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

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.

American journal of epidemiology - 1 Jun 2021

Jiang Lai, Xu Shujing, Mancuso Nicholas, Newcombe Paul J, Conti David V

Abstract excerpt

Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of estimates from association analyses of single-nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a 2-stage instrumental or transcriptome-wide association...

Read the complete abstract on PubMed

Topics

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

Share to Amidohydrolases
Share to Bias

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Body Mass Index

Share to Data Interpretation, Statistical

Share to Mendelian Randomization Analysis

Share to Myocardial Infarction

Share to Phosphoproteins

Share to Prostatic Neoplasms

Source

Open in PubMed Open full text

Identifiers

PMID: 33404048
DOI: 10.1093/aje/kwaa287
PMCID: PMC8521785

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.PMID 33404048Read ↗

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.Semantic proximity Read ↗

A flexible and unified framework for single- and multi-outcome Mendelian randomization using summary statistics.Semantic proximity Read ↗

Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization.Semantic proximity Read ↗

Causal associations between risk factors and common diseases inferred from GWAS summary dataSemantic proximity Read ↗

Causal associations between risk factors and common diseases inferred from GWAS summary data.Semantic proximity Read ↗

Causal effect heterogeneity estimation using summary statisticsSemantic proximity Read ↗

A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics.Semantic proximity Read ↗

Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomizationSemantic proximity Read ↗

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.Semantic proximity Read ↗

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

Inferring a directed acyclic graph of phenotypes from GWAS summary statisticsSemantic proximity Read ↗

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond.Semantic proximity Read ↗

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