2,6 |
D4: Annual
report |
- |
Complete |
All |
D5: Report on QTL detection in commercial pig populations |
- |
Complete |
All |
D6: Evaluation of the usefulness of experimental populations for identifying QTLs of commercial importance |
- |
Complete |
All |
D7: Evaluation of marker assisted backcrossing for the introgression of QTL alleles in commercial populations |
7 |
Complete |
1 |
D8: Refined mapping of the major fatness QTL on pig chromosome 4 |
7 |
Complete |
1 |
D9: Evaluation of the prospects for positional cloning of major QTLs |
7 |
Complete |
1 |
D10: Route map for the commercial application of QTL research |
- |
Complete, attached |
All |
D11: Final
report detailing progress in all areas of the project |
- |
Complete, attached |
All |
D12: Confidential report outlining exploitation plans |
- |
Complete, attached |
All |
The strong links between the participants within this project are well illustrated by the summary of the project in section A (see Tables 2-5). In brief, Participants 3, 4, 5 and 6 collected tissue samples and phenotypic data from commercial pig populations and transferred these to genotyping laboratories and the PigQTech database, respectively. Participants 1, 3 and 7 were responsible for genotyping and transferred the data to the database. Participants 2 and 6 developed methods for the statistical analysis and were responsible for the statistical evaluations of the data generated by the other partners.
The project has been coordinated by regular meetings (two per year) and through frequent email contacts. Some exchange of researchers among the participants has taken place. Gary Evans (PIC) spent two and half months in the autumn of 1997 in Uppsala and worked together with Elisabetta Giuffra (SLU) to accomplish task 1. Alex Clop (UAB) spent altogether two and a half month in Uppsala, Sweden during the autumn of 1998 and in January 2000 to accomplish task 5.
The project has been presented to a wide audience as described in more detail in the individual reports under Task 8 Dissemination.
Good quality pictures of pigs representing some of the populations investigated in this study can be transferred preferably by ftp since the files are quite large.
(Does anyone have other
pictures to provide?)
E. Scientific articles and
books
Several additional papers are in preparation and will be submitted for publication during 2001.
Chatziplis, D. G., Hill, W. G. & Haley, C. S. (1998) Selective genotyping for QTL detection by sib-pair analysis. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, Armidale, N.S.W., 11-16 January, 1998. Vol. 26, pp 249-252.
Chatziplis, D. G., Hamann, H. & Haley, C. S. (2000) Selection and subsequent analysis of sib pair data for QTL detection. Genetical Research (submitted)
Chatziplis, D. G. & Haley, C. S. (2000) Selective genotyping for QTL detection using sib pair analysis in outbred populations with hierarchical structures. Genetics, Selection, Evolution (in press)
George, A. W., Visscher, P. M. & Haley, C. S. (2000) Mapping quantitative trait loci in complex pedigrees: A two step variance component approach. Genetics (in press)
Perez-Enciso, M. & Varona, L.
2000. Quantitative Trait Loci
Mapping in F2 Crosses
Between Outbred Lines. Genetics 155:
391-405.
Perez-Enciso, M., Varona, L. &
Rothschild, M. 2000. Computation of
identity by descent probabilities conditional on DNA markers via a Monte Carlo Markov Chain
method. Genet. Sel. Evol. 32:
467-482.
Varona, L, Garcia-Cortes, L.A.,
Perez-Enciso, M. 2001. Bayes Factor
for detection of Quantitative Trait Loci. Genet. Sel. Evol. (in
press).
A good breeding programme is a prerequisite for an
efficient animal production. For thousands of years animal breeding has involved
the selection of breeding animals on the basis of the phenotypic characters of
the animals. The rapid advance in the field of gene mapping technology holds the
promise to improve these procedures by the possibility to identify animals with
a favourable genetic constitution using DNA markers. In the EC-funded PigQTech
project, four academic partners and three industrial partners from Sweden, Spain
and the UK have now demonstrated how this technology can be applied to
commercial pig populations. The consortium has sampled more than 5,000 pigs from
10 populations and screened these for 10 chromosomal regions of the pig genome.
The project has demonstrated that this technology can be used to identify
regions of the genome controlling important production traits such as growth and
meat quality. We have also established routines for the collection of tissue
samples from commercial populations and statistical methods for the analysis of
data. The project paves the way for important commercial applications that will
strengthen the competitiveness of the European animal industry. The technology
can be used to improve production, fertility and animal health.
We thank the European Commission for the support
to this project.