ArticleJournal of genetics2016
An efficient method to handle the 'large p, small n' problem for genomewide association studies using Haseman-Elston regression.
Article in Journal of genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Rank-based learning: a novel high-throughput algorithm resilient to missing data and effective for datasets with small sample size.Briefings in bioinformatics · 2025Article
- Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases.Frontiers in molecular biosciences · 2021Review
- Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review.Brain communications · 2021Review
- Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data.Genes · 2020Article
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2 authors.
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Abstract
The 'large p, small n' problem in genomewide association studies (GWAS) is an important subject in genetic studies. Many approaches have been proposed for this issue, but none of them successfully combine the Haseman-Elston (H-E) regression with sliding-window scan approaches in GWAS. In this article, we extended H-E regression to GWAS, and replaced original data with different measurements of phenotype of sib pairs. Meanwhile, we also applied hidden Markov model to infer identity by state. Using subsequent simulation studies, we found that it had higher statistical power than the corresponding single-marker association studies. The advantage of the H-E regression was also sufficient to capture about 48.01% of the quantitative trait locus (QTL). Meanwhile, the results show that the power decreases with the increase in the number of QTLs, and the power of H-E regression is sensitive to heritability.
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