Evaluating dimensionality reduction for genomic prediction

Manthena, V and Jarquín, D and Varshney, R K and Roorkiwal, M and Dixit, G P and Bharadwaj, C and Howard, R (2022) Evaluating dimensionality reduction for genomic prediction. Frontiers in Genetics (TSI), 14. pp. 1-16. ISSN 1664-8021

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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 26,817 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.

Item Type: Article
Divisions: Center of Excellence in Genomics and Systems Biology
Uncontrolled Keywords: dimensionality reduction, chickpea, genomic selection, randomized algorithms, genomic prediction
Subjects: Mandate crops > Chickpea
Others > Genetics and Genomics
Depositing User: Mr Nagaraju T
Date Deposited: 25 Oct 2023 04:32
Last Modified: 25 Oct 2023 04:32
URI: http://oar.icrisat.org/id/eprint/12239
Official URL: https://www.frontiersin.org/articles/10.3389/fgene...
Acknowledgement: This research was done using resources provided by the Open Science Grid (Pordes et al., 2007; Sfiligoi et al., 2009), which is supported by the National Science Foundation award #2030508. This work was completed utilizing the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative.
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