Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Methods for Disease-Genotype Association in Genetic Association
  Studies: Calculate P-values Using Exact Conditional Enumeration instead of
  Asymptotic Approximations   In genetic association studies, detecting disease-genotype associations is a
primary goal. For most diseases, the underlying genetic model is unknown, and
we study seven robust test statistics for monotone association. For a given
test statistic, there are many ways to calculate a p-value, but in genetic
association studies, calculations have predominantly been based on asymptotic
approximations or on simulated permutations. We show that when the number of
permutations tends to infinity, the permutation p-value approaches the exact
conditional enumeration p-value, and further that calculating the latter
p-value is much more efficient than performing simulated permutations. We then
answer two research questions. (i) Which of the test statistics under study are
the most powerful for monotone genetic models? (ii) Based on test size, power,
and computational considerations, should asymptotic approximations or exact
conditional enumeration be used for calculating p-values? We have studied
case-control sample sizes with 500-5000 cases and 500-15000 controls, and
significance levels from 5e-8 to 0.05, thus our results are applicable to
genetic association studies with only one genetic marker under study,
intermediate follow-up studies, and genome wide association studies. We find
that if all monotone genetic models are of interest, the best performance is
achieved for a test statistics based on the maximum over a range of
Cochrane-Armitage trend tests with different scores and for a constrained
likelihood ratio test. For significance levels below 0.05, asymptotic
approximations may give a test size up to 20 times the nominal level, and
should therefore be used with caution. Further, calculating p-values based on
exact conditional enumeration is a powerful, valid and computationally feasible
approach, and we advocate its use in genetic association studies.

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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga