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A Unified Framework Integrating Parent-of-Origin Effects for Association Study

Overview of attention for article published in PLOS ONE, August 2013
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Title
A Unified Framework Integrating Parent-of-Origin Effects for Association Study
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0072208
Pubmed ID
Authors

Feifei Xiao, Jianzhong Ma, Christopher I. Amos

Abstract

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 33%
Student > Ph. D. Student 3 25%
Student > Master 2 17%
Student > Doctoral Student 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 42%
Biochemistry, Genetics and Molecular Biology 2 17%
Medicine and Dentistry 2 17%
Social Sciences 1 8%
Mathematics 1 8%
Other 0 0%
Unknown 1 8%