Evidence map›Paper›PMID 19761592›Full record

ArticleBMC genetics2009

Regression-based approach for testing the association between multi-region haplotype configuration and complex trait.

Yanling Hu, Sinnwell Jason, Qishan Wang, Yuchun Pan, Xiangzhe Zhang, Hongbo Zhao, Changlong Li, Libin Sun

Abstract readComparative StudyValidation Study
In one paragraph

Article in BMC genetics, 2009. 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

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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

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
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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.

Yanling HuSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, PR China. ylhu0323@sjtu.edu.cn
Sinnwell Jason
Qishan Wang
Yuchun Pan
Xiangzhe Zhang
Hongbo Zhao
Changlong Li
Libin Sun

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is quite common that the genetic architecture of complex traits involves many genes and their interactions. Therefore, dealing with multiple unlinked genomic regions simultaneously is desirable.

resultsIn this paper we develop a regression-based approach to assess the interactions of haplotypes that belong to different unlinked regions, and we use score statistics to test the null hypothesis of non-genetic association. Additionally, multiple marker combinations at each unlinked region are considered. The multiple tests are settled via the minP approach. The P value of the "best" multi-region multi-marker configuration is corrected via Monte-Carlo simulations. Through simulation studies, we assess the performance of the proposed approach and demonstrate its validity and power in testing for haplotype interaction association.

conclusionOur simulations showed that, for binary trait without covariates, our proposed methods prove to be equal and even more powerful than htr and hapcc which are part of the FAMHAP program. Additionally, our model can be applied to a wider variety of traits and allow adjustment for other covariates. To test the validity, our methods are applied to analyze the association between four unlinked candidate genes and pig meat quality.

Indexed as

Genetic MarkersHaplotypesModels, GeneticRegression AnalysisAnimalsComputer SimulationGene FrequencyLinkage DisequilibriumMeatModels, StatisticalPolymorphism, Single NucleotideQuantitative Trait, HeritableSwineGenetic Markers

Identifiers

PMID19761592
PMCPMC2760580

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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.