Use of gene expression data for predicting continuous phenotypes for animal production and breeding

Abstract

Traits such as disease resistance are costly to evaluate and slow to improve using current methods. Analysis of gene expression profiles (e.g. DNA microarrays) has potential for predicting such phenotypes and has been used in an analogous way to classify cancer types in human patients. However, doubts have been raised regarding the use of classification methods with microarray data for this purpose. Here we propose a method using random regression with cross validation, which accounts for the distribution of variation in the trait and utilises different subsets of patients or animals to perform a complete validation of predictive ability. Published breast tumour data were used to test the method. Despite the small dataset (n , 100), the new approach resulted in a moderate but significant correlation between the predicted and actual phenotypes (0.32). Binary classification of the predicted phenotypes yielded similar classification error rates to those found by other authors (35%). Unlike other methods, the new method gave a quantitative estimate of phenotype that could be used to rank animals and select those with extreme phenotypic performance. Use of the method in an optimal way using larger sample sizes, and combining DNA microarrays and other testing platforms, is recommended.

Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects

“…Apart from the use of gene expression profiles to better understand the function and regulation of genes, Robinson, Goddard and Hayes (2008) have proposed the use of gene expression profiles in indirect selection for disease resistance. The concept is that certain cells (e.g., macrophages or leucocytes) will respond to the disease agent, and the resulting cascades of gene expression changes in these cells could potentially differ between animals that are able to resist versus those that are more susceptible to the disease.…”

“…The use as a selection tool assumes that a method of eliciting a gene expression response from breeding candidates is available, e.g., by challenging cells derived from these individuals to disease. Robinson et al (2008) evaluated a method for formulating prediction equations using random regression with cross‐validation on a set of gene expression data from breast cancer patients, and found a moderate correlation between the predicted and the actual phenotype (0.32±0.06). Based on simulations using gene expression data in a selective breeding programme for Atlantic salmon, Robinson and Hayes (2008) found that use of such data in combination with sib challenge testing would improve both the accuracy and intensity of selection and would thus speed up genetic gain for disease resistance.…”

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype

“…The eQTL are used to develop biomarkers to monitor, diagnose and predict phenotypes (see below). While it is possible to use eQTL for the selection of animals [92], at present, to the best of our knowledge, this is not practiced yet by breeding companies. The main reason for this may be that the structure of the breeding schemes is not optimized for using this type of data, together with the unfamiliarity of the breeding companies with the potential of the eQTL and unavailability of data in the breeding lines.…”

Differential gene expression profiles in Japanese flounder (Paralichthys olivaceus) with different susceptibilities to edwardsiellosis

“…Interestingly, the genes for two histone proteins (HH7(1) and LD1 (8)) were expressed at high levels in the resistant groups ( Table 2). Although histones mainly serve as structural components of nucleosomes, but they can also act as antimicrobial agents [45].…”