ArticleEuropean journal of human genetics : EJHG2019
Exploring predictive biomarkers from clinical genome-wide association studies via multidimensional hierarchical mixture models.
Article in European journal of human genetics : EJHG, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation of VEGF +405C/G Polymorphism with Gastrointestinal Tract Cancers Risk: An Updated Meta-Analysis.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine.Drug metabolism and disposition: the biological fate of chemicals · 2023Review
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Authors and funding
10 authors.
Funding
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Abstract
Although the detection of predictive biomarkers is of particular importance for the development of accurate molecular diagnostics, conventional statistical analyses based on gene-by-treatment interaction tests lack sufficient statistical power for this purpose, especially in large-scale clinical genome-wide studies that require an adjustment for multiplicity of a huge number of tests. Here we demonstrate an alternative efficient multi-subgroup screening method using multidimensional hierarchical mixture models developed to overcome this issue, with application to stroke and breast cancer randomized clinical trials with genomic data. We show that estimated effect size distributions of single nucleotide polymorphisms (SNPs) associated with outcomes, which could provide clues for exploring predictive biomarkers, optimizing individualized treatments, and understanding biological mechanisms of diseases. Furthermore, using this method we detected three new SNPs that are associated with blood homocysteine levels, which are strongly associated with the risk of stroke. We also detected six new SNPs that are associated with progression-free survival in breast cancer patients.
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