Performance Evaluation of Three Methods for Predicting Quantitative Traits

• Activity Number: 77
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Xuesong Li*+ and Lan Zhu
• Companies: Oklahoma State University and Oklahoma State University
• Keywords: Prediction ; Quantitative traits ; Phenotypes

Abstract

Predicting quantitative traits from genomic data provides considerable information in studies of human diseases and livestock breeding programs. Many methods have been developed to predict phenotypic values for individuals from their available genetic and non-genetic information. In this study, we evaluate and compare the performance of three recently published methods (Lee et al, 2008; Yip and Lange, 2010; and Shepherd, Meuwissen, and Woolliams, 2010) for quantitative traits prediction from genomic data. Although all these methods perform reasonably well under assumptions specified in the models, some assumptions are violated in the real data. The robustness of these methods under several common scenarios is evaluated in this study. Specifically, we explore the effect of several main factors, including sample size, number of markers, minor allele frequency, and trait heritability on the performance of these methods. Results from this study can help researchers choose the most appropriate prediction method for their own data analysis.