Genetic parameters

Heritability estimates for flystrike in New Zealand are limited; Brandsma and Blair (1997) estimated heritability in 3 Perendale flocks as 0.18 ± 0.04. There are several estimates in Australian Merinos. Raadsma (1991a) estimated the heritability of body strike as 0.26 ± 0.12 and on the liability scale as 0.53 ± 0.25. Estimates of heritability of on the observed scale for breech strike were 0.32 ± 0.11 and 0.57 ± 0.28 (Greeff and Karlsson, 2009; Smith et al., 2009). In South African Merinos, the heritability for breech strike was 0.33 ± 0.16 in mulesed, and 0.46 ± 0.23 in unmulsed Merinos (Scholtz et al., 2010). The heritabilities estimated in this study on the observed and liability scale are similar to estimates reported in Australian and South African Merinos.

Flystrike is technically better modelled as a censored trait using time from start of the flystrike season to the day strike occurs as the trait. The estimate from Survival Kit V6 (Ducrocq et al., 2010) was very low compared to estimates generated from ASReml. It is however a much more conservative estimate of the heritability of flystrike resistance, and the true heritability is probably between the heritability estimated on the observed or liability scale and that estimated using Survival Kit V6 estimated heritability. The Survival Kit V6 is not compatible with the current SIL system compared to ASReml which limits its usefulness in the New Zealand national sheep genetic evaluation scheme. Random regression models (Veerkamp et al., 2001) and COXF90 (Misztal, 2009) are other programs/methods that could be investigated. Given the flexibility of using ASReml, especially when including correlated traits in breeding value estimations, for example dagginess as in the current study, it is suggested that the industry continues to use a simple model of presence or absence of flystrike over the first spring, summer and autumn period of the lamb’s life and to not implement a survival model.

91 Previous2 _{estimates of the genetic and phenotypic correlations between breech strike}

and dag score in Australian Merinos, are 0.86 and 0.23 for genetic and 0.22 for phenotypic correlation (Greeff and Karlsson, 2009; Smith et al., 2009). In England it was noticed there was a significant difference between dag score in flystrike cases and controls (French et al., 1996), with flystruck animals tending to have higher dag scores and softer faeces. Leathwick and Atkinson (1998) in New Zealand dual-purpose coarse wool sheep also noted a phenotypic association between dag weight and flystrike (r = 0.58 to 0.82).

The correlations found in this study are high and similar to the previous reports and indicate that dag score would be useful as an indirect predictor of flystrike. Turner and Young (1969) demonstrated how to compare the relative efficacy of using indirect selection (on dag score) compared to direct selection (on flystrike).

Using the observed heritability from ASReml, the relative efficacy is 0.55, indicating that direct selection is more efficient. However, using the conservative heritability calculated using the Cox hazard model, relative efficacy is 1.2, and indirect selection on dag score is more efficient. Even with the range of 0.55 to 1.2 for estimates of relative efficacy, for practical reasons it is still more effective to measure and select on dag score for the following reasons. Flystrike is seasonally dependent, it is hard to measure, and for a true measure animals should be left untreated. However, exposing untreated animals raises animal welfare concerns. Therefore indirect selection for this trait is more appropriate.

Correlations of flystrike with breech cover score in Australian Merinos were 0.17 (genetic) and 0.01 to 0.17 (phenotypic) (Greeff and Karlsson, 2009; Smith et al., 2009). In a Merino cross Wiltshire experiment, it was shown that wool shedding, and thus breech bareness, increased as the proportion of Wiltshire in the cross increased, with a concomitant decrease in the proportion of flystruck animals (Rathie et al., 1994). In a New Zealand study it was shown that the proportion of flystrike decreased as breech bareness score increased (Scobie et al., 2002). This study was in a mixed breed resource and could have been a consequence of using some short wool (Finnish Landrace), naturally bare sheep (Wiltshire), and naturally resistant (feral) breeds in the crosses. Genetic and phenotypic correlations of breech bareness with flystrike were low in the

2 _{This chapter is concerned with the correlation of indicator traits with flystrike. Correlations between}

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current study, possibly due to the study involving Romney base flocks, with little variation in breech bareness. Breech bareness would not be a good trait to use for indirect selection of flystrike resistance, in these flocks.

There are only a few published estimates of genetic correlations between wool and fibre traits with flystrike. This is surprising as it is known that wool plays a large part in flystrike susceptibility. Raadsma (1993) estimated genetic and phenotypic correlations of fibre traits with body strike incidence in Australian Merinos. Phenotypic correlations with staple length, MFD, FDSD, and FDCV were -0.11, 0.09, 0.15, and 0.11 respectively. The genetic correlations were -0.69, 0.65, 0.35 and 0.04 respectively. Brandsma and Blair (1997) found that there was a significant association with fleece weight; sheep with lower fleece weight are less at risk of flystrike than those with a heavier fleece. In the current study, FDCV shows the highest potential as an indirect predictor for flystrike, the relative efficacy of indirect selection was estimated at 0.49. The genetic correlations of flystrike with BULK, FDSD, and FDCV suggest that the degree of variation in the fleece and the degree of compactness are important fleece characteristics for flystrike resistance. The high standard errors of the genetic correlations and the low phenotypic correlations mean more work in this area is required before their use as indicators of flystrike.