scholarly journals Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data

2021 ◽  
Author(s):  
Dmitry Kobak ◽  
Yves Bernaerts ◽  
Marissa A. Weis ◽  
Federico Scala ◽  
Andreas S. Tolias ◽  
...  
Keyword(s):  
Multivariate Data ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank

Reduced rank regression in cointegrated models

2002 ◽  
Vol 106 (2) ◽  
pp. 203-216 ◽  
Author(s):  
T.W. Anderson
Keyword(s):  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank

scholarly journals Asymptotic properties of adaptive group Lasso for sparse reduced rank regression

Stat ◽  
2016 ◽  
Vol 5 (1) ◽  
pp. 251-261 ◽  
Author(s):  
Kejun He ◽  
Jianhua Z. Huang
Keyword(s):  
Asymptotic Properties ◽  
Group Lasso ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank

scholarly journals A regression framework to uncover pleiotropy in large-scale electronic health record data

2019 ◽  
Vol 26 (10) ◽  
pp. 1083-1090 ◽  
Author(s):  
Ruowang Li ◽  
Rui Duan ◽  
Rachel L Kember ◽  
Daniel J Rader ◽  
Scott M Damrauer ◽  
...  
Keyword(s):  
Large Scale ◽  
Pleiotropic Effects ◽  
Nucleotide Polymorphisms ◽  
Reduced Rank Regression ◽  
Electronic Health Record Data ◽  
Single Nucleotide ◽  
Rank Regression ◽  
Reduced Rank ◽  
Multiple Phenotypes ◽  
Regression Framework

Abstract Objective Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype–phenotype relationship. Although individual genotype–phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources. Materials and Methods In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework. Results Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods. Conclusion The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

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