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

Accounting for selection and correlation in the analysis of two-stage genome-wide association studies.

Biostatistics (Oxford, England) - 1 Oct 2016

Robertson David S, Prevost A Toby, Bowden Jack

Abstract excerpt

The problem of selection bias has long been recognized in the analysis of two-stage trials, where promising candidates are selected in stage 1 for confirmatory analysis in stage 2. To efficiently correct for bias, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been proposed for a wide variety of trial settings, but where the population parameter estimates are assumed to be...

Read the complete abstract on PubMed

Topics

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

Share to Crohn Disease
Share to IRGM gene
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Polymorphism, Single Nucleotide

Share to Selection Bias

Source

Open in PubMed Open full text

Identifiers

PMID: 26993061
DOI: 10.1093/biostatistics/kxw012
PMCID: PMC5031943

Genes

IRGM

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Accounting for selection and correlation in the analysis of two-stage genome-wide association studies.PMID 26993061Read ↗

A Linear Mixed Model with Measurement Error Correction (LMM-MEC): A Method for Summary-data-based Multivariable Mendelian RandomizationSemantic proximity Read ↗

A Linear Mixed Model With Measurement Error Correction (LMM-MEC): A Method for Summary-Data-Based Multivariable Mendelian Randomization.Semantic proximity Read ↗

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.Semantic proximity Read ↗

Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors.Semantic proximity Read ↗

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

Correcting "winner's curse" in odds ratios from genomewide association findings for major complex human diseases.Semantic proximity Read ↗

Approximation of bias and mean-squared error in two-sample Mendelian randomization analyses.Semantic proximity Read ↗

A general framework for two-stage analysis of genome-wide association studies and its application to case-control studies.Semantic proximity Read ↗

Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data.Semantic proximity Read ↗

Strategies for developing prediction models from genome-wide association studies.Semantic proximity Read ↗

Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease.Semantic proximity Read ↗

Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level dataSemantic proximity Read ↗

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