Evidence map›Paper›PMID 26413181›Full record

ArticleThe annals of applied statistics

BAYESIAN METHODS FOR GENETIC ASSOCIATION ANALYSIS WITH HETEROGENEOUS SUBGROUPS: FROM META-ANALYSES TO GENE-ENVIRONMENT INTERACTIONS.

Xiaoquan Wen, Matthew Stephens

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In one paragraph

Article in The annals of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  8. The Bayesian lens and Bayesian blinkers.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xiaoquan WenDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Matthew StephensDepartment of Statistics and Department of Human Genetics, University of Chicago, 5734 S. University Avenue, Chicago, IL 60637, USA.

Funding

Genome analysis: statistical methods and applicationsR01HG002585 · NHGRI · UNIVERSITY OF WASHINGTON · PI MATTHEW STEPHENS · 2002 to 2026
$8.4M
Statistical analysis of gene expression quantitative trait loci (eQTL)R01MH090951 · NIMH · UNIVERSITY OF CHICAGO · PI PRITCHARD, JONATHAN K · 2010 to 2012
$873k
Genome analysis: statistical methods and applicationsR56HG002585 · NHGRI · UNIVERSITY OF CHICAGO · PI STEPHENS, MATTHEW · 2023 to 2023
$292k
NHGRI NIH HHS R01 HG002585NHGRI NIH HHS R56 HG002585NIMH NIH HHS R01 MH090951
6 · The paper itself

Abstract

Genetic association analyses often involve data from multiple potentially-heterogeneous subgroups. The expected amount of heterogeneity can vary from modest (e.g. a typical meta-analysis), to large (e.g. a strong gene-environment interaction). However, existing statistical tools are limited in their ability to address such heterogeneity. Indeed, most genetic association meta-analyses use a "fixed effects" analysis, which assumes no heterogeneity. Here we develop and apply Bayesian association methods to address this problem. These methods are easy to apply (in the simplest case, requiring only a point estimate for the genetic effect, and its standard error, from each subgroup), and effectively include standard frequentist meta-analysis methods, including the usual "fixed effects" analysis, as special cases. We apply these tools to two large genetic association studies: one a meta-analysis of genome-wide association studies from the Global Lipids consortium, and the second a cross-population analysis for expression quantitative trait loci (eQTLs). In the Global Lipids data we find, perhaps surprisingly, that effects are generally quite homogeneous across studies. In the eQTL study we find that eQTLs are generally shared among different continental groups, and discuss consequences of this for study design.

Identifiers

PMID26413181
PMCPMC4583155

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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.