Data mining, neural nets, trees--problems 2 and 3 of Genetic Analysis Workshop 15.
Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.
Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität zu Lübeck, Ratzeburger Allee 160, Lübeck, Germany. ziegler@imbs.uni-luebeck.de
Andreas
A
Ziegler
Anita L
AL
DeStefano
Inke R
IR
König
Claire
C
Bardel
Dumitru
D
Brinza
Shelley
S
Bull
Zhaohui
Z
Cai
Beate
B
Glaser
Wei
W
Jiang
Kristine E
KE
Lee
Chuang Xing
CX
Li
Jing
J
Li
Xin
X
Li
Paul
P
Majoram
Yan
Y
Meng
Kristin K
KK
Nicodemus
Alexander
A
Platt
Daniel F
DF
Schwarz
Weilang
W
Shi
Yin Yao
YY
Shugart
Hans H
HH
Stassen
Yan V
YV
Sun
Sungho
S
Won
Wenyi
W
Wang
Grace
G
Wahba
Usumah A
UA
Zagaar
Zhenming
Z
Zhao
D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006801:Humans; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis