Evaluating Dimensionality Reduction for Genomic Prediction

• Activity Number: 209 - Statistical methods for genomic and epigenetic data analysis
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Vamsi Manthena* and Rajeev K Varshney and Diego Jarquin and Reka Howard
• Companies: University of Nebraska - Lincoln and Center of Excellence in Genomics & Systems Biology and University of Nebraska - Lincoln and University of Nebraska - Lincoln
• Keywords: dimensionality reduction; genomic selection; randomized algorithms

Abstract

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data.