Evidence map›Paper›PMID 30255770›Full record

ArticleBMC genetics2018

An adaptive gene-level association test for pedigree data.

Jun Young Park, Chong Wu, Wei Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Jun Young ParkDivision of Biostatistics, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
Chong WuDivision of Biostatistics, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
Wei PanDivision of Biostatistics, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA. weip@biostat.umn.edu.

Funding

Genetic and Environmental Determinants of TriglyceridesU01HL072524 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI ARNETT, DONNA K · 2002 to 2008
$11.9M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Genomewide Association Study of Lipid Response to Fenofibrate and Dietary FatR01HL091357 · NHLBI · UNIVERSITY OF KENTUCKY · PI ARNETT, DONNA K · 2008 to 2018
$5.8M
Association analysis of rare variants with sequencing dataR01HL116720 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, WEI, PENG · 2013 to 2020
$3.3M
Powerful Inference and Prediction for Genetic AssociationR01HL105397 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2011 to 2018
$2.9M
Statistical Methods for Genomic DataR01GM113250 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2014 to 2017
$702k
Integrating genomic and imaging endophenotypes in GWASR21AG057038 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2017 to 2018
$405k
NHLBI NIH HHS R01 HL091357NHLBI NIH HHS R01 HL105397NHLBI NIH HHS R01 HL116720NHLBI NIH HHS U01 HL072524NIA NIH HHS R21 AG057038NIGMS NIH HHS R01 GM031575NIGMS NIH HHS R01 GM113250
6 · The paper itself

Abstract

backgroundWe propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient.

resultsWe applied our approach to test for association with the high-density lipoprotein (HDL) ratio of post- and pretreatments in GAW20 data. Using the LMM similar to that used by Aslibekyan et al. (PLos One, 7:48663, 2012), our method identified 2 nearly significant genes (APOA5 and ZNF259) near rs964184, whereas neither the other gene-level tests nor the standard test on each individual single-nucleotide polymorphism (SNP) detected any significant gene in a genome-wide scan.

conclusionsGene-level association testing can be a complementary approach to the SNP-level association testing and our method is adaptive and efficient compared to several other existing gene-level association tests.

Indexed as

Genome-Wide Association StudyApolipoprotein A-VCarrier ProteinsHumansHypertriglyceridemiaHypoglycemic AgentsLinear ModelsLipoproteins, HDLMembrane Transport ProteinsPolymorphism, Single NucleotideAPOA5 protein, humanApolipoprotein A-VCarrier ProteinsHypoglycemic AgentsLipoproteins, HDLMembrane Transport ProteinsZPR1 protein, humanaSPUGWASHDLLinear mixed modelsScore test

Identifiers

PMID30255770
PMCPMC6157189

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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 OpenQuestion graph.