Genome-wide association analysis by lasso penalized logistic regression

Wu, Tong Tong; Chen, Yi Fang; Hastie, Trevor; Sobel, Eric M.; Lange, Kenneth

doi:10.1093/bioinformatics/btp041

Cited by 689 publications

(640 citation statements)

References 27 publications

(26 reference statements)

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“…Among them, penalized regression methods [11][12][13][14] have drawn particular attention in GWAS. However, due to limited sample size of a single GWAS and polygenicity of a complex trait, many existing methods do not have enough power to uncover the remaining risk genetic variants.…”

Section: Introductionmentioning

confidence: 99%

A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies

F¹

2015

J Biom Biostat

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Over one thousand genome-wide association studies (GWAS) have been conducted in the past decade. Increasing biological evidence suggests the polygenic genetic architecture of complex traits: a complex trait is affected by many risk variants with small or moderate effects jointly. Meanwhile, recent progress in GWAS suggests that complex human traits may share common genetic bases, which is known as "pleiotropy". To further improve statistical power of detecting risk genetic variants in GWAS, we propose a penalized regression method to analyze the GWAS dataset of primary interest by incorporating information from other related GWAS. The proposed method does not require the individual-level of genotype and phenotype data from other related GWAS, making it useful when only summary statistics are available.method. Simulation studies showed that the proposed approach had satisfactory performance. We applied the proposed method to analyze a body mass index (BMI) GWAS dataset from a European American (EA) population and achieved improvement over single GWAS analysis.

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Section: Introductionmentioning

confidence: 99%

A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies

F¹

2015

J Biom Biostat

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“…Most investigators ignore the multivariate nature of the predictors and opt for simple linear regression one predictor at time. In addition to delivering these simpler, marginal results, Mendel can perform lasso penalized regression [111,112], a form of continuous model selection on all predictors simultaneously. The lasso penalty discourages predictors with low explanatory power from entering the model.…”

Section: Introductionmentioning

confidence: 99%

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach

Pikkuhookana

Sillanpää

2013

Heredity

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“…To rank SNPs and find SNP combinations, various methods are applied: Bayes factors [3], logistic regression [4,5], Hidden Markov Model (HMM) [6], Support Vector Machine (SVM), [7,8] and Random Forests (RF) [8-12]. Among the applied standard statistical methods and the machine learning-based methods, RF effectively ranks causal SNPs to detect SNP interactions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

Kang

2013

BMC Med Inform Decis Mak

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BackgroundDue to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.MethodsWe aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.ResultsA T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.ConclusionsWe propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.

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Genome-wide association analysis by lasso penalized logistic regression

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 689 publications

References 27 publications

A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies

A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

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