Use of information on identified genes to reduce the selection bias on genetic evaluation

• Ano de defesa: 2003
• Autor(a) principal: Fonseca, Ricardo da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Viçosa
• Palavras-chave em Português: Avaliação genética; Ciências Agrárias
• Link de acesso: http://www.locus.ufv.br/handle/123456789/10560

Data available for genetic evaluation are invariably originated from several generations of selection. However, BLUP, the usual method for predict ge- netic values, assumes that selection has not occurred. If genetic evaluations are performed ignoring changes in variances and means due to selection, predictions are likely to be biased and not minimum variance and ranking alterations may occur. Misclassification of animals reduce the genetic gain per generation and causes economic losses to breeding companies and farm- ers. The selection problem in genetic evaluations is, essentially, a problem of missing data. If all data used to take selection decisions are included in the analysis, selection can be ignored, otherwise, predictions are not obtained from the correct distributions. In order to verify the impact of including sin- gle genes information to recover missing data and to reduce selection bias in the genetic evaluation, a simulation study was carried out. Weaning weight and weight at 550 days were simulated. The first trait was controlled by 20 genes and the second by 40 genes where the first twenty were shared between the traits. Genetic evaluations were always performed for weight at 550 days without single genes included in the model and with 20, 10, 5 and 2 single genes included as fixed effects. Each analysis were carried out in presence and absence of pre-selection on weaning weight. The criteria used to evaluate the technique were: genetic gains and bias. The results showed, in general, that including of the information of the identified genes as fixed effects in the genetic evaluation model did not contribute to reduce the bias due to selection. In some generations, the selection bias was larger when identified genes were used in the genetic evaluation. This situation occured in the setting which 20 and 10 identified genes were included as fixed effects in the model, not being observed in the cases which included 5 and 2 identified genes. Considering the two former cases, it was not ob- served significantly effect of including of the genetic information for nearly all generations.