Genomics

Regularized quantile regression applied to genome-enabled prediction of quantitative traits.

M. Nascimento, Silva, F. F. E., de Resende, M. D. V., Cruz, C. D., Nascimento, A. C. C., Viana, J. M. S., Azevedo, C. F., and Barroso, L. M. A., “Regularized quantile regression applied to genome-enabled prediction of quantitative traits.”, Genet Mol Res, vol. 16, no. 1, 2017.

Multiple-trait genomic evaluation for milk yield and milk quality traits using genomic and phenotypic data in buffalo in Brazil

R. R. Aspilcueta-Borquis, Neto, F. R. Araujo, Santos, D. J. A., Hurtado-Lugo, N. A., Silva, J. A. V., and Tonhati, H., “Multiple-trait genomic evaluation for milk yield and milk quality traits using genomic and phenotypic data in buffalo in Brazil”, vol. 14, pp. 18009-18017, 2015.

The objective of this study was to compare the multi-trait model using pedigree information and a model using genomic information in addition to pedigree information. We used data from 5896 lactations of 2021 buffalo cows, of which 384 were genotyped using the Illumina Infinium® bovine HD BeadChip, considering seven traits related to milk yield (MY305), fat (FY305), protein (PY305), and lactose (LY305), percentages of fat (%F) and protein (%P), and somatic cell score (SCS).

Subscribe to Genomics