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DESIGNING EXPERIMENTS THAT ESTIMATE
GENETIC MARKER, MAJOR GENE AND TREATMENT EFFECTS D.L. Robinson1, L.M. Cafe1, J.M. Thompson2 and P.L. Greenwood1
1 NSW Department of Primary Industries & CRC for Beef Genetic Technologies, BIC, University of New England, Armidale, NSW 2351
2 Division of Animal Sciences, University of New England, Armidale, NSW 2351.
SUMMARY
Once major genes and genetic markers have been identified, it is important to estimate their effects as accurately as possible, so that we can understand their biology and, if practicable, assess how they might be moderated by treatment or environmental effects. In beef cattle, genes controlling the cal- pain/calpastatin system affect protein turnover and consequently post-mortem enzymatic degradation of muscle tissue, which is related to tenderness. Treatments such as hormone implants also affect rates of protein turnover and potentially reduce the benefit of tenderness-related genes.
This paper describes how experimental designs were generated to estimate the effects of 3 differ- ent genes/markers for tenderness involving Brahman cattle with and without hormone implants, taking account of sex, birth herd and the genetic makeup of cattle available for the study. The resultant designs were up to 10% more efficient that less sophisticated designs.
INTRODUCTION
Consumers have consistently rated tenderness as the most important contributor to beef palatability in all markets, including Australia (Hearnshaw and Shorthose 1995), the US (Huffman et al. 1996) and Japan (SMART 1993). One of the main barriers to genetic improvement is the difficulty and expense of identifying the best breeding stock. Until recently, the main source of information was progeny tests. However, genes controlling the calpain/calpastatin system (affecting protein turnover, enzy- matic degradation of muscle tissue post-mortem and feed efficiency) have now been identified.
Treatments such as hormone implants also affect efficiency and protein turnover.
This paper describes a method of generating experimental designs to estimate the effects of sev- eral different markers and genes for tenderness in Brahman and Angus cattle with and without hormone implants, taking account of available resources, including the sex, birth herd, and gene combinations of cattle available for the study.
MATERIALS AND METHODS
Approximately 1100 Brahman cattle from 7 herds and 400 Angus cattle from 2 herds were genotyped to identify homozygotes for 3 tenderness genes/markers (calpastatin, tenderness3 and calpain1).
Although reasonable numbers of Brahmans were identified with 2 copies of the favourable alleles for both the calpastatin gene and the tenderness3 markers (2_2 genotypes) as well as reasonable numbers of 2_0 and 0_0 genotypes, only 30 0_2 Brahmans were identified. The favourable calpain1 marker was even less frequent, only 8 Brahmans had 2 copies. The cattle chosen for the experiment therefore included equal numbers of 2_2 and 0_0 genotypes, slightly more 2_0 genotypes, and the 30 0_2 genotypes. Because of their rarity, cattle carrying either 1 or 2 copies of the favourable calpain1 marker were chosen in preference to those with 0 copies.
Another aim of the experiment was to compare the biology of the calpain/calpastatin system in Brahmans with Angus cattle carrying 2 copies of the favourable alleles for 3 tenderness genes/markers
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(calpastatin, tenderness3 and a different calpain1 gene). However, only 15 such cattle were identi- fied. Consequently, the cattle chosen were 2_2 genotypes with 0, 1 or 2 copies of the Angus calpain1 gene. Table 1 shows numbers of animals selected for the experiment by genotype, sex and birth herd.
Experimental requirements. Treatments such as hormone implants affect protein turnover, as do genes controlling the calpain/calpastatin system. It was therefore considered important to compare the different genotypes with and without hormone implants (treatments) and to measure relevant traits including tenderness, fatness, growth and feed efficiency. Thus the experimental design needed to allocate animals into replicates, hormone treatment groups and feed efficiency pens according to their tenderness genotypes, sex and the herds from which they were obtained.
Design allocation procedures. Robinson (2002) described the statistical theory used to develop a series of experimental designs for the CRC for the Cattle and Beef Industry (Meat Quality). The principles can be illustrated by considering a simple case, involving two factors:
y = Xα + Zβ + error (1)
where y represents the vector of observed data, α the vector of (say) treatments, and β a vector of (say) genotypes. The highest possible efficiency is obtained with orthogonal designs, which can be parameterised so that (e.g. by fitting the grand mean and requiring the levels of the other factors to sum to zero) X/Z = 0. In this parameterisation, treatment effects can be estimated simply by solving:
y = Xα. (and genotypes by solving y = Zβ). It can be shown that in an orthogonal design all geno- types have equal numbers of animals allocated to every treatment, i.e. in an orthogonal design geno- types are 'balanced' or equally distributed across treatments.
When orthogonality cannot be achieved (here it was not possible to obtain equal numbers of ani- mals of each genotype), highly balanced designs generally tend to be more efficient. This is intui- tively obvious in that, if all animals of a single genotype were allocated to the same treatment or treatment combinations and this proved to be above average, it would be impossible to tell whether Table 1. Numbers of animals selected for study, by genotype, sex (H=heifer, S=steer) and herd
Calpastatin_tenderness3 __Calpain1 1___ __Sex__
Breed/birth herd 0_0 0_2 2_0 2_2 0 1 2 H S Total
Br/herd 1 5 4 8 5 12 9 1 22 0 22
Br/herd 2 11 2 10 5 14 13 1 5 23 28
Br/herd 3 7 3 8 9 21 5 1 6 21 27
Br/herd 4 10 10 5 5 11 17 2 13 17 30
Br/herd 5 0 5 6 7 11 5 2 18 0 18
Br/herd 6 10 2 7 8 17 10 0 6 21 27
Br/herd 7 0 4 3 4 3 7 1 11 0 11
All Brahmans 43 30 47 43 89 66 8 81 82 163
Ang/herd 1 24 6 8 10 12 12 24
Ang/herd 2 25 11 9 5 12 13 25
All Angus 49 17 17 15 24 25 49
1The calpain1 marker in Brahmans (Br) is different to the calpain1 gene in Angus (Ang).
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the superior performance were due to the effect of the treatments, the effect of the genotype, or a combination of both.
The same principles apply to experiments with many factors. Robinson (2002) described a suite of computer programs for allocating animals from different sires within herds to achieve maximum balance across the main effects of all treatments and also 2-way and 3-way interactions, as well as aiming to test each sire with as many other sires as possible within all management groups. These programs were modified to accommodate a wider range of genetic effects (including the presence or absence of several different genes, markers and marker/gene combinations) as well as treatments, interactions and management groups, and then used to generate an experimental design for the cattle in this study.
Alternative allocation procedure. To assess the benefits of the resultant design, a simpler allocation procedure was carried out by sorting animals within sex into their calpastatin_tenderness3 genotypes and assigning the first two heifers to treatments 1 and 2 and pen 1, the next two to treatments 1 and 2 and pen 2, and so on to pen 10, then starting again at pen 1 until all heifers were allocated. Steers were allocated to pens 11 to 20 using a similar procedure. The aim was to achieve reasonable balance across treatments and pens for at least the calpastatin_tenderness3 genotypes within each sex. In contrast, the design generation programs aimed for balance for all effects including genetic and treatment effects, management groups and birth herds as well as low standard errors for comparing the effects of the different marker alleles and treatment combinations.
Evaluation of efficiency. The efficiency of the resultant designs was assessed by comparing the accuracy of estimated effects, fitting the model:
y = cs + t3 + cs.t3 + c1 + imp + herd + mgp + pen (2) where cs and t3 = factors representing the effects of having 2 copies of the favourable/unfavourable calpastatin and tenderness3 allelles respectively; cs.t3 the interaction between cs and t3; c1 = the effect of carrying at least one copy of the favourable calpain1 allele; imp = the effect of hormone implants; herd = the birth herd of the animal; mgp = grow-out management group; pen = feed efficiency pen. Sex was not included in the model because it was not feasible to house heifers and steers in the same pen.
The final analysis may require other models to be fitted (e.g. by fitting pen as random and including the effect of sex). However, experimental designs that efficiently estimate effects from model 2 should also be efficient for minor variations of the above.
Standard errors of estimates were calculated assuming a residual variance of 0.5. This value was derived from Reverter et al. (2003), who reported genetic and environmental variances of 0.19 and 0.44 respectively for shear force of the longissimus muscle in 3322 tropically adapted cattle that were finished either on pasture or in the feedlot. Assuming a slightly lower environmental variance for a single cohort of feedlot-finished cattle (say 0.37) and that 30% of genetic variance is attributable to marker effects, a plausible value for the residual variance in this study is about 0.5.
For brevity and simplicity, results are presented here for contrasts between genotypes and treat- ments in Brahman cattle.
RESULTS
The predicted standard errors in Table 2 show that, based on initial estimates of the size of effects (Bill Barendse, pers. communication), it may not be possible to obtain precise estimates of all contrasts. However, the standard error of the difference between 0_0 and 2_2 animals is less than
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half its estimated magnitude, so there is a reasonable chance of detecting an effect of this size. In fact, the effect may be substantially greater. Van Eenennaam et al. (2007) reported differences of up to 0.8 kg in shear force of meat from beef cattle with 2 copies of two tenderness genes compared to those with none (and an estimated 1 kg difference for animals with 2 copies of all 3 tenderness genes studied). Thus the magnitude of effects shown in Table 2 may be somewhat conservative.
Worthwhile improvements in efficiency were obtained by using the sophisticated design alloca- tion procedures (Table 2). For example, for the contrast most likely to be detected (2_2 vs 0_0 genotypes), the simpler allocation procedure resulted in a 10% greater variance, so 10% more animals would be required to achieve an equivalent accuracy.
Table 2. Estimated effects of genes/markers (Bill Barendse, pers. communication) together with predicted standard errors of estimates from an experimental design generated by the suite of computer programs, compared to a simpler allocation for the animals in Table 1
cs 1 t3 1 cs_t3 1 Calpain1 Implant Contrast
0 vs 2 0 vs 2 0_0 vs 2_2 0 vs (1 & 2) yes/no
Size of effect 0.22 0.12 0.34 0.06
Standard errors of estimates (SE)
Design programs 0.118 0.122 0.161 0.121 0.112
Simpler allocation 0.121 0.126 0.169 0.125 0.114
Improvement in efficiency 2 5.4% 5.7% 10.4% 7.2% 4.0%
(Size of effect)/(SE, design programs) 1.86 0.98 2.11 0.50
1 cs=calpastatin; t3=tenderness3; 2 Calculated as 100*(VarS(effect)/VarD(effect) - 1) where VarS and VarD are predicted variances of the effect from the simpler allocation procedure and the design generation programs CONCLUSIONS
For traits such as tenderness which cannot be measured in live breeding stock, genetic improvement has to rely on indirect methods such as progeny tests, information from relatives, or use of major genes. Although the latter has considerable potential, experiments to estimate the effect of major genes may require thousands of animals to be genotyped, a difficult and expensive process, especially if several genes are involved, and the animals are sourced from a number of different backgrounds or breeding herds. Limitations in resources can limit the accuracy of estimates. Sophisticated experi- mental design procedures (which in this case increased the accuracy of estimates by up to 10%) are therefore recommended to maximise the accuracy of estimated effects.
REFERENCES
Hearnshaw H, Shorthose WR (1995). MRC Final Report – Project DAN 37.
Huffman KL, Miller,MF, Hoover, LC, Wu CK, Brittin HC, Ramsey CB (1996). J. Anim. Sci. 74:91.
Reverter A, Johnston DJ, Ferguson DJ et al. (2003) Aust. J. Agric. Res. 54:149 Robinson DL (2002). PhD thesis, University of New England.
SMART (Sensory Market Analysis and Research Technology) (1993) – summary MRC Report.
Van Eenennaam AL, Li J, Thallman RM, et al. (2007). J. Anim. Sci. 85:891.
