Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes.

Human heredity - 1 Jan 2022

Yu Ying, Chen Siyuan, Jones Samantha Jean, Hoque Rawnak, Vishnyakova Olga, Brooks-Wilson Angela, McNeney Brad

Abstract excerpt

INTRODUCTION: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. Sparseness leads to maximum likelihood estimators (MLEs) of log-OR parameters that are biased away from their null value of zero and tests with inflated type I errors. Different...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Association Studies

Share to Likelihood Functions

Share to Logistic Models

Share to Models, Genetic

Share to Monte Carlo Method

Share to Phenotype

Source

Identifiers

PMID: 35767963
DOI: 10.1159/000525650

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes.PMID 35767963Read ↗

Bayesian multiple logistic regression for case-control GWASSemantic proximity Read ↗

Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.Semantic proximity Read ↗

SNP selection in genome-wide and candidate gene studies via penalized logistic regression.Semantic proximity Read ↗

Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits.Semantic proximity Read ↗

Regularized rare variant enrichment analysis for case-control exome sequencing data.Semantic proximity Read ↗

Incorporating scientific knowledge into phenotype development: penalized latent class regression.Semantic proximity Read ↗

Shrinkage methods for variable selection and prediction with applications to genetic dataSemantic proximity Read ↗

Penalized linear mixed models for structured genetic data.Semantic proximity Read ↗

Rare variants analysis using penalization methods for whole genome sequence data.Semantic proximity Read ↗

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS DataSemantic proximity Read ↗

Association screening of common and rare genetic variants by penalized regression.Semantic proximity Read ↗

Bayesian variable selection for survival regression in genetics.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.