ArticleInternational journal of epidemiology2012
Correction for population stratification in random forest analysis.
Article in International journal of epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Towards fine-scale population stratification modeling based on kernel principal component analysis and random forest.Genes & genomics · 2021Article
- Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.BioData mining · 2021Article
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- Genomewide association analyses of fitness traits in captive-reared Chinook salmon: Applications in evaluating conservation strategies.Evolutionary applications · 2018Article
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- Genomic signatures amongEvolutionary applications · 2016Article
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- Regularized machine learning in the genetic prediction of complex traits.PLoS genetics · 2014Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundPopulation structure (PS), including population stratification and admixture, is a significant confounder in genome-wide association studies (GWAS), as it may produce spurious associations. Random forest (RF) has been increasingly applied in GWAS data analysis because of its advantage in analysing high dimensional genetic data. RF creates importance measures for single nucleotide polymorphisms (SNPs), which are helpful for feature selections. However, if PS is not appropriately corrected, RF tends to give high importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results.
methodsIn this study, the authors propose to correct for the confounding effect of PS by including the information of PS in RF analysis. The correction procedure starts by extracting the information of PS using EIGENSTRAT or multi-dimensional scaling clustering procedure from a large number of structure inference SNPs. Phenotype and genotypes adjusted by the information of PS are then used as the outcome and predictors in RF analysis.
resultsExtensive simulations indicate that the importance measure of the causal SNP is increased following the PS correction. By analysing a real dataset, the proposed correction removes the spurious association between the lactase gene and height.
conclusionThe authors propose a simple method to correct for PS in RF analysis on GWAS data. Further studies in real GWAS datasets are required to validate the robustness of the proposed approach.
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