ArticleBMC bioinformatics2011
Multilocus association mapping using generalized ridge logistic regression.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comorbid insomnia among breast cancer survivors and its prediction using machine learning: a nationwide study in Japan.Japanese journal of clinical oncology · 2022Article
- Enlarged Lumen Volume of Proximal Aortic Segment and Acute Type B Aortic Dissection: A Computer Fluid Dynamics Study of Ideal Aortic Models.International journal of general medicine · 2022Article
- DAPT score: predictive model of dual-antiplatelet therapy for acute cerebral infarction.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Article
- Maximal Segmental Score Method for Localizing Recessive Disease Variants Based on Sequence Data.Frontiers in genetics · 2020Article
- Article
- "Noisy beets": impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris.Plant methods · 2016Article
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3 authors.
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Abstract
backgroundIn 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.
resultsWithin 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.
conclusionsOur 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.
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