Evaluation of marker assisted selection in pig enterprises

Livestock Production Science 81 (2003) 197–211 www.elsevier.com / locate / livprodsci

Evaluation of marker assisted selection in pig enterprises a, a,b Ben Hayes *, Mike E. Goddard a

Department of Natural Resources and Environment, Victorian Institute of Animal Science, 475 Mickleham Road, Attwood, Victoria 3049, Australia b Institute of Land and Food Resources, University of Melbourne, Parkville, Victoria 3052, Australia Received 10 December 2001; accepted 17 October 2002

Abstract The profitability of using DNA markers linked to quantitative trait loci (QTL) in a pig enterprise in marker assisted selection (MAS) was evaluated, using a computer simulation of a pig population with segregating QTL and markers. The starting point for MAS was a population of pigs evolved for a large number of generations. Mutation, drift and natural selection affected both the size and frequency of segregating QTL. The QTL affected four independent traits, growth index (GI), net food intake (NFI), pigs born alive (PBA) and a meat quality index (MQI). A genome scan was conducted in the population, using a genome-wide (GW), chromosome-wide (CW) or point-wise (PW) threshold as criteria for taking QTL from the genome scan to MAS. MAS with the significant markers was conducted in a small nucleus population of 20 sows, and selection was on an index of the four quantitative traits. The extra dollar returns from the additional genetic gain from MAS schemes were calculated using an economic model of a pig enterprise (with a 100 sow nucleus, 1000 sow multiplier tier and 10 000 sow commercial tier). Returns were largest with MAS schemes using QTL detected with CW and PW thresholds. However, when the cost of genotyping per animal per marker was $4, profit (extra returns from MAS 2 cost of genotyping) were greatest with the GW criteria, as for this criteria the fewest markers needed to be genotyped. MAS as described in this study may be difficult to implement in commercial herds, as markers and QTL were not closely linked, and linkage phase between QTL and markers had to be established within every family. If markers could be found in linkage disequilibrium with QTL, such that marker–QTL allele associations persist across the whole population, the profitability of MAS could be increased.  2002 Elsevier Science B.V. All rights reserved. Keywords: Quantitative trait loci; Marker assisted selection; Profitability; Pig enterprise

1. Introduction The accuracy of selection in pig breeding programs can be increased by exploiting linkage be*Corresponding author. Tel.: 161-3-9217-4254; fax: 161-39217-4359. E-mail address: [email protected] (B. Hayes).

tween molecular markers and quantitative trait loci (QTL) with effects on economic traits, using marker assisted selection (MAS) (e.g. Visscher and Haley, 1995). A number of studies have investigated the increase in response from using MAS (e.g. Lande and Thompson, 1990; Meuwissen and Goddard, 1996; Meuwissen and Van Arendonk, 1992). While these studies have predicted increases in response

0301-6226 / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved. doi:10.1016/S0301-6226(02)00257-9

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from using MAS in breeding programs in the range of 8–64% (depending on whether phenotypic records are available for selection candidates before or after selection), they have not assessed the profitability of MAS in pig breeding programs. Profitability of MAS schemes can be defined as the extra discounted accumulated returns from MAS minus the discounted cost of genotyping. MAS is generally implemented in four steps (1) search for genetic markers, (2) establishment of a linkage map of the markers, (3) detection of associations between QTL and markers, and (4) use of markers in a breeding program (Meuwissen and Van Arendonk, 1992). Linkage maps of markers on the pig genome are well developed (Visscher and Haley, 1995), and only steps 3 and 4 will be considered here. Linkage between markers and QTL are usually detected with a genome scan, where markers are placed at intervals along the entire genome. An important decision which must be made is how many QTL to take from the genome scan and use in the MAS program. This will determine the proportion of variance explained by detected QTL, which in turn is an important factor determining the accuracy of MAS (Lande and Thompson, 1990). The number of QTL detected in a genome scan is controlled by three factors, the distribution of QTL effects, the power of the mapping experiment, and the level of stringency of the statistical test used to set the size of the significance threshold, above which a QTL is detected. The less stringent the threshold, the greater the number of QTL detected, and the higher the proportion of genetic variance exploited by MAS using these detected QTL. The risk of using less stringent thresholds is the higher number of false positives detected. False positives reduce the accuracy of MAS, as the variance explained by marked QTL is overestimated. The number of QTL taken from the genome scan to MAS will influence the profitability of the MAS program. For example, using a single QTL which explains a large proportion of the genetic variance would be more profitable than using a number of QTL which each explain a small proportion of the genetic variance, as the latter incurs a higher genotyping cost. This paper investigated the effect on profitability

of pig breeding schemes of the number of QTL used in MAS, determined by the stringency level set in the genome scan. The aim was to identify the most profitable protocol for implementing MAS in commercial pig populations. The results depend on the distribution of QTL effects assumed. Hayes and Goddard (2001) estimated the distribution of QTL effects from the results of QTL mapping experiments. They estimated that 5% of QTL with the largest effects would explain up to 60% of the variance for a quantitative trait. We have attempted to simulate the evolution of quantitative traits, with the aim of producing a population of pigs with QTL segregating. The QTL effects had similar distribution to that estimated by Hayes and Goddard (2001). In the past, arbitrary assumptions have been made about the gene frequencies at QTL. As a result of simulating the evolution of a population of pigs, frequencies of QTL alleles in our starting population for MAS may be more realistic. Genome scans and MAS were carried out in the simulated population of pigs, with a range of significance thresholds determining how many QTL were taken from the genome scan to MAS. The extra dollar returns from the additional genetic gain from MAS schemes were calculated using an economic model of a pig enterprise (with a 100 sow nucleus, 1000 sow multiplier tier and 10 000 sow commercial tier). Profit was considered as the increase in discounted accumulated response from using MAS minus the discounted costs of genotyping.

2. Methods The methods are summarised in Fig. 1, and consist of four parts: (1) simulation of base population, (2) genome scans, (3) MAS programs, and (4) cost benefit analysis.

2.1. Simulation of QTL and marker evolution There are potentially a large number of traits of interest to pig breeders. However, some traits have similar properties with respect to MAS. For instance, growth rate and fat depth are both moderately heritable and are measured on both sexes before

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gamma distributed with shape parameter 0.5 and scale parameter 26. Fitness effects were always deleterious. Quantitative effects were given a negative sign with probability 0.5. The correlation of absolute value of fitness effects with the absolute value of the effect on GI, MQI, PBA and NFI was 0.4, 0.2, 0.8 and 0.4 respectively. There was no correlation between the effects of the quantitative traits. The quantitative value for GI for offspring i from sire j and dam k were calculated as:

OOg 18

a i GI 5

6

r 51 t 51

Fig. 1. Breeding scheme for pig population.

selection. Therefore, we grouped traits with similar properties into a sub-index. This resulted in four sub-indices called GI (growth index), MQI (meat quality index), PBA (pigs born alive) and NFI (net feed intake). We simulated these four quantitative traits in a population of pigs. Each pig had a genome of 18 chromosomes. On each chromosome there were 13 loci, six of which were QTL and seven of which were markers. Every second locus was a QTL. Initially, all QTL and marker loci had a 0 allele. The effect of the 0 allele at a QTL on the quantitative traits was sampled from a gamma distribution. The effect of the 0 allele at a QTL on fitness was 0. To create an offspring, for each parent in a mating pair, a gamete was formed from its chromosome pairs by sampling the number of crossovers for each chromosome pair from a Poisson distribution, with mean of 1.5 (so recombination distance between adjacent markers was 25 cM). Crossover points were randomly positioned along chromosome pairs. The haploid gametes were mutated (mutation rates are given below), and if a locus was mutated, a new allele was added. The effect of the mutant allele on fitness and the four quantitative traits was sampled from a multivariate gamma distribution. The distribution of QTL effects (where the effect of a QTL is 1 / 2 the difference between homozygotes) on each of the quantitative traits, immediately after a mutation was gamma distributed with shape parameter 0.5 and scale parameter 2. The distribution of QTL effects on fitness, immediately after a mutation, was

i,r,t GI

where r is the chromosome number, t is the number of the QTL: 1 gi,r,t GI 5 ]sval j,r,t GI 1 val k,r,t GId 2 where val j,r,t GI is the value of the paternal allele at QTL t on chromosome r for GI, and likewise for the maternal allele from dam k. Quantitative value for the other traits were calculated similarly. An individuals’ fitness was calculated as:

S OO D 18

6

fit i 5 exp 2

fi,r,t

r 51 t 51

1 where fi,r,t 5 ]ss j,r,t 1 s k,r,td, and s j , r , t is the effect on 2 fitness of the paternal allele at QTL t on chromosome r, and likewise for the maternal allele. Offspring survived to breed the next generation if ( fit i / max fitness).dev, where max fitness is the fitness of the offspring with the highest fitness in the population and dev is uniform random deviate between 0 and 1. To generate a realistic starting population for MAS, we simulated a long period of natural selection followed by 10 generations of artificial selection on GI. The evolution of gene frequencies depends mainly on Ne s, Ne c and Ne m, where Ne is the effective population size, s is the coefficient of selection, c is the average number of recombinations per chromosome per gametogenesis and m is the per locus mutation rate at QTL. In order to reduce the computer time needed, we simulated a period of natural selection with Ne reduced by a factor 10 and s, c and m increased by a factor of 10. That is, for

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1000 generations there was natural selection for fitness in a population with effective size Ne 5100. The coefficient of selection was 0.1. Mutation rates per locus per gamete per generation for generations 1 to 1000 were 1.4310 22 for markers and 2.5310 25 for QTL, and the mean number of recombinations was 15 per chromosome per gametogenesis. To mimic the effect of declining Ne , after generation 1000, values of Ne s, Ne c and Ne m were reduced by a factor of 10 by reducing values of s, m and c by a factor of 10. Mutation rates per locus per gamete per generation for generations 1001 to 1010 were 1.4310 23 for markers and 2.5310 26 for QTL. Effects of the QTL on fitness were divided by 10 to give coefficients of selection of 0.01. The mean number of recombinations was 1.5 per chromosome per gametogenesis. Artificial selection in generation 1001 to 1010 was on phenotype for GI only, where phenotype for individual i was y i,GI 5 a i,GI 1 ]] ran*œVe,GI , where ran is a random normal deviate and Ve,GI is the error variance of GI measured in trait units. Intensity of artificial selection i51. In generation 1011, a large population of 6250 individuals was created from the population of 100 animal in generation 1010. Breeding continued in the large population for seven generations, with the 25 boars and 625 sows ranked highest on GI phenotype used to breed the population each generation. Ten piglets were born per mating. The quantitative value of PBA for a sow did not affect the number of piglets born to that sow (which was always 10 for selected sows), but was important in the economic analysis.

2.2. QTL mapping In generation 1015, the five highest ranked sires on GI phenotype were selected and either 50 (scan S) or 200 progeny (scan L) bred from each sire. For each of the six marker brackets on the 18 chromosomes, the sire’s progeny were separated into those that inherited the paternal bracket and those that inherited the maternal bracket. QTL mapping for GI is described, mapping for the other traits was identical. The variance for GI from the QTL in bracket t on chromosome r was estimated as:

O ]41 sy¯ 5

V2 QTLr,t,GI 5

j 51

p ,r,t, j,GI

2Ve,GI 2 y¯ m ,r,t, j,GId 2 2 ]] N

where y¯ p ,r,t, j,GI is the average of the progeny of the j th sire whom received that sires paternal marker alleles at bracket t on chromosome r for GI, and y¯ m ,r,t, j,GI is the average of the progeny of the j th sire who have received the sire’s maternal marker alleles at bracket t on chromosome r for GI, N is the number of progeny per sire (50 or 200) and Ve,GI is the error variance of GI measured in trait units. Progeny with recombinant marker brackets were ignored. QTL were ‘detected’ when the estimated variance exceeded a significance threshold. Three significance thresholds of decreasing stringency were set by permutation testing for each trait (Churchill and Doerge, 1994). The probabilities of a false positive for the four thresholds when testing an individual marker bracket were, 0.0008 (corresponding to less than 5% false positives for the whole experiment), 0.014 (corresponding to less than 5% false positives for each chromosome tested), and 0.05. These thresholds were designated GW (genome-wise), CW (chromosome-wise) and PW (point-wise). Scans were given a code based on the number of progeny per sire and significance threshold used. For example, SGW refers to a scan with 50 progeny for each of the five sires using a genome wide significance threshold of 5% false positives. LPW refers to a scan with 200 progeny for each of the five sires, using a point wise significance threshold of 5% false positives. The results of a genome scan are used to fill a matrix D scan for each trait. If V2 QTLr,t,GI exceeds the significance threshold, and V2 QTLr,t,GI is the largest estimated variance of all brackets on chromosome r, then D scan,r , t 51, otherwise D scan,r , t 50 (i.e. there was a maximum of one QTL per trait per chromosome.

2.3. Marker assisted selection Breeding values for selection were estimated either with (MEBVs) or without marker information (EBVs). Breeding values were estimated for each trait in a separate analysis, and only the markers linked to significant QTL for that trait were used to predict breeding values for the trait. The number of QTL traced by marker brackets to calculate MEBVs depends on the scan used, for example MAS–SGW would trace those marker brackets detected as significant in scan SGW. Estimation of breeding values

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with marker brackets follows Goddard (1992) and Meuwissen and Goddard (1996). Given the QTL detected in a genome scan, records for a trait were analysed by the model: y 5 Zu 1

OOD 18

6

r

t

scan ,r,t

F

Q 1,3 9Z9Z

F

Z9y 5 Q 9Z9y 1,3

squared distribution in their V2 QTLr,t BAYES was calculated as!!:

appendix.

Then,

VˆQTLr,t 1 y ]]] V2 QTLr,t ( BAYES ) 5 s 1 4.012

ZQ r,t qr,t 1 e

Where y5vector of records, u5vector of polygenic effects, Z5incidence matrix linking animals to records, qr . t 5vector of allelic effects for QTL r , t , Q r , t 5incidence matrix linking QTL alleles to animals (every animal has two QTL alleles, hence every row of Q r , t has two elements51 and the remaining elements are zero). Meuwissen and Goddard (1996) describe and give an example of formation of the Q r , t matrices. Estimates for u and qr , t are obtained by solving the mixed model equations. For example, if one QTL was detected for trait GI, on chromosome 1 in bracket 3, the equations: Z9Z 1 A21 l

201

Q 1,3 9Z9ZQ 1,3 1 G 1,3

GF G uˆ

Z9ZQ 1,3 21

Ve

qˆ 1,3

G

are solved where l 5Ve /sVa 2VQTL1,3d and the variance components Ve and Va were assumed to be known. The G matrix was formed as described by Meuwissen and Goddard (1996). Extension to multiple QTL is straight forward. The value of V2 QTLr,t,GI for deriving lambda was estimated using a bayesian approach. Variances for QTL when calculated directly from genome scans are likely to be overestimates, a result of imposing a significance threshold which the observed (true1 error) QTL effect must exceed (Georges et al., 1995). We therefore combined the estimate of VQTLr,t from the genome scan with a prior distribution of QTL variances to obtain V2 QTLr,t BAYES , the variance used in MEBV estimation. The prior distribution was an inverted chi-squared distribution, VQTLr,t ( prior) | x 22 (4.012,0.0020) with parameters 4.012 and 0.0020 chosen such that the mean and variance of VQTLr,t ( prior) equals that of the distribution estimated by Hayes and Goddard (2001). Meuwissen et al. (2001) describe the derivation of the inverted chi-

where VˆQTLr,t is from the genome scan, s is the number of sires (5), y 50.001, the mean of x 22 (4.012,0.0020). Breeding values for individuals for each trait were estimated as: MEBV5 uˆ 1

OOD 18

6

r

t

scan, r,t

Q r,t qˆ r,t

when marker information was available. When marker information was not available, the equation f Z9Z 1 A21 g uˆ 5 Z9y was solved, to obtain estimates of breeding value, where EBV5 aˆ and l 5Ve /Va . Phenotypic information was collected for all individuals on GI before selection, on slaughtered relatives of selected males after selection for MQI, on breeding females after selection for PBA, and on males before selection for NFI. The four sub-indices were assumed to be un-correlated which could be achieved by, for instance, correcting meat quality traits for GI when forming the MQI. Economic weights were appropriate for the current payment system for Australian slaughter pigs. Animals were selected on an index of their MEBVs for MAS or EBVs for non-MAS. The index for MAS was: MEBVI 5 $2.1MEBVGI 1 $1.0MEBVMQI 1 $2.8MEBVPBA 2 $4.2MEBVNFI (or with EBVs for non-MAS). Index weights are expressed as $ / genetic standard deviation.

2.4. Breeding schemes A closed nucleus scheme with discrete generations was simulated. Parameters of the closed nucleus scheme are summarised in Table 1. The nucleus was formed in generation 1015 by selecting (on GI phenotype) four boars and 20 sows from the large

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Table 1 Parameters of the nucleus scheme Number of animals per generation Males1 Females No. of sires selected per generation No. of dams selected per generation Progeny per dam Genetic variance in large population, VA (trait units) GI MQI PBA NFI Environmental variance in large population, Ve (trait units) GI MQI PBA NFI Number of generation of implementing MAS Average heterozygosity of markers generation 1015 Average heterozygosity of markers generation 1017–1021

100 100 4 20 10 0.7 1.5 1.2 1.4 1.5 3.6 9.9 7.5 5 0.85 0.65

Unless otherwise stated, default parameters (in bold) were used. Genetic variances and marker heterozygosities are the outcome of the simulation model in generation 1015 averaged over 100 replicates.

population. None of the selected boars were the same as those used in the genome scan. DNA from nucleus founders was stored so marker genotypes could be assessed. One more generation of selection in the nucleus on GI phenotype followed, during which marker and phenotypes were recorded. Phenotypes for other traits were generated as described for GI in Section 2.1. There were five generations of selection in the nucleus on MEBVI ., for MAS, or on EBVI for non MAS. One hundred replicates of the base population were generated, and each scheme (MAS–SGW etc) was implemented for each these 100 replicates.

2.5. Economic model of a pig enterprise and cost benefit analysis A gene flow method was used (Hill, 1974) to calculate the extra discounted returns from MAS. Gains from increase in genetic merit in the nucleus population were realised from sale of slaughter pigs from a nucleus, multiplier tier and commercial tier. In the gene flow model, there were 100 sows in the nucleus, 1000 sows in the multiplier tier and 10 000 sows in the commercial tier. The gene flow model requires a vector ( g) of additional gains from MAS per 6-month time period

in the nucleus. By considering one generation to be 18 months, g (dimensions 1531) was derived from a 531 vector of additional gains per generation from MAS from the simulations, using the method described in Appendix A. In the gene flow model, boars and sows both reached puberty at six months of age. Boars selected for breeding in the nucleus were used for six months, and then transferred to the multiplier herd where they were used for another 24 months. Boars not selected for breeding in the nucleus were either slaughtered or used for breeding in the commercial tier, where they were used for 18 months. One boar could mate 50 sows in a 6-month period (some artificial insemination was used). All sows weaned nine piglets per mating. In the nucleus, half of the piglets produced were from sows in their first parity, 1 / 3 from sows in their second parity and 1 / 6 from sows in their third parity. In the multiplier tier, 30% of piglets were from sows in their first parity, 26% of piglets were from sows in their second parity, 23% of piglets were from sows in their third parity, and 21% of piglets were from sows in their fourth parity. Gilts produced in the nucleus and not selected for use in the nucleus were slaughtered. Gilts produced in the multiplier were either used for breeding in the multiplier, slaughtered, or used for breeding in the

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Returns from selection in the economic model at time v were calculated as: r v 5sP v 2 Q vdsmgv 1 fgvd

Fig. 2. Pig enterprise structure, flow of animals between tiers and to slaughter. Numbers are pigs per 6 months.

commercial tier. Fig. 2 gives the flow of animals between tiers and to slaughter for a six month period (culled animals have been ignored for simplification). The gene flow method requires the definition of tier–sex–age classes. The definitions are given in Table 2. Table 2 Definition of tier sex age classes used in the economic model Tier–sex– age class

Tier

Sex

Age (months)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Nucleus Nucleus Nucleus Nucleus Nucleus Nucleus Nucleus Multiplier Multiplier Multiplier Multiplier Multiplier Multiplier Multiplier Multiplier Multiplier Multiplier Commercial Commercial Commercial Commercial Commercial Commercial Commercial Commercial Commercial

Male Male Male Female Female Female Female Male Male Male Male Male Female Female Female Female Female Male Male Male Male Female Female Female Female Female

0–6 6–12 12–18 0–6 6–12 12–18 18–24 0–6 6–12 12–18 18–24 24–30 0–6 6–12 12–18 18–24 24–30 0–6 6–12 12–18 18–24 0–6 6–12 12–18 18–24 24–30

where v5 time, with one time unit56 months, m5a (1326) vector with element m i the proportion of genes in individuals in tier–sex–age class i derived from nucleus males at 6 months (tier–sex– age class 1) at time 0, m5[1 0 0 ? ? ?]. f 5a (1326) vector of with element fi the proportion of genes in individual in tier–sex–age class i derived from nucleus females at age 6 months (tier sex age class 4) at time 0, f 5[0 0 0 1 0 0 ? ? ?] P5(26326) matrix corresponding to alternative pathways of genes, where element pij is the proportion of genes in animals of tier–sex–age class i coming from animals of tier–sex–age class j at time v21. The P matrix that was used is given in Appendix B. Q5(26326) matrix defining the passage of genes due to reproduction alone. gv 5element of g (1531) the difference in response from MAS and non-MAS for males in the nucleus at time v (see Appendix A). Returns in time period v from a single cycle of selection in the current time period were be calculated as x v 5 d v w9r v . The value of d (discount rate) was 1 /(1.049) (for a 4.9% per six months discount rate), and w was a vector with elements the number of animals in each tier–sex–age class sent to the abattoir in each 6-month period: w9 5 [378 0 0 400 0 0 0 4500 0 0 0 0 1200 0 0 0 0 45000 0 0 0 45000 0 0 0 0] At the limit, returns from a single round of selection continue and are accumulated over an infinite number of time periods. We approximated returns from a single round of selection over an infinite number of time periods by calculating returns from a single round of selection over 100 time periods. Total returns R v for the 100 time periods after a single cycle of selection in generation v discounted back to the time of selection were R v 5 o z1001v xz 5v The extra cost of the breeding program from implementing MAS was assumed to be only genotyping costs. Assuming a cost of genotyping a marker in a single pig of $4, with 9 piglets*100

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sows5900 candidates for selection in each time unit, and two markers to trace each QTL (a marker bracket), cost of implementing MAS at time v was * d v , where n scan is the number of Cv 5 4*900*2*n scan QTL detected in the genome scan and d was the discount rate. Profit from implementing each MAS scheme was 15 calculated as o 15 v 50 R v 2 o v 50 Cv .

Table 3 Number of QTL detected and proportion of variance explained by detected QTL in genome scans (results are averages over 100 replicates) Scan

Trait

SGW

GI MQI PBA NFI Total

0.7 0.4 0.2 0.2 1.5

9 5 2 2

SCW

GI MQI PBA NFI Total

1.8 1.4 0.9 1.4 5

12 9 6 3

SPW

GI MQI PBA NFI Total

4.3 4.1 3.2 3.5 15.1

17 13 9 11

LGW

GI MQI PBA NFI Total

1.3 1.1 0.3 0.8

22 22 10 16

LCW

GI MQI PBA NFI Total

3.2 2.5 1.2 2.1 9.0

29 28 13 22

LPW

GI MQI PBA NFI Total

5.1 5.2 3.3 4.5 18.1

33 35 18 29

3. Results

3.1. Proportion of variance explained by QTL detected in genome scans The proportion of genetic variance accounted for by detected QTL increased as the stringency of the significance threshold was reduced. This was true for both sizes of genome scan (Table 3). The proportion of variance explained by detected QTL was substantially greater in the large genome scan than the small genome scan for all traits. Fewer QTL were detected for traits with lower heritabilities (PBA and NFI) at all significance thresholds, for both sizes of genome scan. The advantage of using a large genome scan, in terms of the proportion of variance explained by detected QTL, was greater for PBA and NFI.

3.2. Gains from MAS We first investigated the contribution of individual traits in the index to the increased returns from MAS (Fig. 3). Results are averages over 100 replicate simulations. For clarity, only results for MAS–LGW are presented. For all traits, genetic merit from implementing MAS–LCW was superior to genetic merit from nonMAS. The difference in genetic gain between nonMAS and MAS–LGW is not large in early generations for any trait, probably because QTL allele effects are not estimated very accurately with the limited information available in early generations. For all traits, the advantage of MAS–LGW over non-MAS increased with generation number, showing no evidence of decline with time. As more phenotypic information accumulates, QTL allele effects are estimated with increasing accuracy, and

QTL detected

Percentage of total genetic variance explained by detected QTL (%)

the accuracy of selection increases. The advantage of MAS–LCW over non-MAS, expressed as a percentage, was greatest for MQI in generation 5 at 62%, followed by PBA in generation 4 at 30%, NFI at 18% in generation 4 and GI at 12% in generation 2. When the extra genetic gain from MAS above non-MAS is expressed as genetic merit for the index, there is up to a 17% gain in index value (for MAS–LPW generation 2) (Table 4). Over all generations, the greatest increase in response from MAS compared to non-MAS was

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Fig. 3. Response in component traits of the index for non-MAS and MAS–LGW. Note the scale on the y-axis for each trait is different.

Table 4 Genetic merit ($) of the nucleus for the index as a result of implementing different MAS schemes (genetic merit is $0 in generation 0) Generation

Non-MAS

MAS–SGW

MAS–SCW

MAS–SPW

MAS–LGW

MAS–LCW

MAS–LPW

1017 1018 1019 1020 1021

3.0 5.3 7.6 9.6 11.4

3.2 5.9 8.7 11.0 12.8

3.3 6.1 8.7 11.0 12.8

3.2 6.0 8.1 9.9 11.5

3.2 6.3 8.9 11.1 12.8

3.3 6.3 8.8 11.1 12.9

3.5 6.4 8.9 11.1 12.9

from MAS–LPW, for the large scans, and MAS– SCW for the small scans. Although scans MAS– SPW detected QTL which explain more of the genetic variance than MAS–SCW, due to the lower stringency of significance testing, a considerable

proportion (8%) of these additional QTL detected were in fact false positives. As a result of these false positives, the variance associated with QTL is likely to be overestimated, explaining the lower genetic gain from SPW than SCW in all generations. With

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the large genome scans QTL variances are estimated more accurately, so the genetic gain from MAS still improves using MAS–LPW relative to MAS–LCW and MAS–LGW.

3.3. Economic appraisal Returns were largest from MAS–SCW (small scans) and MAS–LPW (large scans) (Table 5). However, profit was greatest with for SGW or LGW criteria, a result of the lower cost of genotyping for these strategies.

4. Discussion This paper provides a clear protocol for the implementation of MAS in commercial pig enterprises, and determines the profitability in such enterprises of using linked markers for MAS. The results indicate MAS using linked markers is reasonably profitable with current genotyping costs, and using genome-wide stringency thresholds to detect QTL for MAS (Table 5). Although using less stringent thresholds in genome scans allows detection of additional QTL, the return from using markers linked to the additional QTL does not justify the cost of genotyping. Periodic re-estimation of QTL variance is required to avoid over-estimation of QTL variance as QTL are driven toward fixation. To the best of our knowledge, no other studies have integrated the implementation of MAS considering both the detection of associations between markers and QTL and the exploitation of these detected QTL in MAS. In addition, both genome scans and MAS were carried out in a population of Table 5 Extra returns, costs and profit for implementing MAS strategies, in millions of dollars (costs, returns and profits are additional to those for implementing non-MAS) Scheme

Returns ($)

Cost ($)

Profit

MAS–SGW MAS–SCW MAS–SPW MAS–LGW MAS–LCW MAS–LPW

1.03 1.07 0.23 1.10 1.16 1.20

0.10 0.36 1.13 0.24 0.61 1.33

0.93 0.71 20.90 0.86 0.55 20.13

pigs derived from the simulation of the evolution of a quantitative trait.

4.1. Simulation of quantitative traits The intention was to simulate a distribution of QTL effects and allele frequencies similar to distributions of QTL effects and allele frequencies for QTL which are segregating in real populations. Other studies (e.g. Gomez-Raya and Klemetsdal, 1999; Henshell and Goddard, 1997) arbitrarily assume the frequency of the favourable allele at marked QTL to be 0.5 or 0.1, or the variance due to the QTL is assumed to be 0.1 or 0.25 of the additive variance (Meuwissen and Van Arendonk, 1992; Meuwissen and Goddard, 1996). Despite our attempts to simulate realistic distributions of QTL effects, results of our simulations departed from observations of quantitative traits in real livestock populations in that the genetic variance of the traits, and a result response to selection, declined over time in our simulation [for example no decline in selection response for milk yield has been observe in dairy cattle, Van Vleck (1998)]. For example, reductions in heritabilities of quantitative traits in real populations have not been reported. Possibly this indicates our simulation is still somewhat simplistic. For example, there is some evidence that dominance and epistasis may play a role in the maintenance of quantitative variation (Cabellero and Keightley, 1994), and neither dominance nor epistasis were simulated here.

4.2. Response from MAS—comparison with other studies Meuwissen and Goddard (1996) also investigated the response from marker assisted selection with loosely linked markers (markers .10 cM from the QTL). Meuwissen and Goddard (1996) reported increases in the rate of genetic gain in the first generation with MAS of 8.8% when selection was after the recording of the trait. We observed a similar increase of 11% for GI (MAS–LGW). The magnitude of gain for NFI was similar to GI. For carcass traits (where recording is after selection and on males only), Meuwissen and Goddard (1996) reported increases in rates of gain due to MAS of 24%.

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We observed a larger increase in the rate of genetic gain for MQI of 50% in the second generation (MAS–LGW). The increase in genetic gain for PBA due to MAS in our study (30% in MAS–LGW) was similar to the 38% gain Meuwissen and Goddard (1996) reported for fertility traits. The question could be asked, given that our simulations show MAS can improve the rate of genetic gain for a trait like MQI by up to 62%, why is the profitability of MAS not higher than our results suggest? Our index gives most weight to GI, NFI and PBA. MQI receives the smallest weighting in the index. The increased genetic gain for the index was therefore much closer to the increase in genetic gain for GI and NFI than the increase in genetic gain for MQI, and the profitability of MAS reflects the increase in genetic gain in the index.

QTL were marked, there would be no difference in the selection intensity of MAS and non-MAS on the QTL. A further explanation is that using a BLUP methodology to predict both the polygenic and QTL component of breeding value circumvents the problem of weighting the QTL and polygenic component. BLUP methodology was used both in this study and that of Henshell and Goddard (1997). In the studies of Gibson (1994) and Dekkers and van Arendonk (1998), phenotypic selection was used to select for the polygenic component of breeding value. An animal with an excellent polygenic breeding value but poor QTL effect is less likely to be selected with phenotypic selection plus major gene effect than with selection based on BLUP MEBVs.

4.3. Long term response from MAS

4.4. Cost benefit analysis

In our simulations of MAS, using the marker information never resulted in decreased response compared to non-MAS, and in fact the advantage of MAS over non-MAS appeared to increase over time. Henshell and Goddard (1997) observed similar results, which are contrary to the observations of Gibson (1994) and Dekkers and van Arendonk (1998). Both Gibson (1994) and Dekkers and van Arendonk (1998) observed a reduced average total genetic value in a population undergoing ‘gene assisted’ selection relative to a population undergoing phenotypic selection after nine and six generations respectively in their deterministic predictions. We simulated five generations of selection with marker information. This may not have been long enough to observe a decrease in response from MAS relative to non-MAS due to decreased response in the un-marked portion of breeding value. Another possible reason the reduction in response noted by Gibson (1994) and Dekkers and van Arendonk (1998) was not observed in our simulations was because multiple QTL were marked. In general, the more QTL were included in MAS, the longer the advantage of MAS persisted for. This is because with multiple marked QTL, the selection intensity on individual QTL is not as large, and because the un-marked QTL contribute a smaller proportion of the total variance. At the limit, if all

One shortcoming of the cost benefit analysis was the way in which returns from change in PBA were modelled. An increase in genetic level for PBA would increase the average litter size for sows. In turn, selection intensities could be increased, and a greater number of pigs turned off. We have approximated these benefits by using an economic weight for PBA which is expressed per slaughter pig, rather than attempt to actually increase litter size with time as a result of selection in the model. The limitations of the cost benefit analysis are unlikely to affect the main conclusion, that MAS is most profitable when the only the largest QTL (in terms of genetic variance explained) are marked (e.g. MAS–SGW and MAS–LGW). Although using more QTL means a greater proportion of variance is explained by marked QTL, an increasing number of QTL are required to capture a decreasing proportion of genetic variance. This is a function of the distribution of QTL effects simulated. With current costs of genotyping, it is not profitable to trace the host of smaller QTL. It will obviously be profitable to trace the largest QTL only until they are fixed in the population. This implies re-estimating the variance at these QTL periodically, until they contribute little of the genetic variance. One cost not considered in our cost benefit analysis was the cost of the genome scan. Consider-

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ing the cost of the markers only the cost of each scan would be (large scan) 1000 progeny*18 chromosomes*7 markers / chromosome*$4 / marker / chromosome / animal5$504 000 or 250 (small scan) progeny*18*7*$45$126 000. The real of cost of the genome scans is likely to be much higher, if the costs of phenotypic measurements, analysis and other factors are included. If the genome scan were completely funded by the pig enterprise, the cost of the genome scan would considerably reduce the profitability of MAS. In practise, the genome scan may be a joint venture between a pig enterprise and a research organisation, or a number of research organisations. This would reduce the cost of the genome scan to the pig enterprise.

4.5. Practicalities of implementing MAS in commercial pig enterprises The statistical analysis of the genome scans used in this study was simplistic, in order to reduce computational time with many replicates. In practice, QTL would be detected using information from all markers on the chromosome simultaneously (Knott et al., 1998) and / or multiple trait approaches (Jiang and Zeng, 1995). The increased accuracy of positioning QTL and estimating QTL effects from these approaches should increase the accuracy of subsequent MAS. The genome scans to detect segregating QTL were conducted in the same population as which the nucleus was derived (though a different group of sires were used to found the nucleus). Many of the genome scans which have been conducted in real pig populations have used wide-cross F2 designs, such as large white by wild boar crosses (Andersson et al., 1994; Knott et al., 1998). It is questionable whether QTL detected in these experimental populations would be segregating in commercial pig populations. An alternative approach is to map QTL in the commercial population of interest using a half sib design, e.g. Kerr et al. (1999). QTL detected in this way are much more likely to be segregating in the commercial population. The mapping experiment described by Kerr et al. (1999) detected QTL for growth, backfat, NFI and meat quality traits. If these QTL could be detected using a genome-wide thres-

hold, they would be suitable for inclusion in the MAS strategies described here. As the markers in this study were only loosely linked to QTL, linkage phase between the QTL and linked markers had to be established for each family. An alternative would be to find markers in linkage disequilibrium (LD) with QTL, such that the marker–QTL associations persist across populations. The most likely scenario is that marker haplotypes (a group of marker alleles) in strong linkage disequilibrium will be used [At the limit, tests for the favourable mutation at the gene causing the QTL effect can be devised, such as the test for the Hal gene]. Use of LD markers also makes the implementation of MAS much simpler, as linkage phase for each sire family does not have to be established and the effects within each family do not have to be estimated, and so generations of phenotype and genotype data do not have to be accumulated. Our results suggest MAS with loosely linked markers in commercial pig enterprises are reasonably profitable. There are two ways in which MAS with loosely linked markers could become more attractive. In our simulations, the increase in genetic gain from MAS for meat quality traits was as large as 62%. If meat quality becomes more important, and is given a larger economic weight in the breeding objective, the increase in genetic gain from MAS in the selection index would be much greater, and profits from MAS could be greatly increased. The returns from MAS were for schemes such as MAS–LCW and MAS–LPW, where marked QTL explain up to 30% of the genetic variance. However the high cost of genotyping nucleus progeny for the many markers linked to QTL detected in scan LCW, meant that the scheme was less profitable than MAS–LGW. If the cost of genotyping could be reduced, say to $0.50 per marker per pig, and provided QTL variances were re-estimated in later generations, increased profits could be made from schemes with markers linked to 2–3 QTL per trait.

Acknowledgements The authors are grateful for funding from the Pig

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Research and Development Corporation, grant U43 ‘Marker assisted selection for profitable pigs’ to complete this research. Many thanks to Dr Susanne Hermesch and Dr Brian Luxford for useful discussions concerning the economic value of genetic gains in the pig industry. The comments of an anonymous reviewer added considerably to the quality of the manuscript.

Appendix A. Conversion of extra gains from MAS in the simulated population to extra gains from MAS in the economic model

therefore one third of the gain in one generation in the simulated population. For example the gain in time period one in the economic model nucleus is one third the gain from the simulation in generation one. The difference in increase in genetic value of the nucleus each generation between MAS and nonMAS from the simulations was calculated at each generation, to fill a 531 vector gsim , for generations 1017 to 1021, from Table 4). Then gains in the economic model nucleus were calculated as a function of gains in the simulation as:

11 // 33 00 g 5 0 . .0 00 1/3

There were five generations of MAS in the simulated population. In the economic model, the average generation time for both males and females in the nucleus was 18 months, so one generation is equivalent to three time periods of 6 months (the time period used in the economic model), and there were 15 time periods in total. The genetic gain in the nucleus in one time period in the economic model is

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0 0 0 1/3 1/3 1/3 . . 0 0 0

0 0 0 0 0 0 . . 0 0 0

0 0 0 0 0 0 . . 0 0 0

0 0 0 0 0 0 . . 1/3 1/3 1/3

  g  

sim

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Appendix B The P matrix used in gene-flow calculations was: 0 1 0

0.5 0 1

0 0 0

0 0 0

0.25 0 0

0.17 0 0

0.08 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0 0

0.5 0 0 0

0 0 0 0

0 1 0 0

0.25 0 1 0

0.17 0 0 1

0.08 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0 0

0 0 0 0 0

0.5 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

0 0 0 0 0

0 0 0 0 0

0.15 0 0 0 0

0.13 0 0 0 0

0.12 0 0 0 0

0.1 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0.5 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 1 0 0 0

0.15 0 1 0 0

0.13 0 0 1 0

0.12 0 0 0 1

0.1 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0

0.5 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0.5 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0 0

0.5 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0.5 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

0 0 0 0 0

Mf Mf Mf Mf Mf Mf

to Nm to Nf to Mm to Mf to Cm to Cf

Cm Cm Cm Cm Cm Cm

to Nm to Nf to Mm to Mf to Cm to Cf

Where the blocks of P refer to the passage of genes: Nm Nm Nm Nm Nm Nm

to Nm to Nf to Mm to Mf to Cm to Cf

Nf Nf Nf Nf Nf Nf

to Nm to Nf to Mm to Mf to Cm to Cf

Mm Mm Mm Mm Mm Mm

to Nm to Nf to Mm to Mf to Cm to Cf

Cf Cf Cf Cf Cf Cf

to Nm to Nf to Mm to Mf to Cm to Cf

N, M and C denote nucleus, multiplier and commercial animals respectively, with subscript m denoting males and subscript f denoting females. References Andersson, L., Haley, C.S., Ellegren, H., Knott, S.A., Johansson, M., Andersson, K., Andersson-Eklund, L., Edfors-Lilja, I., Fredholm, M., Hansson, I., Hakansson, J., Lundstron, K., 1994. Genetic mapping of quantitative trait loci for growth and fatness in pigs. Science 263, 1771–1774. Cabellero, A., Keightley, P.D., 1994. A pleiotropic nonadditive

model of variation in quantitative traits. Genetics 138, 883– 900. Churchill, G.A., Doerge, R.W., 1994. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. Dekkers, J.C.M., van Arendonk, J.A.M., 1998. Optimizing selection response for quantitative traits with information on an identified locus in an outbred population. Genet. Res. 71, 257–275.

B. Hayes, M.E. Goddard / Livestock Production Science 81 (2003) 197–211 Georges, M., Nielson, D., Mackinnon, M., Mishra, A., Okimoto, R., Pasquino, A.T., Sargeant, L.S., Sorenson, A., Steele, M.R., Zhao, X., Womack, J.E., Hoeschele, I., 1995. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139, 907–920. Gibson, J.P., 1994. Short term gain at the expense of long term response with selection of identified loci. Proc. 5th World Congr. Genet. Appl. Livest. Prod. 21, 201–204. Goddard, M.E., 1992. A mixed model analyses of data on multiple genetic markers. Theor. Appl. Genet. 83, 878–886. Gomez-Raya, L., Klemetsdal, G., 1999. Two-stage selection strategies utilizing marker-quantitative trait locus information and individual performance. J. Anim. Sci 77, 2008–2018. Hayes, B.J., Goddard, M.E., 2001. The distribution of the effects of genes affecting quantitative traits in livestock. Genet. Sel. Evol. 33, 209–229. Henshell, J., Goddard, M.E., 1997. Comparison of marker assisted selection using mixed model (BLUP) and mixed model with a test for the QTL. In: Proceedings of the 12th Conference of the Association for the Advancement of Animal Breeding and Genetics, Dubbo, Vol. 12, pp. 217–221. Hill, W.G., 1974. Prediction and evaluation of response to selection with overlapping generations. Anim. Prod. 18, 117– 139. Jiang, C., Zeng, Z.B., 1995. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111–1127. Kerr, R.J., Chen, Y., Henshall, J.M., Moran, C., 1999. Mapping

211

QTL for meat quality traits, carcase traits and growth in commercial pigs in Australia. Proc. Assoc. Adv. Anim. Breed. Genet. 13, 215–218. Knott, S.A., Marklund, L., Haley, C.S., Andersson, K., Davies, W., Ellegren, H., Fredholm, M., Hansson, I., Hoyheim, B., Lundstrom, K., Moller, M., Andersson, L., 1998. Multiple marker mapping of quantitative trait loci in a cross between outbred wild boar and large white pigs. Genetics 149, 1069–1080. Lande, R., Thompson, R., 1990. Efficiency of marker assisted selection in the improvement of quantitative traits. Genetics 124, 743–756. Meuwissen, T.H.E., Van Arendonk, J.A.M., 1992. Potential improvements in rate of genetic gain from marker assisted selection in dairy cattle breeding schemes. J. Dairy Sci. 75, 1651–1659. Meuwissen, T.H.E., Goddard, M.E., 1996. The use of marker haplotypes in animal breeding schemes. Genet. Sel. Evol. 28, 1. Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome wide dense marker maps. Genetics (in press). Van Vleck, L.D., 1998. Observations on selection advances in dairy cattle. In: Weir, B.S., Eisen, E.J., Goodman, M.M., Namkong, P. (Eds.), Proceedings of the Second International Conference on Quantitative Genetics. Sinauer Associates, Boston, MA. Visscher, P.M., Haley, C.S., 1995. Utilizing genetic markers in pig breeding schemes. Anim. Breed. Abstr. 63, 1–8.