A Bayesian approach for applying Haseman-Elston methods

Seungtai Yoon, Young Ju Suh, Nancy Role Mendell, Qian K. Ye

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDXI variable as our response.

Original languageEnglish (US)
Article numberS39
JournalBMC Genetics
Volume6
Issue numberSUPPL.1
DOIs
StatePublished - Dec 30 2005

Fingerprint

Bayes Theorem
Microsatellite Repeats
Alcoholism

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

A Bayesian approach for applying Haseman-Elston methods. / Yoon, Seungtai; Suh, Young Ju; Mendell, Nancy Role; Ye, Qian K.

In: BMC Genetics, Vol. 6, No. SUPPL.1, S39, 30.12.2005.

Research output: Contribution to journalArticle

Yoon, Seungtai ; Suh, Young Ju ; Mendell, Nancy Role ; Ye, Qian K. / A Bayesian approach for applying Haseman-Elston methods. In: BMC Genetics. 2005 ; Vol. 6, No. SUPPL.1.
@article{dab0830036bf4c0385456ac59333901d,
title = "A Bayesian approach for applying Haseman-Elston methods",
abstract = "The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDXI variable as our response.",
author = "Seungtai Yoon and Suh, {Young Ju} and Mendell, {Nancy Role} and Ye, {Qian K.}",
year = "2005",
month = "12",
day = "30",
doi = "10.1186/1471-2156-6-S1-S39",
language = "English (US)",
volume = "6",
journal = "BMC Genetics",
issn = "1471-2156",
publisher = "BioMed Central",
number = "SUPPL.1",

}

TY - JOUR

T1 - A Bayesian approach for applying Haseman-Elston methods

AU - Yoon, Seungtai

AU - Suh, Young Ju

AU - Mendell, Nancy Role

AU - Ye, Qian K.

PY - 2005/12/30

Y1 - 2005/12/30

N2 - The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDXI variable as our response.

AB - The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDXI variable as our response.

UR - http://www.scopus.com/inward/record.url?scp=30344460861&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=30344460861&partnerID=8YFLogxK

U2 - 10.1186/1471-2156-6-S1-S39

DO - 10.1186/1471-2156-6-S1-S39

M3 - Article

C2 - 16451649

AN - SCOPUS:30344460861

VL - 6

JO - BMC Genetics

JF - BMC Genetics

SN - 1471-2156

IS - SUPPL.1

M1 - S39

ER -