Evidence map›Paper›PMID 33290408›Full record

ArticlePLoS genetics2020

A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.

Debashree Ray, Nilanjan Chatterjee

Abstract read
In one paragraph

Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
153citing papers in PubMed, 7 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

153 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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93 more citing papers are in PubMed but not listed here.

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.

Debashree RayDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0002-0979-2935
Nilanjan ChatterjeeDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0002-9060-008X

Funding

ECHODAC (Environmental Influences on Child Health Outcomes Data Analysis Center)U24OD023382 · OD · JOHNS HOPKINS UNIVERSITY · PI Diane J Catellier, LISA P JACOBSON · 2016 to 2026
$148.4M
Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk PredictionR01HG010480 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI CHATTERJEE, NILANJAN · 2019 to 2023
$2.8M
Statisticalmethods and analysis to detect pleiotropy in GWAS of oral cleft triosR03DE029254 · NIDCR · JOHNS HOPKINS UNIVERSITY · PI RAY, DEBASHREE · 2020 to 2021
$328k
NHGRI NIH HHS R01 HG010480NIDCR NIH HHS R03 DE029254NIH HHS U24 OD023382
6 · The paper itself

Abstract

There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the genetic variants across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of variants to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions: 3q23 (ZBTB38), 6q25.3 (RGS17), 9p22.1 (HAUS6), 9p13.3 (UBAP2), 11p11.2 (RAPSN), 14q12 (AKAP6), 15q15 (KNL1) and 18q23 (ZNF236).

Indexed as

Genetic PleiotropyQuantitative Trait LociDiabetes Mellitus, Type 2Genome-Wide Association StudyHumansMaleModels, GeneticProstatic Neoplasms

Identifiers

PMID33290408
PMCPMC7748289

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.