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FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

Tom Cattaert, Victor Urrea, Adam C Naj, Lizzy De Lobel UGent, Vanessa De Wit, Mao Fu, Jestinah M Mahachie John, HaiQing Shen, M Luz Calle and Marylyn D Ritchie, et al. (2010) PLOS ONE. 5(4).
abstract
We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
OLD ORDER AMISH, GENOME-WIDE ASSOCIATION, GENE-ENVIRONMENT INTERACTIONS, BETA(3)-ADRENERGIC RECEPTOR GENE, QUANTITATIVE TRAIT LOCI, DROSOPHILA-MERCATORUM, ECOLOGICAL GENETICS, INSULIN-RESISTANCE, ABNORMAL-ABDOMEN, HUMAN-DISEASES
journal title
PLOS ONE
PLoS One
volume
5
issue
4
Web of Science type
Article
Web of Science id
000276952600021
JCR category
BIOLOGY
JCR impact factor
4.411 (2010)
JCR rank
12/84 (2010)
JCR quartile
1 (2010)
ISSN
1932-6203
DOI
10.1371/journal.pone.0010304
language
English
UGent publication?
yes
classification
A1
additional info
article no. e10304 (15 p.)
copyright statement
I have retained and own the full copyright for this publication
id
946644
handle
http://hdl.handle.net/1854/LU-946644
date created
2010-05-17 10:50:48
date last changed
2010-05-26 11:26:23
@article{946644,
  abstract     = {We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.},
  author       = {Cattaert, Tom and Urrea, Victor and Naj, Adam C and De Lobel, Lizzy and De Wit, Vanessa and Fu, Mao and John, Jestinah M Mahachie and Shen, HaiQing and Calle, M Luz and Ritchie, Marylyn D and Edwards, Todd L and Van Steen, Kristel},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {OLD ORDER AMISH,GENOME-WIDE ASSOCIATION,GENE-ENVIRONMENT INTERACTIONS,BETA(3)-ADRENERGIC RECEPTOR GENE,QUANTITATIVE TRAIT LOCI,DROSOPHILA-MERCATORUM,ECOLOGICAL GENETICS,INSULIN-RESISTANCE,ABNORMAL-ABDOMEN,HUMAN-DISEASES},
  language     = {eng},
  number       = {4},
  title        = {FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals},
  url          = {http://dx.doi.org/10.1371/journal.pone.0010304},
  volume       = {5},
  year         = {2010},
}

Chicago
Cattaert, Tom, Victor Urrea, Adam C Naj, Lizzy De Lobel, Vanessa De Wit, Mao Fu, Jestinah M Mahachie John, et al. 2010. “FAM-MDR: a Flexible Family-based Multifactor Dimensionality Reduction Technique to Detect Epistasis Using Related Individuals.” Plos One 5 (4).
APA
Cattaert, T., Urrea, V., Naj, A. C., De Lobel, L., De Wit, V., Fu, M., John, J. M. M., et al. (2010). FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLOS ONE, 5(4).
Vancouver
1.
Cattaert T, Urrea V, Naj AC, De Lobel L, De Wit V, Fu M, et al. FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLOS ONE. 2010;5(4).
MLA
Cattaert, Tom, Victor Urrea, Adam C Naj, et al. “FAM-MDR: a Flexible Family-based Multifactor Dimensionality Reduction Technique to Detect Epistasis Using Related Individuals.” PLOS ONE 5.4 (2010): n. pag. Print.