Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study

Hyo Jung Lee, Jae Won Lee, Seohoon Jin, Hee Jeong Yoo, Mira Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
EditorsKevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1767-1770
Number of pages4
ISBN (Electronic)9781509016105
DOIs
StatePublished - 17 Jan 2017
Event2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
Duration: 15 Dec 201618 Dec 2016

Other

Other2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
CountryChina
CityShenzhen
Period15/12/1618/12/16

Fingerprint

Genes
Association reactions
Genome-Wide Association Study
Multifactor Dimensionality Reduction
Bayes Theorem
Pedigree
Single Nucleotide Polymorphism
Logistic Models
Support vector machines
Logistics
Statistics
Population

Keywords

  • Gene-gene interactions
  • Genetic associations
  • Markov-Blanket
  • Transmission disequilibrium test

Cite this

Lee, H. J., Lee, J. W., Jin, S., Yoo, H. J., & Park, M. (2017). Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, ... G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1767-1770). [7822786] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822786
Lee, Hyo Jung ; Lee, Jae Won ; Jin, Seohoon ; Yoo, Hee Jeong ; Park, Mira. / Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1767-1770
@inproceedings{9a9f6b7d86164d2996ca3ae843142f1f,
title = "Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study",
abstract = "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.",
keywords = "Gene-gene interactions, Genetic associations, Markov-Blanket, Transmission disequilibrium test",
author = "Lee, {Hyo Jung} and Lee, {Jae Won} and Seohoon Jin and Yoo, {Hee Jeong} and Mira Park",
year = "2017",
month = "1",
day = "17",
doi = "10.1109/BIBM.2016.7822786",
language = "English",
pages = "1767--1770",
editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, {Xiaohua Tony} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",
booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Lee, HJ, Lee, JW, Jin, S, Yoo, HJ & Park, M 2017, Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822786, Institute of Electrical and Electronics Engineers Inc., pp. 1767-1770, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822786

Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study. / Lee, Hyo Jung; Lee, Jae Won; Jin, Seohoon; Yoo, Hee Jeong; Park, Mira.

Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1767-1770 7822786.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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

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

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.

ER -

Lee HJ, Lee JW, Jin S, Yoo HJ, Park M. Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1767-1770. 7822786 https://doi.org/10.1109/BIBM.2016.7822786