Performance of random forests and logic regression methods using mini-exome sequence data

Performance of random forests and logic regression methods using mini-exome sequence data

Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, a...

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Bibliographic Details
Main Authors: Kim, Yoonhee, Li, Qing, Cropp, Cheryl D, Sung, Heejong, Cai, Juanliang, Simpson, Claire L, Perry, Brian, Dasgupta, Abhijit, Malley, James D, Wilson, Alexander F, Bailey-Wilson, Joan E
Format: Online
Language:English
Published: BioMed Central 2011
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287827/
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Summary:Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.

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