Evidence map›Paper›PMID 25519384›Full record

ArticleBMC proceedings2014

Adjustment of familial relatedness in association test for rare variants.

Cong Li, Can Yang, Mengjie Chen, Xiaowei Chen, Lin Hou, Hongyu Zhao

Abstract read
In one paragraph

Article in BMC proceedings, 2014. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

6 authors.

Cong LiProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Can YangDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
Mengjie ChenProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Xiaowei ChenProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Lin HouDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
Hongyu ZhaoDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.

Funding

Yale University Clinical and Translational Science Award ProgramUL1TR000142 · NCATS · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2012 to 2015
$31.8M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
NCATS NIH HHS UL1 TR000142NIGMS NIH HHS R01 GM031575
6 · The paper itself

Abstract

High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.

Identifiers

PMID25519384
PMCPMC4143885

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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.