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CLC number: Q39

On-line Access: 2018-12-03

Received: 2018-03-14

Revision Accepted: 2018-07-12

Crosschecked: 2018-11-08

Cited: 0

Clicked: 3539

Citations:  Bibtex RefMan EndNote GB/T7714


Cheng-yin Ye


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Journal of Zhejiang University SCIENCE B 2018 Vol.19 No.12 P.935-947


An ensemble-based likelihood ratio approach for family-based genomic risk prediction

Author(s):  Hui An, Chang-shuai Wei, Oliver Wang, Da-hui Wang, Liang-wen Xu, Qing Lu, Cheng-yin Ye

Affiliation(s):  Department of Health Management, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China; more

Corresponding email(s):   yechengyin@hznu.edu.cn

Key Words:  Family-based study, Genetic risk prediction, High-dimensional data

Hui An, Chang-shuai Wei, Oliver Wang, Da-hui Wang, Liang-wen Xu, Qing Lu, Cheng-yin Ye. An ensemble-based likelihood ratio approach for family-based genomic risk prediction[J]. Journal of Zhejiang University Science B, 2018, 19(12): 935-947.

@article{title="An ensemble-based likelihood ratio approach for family-based genomic risk prediction",
author="Hui An, Chang-shuai Wei, Oliver Wang, Da-hui Wang, Liang-wen Xu, Qing Lu, Cheng-yin Ye",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T An ensemble-based likelihood ratio approach for family-based genomic risk prediction
%A Hui An
%A Chang-shuai Wei
%A Oliver Wang
%A Da-hui Wang
%A Liang-wen Xu
%A Qing Lu
%A Cheng-yin Ye
%J Journal of Zhejiang University SCIENCE B
%V 19
%N 12
%P 935-947
%@ 1673-1581
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1800162

T1 - An ensemble-based likelihood ratio approach for family-based genomic risk prediction
A1 - Hui An
A1 - Chang-shuai Wei
A1 - Oliver Wang
A1 - Da-hui Wang
A1 - Liang-wen Xu
A1 - Qing Lu
A1 - Cheng-yin Ye
J0 - Journal of Zhejiang University Science B
VL - 19
IS - 12
SP - 935
EP - 947
%@ 1673-1581
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1800162

Objective: As one of the most popular designs used in genetic research, family-based design has been well recognized for its advantages, such as robustness against population stratification and admixture. With vast amounts of genetic data collected from family-based studies, there is a great interest in studying the role of genetic markers from the aspect of risk prediction. This study aims to develop a new statistical approach for family-based risk prediction analysis with an improved prediction accuracy compared with existing methods based on family history. Methods: In this study, we propose an ensemble-based likelihood ratio (ELR) approach, Fam-ELR, for family-based genomic risk prediction. Fam-ELR incorporates a clustered receiver operating characteristic (ROC) curve method to consider correlations among family samples, and uses a computationally efficient tree-assembling procedure for variable selection and model building. Results: Through simulations, Fam-ELR shows its robustness in various underlying disease models and pedigree structures, and attains better performance than two existing family-based risk prediction methods. In a real-data application to a family-based genome-wide dataset of conduct disorder, Fam-ELR demonstrates its ability to integrate potential risk predictors and interactions into the model for improved accuracy, especially on a genome-wide level. Conclusions: By comparing existing approaches, such as genetic risk-score approach, Fam-ELR has the capacity of incorporating genetic variants with small or moderate marginal effects and their interactions into an improved risk prediction model. Therefore, it is a robust and useful approach for high-dimensional family-based risk prediction, especially on complex disease with unknown or less known disease etiology.




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[32]List of electronic supplementary materials

[33]Table S1 Significant interaction effects identified by logistic regression in the genome-wide prediction

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