Evidence map›Paper›PMID 18096066›Full record

ArticleBMC genetics2007

EFBAT: exact family-based association tests.

Kady Schneiter, James H Degnan, Christopher Corcoran, Xin Xu, Nan Laird

Abstract read
In one paragraph

Article in BMC genetics, 2007. 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.

Kady SchneiterDepartment of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT 84322-3900, USA. kady.schneiter@usu.edu
James H Degnan
Christopher Corcoran
Xin Xu
Nan Laird

Funding

FAMILY BASED TESTS OF ASSOCIATION FOR COMPLEX DISEASESR01MH059532 · NIMH · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI LAIRD, NAN MCKENZIE · 1998 to 2010
$2.9M
NIMH NIH HHS MH59532NIMH NIH HHS R01 MH059532
6 · The paper itself

Abstract

backgroundFamily-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers.

resultsThe program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data.

conclusionThe general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models.

Indexed as

Chromosomes, Human, XGenes, X-LinkedGenetic Predisposition to DiseaseSoftwareGenetic LinkageGenetic MarkersHumansModels, GeneticQuantitative Trait LociGenetic Markers

Identifiers

PMID18096066
PMCPMC2234422

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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.