ArticleFrontiers in genetics2022
Evaluating dimensionality reduction for genomic prediction.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.The plant genome · 2026Article
- Trait Association for Flowering Time in Lentil from Global Multi-Environment Data Using GWAS and Machine Learning.Plants (Basel, Switzerland) · 2026Article
- Statistics and data science unlock the predictive power of quantitative genetics.Frontiers in plant science · 2026Review
- Integrative multi-omics and computer-aided biofungicide design approach to combat fusarium wilt of chickpea.Planta · 2025Review
- Article
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
- Obscured-ensemble models for genomic prediction.PloS one · 2025Article
- A tied-weight autoencoder for the linear dimensionality reduction of sample data.Scientific reports · 2024Article
- CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes.Database : the journal of biological databases and curation · 2024Article
- Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.Plants (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
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 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.
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