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.

Maternal dietary patterns during pregnancy derived by reduced-rank regression and birth weight in the Chinese population

2020 ◽  
Vol 123 (10) ◽  
pp. 1176-1186
Author(s):  
Danmeng Liu ◽  
Yue Cheng ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
Pengfei Qu ◽  
...  
Keyword(s):  
Birth Weight ◽  
Pregnant Women ◽  
Dietary Patterns ◽  
Large Scale ◽  
Reduced Rank Regression ◽  
Cross Sectional Survey ◽  
Rank Regression ◽  
Reduced Rank ◽  
Animal Source Foods ◽  
Animal Source

AbstractFew studies have investigated the association between maternal dietary patterns (DP) during pregnancy, derived from reduced-rank regression (RRR), and fetal growth. This study aims to identify DP during pregnancy associated with macro- and micronutrient intakes, using the RRR method, and to examine their relationship with birth weight (BW). We used data of 7194 women from a large-scale cross-sectional survey in Northwest China. Dietary protein, carbohydrate, haem Fe density and the ratio of PUFA and MUFA:SFA were used as the intermediate variables in the RRR model to extract DP. Generalised estimating equation models were applied to evaluate the associations between DP and BW and related outcomes (including BW z-score, low birth weight (LBW) and small for gestational age (SGA)). Four DP during pregnancy were identified. Socio-demographically disadvantaged pregnant women were more likely to have lower BW and lower adherence to DP1 (high legumes, soyabean products, vegetables and animal-source foods, with relative low wheat and oils). Women with medium and high adherence to DP1 had significantly increased BW (medium 28·6 (95 % CI 7·1, 50·1); high 25·2 (95 % CI 2·7, 47·6)) and BW z-score and had significantly reduced risks of LBW and SGA. The associations were stronger among women with babies <3100 g. There is no association between other DP and outcomes. Higher adherence to the DP that was high in legumes, soyabean products, vegetables and animal-source foods was associated with improved BW in the Chinese pregnant women, particularly among those with disadvantageous socio-demographic conditions.

scholarly journals Large-Scale Sparse Regression for Multiple Responses with Applications to UK Biobank

2020 ◽  
Author(s):  
Junyang Qian ◽  
Yosuke Tanigawa ◽  
Ruilin Li ◽  
Robert Tibshirani ◽  
Manuel A. Rivas ◽  
...  
Keyword(s):  
Iterative Algorithm ◽  
Large Scale ◽  
Missing Values ◽  
Uk Biobank ◽  
Reduced Rank Regression ◽  
Sparse Regression ◽  
Regression Problem ◽  
Rank Regression ◽  
Reduced Rank ◽  
The Uk

AbstractIn high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the UK Biobank population-based cohort study, where we are faced with large-scale, ultrahigh-dimensional features, and have access to a large number of outcomes (phenotypes): lifestyle measures, biomarkers, and disease outcomes. We are hence led to fit sparse reduced-rank regression models, using computational strategies that allow us to scale to problems of this size. We use an iterative algorithm that alternates between solving the sparse regression problem and solving the reduced rank decomposition. For the sparse regression component, we propose a scalable iterative algorithm based on adaptive screening that leverages the sparsity assumption and enables us to focus on solving much smaller sub-problems. The full solution is reconstructed and tested via an optimality condition to make sure it is a valid solution for the original problem. We further extend the method to cope with practical issues such as the inclusion of confounding variables and imputation of missing values among the phenotypes. Experiments on both synthetic data and the UK Biobank data demonstrate the effectiveness of the method and the algorithm. We present multiSnpnet package, available at http://github.com/junyangq/multiSnpnet that works on top of PLINK2 files, which we anticipate to be a valuable tool for generating polygenic risk scores from human genetic studies.

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

scholarly journals A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 639-647 ◽  
Author(s):  
ELEAZAR ESKIN ◽  
RODED SHARAN ◽  
ERAN HALPERIN
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Novel Approach ◽  
Maximum Likelihood Criterion ◽  
The Common ◽  
Genomic Regions ◽  
High Computational Cost ◽  
Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

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