The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16.
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
An
Ping
P
Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
Mukherjee
Odity
O
Chanda
Pritam
P
Yao
Li
L
Engelman
Corinne D
CD
Huang
Chien-Hsun
CH
Zheng
Tian
T
Kovac
Ilija P
IP
Dubé
Marie-Pierre
MP
Liang
Xueying
X
Li
Jia
J
de Andrade
Mariza
M
Culverhouse
Robert
R
Malzahn
Doerthe
D
Manning
Alisa K
AK
Clarke
Geraldine M
GM
Jung
Jeesun
J
Province
Michael A
MA
D000483:Alleles; D001185:Artificial Intelligence; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D017720:Molecular Epidemiology; D019683:Penetrance; D025341:Principal Component Analysis; D016016:Proportional Hazards Models; D018709:Statistics, Nonparametric