ReviewTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2012
Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection.
Review in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
121 citing papers in PubMed, 283 citations in OpenAlex.
- Functional gradient analysis reveals potential therapeutic mechanisms of nrTMS for postoperative motor deficits in glioma patients: A randomized controlled trial.NeuroImage. Clinical · 2026Trial
- A serum proteomics-based predictive model for steroid-resistant immune checkpoint inhibitor associated myocarditis.Clinical and translational medicine · 2026Article
- Identification of a Lactylation-Related Gene Signature and Candidate Biomarkers for Dilated Cardiomyopathy.Journal of cellular and molecular medicine · 2026Article
- Climate change and crop resilience: harnessing metabolomics for predicting stress tolerance.The New phytologist · 2026Review
- Machine learning identifies neutrophil-related signatures for diagnostic value in neonatal sepsis.Journal of clinical biochemistry and nutrition · 2026Article
- Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Development of immune-derived molecular markers for coronary heart disease via multimachine learning.iScience · 2026Article
- Molecular and Pharmacogenetic Marker Evaluation in Relation to the Toxicity and Clinical Response of Acute Lymphoblastic Leukemia Treatment in Indian Children (MPGx-INDALL): Protocol for a Prospective Observational Cohort Study.JMIR research protocols · 2026Article
- Integrated Transcriptomic Analysis Identifies TAP1 as a Key Regulator of PANoptosis in Diabetic Kidney Disease Tubular Injury.Inflammation · 2026Article
- Machine Learning-Driven Design of Sustainable Polymer Membranes: Integrated Prediction of Gas Permeability, Selectivity, and Biodegradability.ACS omega · 2026Article
- A Predictive Model for Anemia and Coronary Heart Disease Based on Bidirectional Two-Sample Mendelian Randomization and Machine Learning.Endocrine, metabolic & immune disorders drug targets · 2026Article
- Serum proteomics reveals biomarkers for diagnosis, stratification, and mechanistic insights into cerebral microbleeds.Frontiers in aging neuroscience · 2026Article
- Identification and analysis of diagnostic senescence-related gene signatures for acute myocardial infarction based on multi-omics data and machine learning.Frontiers in immunology · 2026Article
- Early Identification of Endometrial Malignancy in Postmenopausal Women with Asymptomatic Endometrial Thickening: A Novel Explainable Machine Learning Model.International journal of medical sciences · 2026Article
- Machine learning and transcriptomic analysis identify tubular injury biomarkers in patients with chronic kidney disease.International urology and nephrology · 2025Article
- Development of a Model for Predicting the Thermophysical Properties of Carbon Materials and Proposal of Manufacturing Conditions Using the Model.Analytical science advances · 2025Article
- IGFBP1 facilitates tumor proliferation, invasion and migration by modulating the MAPK-ERK signaling pathway in lung adenocarcinoma.BMC cancer · 2025Article
- Integrated transcriptome profiling of plasma exosomes reveals molecular stratification of exocrine and endocrine disorders and S100A8-mediated cell interactions in chronic pancreatitis.Cell discovery · 2025Article
- Biomarker signatures distinguish diabetic retinopathy and diabetic nephropathy.Diabetology international · 2025Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative trait loci (QTL)/association mapping aims at finding genomic loci associated with the phenotypes, whereas genomic selection focuses on breeding value prediction based on genomic data. Variable selection is a key to both of these tasks as it allows to (1) detect clear mapping signals of QTL activity, and (2) predict the genome-enhanced breeding values accurately. In this paper, we provide an overview of a statistical method called least absolute shrinkage and selection operator (LASSO) and two of its generalizations named elastic net and adaptive LASSO in the contexts of QTL mapping and genomic breeding value prediction in plants (or animals). We also briefly summarize the Bayesian interpretation of LASSO, and the inspired hierarchical Bayesian models. We illustrate the implementation and examine the performance of methods using three public data sets: (1) North American barley data with 127 individuals and 145 markers, (2) a simulated QTLMAS XII data with 5,865 individuals and 6,000 markers for both QTL mapping and genomic selection, and (3) a wheat data with 599 individuals and 1,279 markers only for genomic selection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.