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Random Forests for Genetic Association Studies

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  • Goldstein Benjamin A
  • Polley Eric C
  • Briggs Farren B. S.

Abstract

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

Suggested Citation

  • Goldstein Benjamin A & Polley Eric C & Briggs Farren B. S., 2011. "Random Forests for Genetic Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-34, July.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:32
    DOI: 10.2202/1544-6115.1691
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    References listed on IDEAS

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    1. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
    2. Tuglus Catherine & van der Laan Mark J., 2009. "Modified FDR Controlling Procedure for Multi-Stage Analyses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-17, February.
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    Cited by:

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    2. Florian Marcel Nuţă & Alina Cristina Nuţă & Cristina Gabriela Zamfir & Stefan-Mihai Petrea & Dan Munteanu & Dragos Sebastian Cristea, 2021. "National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO 2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data," Energies, MDPI, vol. 14(10), pages 1-23, May.
    3. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    4. Cihan Şahin, 2023. "Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(1), pages 29-40, January.
    5. Maria Angela Echeverry-Galvis & Jennifer K Peterson & Rajmonda Sulo-Caceres, 2014. "The Social Nestwork: Tree Structure Determines Nest Placement in Kenyan Weaverbird Colonies," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.
    6. Dinesh Reddy Vangumalli & Konstantinos Nikolopoulos & Konstantia Litsiou, 2019. "Clustering, Forecasting and Cluster Forecasting: using k-medoids, k-NNs and random forests for cluster selection," Working Papers 19016, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    7. Michel Fuino & Andrey Ugarte Montero & Joël Wagner, 2022. "On the drivers of potential customers' interest in long‐term care insurance: Evidence from Switzerland," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(3), pages 271-302, September.
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    9. Lauric A Ferrat & Marc Goodfellow & John R Terry, 2018. "Classifying dynamic transitions in high dimensional neural mass models: A random forest approach," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-27, March.
    10. Silke Janitza & Roman Hornung, 2018. "On the overestimation of random forest’s out-of-bag error," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-31, August.

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