Evidence map›Paper›PMID 27325607›Full record

ArticleGenetic epidemiology2016

FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.

Sungkyoung Choi, Sungyoung Lee, Dandi Qiao, Megan Hardin, Michael H Cho, Edwin K Silverman, Taesung Park, Sungho Won

Abstract read
In one paragraph

Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

8 authors.

Sungkyoung ChoiInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Sungyoung LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Dandi QiaoChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Megan HardinChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Michael H ChoChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Edwin K SilvermanChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Taesung ParkInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Sungho WonInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.

Funding

Respiratory Computational Discovery CoreP01HL114501 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHOI, MARY E · 2013 to 2025
$24.9M
UW Center for Mendelian GenomicsU54HG006493 · NHGRI · UNIVERSITY OF WASHINGTON · PI BAMSHAD, MICHAEL JOSEPH, NICKERSON, DEBORAH A · 2012 to 2015
$20.0M
UW Center for Mendelian GenomicsUM1HG006493 · NHGRI · UNIVERSITY OF WASHINGTON · PI BAMSHAD, MICHAEL JOSEPH, LEAL, SUZANNE M · 2016 to 2020
$15.3M
Systems Biology, Bioinformatics, and BiostatisticsP01HL105339 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2011 to 2015
$12.2M
Fine Mapping of COPD Susceptibility GenesR01HL075478 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2004 to 2012
$6.4M
Identifying Genetic Determinants of Severe, Early-Onset COPDR01HL113264 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2012 to 2016
$6.0M
Network-based Methods for Understanding Genetic Determinants and Epistasis in COPDK01HL129039 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI QIAO, DANDI · 2015 to 2019
$844k
NHGRI NIH HHS U54 HG006493NHGRI NIH HHS UM1 HG006493NHLBI NIH HHS K01 HL129039NHLBI NIH HHS P01 HL105339NHLBI NIH HHS P01 HL114501NHLBI NIH HHS R01 HL075478NHLBI NIH HHS R01 HL113264
6 · The paper itself

Abstract

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

Indexed as

Genes, X-LinkedModels, GeneticAllelesChromosomes, Human, XFemaleGenetic VariationHumansPedigreePhenotypeX Chromosome Inactivationextended familiesgenetic association analysisrare variantsX chromosomeX chromosome inactivation

Identifiers

PMID27325607
PMCPMC4981534

What OpenQuestion holds

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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.