title: Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle.
abstract: Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.
authors.affiliation: Animal and Dairy Science Department, University of Georgia, Athens 30602-2771, USA.
authors.forename: K A
authors.initials: KA
authors.lastname: Donoghue
authors.forename: R
authors.initials: R
authors.lastname: Rekaya
authors.forename: J K
authors.initials: JK
authors.lastname: Bertrand
authors.forename: I
authors.initials: I
authors.lastname: Misztal
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding / Q000379:methods*; D002417:Cattle / Q000235:genetics* / Q000502:physiology; D005260:Female; D005298:Fertility / Q000235:genetics* / Q000502:physiology; D014644:Genetic Variation; D007315:Insemination, Artificial / Q000706:statistics & numerical data / Q000662:veterinary*; D008297:Male; D011247:Pregnancy; D011270:Pregnancy, Animal / Q000235:genetics* / Q000502:physiology; D012641:Selection, Genetic; D013997:Time Factors