ArticleBMC genetics2017
Resampling-based tests for Lasso in genome-wide association studies.
Article in BMC genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- FAST VARIABLE SELECTION FOR DISTRIBUTIONAL REGRESSION WITH APPLICATION TO CONTINUOUS GLUCOSE MONITORING DATA.The annals of applied statistics · 2025Article
- Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.Scientific reports · 2025Article
- GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning.American journal of human genetics · 2024Article
- GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.bioRxiv : the preprint server for biology · 2024Article
- Molecular characterization of m6A RNA methylation regulators with features of immune dysregulation in IgA nephropathy.Clinical and experimental medicine · 2024Article
- A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization.Sensors (Basel, Switzerland) · 2023Article
- Common genetic substrates of alcohol and substance use disorder severity revealed by pleiotropy detection against GWAS catalog in two populations.Addiction biology · 2021Article
- Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.Bioinformatics (Oxford, England) · 2020Article
- Genetic Variants Detection Based on Weighted Sparse Group Lasso.Frontiers in genetics · 2020Article
- TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.Scientific reports · 2019Article
- Honey bee predisposition of resistance to ubiquitous mite infestations.Scientific reports · 2019Article
- Seven-senescence-associated gene signature predicts overall survival for Asian patients with hepatocellular carcinoma.World journal of gastroenterology · 2019Article
- Combining Sparse Group Lasso and Linear Mixed Model Improves Power to Detect Genetic Variants Underlying Quantitative Traits.Frontiers in genetics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundGenome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings.
methodsWe consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study.
resultsWe show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions.
conclusionOur results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies.
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.