Evidence map›Paper›PMID 28132020›Full record

ArticleGenetics2017

Simultaneous Modeling of Disease Status and Clinical Phenotypes To Increase Power in Genome-Wide Association Studies.

Michael Bilow, Fernando Crespo, Zhicheng Pan, Eleazar Eskin, Susana Eyheramendy

Abstract read
In one paragraph

Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Michael BilowDepartment of Computer Science, University of California, Los Angeles, California.
Fernando CrespoDepartment of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.
Zhicheng PanBioinformatics Program, University of California, Los Angeles, California.
Eleazar EskinDepartment of Computer Science, University of California, Los Angeles, California.
Susana EyheramendyDepartment of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile susana@mat.puc.cl.

Funding

ULTRASTRUCTURE OF THE INTIMA IN EARLY LESION FORMATIONP01HL030568 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FOGELMAN, ALAN M · 1985 to 2019
$55.2M
Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LUSIS, ALDONS JAKE · 1985 to 2019
$50.2M
Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentU01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2007 to 2010
$10.8M
Training U54EB020403 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2018
$10.1M
Integrative Center for Neurogenetics and Neurogenomics - OverallP30NS062691 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COPPOLA, GIOVANNI · 2009 to 2018
$7.4M
INTEGRATIVE GENETIC APPROACHES TO GENE-AIR POLLUTION INTERACTIONS IN ASTHMAR01ES021801 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AKBARI, OMID, ALLAYEE, HOOMAN · 2012 to 2016
$3.9M
An integrated molecular approach to understand variation in iron metabolismR01GM083198 · NIGMS · UNIVERSITY OF FLORIDA · PI VULPE, CHRISTOPHER D · 2012 to 2015
$2.7M
Genetic Regulation of Gene Expression and its Impact on Phenotypes - SupplementR01MH101782 · NIMH · STANFORD UNIVERSITY · PI SABATTI, CHIARA · 2013 to 2016
$1.3M
Correcting for Population Structure in Gene-by-Environment Interaction StudiesR01ES022282 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ESKIN, ELEAZAR · 2013 to 2015
$1.2M
Discovering the Genetic Basis of HypertensionK25HL080079 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ESKIN, ELEAZAR · 2006 to 2010
$685k
NHLBI NIH HHS K25 HL080079NHLBI NIH HHS P01 HL028481NHLBI NIH HHS P01 HL030568NIBIB NIH HHS U54 EB020403NIDA NIH HHS U01 DA024417NIEHS NIH HHS R01 ES021801NIEHS NIH HHS R01 ES022282NIGMS NIH HHS R01 GM083198NIMH NIH HHS R01 MH101782NINDS NIH HHS P30 NS062691
6 · The paper itself

Abstract

Genome-wide association studies have identified thousands of variants implicated in dozens of complex diseases. Most studies collect individuals with and without disease and search for variants with different frequencies between the groups. For many of these studies, additional disease traits are also collected. Jointly modeling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this approach increases the potential for discovering genetic variants that are associated with both a clinical phenotype and a disease. Standard multivariate techniques fail to effectively solve this problem, because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case-control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate. We demonstrate the utility of our method using both simulated data and the Northern Finland Birth Cohort data.

Indexed as

Genetic Predisposition to DiseaseModels, GeneticPhenotypeGenetic Diseases, InbornGenome-Wide Association StudyHumanscovariatesmultivariate analysis

Identifiers

PMID28132020
PMCPMC5340321

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.