Dataset Information

A novel kernel for correcting size bias in the logistic kernel machine test with an application to rheumatoid arthritis.

ABSTRACT: The logistic kernel machine test (LKMT) is a testing procedure tailored towards high-dimensional genetic data. Its use in pathway analyses of case-control genome-wide association studies results from its computational efficiency and flexibility in incorporating additional information via the kernel. The kernel can be any positive definite function; unfortunately, its form strongly influences the test's power and bias. Most authors have recommended the use of a simple linear kernel. We demonstrate via a simulation that the probability of rejecting the null hypothesis of no association just by chance increases with the number of SNPs or genes in the pathway when applying a simple linear kernel.We propose a novel kernel that includes an appropriate standardization in order to protect against any inflation of false positive results. Moreover, our novel kernel contains information on gene membership of SNPs in the pathway.When applying the novel kernel to data from the North American Rheumatoid Arthritis Consortium, we find that even this basic genomic structure can improve the ability of the LKMT to identify meaningful associations. We also demonstrate that the standardization effectively eliminates problems of size bias.We recommend the use of our standardized kernel and urge caution when using non-adjusted kernels in the LKMT to conduct pathway analyses.

SUBMITTER: Freytag S

PROVIDER: S-EPMC3779069 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel kernel for correcting size bias in the logistic kernel machine test with an application to rheumatoid arthritis.

Freytag Saskia S Bickeböller Heike H Amos Christopher I CI Kneib Thomas T Schlather Martin M

Human heredity 20120101 2

<h4>Objectives</h4>The logistic kernel machine test (LKMT) is a testing procedure tailored towards high-dimensional genetic data. Its use in pathway analyses of case-control genome-wide association studies results from its computational efficiency and flexibility in incorporating additional information via the kernel. The kernel can be any positive definite function; unfortunately, its form strongly influences the test's power and bias. Most authors have recommended the use of a simple linear ke ...[more]

PMID: 23466369

Similar Datasets

Project description:The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the "treatment in the Rotterdam Early Arthritis CoHort" (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Finally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68-0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67-0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61-0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression's sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response.

Project description:IntroductionWe investigated whether rheumatoid arthritis (RA)-related autoantibodies were associated with abnormalities on pulmonary function tests (PFTs).MethodsWe studied RA serostatus and PFT abnormalities within a RA registry. RA serostatus was assessed by research assays for cyclic citrullinated peptide (CCP) and rheumatoid factor (RF). Outcomes were abnormalities on clinically indicated PFTs, including restriction, obstruction, and diffusion abnormality. Logistic regression was used to obtain ORs and 95% CIs for the PFT abnormalities by RA serologic phenotypes independent of lifestyle and RA characteristics.ResultsAmong 1272 analyzed subjects, mean age was 56.3 years (SD 14.1), 82.2% were female, and 69.5% were seropositive. There were 100 subjects with abnormal PFTs. Compared with seronegativity, seropositivity was associated with increased odds of any PFT abnormality (multivariable OR 2.29, 95% CI 1.30-4.03). When analyzing type of PFT abnormality, seropositivity was also associated with restriction, obstruction, and diffusion abnormalities; multivariable ORs were 2.48 (95% CI 1.26-4.87), 3.12 (95% CI 1.28-7.61), and 2.30 (95% CI 1.09-4.83), respectively. When analyzing by CCP and RF status, the associations were stronger for RF+ than for CCP+ (any PFT abnormality OR 1.99, 95% CI 1.21-3.27 for RF+ vs. RF-; OR 1.67, 95% CI 1.03-2.69 for CCP+ vs. CCP-) with a dose effect of higher RF titer increasing odds for each PFT abnormality (p for trend < 0.05).ConclusionsSeropositive RA patients had two-fold increased risk for abnormalities on PFTs performed for clinical indications compared with seronegative RA. Patients with seropositive RA, particularly those with high-titer RF positivity, may be more likely to have obstructive and restrictive abnormalities, independent of smoking.Key points• Due to the known excess pulmonary morbidity/mortality in RA, we studied the relationship of rheumatoid arthritis (RA)-related autoantibodies with pulmonary function test (PFT) abnormalities using a large RA registry.• We evaluated whether presence and levels of cyclic citrullinated peptide (CCP) and rheumatoid factor (RF) were associated with restriction, obstruction, and diffusion abnormalities on PFTs among 1272 subjects with RA.• Seropositivity was associated with two-fold increased risk for any PFT abnormality, independent of confounders including smoking. Higher titers of RF conferred greatest risk for all PFT outcomes: obstruction, restriction, and diffusion abnormality.• These results provide evidence that patients with RA should be closely monitored for pulmonary involvement, particularly those with high-titer RF seropositivity.

Dataset Information

A novel kernel for correcting size bias in the logistic kernel machine test with an application to rheumatoid arthritis.

Publications

A novel kernel for correcting size bias in the logistic kernel machine test with an application to rheumatoid arthritis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets