Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Model Selection in Complex Linear Systems, as Illustrated in
  Genetic Association Studies   Motivated by examples from genetic association studies, this paper considers
the model selection problem in a general complex linear model system and in a
Bayesian framework. We discuss formulating model selection problems and
incorporating context-dependent {\it a priori} information through different
levels of prior specifications. We also derive analytic Bayes factors and their
approximations to facilitate model selection and discuss their theoretical and
computational properties. We demonstrate our Bayesian approach based on an
implemented Markov Chain Monte Carlo (MCMC) algorithm in simulations and a real
data application of mapping tissue-specific eQTLs. Our novel results on Bayes
factors provide a general framework to perform efficient model comparisons in
complex linear model systems.

28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn