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Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies

Overview of attention for article published in BMC Genetics, January 2016
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Title
Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies
Published in
BMC Genetics, January 2016
DOI 10.1186/s12863-016-0336-y
Pubmed ID
Authors

Yumei Li, Yang Xiang

Abstract

Major advances in genotyping technology have generated high-density maps of single nucleotide polymorphism (SNP) markers that provide an unprecedented opportunity to identify genes underlying complex traits. Several family-based statistical methods showing robust population stratification have been developed to test the association between multiple markers and disease-susceptibility genes. Only a few methods focus on testing for maternally mediated genetic effects, which is a critical risk for birth defects. The present study focuses on testing for association and maternally mediated genetic effects with family-based methods. In the present study, we proposed a new method, max_PC integrating principal component analysis, to test association or maternally mediated genetic effects with case-parent data. The proposed method only uses the genotypes of case-parents triads and accommodates missing SNP data. Our results demonstrated that this method is powerful to test association or maternally mediated genetic effects and attractive because it provides a tool for testing the null hypothesis of no association and no maternally mediated genetic effects. Simulations with the permutation procedure as well as an application in the Crohn's disease study showed that the type I error rates of the proposed statistic were nominal with slightly higher power as compared to those of the max_Z(2) test. We conclude that the max_PC is a good approach to test association or maternally mediated genetic effects.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 50%
Student > Ph. D. Student 1 25%
Student > Master 1 25%
Readers by discipline Count As %
Medicine and Dentistry 2 50%
Social Sciences 1 25%
Agricultural and Biological Sciences 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2016.
All research outputs
#6,007,464
of 6,997,887 outputs
Outputs from BMC Genetics
#524
of 642 outputs
Outputs of similar age
#258,613
of 315,481 outputs
Outputs of similar age from BMC Genetics
#37
of 48 outputs
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