ArticleBMC proceedings2009
Predictive modeling in case-control single-nucleotide polymorphism studies in the presence of population stratification: a case study using Genetic Analysis Workshop 16 Problem 1 dataset.
Article in BMC proceedings, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Review
- Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods.Frontiers in plant science · 2021Article
- Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset.Journal of advanced research · 2019Article
- Risk estimation and risk prediction using machine-learning methods.Human genetics · 2012Review
- Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.Biology direct · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we apply the gradient-boosting machine predictive model to the rheumatoid arthritis data for predicting the case-control status. QQ-plot suggests severe population stratification. In univariate genome-wide association studies, a correction factor for ethnicity confounding can be derived. Here we propose a novel strategy to deal with population stratification in the context of multivariate predictive modeling. We address the problem by clustering the subjects on the axes of genetic variations, and building a predictive model separately in each cluster. This allows us to control ethnicity without explicitly including it in the model, which could marginalize the genetic signal we are trying to discover. Clustering not only leads to more similar ethnicity groups but also, as our results show, increases the accuracy of our model when compared to the non-clustered approach. The highest accuracy is achieved with the model adjusted for population stratification, when the genetic axes of variation are included among the set of predictors, although this may be misleading given the confounding effects.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.