ArticleBMC bioinformatics2019
AUTALASSO: an automatic adaptive LASSO for genome-wide prediction.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 31 citations in OpenAlex.
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- A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia.Molecular therapy. Nucleic acids · 2022Article
- Comprehensive analysis of competitive endogenous RNAs network: Identification and validation of prediction model composed of mRNA signature and miRNA signature in gastric cancer.Oncology letters · 2022Article
- NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction.Frontiers in plant science · 2022Article
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- A proximal LAVA method for genome-wide association and prediction of traits with mixed inheritance patterns.BMC bioinformatics · 2021Article
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- Identification of a long non-coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma.Oncology letters · 2020Article
- Predicting Grating Orientations With Cross-Frequency Coupling and Least Absolute Shrinkage and Selection Operator in V1 and V4 of Rhesus Monkeys.Frontiers in computational neuroscience · 2020Article
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- On the Use of the Pearson Correlation Coefficient for Model Evaluation in Genome-Wide Prediction.Frontiers in genetics · 2019Article
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-wide prediction has become the method of choice in animal and plant breeding. Prediction of breeding values and phenotypes are routinely performed using large genomic data sets with number of markers on the order of several thousands to millions. The number of evaluated individuals is usually smaller which results in problems where model sparsity is of major concern. The LASSO technique has proven to be very well-suited for sparse problems often providing excellent prediction accuracy. Several computationally efficient LASSO algorithms have been developed, but optimization of hyper-parameters can be demanding.
resultsWe have developed a novel automatic adaptive LASSO (AUTALASSO) based on the alternating direction method of multipliers (ADMM) optimization algorithm. The two major hyper-parameters of ADMM are the learning rate and the regularization factor. The learning rate is automatically tuned with line search and the regularization factor optimized using Golden section search. Results show that AUTALASSO provides superior prediction accuracy when evaluated on simulated and real bull data compared to the adaptive LASSO, LASSO and ridge regression implemented in the popular glmnet software.
conclusionsThe AUTALASSO provides a very flexible and computationally efficient approach to GWP, especially when it is important to obtain high prediction accuracy and genetic gain. The AUTALASSO also has the capability to perform GWAS of both additive and dominance effects with smaller prediction error than the ordinary LASSO.
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