ArticleG3 (Bethesda, Md.)2014
Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation.
Article in G3 (Bethesda, Md.), 2014. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Introgression of rye chromosome arm 1RS enhances climate resilience in German winter wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.Frontiers in plant science · 2023Article
- Genomic prediction with haplotype blocks in wheat.Frontiers in plant science · 2023Article
- Genomic prediction applied to high-biomass sorghum for bioenergy production.Molecular breeding : new strategies in plant improvement · 2018Article
- Genomic selection of crossing partners on basis of the expected mean and variance of their derived lines.PloS one · 2017Article
- Genomic selection across multiple breeding cycles in applied bread wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Article
- Article
- Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing.BMC genomics · 2015Article
- The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.BioMed research international · 2015Review
- A Ranking Approach to Genomic Selection.PloS one · 2015Article
- Genome-wide prediction methods for detecting genetic effects of donor chromosome segments in introgression populations.BMC genomics · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Ridge regression with heteroscedastic marker variances provides an alternative to Bayesian genome-wide prediction methods. Our objectives were to suggest new methods to determine marker-specific shrinkage factors for heteroscedastic ridge regression and to investigate their properties with respect to computational efficiency and accuracy of estimated effects. We analyzed published data sets of maize, wheat, and sugar beet as well as simulated data with the new methods. Ridge regression with shrinkage factors that were proportional to single-marker analysis of variance estimates of variance components (i.e., RRWA) was the fastest method. It required computation times of less than 1 sec for medium-sized data sets, which have dimensions that are common in plant breeding. A modification of the expectation-maximization algorithm that yields heteroscedastic marker variances (i.e., RMLV) resulted in the most accurate marker effect estimates. It outperformed the homoscedastic ridge regression approach for best linear unbiased prediction in particular for situations with high marker density and strong linkage disequilibrium along the chromosomes, a situation that occurs often in plant breeding populations. We conclude that the RRWA and RMLV approaches provide alternatives to the commonly used Bayesian methods, in particular for applications in which computational feasibility or accuracy of effect estimates are important, such as detection or functional analysis of genes or planning crosses.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.