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A Model Selection Approach for Expression Quantitative Trait Loci (eQTL) Mapping
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DOI：10.1534/genetics.110.122796.png)
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Abstract
En 中文
Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expressionQTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.
Keywords:
LIPOPROTEIN CHOLESTEROL LEVELS
STATISTICAL-METHODS
GENE-EXPRESSION
SUSCEPTIBILITY
INTERCROSS
129S1/SVIMJ
CAST/EI
IDENTIFICATION
FRAMEWORK
C57BL/6J
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