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Evaluating dimensionality reduction for genomic prediction

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Relation http://oar.icrisat.org/12239/
https://www.frontiersin.org/articles/10.3389/fgene.2022.958780/full
https://doi.org/10.3389/fgene.2022.958780
 
Title Evaluating dimensionality reduction for genomic prediction
 
Creator Manthena, V
Jarquín, D
Varshney, R K
Roorkiwal, M
Dixit, G P
Bharadwaj, C
Howard, R
 
Subject Chickpea
Genetics and Genomics
 
Description 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.
 
Publisher Frontiers Media
 
Date 2022-10-14
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_attribution
 
Identifier http://oar.icrisat.org/12239/1/Frontiers%20in%20Genetics_13_1-16_2022.pdf
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