FAM-MDR

A flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

Tom Cattaert, Víctor Urrea, Adam C. Naj, Lizzy de Lobel, Vanessa de Wit, Mao Fu, Jestinah M. Mahachie John, Haiqing Shen, M. Luz Calle, Marylyn Deriggi Ritchie, Todd L. Edwards, Kristel van Steen

Research output: Contribution to journalArticle

39 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere10304
JournalPloS one
Volume5
Issue number4
DOIs
StatePublished - Sep 14 2010

Fingerprint

Multifactor Dimensionality Reduction
epistasis
pedigree
Pedigree
methodology
noninsulin-dependent diabetes mellitus
Medical problems
Genes
gene interaction
Type 2 Diabetes Mellitus
testing
Testing
Amish
diabetes
Endophenotypes
phenotype

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Cattaert, Tom ; Urrea, Víctor ; Naj, Adam C. ; de Lobel, Lizzy ; de Wit, Vanessa ; Fu, Mao ; Mahachie John, Jestinah M. ; Shen, Haiqing ; Luz Calle, M. ; Ritchie, Marylyn Deriggi ; Edwards, Todd L. ; van Steen, Kristel. / FAM-MDR : A flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. In: PloS one. 2010 ; Vol. 5, No. 4.
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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.",
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Cattaert, T, Urrea, V, Naj, AC, de Lobel, L, de Wit, V, Fu, M, Mahachie John, JM, Shen, H, Luz Calle, M, Ritchie, MD, Edwards, TL & van Steen, K 2010, 'FAM-MDR: A flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals', PloS one, vol. 5, no. 4, e10304. https://doi.org/10.1371/journal.pone.0010304

FAM-MDR : A flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. / Cattaert, Tom; Urrea, Víctor; Naj, Adam C.; de Lobel, Lizzy; de Wit, Vanessa; Fu, Mao; Mahachie John, Jestinah M.; Shen, Haiqing; Luz Calle, M.; Ritchie, Marylyn Deriggi; Edwards, Todd L.; van Steen, Kristel.

In: PloS one, Vol. 5, No. 4, e10304, 14.09.2010.

Research output: Contribution to journalArticle

TY - JOUR

T1 - FAM-MDR

T2 - A flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

AU - Cattaert, Tom

AU - Urrea, Víctor

AU - Naj, Adam C.

AU - de Lobel, Lizzy

AU - de Wit, Vanessa

AU - Fu, Mao

AU - Mahachie John, Jestinah M.

AU - Shen, Haiqing

AU - Luz Calle, M.

AU - Ritchie, Marylyn Deriggi

AU - Edwards, Todd L.

AU - van Steen, Kristel

PY - 2010/9/14

Y1 - 2010/9/14

N2 - 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.

AB - 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.

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