Analysis of North American Rheumatoid Arthritis Consortium data using a penalized logistic regression approach

Croiseau, Pascal; Cordell, Heather J.

doi:10.1186/1753-6561-3-s7-s61

Cited by 13 publications

(15 citation statements)

References 12 publications

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“…Incorporating information about multiple correlated genetic variants led to the identification of an SNP near the HLA-B gene that was not significant in single-SNP analyses. In contrast, Croiseau and Cordell [2009] observed no advantage of group LASSO over the standard trend test for the detection and localization of SNPs associated with RA.…”

Section: Resultsmentioning

confidence: 74%

“…For this reason, sets of possibly interesting SNPs were selected based on previous findings or by statistical methods. Sun et al [2009] and Croiseau and Cordell [2009] restricted their analyses to SNPs in regions on chromosomes 1, 6, and 9 with known susceptibility loci for RA . Similarly, D'Angelo et al [2009] focused on SNPs in RA candidate genes on chromosome 6.…”

Section: Specific Problems and Solutionsmentioning

confidence: 99%

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Machine learning in genome‐wide association studies

Szymczak

Biernacka

Cordell

et al. 2009

Genetic Epidemiology

141

122

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Recently, genome-wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single-nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify SNPs that influence disease risk jointly or in complex interactions. Experimental and simulated genome-wide SNP data provided by the Genetic Analysis Workshop 16 afforded an opportunity to analyze the applicability and benefit of several machine learning methods. Penalized regression, ensemble methods, and network analyses resulted in several new findings while known and simulated genetic risk variants were also identified. In conclusion, machine learning approaches are promising complements to standard single-and multi-SNP analysis methods for understanding the overall genetic architecture of complex human diseases. However, because they are not optimized for genome-wide SNP data, improved implementations and new variable selection procedures are required.

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Section: Resultsmentioning

confidence: 74%

Section: Specific Problems and Solutionsmentioning

confidence: 99%

Machine learning in genome‐wide association studies

Szymczak

Biernacka

Cordell

et al. 2009

Genetic Epidemiology

141

122

View full text Add to dashboard Cite

show abstract

“…Depending on the degree of penalisation, Lasso regression drives some coefficients exactly to zero, excluding them from the model, and thus performing variable selection. In the context of GWA studies, sparse generalised linear models, and specifically logistic regression, have been used to select genetic markers that are highly predictive of the disease status (Hoggart et al, 2008; Cantor et al, 2010; Wu et al, 2009; Croiseau and Cordell, 2009). …”

Section: Introductionmentioning

confidence: 99%

Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach

Vounou¹,

Nichols²,

Montana³

2010

NeuroImage

200

239

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There is growing interest in performing genome-wide searches for associations between genetic variants and brain imaging phenotypes. While much work has focused on single scalar valued summaries of brain phenotype, accounting for the richness of imaging data requires a brain-wide, genome-wide search. In particular, the standard approach based on mass-univariate linear modelling (MULM) does not account for the structured patterns of correlations present in each domain. In this work, we propose sparse Reduced Rank Regression (sRRR), a strategy for multivariate modelling of high-dimensional imaging responses (measurements taken over regions of interest or individual voxels) and genetic covariates (single nucleotide polymorphisms or copy number variations) that enforces sparsity in the regression coefficients. Such sparsity constraints ensure that the model performs simultaneous genotype and phenotype selection. Using simulation procedures that accurately reflect realistic human genetic variation and imaging correlations, we present detailed evaluations of the sRRR method in comparison with the more traditional MULM approach. In all settings considered, sRRR has better power to detect deleterious genetic variants compared to MULM. Important issues concerning model selection and connections to existing latent variable models are also discussed. This work shows that sRRR offers a promising alternative for detecting brain-wide, genome-wide associations.

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“…the SSU, SSUw and LKMR. This is not the first negative report on penalized regression for genetic association analysis; see Croiseau and Cordell (2009) for a case study and Martinez et al (2010) for disappointing performance of penalized regression in a different context. Note that in our simulations, the data-generating models were indeed sparse, favoring variable selection by Lasso while the random-effects assumption utilized by the SSU, SSUw and LKMR was violated; if the true models contained many more non-zero and small coefficients, the SSU, SSUw and LKMR methods would be expected to perform even better.…”

Section: Discussionmentioning

confidence: 94%

Multilocus association testing with penalized regression

Basu

Pan

Shen

et al. 2011

Genetic Epidemiology

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In multi-locus association analysis, since some markers may not be associated with a trait, it seems attractive to use penalized regression with the capability of automatic variable selection. On the other hand, in spite of a rapidly growing body of literature on penalized regression, most focus on variable selection and outcome prediction, for which penalized methods are generally more effective than their non-penalized counterparts. However, for statistical inference, i.e. hypothesis testing and interval estimation, it is less clear how penalized methods would perform, or even how to best apply them, largely due to lack of studies on this topic. In our motivating data for a cohort of kidney transplant recipients, it is of primary interest to assess whether a group of genetic variants are associated with a binary clinical outcome, acute rejection at 6 months. In this paper, we study some technical issues and alternative implementations of hypothesis testing in Lasso penalized logistic regression, and compare their performance with each other and with several existing global tests, some of which are specifically designed as variance component tests for high-dimensional data. The most interesting, and perhaps surprising, conclusion of this study is that, for low to moderately high-dimensional data, statistical tests based on Lasso penalized regression are not necessarily more powerful than some existing global tests. In addition, in penalized regression, rather than building a test based on a single selected “best” model, combining multiple tests, each of which is built on a candidate model, might be more promising.

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Analysis of North American Rheumatoid Arthritis Consortium data using a penalized logistic regression approach

Cited by 13 publications

References 12 publications

Machine learning in genome‐wide association studies

Machine learning in genome‐wide association studies

Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach

Multilocus association testing with penalized regression

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