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Cited by 440Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenetic Associations and Epidemiology

Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions.

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Ogutu, Joseph O, Schulz-Streeck, Torben, Piepho, Hans-Peter·2012·BMC Proceedings
DOI 10.1186/1753-6561-6-s2-s10PMC PMC3363152
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Abstract

BACKGROUND: Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central and recurring challenge in plant and animal breeding. The existence of a bewildering array of approaches for predicting breeding values using markers underscores the importance of identifying approaches able to efficiently and accurately predict breeding values. Here, we comparatively evaluate the predictive performance of six regularized linear regression methods-- ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net and adaptive elastic net-- for predicting GEBV using dense SNP markers. METHODS: We predicted GEBVs for a quantitative trait using a dataset on 3000 progenies of 20 sires and 200 dams and an accompanying genome consisting of five chromosomes with 9990 biallelic SNP-marker loci simulated for the QTL-MAS 2011 workshop. We applied all the six methods that use penalty-based (regularization) shrinkage to handle datasets with far more predictors than observations. The lasso, elastic net and their adaptive extensions further possess the desirable property that they simultaneously select relevant predictive markers and optimally estimate their effects. The regression models were trained with a subset of 2000 phenotyped and genotyped individuals and used to predict GEBVs for the remaining 1000 progenies without phenotypes. Predictive accuracy was assessed using the root mean squared error, the Pearson correlation between predicted GEBVs and (1) the true genomic value (TGV), (2) the true breeding value (TBV) and (3) the simulated phenotypic values based on fivefold cross-validation (CV). RESULTS: The elastic net, lasso, adaptive lasso and the adaptive elastic net all had similar accuracies but outperformed ridge regression and ridge regression BLUP in terms of the Pearson correlation between predicted GEBVs and the true genomic value as well as the root mean squared error. The performance of RR-BLUP was also somewhat better than that of ridge regression. This pattern was replicated by the Pearson correlation between predicted GEBVs and the true breeding values (TBV) and the root mean squared error calculated with respect to TBV, except that accuracy was lower for all models, most especially for the adaptive elastic net. The correlation between the predicted GEBV and simulated phenotypic values based on the fivefold CV also revealed a similar pattern except that the adaptive elastic net had lower accuracy than both the ridge regression methods. CONCLUSIONS: All the six models had relatively high prediction accuracies for the simulated data set. Accuracy was higher for the lasso type methods than for ridge regression and ridge regression BLUP.

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  • The elastic net algorithm was used because penalization improves prediction accuracy and maintains clinical interpretability compared with less transparent machine learning algorithms.

    “We used the elastic net algorithm, given substantial evidence that penalization improves prediction accuracy,25–27 alongside its well-established accuracy and robustness28,29 and ability to maintain clinical interpretability compared with less transparent machine learning algorithms (eg, random forests, neural networks).”

    From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [28]

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  1. Ytournel F Linkage disequilibrium and QTL fine mapping in a selected population PhD thesis, Station de Génétique Quantitative et Appliquée, INRA 2008

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Authors
Ogutu, J. O., Schulz-Streeck, T., & Piepho, H.-P.
Affiliations
University of Hohenheim (DE)
Corresponding author
Ogutu, Joseph O
ORCID
Venue
BMC Proceedings
Publisher
Springer Science and Business Media LLC
Published
2012-05-21
Volume
6
Issue
S2
Pages
S10-S10
ISSN
1753-6561 (ISSN-L)
DOI
10.1186/1753-6561-6-s2-s10
PMC
PMC3363152
OpenAlex
W2125459357
Cited by
440
References
31
Citations by year
2026: 43 · 2025: 59 · 2024: 48 · 2023: 52 · 2022: 42 · 2021: 45 · 2020: 41 · 2019: 27 · 2018: 21 · 2017: 15 · 2016: 16 · 2015: 12 · 2014: 10 · 2013: 5 · 2012: 4
FWCI
5.88
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Proceedings (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Bundesministerium für Bildung und Forschung (0315526)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
genomic selection, genomic breeding value prediction, regularized linear regression, ridge regression, LASSO, elastic net, adaptive LASSO, adaptive elastic net, ridge regression BLUP, SNP markers, marker selection, prediction accuracy, simulated data, plant breeding, animal breeding, high-dimensional data, cross-validation

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