Evidence map›Paper›PMID 27980630›Full record

ArticleBMC proceedings2016

A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures.

Alden Green, Kaitlyn Cook, Kelsey Grinde, Alessandra Valcarcel, Nathan Tintle

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Alden GreenDepartment of Statistics, Harvard University, Cambridge, MA 02138 USA.
Kaitlyn CookDepartment of Mathematics and Statistics, Carleton College, Northfield, MN 55057 USA.
Kelsey GrindeDepartment of Biostatistics, University of Washington, Seattle, WA 98195 USA.
Alessandra ValcarcelDepartment of Statistics, University of Connecticut, Storrs, CT 06269 USA.
Nathan TintleDepartment of Mathematics, Statistics, and Computer Science, Dordt College, Sioux Center, IA 51250 USA.

Funding

Multiethnic Study of Type 2 Diabetes GenesU01DK085526 · NIDDK · BROAD INSTITUTE, INC. · PI ALTSHULER, DAVID, JABLONSKI, KATHLEEN ANN · 2009 to 2014
$15.7M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Discovery of Functional Variants in Type 2 Diabetes Genes in Mexican AmericansU01DK085524 · NIDDK · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI BLANGERO, JOHN, DEFRONZO, RALPH A · 2009 to 2019
$5.9M
NIDDM SUSCEPTIBILITY GENES IN MEXICAN-AMERICANSR01DK047482 · NIDDK · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI LEHMAN, DONNA M · 1993 to 2010
$5.4M
Identifying T2D Variants by DNA Sequencing in Multiethnic SamplesU01DK085584 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO, BOEHNKE, MICHAEL L · 2009 to 2013
$4.1M
Identifying variants causal for Type 2 Diabetes in Major human populationsU01DK085545 · NIDDK · UNIVERSITY OF OXFORD · PI CHAN, JULIANA CN, EBRAHIM, SHAH BRIAN · 2009 to 2013
$2.8M
Identification and Replication of Type 2 Diabetes Genes in Mexican AmericansU01DK085501 · NIDDK · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HANIS, CRAIG L · 2009 to 2013
$2.7M
Predoctoral Research Training in Statistical Genetics Administrative SupplementT32GM081062 · NIGMS · UNIVERSITY OF WASHINGTON · PI THORNTON, TIMOTHY ALVIN · 2007 to 2021
$2.4M
GENETICS OF GALLBLADDER DISEASE IN MEXICAN AMERICANSR01DK053889 · NIDDK · UNIVERSITY OF TEXAS HLTH SCI CTR SAN ANT · PI DUGGIRALA, RAVINDRANATH · 1998 to 2011
$2.3M
Analyzing the behavior and interpreting the results of gene based tests of rare variant associationR15HG006915 · NHGRI · DORDT COLLEGE · PI TINTLE, NATHAN L · 2012 to 2020
$1.3M
NHGRI NIH HHS R15 HG006915NIDDK NIH HHS R01 DK047482NIDDK NIH HHS R01 DK053889NIDDK NIH HHS U01 DK085501NIDDK NIH HHS U01 DK085524NIDDK NIH HHS U01 DK085526NIDDK NIH HHS U01 DK085545NIDDK NIH HHS U01 DK085584NIGMS NIH HHS R01 GM031575NIGMS NIH HHS T32 GM081062
6 · The paper itself

Abstract

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype-phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary

Identifiers

PMID27980630
PMCPMC5133502

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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.