Alternative measures to evaluate the accuracy and bias of genomic predictions with censored records

Geraldo Magela da Cruz  Pereira; Sebastião  Martins Filho; Renata Veroneze; Luiz Fernando  Brito; Vinícius Silva dos  Santos; Leonardo Siqueira  Glória

doi:10.4025/actascianimsci.v45i1.61509

Geraldo Magela da Cruz Pereira Universidade Federal de Lavras https://orcid.org/0000-0001-6280-4870
Sebastião Martins Filho Universidade Federal de Viçosa https://orcid.org/0000-0002-8317-4318
Renata Veroneze Universidade Federal de Viçosa https://orcid.org/0000-0003-2715-7545
Luiz Fernando Brito Purdue University https://orcid.org/0000-0002-5819-0922
Vinícius Silva dos Santos Universidade Federal do Acre https://orcid.org/0000-0002-8387-2917
Leonardo Siqueira Glória Universidade Estadual do Norte Fluminense https://orcid.org/0000-0002-2756-5939

DOI: https://doi.org/10.4025/actascianimsci.v45i1.61509

Keywords: genomic selection; statistical modeling; simulation; mixed Cox model; truncated normal model.

Abstract

This study aimed to propose and compare metrics of accuracy and bias of genomic prediction of breeding values for traits with censored data. Genotypic and censored-phenotypic information were simulated for four traits with QTL heritability and polygenic heritability, respectively: C1: 0.07-0.07, C2: 0.07-0.00, C3: 0.27-0.27, and C4: 0.27-0.00. Genomic breeding values were predicted using the Mixed Cox and Truncated Normal models. The accuracy of the models was estimated based on the Pearson (PC), maximal (MC), and Pearson correlation for censored data (PCC) while the genomic bias was calculated via simple linear regression (SLR) and Tobit (TB). MC and PCC were statistically superior to PC for the trait C3 with 10 and 40% censored information, for 70% censorship, PCC yielded better results than MC and PC. For the other traits, the proposed measures were superior or statistically equal to the PC. The coefficients associated with the marginal effects (TB) presented estimates close to those obtained for the SLR method, while the coefficient related to the latent variable showed almost unchanged pattern with the increase in censorship in most cases. From a statistical point of view, the use of methodologies for censored data should be prioritized, even for low censoring percentages.

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