Principal interactions analysis for repeated measures data: application to gene-gene and gene-environment interactions.
Principal interactions analysis for repeated measures data: application to gene-gene and gene-environment interactions.
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DOI：
10.1002/sim.5315
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发表时间：
2012-09-28
影响因子：
2
通讯作者：
Chen, Jinbo
中科院分区：
文献类型：
--
作者：
Mukherjee, Bhramar;Ko, Yi-An;VanderWeele, Tyler;Roy, Anindya;Park, Sung Kyun;Chen, Jinbo
关键词：
Many existing cohorts with longitudinal data on environmental exposures, occupational history, lifestyle/behavioral characteristics and health outcomes have collected genetic data in recent years. In this paper, we consider the problem of modeling gene-gene, gene-environment interactions with repeated measures data on a quantitative trait. We review possibilities of using classical models proposed by Tukey (1949) and Mandel (1961) using the cell means of a two-way classification array for such data. Whereas these models are effective for detecting interactions in presence of main effects, they fail miserably if the interaction structure is misspecified. We explore a more robust class of interaction models that are based on a singular value decomposition of the cell means residual matrix after fitting the additive main effect terms. This class of additive main effects and multiplicative interaction (AMMI) models (Gollob, 1968) provide useful summaries for subject-specific and time-varying effects as represented in terms of their contribution to the leading eigenvalues of the interaction matrix. It also makes the interaction structure more amenable to geometric representation. We call this analysis “Principal Interactions Analysis” (PIA). While the paper primarily focusses on a cell-mean based analysis of repeated measures outcome, we also introduce resampling-based methods that appropriately recognize the unbalanced and longitudinal nature of the data instead of reducing the response to cell-means. The proposed methods are illustrated by using data from the Normative Aging Study, a longitudinal cohort study of Boston area veterans since 1963. We carry out simulation studies under an array of classical interaction models and common epistasis models to illustrate the properties of the PIA procedure in comparison to the classical alternatives.
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DOI：
10.1186/1297-9686-41-21
发表时间：
2009-01-30
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Meyer K
通讯作者：
Meyer K
DOI：
10.1111/j.1467-9868.2008.00671.x
发表时间：
2009-01-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Maity A;Carroll RJ;Mammen E;Chatterjee N
通讯作者：
Chatterjee N
影响因子：
3.7
作者：
JOHNSON, DE;GRAYBILL, FA
通讯作者：
GRAYBILL, FA
影响因子：
3
作者：
GOLLOB, HF
通讯作者：
GOLLOB, HF
影响因子：
2.7
作者：
GABRIEL, KR
通讯作者：
GABRIEL, KR