The objective of the current study was to compare the performances of a longitudinal multiple-trait (MT) and cumulative single-trait (ST) best linear unbiased prediction (BLUP) analyses for eggs set (E), percentage fertility (F), and percentage hatched of fertile eggs (H) based on rank correlations of hens and roosters (ROO). Longitudinal data consisted of weekly records, and the ST analysis used cumulative records of the 3 traits. Comparisons were carried out using simulated data, in which true values assigned during the simulation were compared with estimates obtained from MT and ST analyses. Two methods were used to calculate breeding values and ROO effects. Weekly E records equal to zero were assumed as valid records, and missing weekly F and H records were predicted in MT analysis. However, missing F and H were not included in the cumulative record. Weekly E, F, and H records were converted to a cumulative record following a procedure used by a commercial broiler company. Further, ST models were developed to evaluate hens and ROO separately as currently practiced in the industry. There was virtually no difference in hen rank correlations between true and predicted breeding values obtained from MT (0.64) and ST (0.62) analyses for E, thus suggesting that hens would rank similarly when weekly or cumulative data were used for genetic evaluation of E. With ST analysis, rank correlations between true and estimated hen breeding values decreased, compared with MT analysis, by 74 and 75 % for F and H, respectively. For F (H), the rank correlation between true and estimated ROO effects were 0.64 (0.67) and 0.17 (0.12) with MT and ST analyses, respectively. The results suggested that longitudinal NIT BLUP, which handles missing records, was better at estimating true hen and ROO effects than ST BLUP. Therefore, it was recommended that the MT BLUP method be used for genetic evaluation of hens and ROO for E, F, and H.
