TY - GEN
T1 - Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study
AU - Lee, Hyo Jung
AU - Lee, Jae Won
AU - Jin, Seohoon
AU - Yoo, Hee Jeong
AU - Park, Mira
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - In recent years, detecting interactions between different genes has become a hot topic, for better understanding multigenic, complex diseases. For population-based genome-wide association studies (GWAS), a number of methods to detect gene-gene interactions such as logistic regression, multifactor dimensionality reduction (MDR) and support vector machine (SVM), have been applied. Bayesian approaches such as BEAM (Bayesian marker partition model) and DASSO-MB (detection of association using Markov Blanket) have also been suggested. However, the studies for family-based GWAS have been limited. In this study, we developed a new Markov Blanket-based algorithm called MB-TDT to find gene-gene interactions for pedigree data. A transmission disequilibrium test statistic was used as an association measure and the incremental association a Markov Blanket (IAMB) algorithm was applied to find Markov Blanket. This proposed MB-TDT method can identify a minimal set of causal SNPs, associated with a specific disease, thus avoiding an exhaustive search. By conducting a simulation study to compare MB-TDT with current methods, we show its superior high power in many cases, and lower false positive rates, in others.
AB - In recent years, detecting interactions between different genes has become a hot topic, for better understanding multigenic, complex diseases. For population-based genome-wide association studies (GWAS), a number of methods to detect gene-gene interactions such as logistic regression, multifactor dimensionality reduction (MDR) and support vector machine (SVM), have been applied. Bayesian approaches such as BEAM (Bayesian marker partition model) and DASSO-MB (detection of association using Markov Blanket) have also been suggested. However, the studies for family-based GWAS have been limited. In this study, we developed a new Markov Blanket-based algorithm called MB-TDT to find gene-gene interactions for pedigree data. A transmission disequilibrium test statistic was used as an association measure and the incremental association a Markov Blanket (IAMB) algorithm was applied to find Markov Blanket. This proposed MB-TDT method can identify a minimal set of causal SNPs, associated with a specific disease, thus avoiding an exhaustive search. By conducting a simulation study to compare MB-TDT with current methods, we show its superior high power in many cases, and lower false positive rates, in others.
KW - Gene-gene interactions
KW - Genetic associations
KW - Markov-Blanket
KW - Transmission disequilibrium test
UR - http://www.scopus.com/inward/record.url?scp=85013231171&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2016.7822786
DO - 10.1109/BIBM.2016.7822786
M3 - Conference contribution
AN - SCOPUS:85013231171
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 1767
EP - 1770
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2016 through 18 December 2016
ER -