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Title:
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FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals
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Author:
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Cattaert, Tom; Urrea Gales, Víctor; Naj, Adam C.; De Lobel, Lizzy; De Wit, Vanessa; Fu, Mao; Mahachie John, Jestinah M.; Shen, Haiqing; Calle, M. Luz; Ritchie, Marylyn D.; Edwards, Todd L.; Van Steen, Kristel
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Other authors:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Bioinformàtica i Estadística Mèdica |
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Notes:
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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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Subject(s):
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-Epidemiologia genètica
-Bioinformàtica |
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Rights:
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Aquest document està subjecte a aquesta llicència Creative Commons
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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Document type:
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Article
Article - Published version |
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