title: Survival analysis applied to genetic evaluation for female fertility in dairy cattle.
abstract: The objective of this research was to study whether survival analysis results in a more accurate genetic evaluation for female fertility traits compared with the usual methodology based on linear models. The fertility trait studied was interval between calving and last insemination. A stochastic simulation describing the reproductive cycle of first-parity cows was done, in which true breeding values for conception rate were created. A model containing effects of sire and herd was used both with survival analysis and with mixed linear model analysis to predict sire breeding values. Correlations between true breeding values for conception rate and breeding values for calving to last insemination predicted by the best survival analysis model or the best linear model were 0.77 and 0.68, respectively. The results showed that when pregnancy status is known, survival analysis is a better method than linear models for genetic evaluation of conception rate when using observations on the interval between calving and last insemination.
authors.affiliation: Centre for Reproductive Biology in Uppsala and Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, SE-75007 Uppsala, Sweden.
authors.forename: M del P
authors.initials: Mdel P
authors.lastname: Schneider
authors.forename: E
authors.initials: E
authors.lastname: Strandberg
authors.forename: V
authors.initials: V
authors.lastname: Ducrocq
authors.forename: A
authors.initials: A
authors.lastname: Roth
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D005260:Female; D005298:Fertility / Q000235:genetics*; D005306:Fertilization; D007774:Lactation / Q000235:genetics; D016014:Linear Models; D008297:Male; D008954:Models, Biological; D010060:Ovulation; D010641:Phenotype; D011247:Pregnancy; D016016:Proportional Hazards Models; D019655:Quantitative Trait, Heritable; D016019:Survival Analysis; D013997:Time Factors