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

Hui AN; Chang-shuai Wei; Oliver Wang; Da-hui Wang; Liang-wen XU; Qing LU; Cheng-yin YE

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

Author: Hui AN ¹ ; Chang-Shuai WEI ² ; Oliver WANG ³ ; Da-Hui WANG ¹ ; Liang-Wen XU ⁴ ; Qing LU ⁵ ; Cheng-Yin YE ¹
Author Information

1. Department of Health Management, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China.
2. Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.
3. HBI Solutions Inc, Palo Alto, CA 94301, USA.
4. Department of Preventive Medicine, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China.
5. Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA.
Publication Type:Journal Article
Keywords: Family-based study; Genetic risk prediction; High-dimensional data
MeSH: Area Under Curve; Computer Simulation; Conduct Disorder/physiopathology*; Family Health; Female; Genetic Markers; Genetic Predisposition to Disease; Genetic Variation; Genome, Human; Genome-Wide Association Study; Genomics; Humans; Likelihood Functions; Male; Models, Genetic; Odds Ratio; Pedigree; ROC Curve; Reproducibility of Results; Risk Factors
From: Journal of Zhejiang University. Science. B 2018;19(12):935-947
CountryChina
Language:English
Abstract: 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.