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Estimating association of CNVs using penalized regression with Lasso and weighted fusion penalties

Abstract Number:

3306 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Yaqin Si (1), Wenbin Lu (1), Albert Tucci (1), Hui Wang (2), Yuhuan Cheng (1), Li-San Wang (2), Gerard Schellenberger (2), Wan-Ping Lee (2), Jung-Ying Tzeng (1)

Institutions:

(1) North Carolina State University, NC, (2) Perelman School of Medicine, University of Pennsylvania, PA

Co-Author(s):

Wenbin Lu  
North Carolina State University
Albert Tucci  
North Carolina State University
Hui Wang  
Perelman School of Medicine, University of Pennsylvania
Yuhuan Cheng  
North Carolina State University
Li-San Wang  
Perelman School of Medicine, University of Pennsylvania
Gerard Schellenberger  
Perelman School of Medicine, University of Pennsylvania
Wan-Ping Lee  
Perelman School of Medicine, University of Pennsylvania
Jung-Ying Tzeng  
North Carolina State University

First Author:

Yaqin Si  
North Carolina State University

Presenting Author:

Yaqin Si  
N/A

Abstract Text:

CNVs are DNA gains or losses involving ≥50 base pairs. Estimating CNV association effects requires considering a few factors, e.g., 1) variations in CNV dosage and length need to be accounted for; and 2) all CNVs in a genomic region should be jointly assessed. Here we propose a penalized regression model for CNV association analysis. We model an individual's CNVs as a piecewise constant curve to naturally capture CNV length and dosage. To jointly model all CNVs in a genomic region, we use Lasso penalty to select CNVs associated with the outcome and integrate a weighted fusion penalty to encourage similar effects of adjacent CNVs when supported by the data. Our simulations show that the proposed model can more effectively identify causal CNVs without introducing additional false positives compared to the baseline methods (Lasso and gBridge); and yield more precise effect size estimation in different simulation settings. In the real data application to identify CNVs associated with Alzheimer's Disease (AD), the CNVs identified by our methods overlap genes that are significantly enriched in pathways related to neuron structure and neuron function and yield higher predictive accuracy.

Keywords:

Penalized Regression|Association|Weighted Fusion|Lasso|Effect estimation|Copy number variants

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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