Bootstrap Bayesian analysis with applications to gene-environment interaction

Adina Crainiceanu, Kung Yee Liang, Ciprian M Crainiceanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a novel statistical model and inferential algorithm for gene environment interaction. Our methodology was motivated by and applied to identity by descent (IBD) sharing for sibling pairs affected by schizophrenia. Our analysis confirms some of the previous findings on the same data set, e.g. the estimated location of the disease gene and the existence of the interaction between the location of disease gene and environment. Our analysis also provides new insights by better accounting for overall variability in the data. We show that taking into account sampling variability may increase the length of posterior credible intervals for the true location of the disease gene by as much as 140%. Moreover, the posterior distribution is shown to be non-Gaussian, which more closely matches the data.

Original languageEnglish (US)
Title of host publication2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009
Pages651-656
Number of pages6
DOIs
StatePublished - 2009
Event2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009 - Guzelyurt, Cyprus
Duration: Sep 14 2009Sep 16 2009

Other

Other2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009
CountryCyprus
CityGuzelyurt
Period9/14/099/16/09

Fingerprint

Genes
Sampling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Crainiceanu, A., Liang, K. Y., & Crainiceanu, C. M. (2009). Bootstrap Bayesian analysis with applications to gene-environment interaction. In 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009 (pp. 651-656). [5291900] https://doi.org/10.1109/ISCIS.2009.5291900

Bootstrap Bayesian analysis with applications to gene-environment interaction. / Crainiceanu, Adina; Liang, Kung Yee; Crainiceanu, Ciprian M.

2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009. 2009. p. 651-656 5291900.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Crainiceanu, A, Liang, KY & Crainiceanu, CM 2009, Bootstrap Bayesian analysis with applications to gene-environment interaction. in 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009., 5291900, pp. 651-656, 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009, Guzelyurt, Cyprus, 9/14/09. https://doi.org/10.1109/ISCIS.2009.5291900
Crainiceanu A, Liang KY, Crainiceanu CM. Bootstrap Bayesian analysis with applications to gene-environment interaction. In 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009. 2009. p. 651-656. 5291900 https://doi.org/10.1109/ISCIS.2009.5291900
Crainiceanu, Adina ; Liang, Kung Yee ; Crainiceanu, Ciprian M. / Bootstrap Bayesian analysis with applications to gene-environment interaction. 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009. 2009. pp. 651-656
@inproceedings{cbccbb4162b443e5a0c1e58c47846e74,
title = "Bootstrap Bayesian analysis with applications to gene-environment interaction",
abstract = "We propose a novel statistical model and inferential algorithm for gene environment interaction. Our methodology was motivated by and applied to identity by descent (IBD) sharing for sibling pairs affected by schizophrenia. Our analysis confirms some of the previous findings on the same data set, e.g. the estimated location of the disease gene and the existence of the interaction between the location of disease gene and environment. Our analysis also provides new insights by better accounting for overall variability in the data. We show that taking into account sampling variability may increase the length of posterior credible intervals for the true location of the disease gene by as much as 140{\%}. Moreover, the posterior distribution is shown to be non-Gaussian, which more closely matches the data.",
author = "Adina Crainiceanu and Liang, {Kung Yee} and Crainiceanu, {Ciprian M}",
year = "2009",
doi = "10.1109/ISCIS.2009.5291900",
language = "English (US)",
isbn = "9781424450237",
pages = "651--656",
booktitle = "2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009",

}

TY - GEN

T1 - Bootstrap Bayesian analysis with applications to gene-environment interaction

AU - Crainiceanu, Adina

AU - Liang, Kung Yee

AU - Crainiceanu, Ciprian M

PY - 2009

Y1 - 2009

N2 - We propose a novel statistical model and inferential algorithm for gene environment interaction. Our methodology was motivated by and applied to identity by descent (IBD) sharing for sibling pairs affected by schizophrenia. Our analysis confirms some of the previous findings on the same data set, e.g. the estimated location of the disease gene and the existence of the interaction between the location of disease gene and environment. Our analysis also provides new insights by better accounting for overall variability in the data. We show that taking into account sampling variability may increase the length of posterior credible intervals for the true location of the disease gene by as much as 140%. Moreover, the posterior distribution is shown to be non-Gaussian, which more closely matches the data.

AB - We propose a novel statistical model and inferential algorithm for gene environment interaction. Our methodology was motivated by and applied to identity by descent (IBD) sharing for sibling pairs affected by schizophrenia. Our analysis confirms some of the previous findings on the same data set, e.g. the estimated location of the disease gene and the existence of the interaction between the location of disease gene and environment. Our analysis also provides new insights by better accounting for overall variability in the data. We show that taking into account sampling variability may increase the length of posterior credible intervals for the true location of the disease gene by as much as 140%. Moreover, the posterior distribution is shown to be non-Gaussian, which more closely matches the data.

UR - http://www.scopus.com/inward/record.url?scp=73949127069&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73949127069&partnerID=8YFLogxK

U2 - 10.1109/ISCIS.2009.5291900

DO - 10.1109/ISCIS.2009.5291900

M3 - Conference contribution

AN - SCOPUS:73949127069

SN - 9781424450237

SP - 651

EP - 656

BT - 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009

ER -