Evidence map›Paper›PMID 20233392›Full record

ArticleBMC bioinformatics2010

ProbABEL package for genome-wide association analysis of imputed data.

Yurii S Aulchenko, Maksim V Struchalin, Cornelia M van Duijn

Abstract read
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 272 papers, 48 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
272citing papers in PubMed, 48 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

272 citing papers in PubMed, 48 syntheses or guidelines pooled it.

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  12. Genome Wide Association Study Identifies theJournal of the American Heart Association · 2018
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212 more citing papers are in PubMed but not listed here.

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

3 authors.

Yurii S AulchenkoDepartment of Epidemiology, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands. i.aoultchenko@erasmusmc.nl
Maksim V Struchalin
Cornelia M van Duijn

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOver the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account.

resultsWe developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations.

conclusionsProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.

Indexed as

SoftwareGenome-Wide Association StudyGenotypeHumansModels, GeneticPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID20233392
PMCPMC2846909

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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.