Evidence map›Paper›PMID 27980645›Full record

ArticleBMC proceedings2016

Powerful association test combining rare variant and gene expression using family data from Genetic Analysis Workshop 19.

Yen-Yi Ho, Weihua Guan, Michael O'Connell, Saonli Basu

Abstract read
In one paragraph

Article in BMC proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

4 authors.

Yen-Yi HoDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 USA.
Weihua GuanDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 USA.
Michael O'ConnellDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 USA.
Saonli BasuDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 USA.

Funding

Women's CancerP30CA077598 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Douglas Yee · 1998 to 2026
$100.4M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
Research CoreU54MD008620 · NIMHD · UNIVERSITY OF MINNESOTA · PI KONETY, BADRINATH R, SHIKANY, JAMES M · 2013 to 2017
$13.5M
Tobacco Constituent and Biomarker Assessment CoreU19CA157345 · NCI · UNIVERSITY OF MINNESOTA · PI HATSUKAMI, DOROTHY K, SHIELDS, PETER G. · 2012 to 2016
$13.3M
UAB / UMN SPORE in Pancreatic CancerP50CA101955 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BUCHSBAUM, DONALD J · 2010 to 2014
$11.1M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Statistical Methods for detection of genome-wide GxE interactions in longitudinalR01DA033958 · NIDA · UNIVERSITY OF MINNESOTA · PI BASU, SAONLI · 2013 to 2016
$1.1M
NCATS NIH HHS UL1 TR000114NCI NIH HHS P30 CA077598NCI NIH HHS P50 CA101955NCI NIH HHS U19 CA157345NIDA NIH HHS R01 DA033958NIGMS NIH HHS R01 GM031575NIMHD NIH HHS U54 MD008620
6 · The paper itself

Abstract

backgroundGenetic association studies aim to test for disease or trait association with genetic variants, either throughout the human genome or in regions of interest. However, for most diseases and traits, the combined effects of associated genetic variants explain only a small proportion of the genetic variation. This "missing heritability" may be a result of the small effects of common variants considered in the genetic association studies. Rare variants may also play an important role in understanding the missing heritability of complex traits.

methodWe propose a novel weight-adjustment approach to combine gene expression into rare variant analysis. Results from previous simulation studies suggested that incorporating gene expression information can lead to substantial gain in statistical power.

resultsUsing the family data set provided through the Genetic Analysis Workshop 19, we identified susceptible genes associated with blood pressure regulation.

conclusionsThese findings provide valuable information for further functional studies for blood pressure control and mechanism.

Identifiers

PMID27980645
PMCPMC5133497

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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.