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