Random Forests for Genetic Association Studies

被引：195

作者：

Goldstein, Benjamin A. ^{[1
]}

Polley, Eric C. ^{[2
]}

Briggs, Farren B. S. ^{[3
]}

机构：

[1] Stanford Univ, Dept Med, Quantitat Sci Unit, Stanford, CA 94305 USA

[2] NCI, Biometr Res Branch, NIH, Bethesda, MD 20892 USA

[3] Univ Calif Berkeley, Genet Epidemiol & Genom Lab, Berkeley, CA 94720 USA

来源：

STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY | 2011年 / 10卷 / 01期

基金：

美国国家卫生研究院;

关键词：

machine learning; SNP; genome wide association studies; GENOME-WIDE ASSOCIATION; VARIABLE IMPORTANCE; SELECTION; SNPS; BIAS;

D O I：

10.2202/1544-6115.1691

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：35

共 64 条

[1] A map of human genome variation from population-scale sequencing [J].

Altshuler, David ;

Durbin, Richard M. ;

Abecasis, Goncalo R. ;

Bentley, David R. ;

Chakravarti, Aravinda ;

Clark, Andrew G. ;

Collins, Francis S. ;

De la Vega, Francisco M. ;

Donnelly, Peter ;

Egholm, Michael ;

Flicek, Paul ;

Gabriel, Stacey B. ;

Gibbs, Richard A. ;

Knoppers, Bartha M. ;

Lander, Eric S. ;

Lehrach, Hans ;

Mardis, Elaine R. ;

McVean, Gil A. ;

Nickerson, DebbieA. ;

Peltonen, Leena ;

Schafer, Alan J. ;

Sherry, Stephen T. ;

Wang, Jun ;

Wilson, Richard K. ;

Gibbs, Richard A. ;

Deiros, David ;

Metzker, Mike ;

Muzny, Donna ;

Reid, Jeff ;

Wheeler, David ;

Wang, Jun ;

Li, Jingxiang ;

Jian, Min ;

Li, Guoqing ;

Li, Ruiqiang ;

Liang, Huiqing ;

Tian, Geng ;

Wang, Bo ;

Wang, Jian ;

Wang, Wei ;

Yang, Huanming ;

Zhang, Xiuqing ;

Zheng, Huisong ;

Lander, Eric S. ;

Altshuler, David L. ;

Ambrogio, Lauren ;

Bloom, Toby ;

Cibulskis, Kristian ;

Fennell, Tim J. ;

Gabriel, Stacey B. .

NATURE, 2010, 467 (7319) :1061-1073

[2] SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivation [J].

Blewitt, Marnie E. ;

Gendrel, Anne-Valerie ;

Pang, Zhenyi ;

Sparrow, Duncan B. ;

Whitelaw, Nadia ;

Craig, Jeffrey M. ;

Apedaile, Anwyn ;

Hilton, Douglas J. ;

Dunwoodie, Sally L. ;

Brockdorff, Neil ;

Kay, Graham F. ;

Whitelaw, Emma .

NATURE GENETICS, 2008, 40 (05) :663-669

[3]

Breiman L, 1996, ANN STAT, V24, P2350

[4] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[5]

Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350

[6]

BREIMAN L, 2010, RANDOM FORESTS

[7]

Breiman L., 1996, 460 U CAL DEP STAT

[8] Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12 566 individuals [J].

Briggs, Farren B. S. ;

Bartlett, Selena E. ;

Goldstein, Benjamin A. ;

Wang, Joanne ;

McCauley, Jacob L. ;

Zuvich, Rebecca L. ;

De Jager, Philip L. ;

Rioux, John D. ;

Ivinson, Adrian J. ;

Compston, Alastair ;

Hafler, David A. ;

Hauser, Stephen L. ;

Oksenberg, Jorge R. ;

Sawcer, Stephen J. ;

Pericak-Vance, Margaret A. ;

Haines, Jonathan L. ;

Barcellos, Lisa F. .

HUMAN MOLECULAR GENETICS, 2010, 19 (21) :4286-4295

[9] Variation Within DNA Repair Pathway Genes and Risk of Multiple Sclerosis [J].