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. 2020 Jun 23;10(6):1085.
doi: 10.3390/ani10061085.

Genetics of Lifetime Reproductive Performance in Italian Heavy Draught Horse Mares

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Genetics of Lifetime Reproductive Performance in Italian Heavy Draught Horse Mares

Roberto Mantovani et al. Animals (Basel). .

Abstract

Our aims were to find a phenotypic variable to express mares' lifetime reproductive performance after 6 breeding seasons (BS) in Italian Heavy Draught Horse breed (IHDH), and to estimate its heritability. At first, 1487 mares in a training dataset were used to implement and validate a set of predictive coefficients (LFR-C) or equations (LFR-E) to estimate a lifetime foaling rate (LFR) after 6 BS, i.e., the number of foals generated divided by the opportunities to do so. Then, 3033 mares in a dataset with at least 3 registered BS, was used to estimate LFR for mares with 3, 4, or 5 registered RS. This dataset contained actual (n = 1950) and estimated (n = 1443) LFR, obtained by LFR-C, and LFR-E; Arcsine transformation of LFR-C and LFR-E were also analyzed in single trait animal models to estimate heritability. Overall, the LFR showed a moderate but significant genetic variation, and the heritability of the trait was high (0.24) considering it is a fitness trait. The arcsine transformation of LFR did not show any improvement of heritability. The present study indicates the possible use of a linear LFR variable for breeding purposes in IHDH breed considering both complete and incomplete reproductive careers.

Keywords: Italian Heavy Draught; fertility; fitness; genetic parameters; heavy breeds; horse; lifetime; mares.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Class distribution of lifetime fertility rate (LFR) obtained in the full dataset (n = 3033) combining actual and predicted foals after the 6th breeding season; In (a) distribution of linear LFR obtained by predicting incomplete reproductive career with coefficients (LFR-C) or equations (LFR-E); in (b) normal distribution of arcsine transformed LFR obtained by predicting LFR with coefficients (Arcsine LFR-C) or equations (Arcsine LFR-E).

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