Dataset Information

Multilocus association mapping using generalized ridge logistic regression.

ABSTRACT: BACKGROUND: In genome-wide association studies, it is widely accepted that multilocus methods are more powerful than testing single-nucleotide polymorphisms (SNPs) one at a time. Among statistical approaches considering many predictors simultaneously, scan statistics are an effective tool for detecting susceptibility genomic regions and mapping disease genes. In this study, inspired by the idea of scan statistics, we propose a novel sliding window-based method for identifying a parsimonious subset of contiguous SNPs that best predict disease status. RESULTS: Within each sliding window, we apply a forward model selection procedure using generalized ridge logistic regression for model fitness in each step. In power simulations, we compare the performance of our method with that of five other methods in current use. Averaging power over all the conditions considered, our method dominates the others. We also present two published datasets where our method is useful in causal SNP identification. CONCLUSIONS: Our method can automatically combine genetic information in local genomic regions and allow for linkage disequilibrium between SNPs. It can overcome some defects of the scan statistics approach and will be very promising in genome-wide case-control association studies.

SUBMITTER: Liu Z

PROVIDER: S-EPMC3224109 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multilocus association mapping using generalized ridge logistic regression.

Liu Zhe Z Shen Yuanyuan Y Ott Jurg J

BMC bioinformatics 20110929

<h4>Background</h4>In genome-wide association studies, it is widely accepted that multilocus methods are more powerful than testing single-nucleotide polymorphisms (SNPs) one at a time. Among statistical approaches considering many predictors simultaneously, scan statistics are an effective tool for detecting susceptibility genomic regions and mapping disease genes. In this study, inspired by the idea of scan statistics, we propose a novel sliding window-based method for identifying a parsimonio ...[more]

PMID: 21958005

Similar Datasets

Project description:BackgroundGeneralized anxiety disorder is characterized by excessive and uncontrollable worry about a variety of events. It is critical to ensure a pregnant mother's mental health in order to reduce pregnancy and birth-related problems. The major goal of current study was to identify the factors associated with generalized anxiety disorder among mothers attending perinatal services in the study area during COVID-19 using ordinal logistic regression.MethodsThe institution-based cross-sectional study was conducted from July 10th, 2020 to August 10th, 2020 at Kembata Tembaro zone, Southern Ethiopia. The current study included 423 mothers. The GAD-7 scale was used to assess the anxiety level among mothers. An Ordered logit model was used to identify the determinants of GAD. Brant test of the parallel line was utilized to check proportionality assumption. The statistical significance was determined using an adjusted proportional odd ratio with a 95%CI, and a p-value <5%. STATA software version 14 was used to analyze statistical data.ResultsOf all 423 mothers attending perinatal service during COVID-19; 134(31.7%), 171(40.4%), 85(20.1%), and 33 (7.8%) had non/minimal to severe generalized anxiety disorder respectively. The results of multivariable proportional odds model (POM) showed that the variables town residents [aPOR = 1.827; 95% CI:1.233-2.708], having alcohol habit [aPOR = 3.437, 95% CI = 1.397-8.454], having occupation [aPOR = 0.509, 95% CI: 0.303-0.857], being health care worker [aPOR = 0.117, 95% CI = 0.044-0.311], having chronic illness [aPOR = 7.685, 95% CI = 3.045-19.39], having family history of anxiety/mood disorder [aPOR = 7.839, 95% CI = 2.656-23.12], fear of contracting COVID-19 [aPOR = 1.704, 95% CI = 1.152-2.521], having moderate social support [aPOR = 0.648, 95% CI = 0.425-0.989], having strong social support [aPOR = 0.495, 95% CI = 0.272-0.901] were significantly associated with generalized anxiety disorder at 5% level of significance.ConclusionCurrent findings concluded that the prevalence of GAD among mothers attending perinatal service during COVID-19 was high. The covariates like being town resident, lower-income status, occupation status, having a chronic illness, having a positive family history of anxiety or mood disorder, perceived social support, and fear of the COVID-19 were significantly associated with generalized anxiety disorder among mothers. Mothers who visit perinatal services should be given special consideration to improve health care services and ensure their mental health.

Dataset Information

Multilocus association mapping using generalized ridge logistic regression.

Publications

Multilocus association mapping using generalized ridge logistic regression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets