Repeatability of litter phenotypein the sow population
Jennifer PattersonSwine Reproduction and Development Program (SRDP),
University of Alberta
John HardingWestern College of Veterinary Medicine, University of
Saskatchewan
Introduction
• Management advances and selection for prolificacy have greatly increased litter size in swine (Estienne, 2012)
Title Here
Title Here, Optional or Unit Identifier
National Animal Health Monitoring System, 2008
Early-in-life experiences impact lifetime reproductive performance and longevity in sows
8
9
10
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1990 1995 2000 2006Li
tter S
ize
Year
BornBorn LiveWeaned
• Consequences have been an increase in the variation of birth weight & the proportion of low birth weight pigs due to IUGR (Estienne, 2012)
Taken from Estienne, 2012
IUGR --- Within-litter variation: Considerable negative economic impact for pork
production systems (Foxcroft et al., 2009).
Effects of within-litter variation in birth weight on pre- and post-natal development:
Intra-Uterine Growth Retardation
Between-litter variation
1. Evidence for induced “litter phenotypes” in commercial sow populations.
2. Low birth weight phenotypes 3. Hyper-prolific and higher parity sows are most
susceptible.
Smit, 2007
Litter phenotypes – between litter variation
HIGH
LOW
Characteristics of High and Low average birth-weight litters (n = 1,094)
“High” “Low” P-ValueAve Birth Weight 1.8 ± 0.01 1.2 ± 0.01 < 0.001
Total born 12.3 ± 0.08 12.3 ± 0.07 0.91Born Alive 11.7 ± 0.09 11.0 ± 0.09 < 0.001Born Dead 0.6 ± 0.07 1.2 ± 0.06 < 0.001Weaned 10.8 ± 0.10 9.4 ± 0.10 < 0.001
(M. Smit, 2007. MSc thesis – Univ. Alberta / Univ. Wageningen)
Effect of average litter weight on body weight
0.56 Kg difference
0.81 Kg difference
3.05 Kg difference
6.92 Kg difference
It took the low bw litters 9 days longer to reach the same slaughter weight as high bw litters
Smit, Leman Conference 2010
Impact on production systems.
This constraint may reduce the lean growth potential of the offspring of the entire litter not just the small pigs!
– Increased pre-weaning morality, – reduced survivability, – reduced growth rates and efficiency – Increased variation in pig market weights– Slow growing pigs need to stay in barn longer to
hit carcass weight targets
Smit, 2010
Low birth-weight phenotype
Genotype
Phenotype
Ovulation Rate Uterine Capacity
Embryonic/fetal survival
Placental function
Ovulation rate in multiparous sows
(Patterson et al., 2008:J. Anim. Sci., 86, 1996-2004)
0%2%4%6%8%
10%12%14%16%18%20%
4 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 28
Ove
rall
perc
ent (
%)
Embryo/Fetus No.
D30
D50
Evidence for early intra-uterine crowding and a wave of fetal losses by day 50
(From Patterson et al., 2008)
Origin of litter phenotype
Postnatal growth performance
Ovulation Rate Uterine Capacity
Embryonic/fetal survival
Placental function
Average litter birth weightLimitations in
postnatal growth
Identifying litter phenotype –
Develop selection & production
strategies
REPEATABILITY OF LITTER PHENOTYPE
Knol E et al. 2010
Repeatability of low litter birth weight phenotype
Identifying litter phenotype Obtained from a collaborating farrow to finish farm in
Saskatchewan Production nucleus and multiplier tiers (large
white/landrace females) 8999 individual parity records, from 2223 multiparous
sows (parity <= 10) over 6 years (2006-2011). Total weight of piglets born alive was collected Average birth weight was calculated as total born alive litter weight divided by # born alive
University of Alberta, unpublished data
Variation in average litter birth weight controlled for total born litter size
Summary Statistics (mean ± stddev)
Each cell has a unique distribution, mean and standard deviation.
Distributions
1462.71 ± 252
1179.33 ± 192
Average Litter Weight
Perc
ent d
istr
ibuti
on
LOW Birth Weight HIGH Birth WeightMEAN
Z-Score – comparing values from different distributions
Tells us how a single data point compares to the rest of the population, represented by a normal curve.
It shows whether the point (weight) is above or below average, but how distant the measurement is from the average.
Z-Score normal distribution
65 % of data
95 % of data
99.7 % of data
"low" phenotype "high" phenotype
High vs low phenotype -- litter size & weight.
Variable High Low P-Value
Total Born 12.7 ± 0.06 13.6 ± 0.06 0.0001
Born Alive 11.7 ± 0.06 12.6 ± 0.06 0.0001Average litter birth weight (g)
1523.6 ± 4.0 1141.5 ± 4.0 0.0001
Q - Is litter phenotype repeatable? Can litter phenotype after P1 be used to predict
phenotype in subsequent parities? Look at the correlation between parity records
CorrelationCoefficient Descriptor
0.0-0.1 trivial, very small, insubstantial, tiny, practically zero
0.1-0.3 small, low, minor
0.3-0.5 moderate, medium
0.5-0.7 large, high, major
0.7-0.9 very large, very high, huge
0.9-1 nearly, practically, or almost: perfect, distinct, infinite
Hopkins, 2002
Correlation – P1 to subsequent parities
Parity ValueParity
1 2 3 4 5 61
Giltsr 1 0.303 .274 .281 .206 .198n 1232 1221 1218 1224 950 673
Low to moderate correlation
Correlation – P1 to subsequent parities
Decreasing ability to predict
Gilts at matingSelection
Body WeightImmunity Level
Stall AcclimationPhysiological AgeChronological Age
Parity ValueParity
1 2 3 4 5 61
Giltsr 1 0.303 .274 .281 .206 .198n 1232 1221 1218 1224 950 673
Take Home Message:Phenotype at Parity 1
can not be used to predict phenotype in
later parities.
Parity ValueParity
2 3 4 5 6
2r 1 .355 .364 .373 .334n 1233 1219 1225 951 675
3r 1 .397 .407 .391n 1230 1222 948 672
4r 1 .420 .410n 1236 954 676
5r 1 .401n 962 674
Take Home Messages:highest correlations between subsequent parities correlations are stronger in more mature sows
Q – Can P2 phenotype predict subsequent parities?
A moderate correlation between P2 and P3 Z-scores (r = 0.355)
Q -Is it likely (probable) that a sow that is “low” is P2, will be “low” in P3?
Low P2, Low P3
Low P2, High P3 High P2, High P3
High P2, Low P3
Probability – flipping a coin A number between zero and one. A probability of one means that the
event is certain (toss a coin, it will be heads or tails).
A probability of zero means that an event is impossible (toss a coin, you cannot get both a head and a tail at the same time, so this has zero probability).
Probabilities do not tell you what is going to happen, they merely tell you what is likely to happen!
http://gwydir.demon.co.uk/jo/probability/info.htm
Prediction Probabilities – Low Phenotype
Observed Parity (ies)
Predicted Parity
2 3 4 5 6Prediction Probability
2 3 L L - - - 0.630
Probability = 0.630 slightly higher than chance
Observed Parity (ies)
Predicted Parity
2 3 4 5 6Prediction Probability
2 3 L L - - - 0.630
2, 3 4*L H L - - 0.592L L L - - 0.815
If the same sow delivered 2 consecutive “low” litters (P2 and P3) she was far more likely to deliver a below average
BW litter in her 4th parity (probability=0.82)
Observed Parity (ies)
Predicted Parity
2 3 4 5 6Prediction Probability
2, 3 4* L L L - - 0.815
2, 3, 4 5
L H H L - 0.444L H L L - 0.621L L H L - 0.647L L L L - 0.824
Sows that are classified “low” in P2-4, it is very probable, she will be “low” in P5.
Probability did not increase when a sow delivered “low” for 3 or 4 consecutive litters
2,3,4,5 6L H L L L 0.698L L L H L 0.655L L L L L 0.805
Prediction Probabilities – High PhenotypeObserved Parity (ies)
Predicted Parity
2 3 4 5 6Prediction Probability of a
ABOVE average litter weight2 3 H H - - - 0.607
2, 3 4H L H - - 0.440H H H - - 0.625
2, 3, 4* 5
H L L H - 0.375H L H H - 0.552H H L H - 0.579H H H H - 0.756
If the same sow delivered 2 consecutive “high” litters (P2 and P3) the probability of a “high” litter in P4 is a little above chance
(probability=0.63)
If the same sow delivered 3 consecutive “high” litters (P2 – P4) she is more likely to have “high” litter in P5 (probability=0.76)
Prediction Probabilities – High PhenotypeObserved Parity (ies)
Predicted Parity
2 3 4 5 6Prediction Probability of a
ABOVE average litter weight
2,3,4,5 6
H L L L H 0.314H H L L H 0.421H L L H H 0.464H L H L H 0.570H H L H H 0.571H H H L H 0.677H L H H H 0.720H H H H H 0.827
If the same sow delivered 4 consecutive “high” litters (P2 – P5) she is more likely to have “high” litter in P6 (probability=0.83)
LOW phenotype Sows producing below average BW litters can be most
accurately predicted after parity 3. This is when intervention should be made.
Do not wait until after parity 4, the accuracy of prediction does not get any better.
Is litter phenotype repeatable & predictable?
Production strategies at sow/litter level :
Segregate sows into farrowing rooms based on expected birth weight phenotype.
Adjust nutrient requirements to reflect expected lean growth potential
Market progeny of different birth-weight litters at different market weights or different ages
Segregate different birth-weight litters into different nursery/grow-finish flows.
Target nutritional interventions at sows with a predicted low litter birth weight phenotype.
LOW phenotype Sows producing below average BW litters can be most
accurately predicted after parity 3. This is when intervention should be made. When sows produce the “low” phenotype for 5 consecutive
parities or fall “extreme low”, consider culling them.
Management Options:
Management Options --- Strategic culling?
2 3 4 5
64 5
23 45
LOW Birth Weight HIGH Birth Weight
HIGH phenotype Sows producing above average BW litters are most accurately
predicted after their 4th parity. This may be because uterine capacity could limit the full
expression of birth weight in younger parities.
Is litter phenotype repeatable & predictable?
The next generation – productivity of H or L female
Selecting replacement females?Minimum birth weights?
Future:
Is Phenotype repeatable within generation?
Summary:
Litter average birth weight is predictable within sows. can be used as tool as a management tool
Sows producing: below average BW litters can be most accurately
predicted after parity 3. above average BW litters are most accurately
predicted after their 4th parity. Management strategies are available to be used Take into consideration when selecting replacement
gilts.
Acknowledgements