SearcharxivSearch

arXiv · 1307.7536

Robust Methods for Disease-Genotype Association in Genetic Association Studies: Calculate P-values Using Exact Conditional Enumeration instead of Asymptotic Approximations

Abstract

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.

Explore related subjects

Keep this discovery

BibTeXRIS

Mette Langaas, Øyvind Bakke. 2013-07-29. Robust Methods for Disease-Genotype Association in Genetic Association Studies: Calculate P-values Using Exact Conditional Enumeration instead of Asymptotic Approximations. https://doi.org/10.1515/sagmb-2013-0084

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Surprise Reduction and Nullification in Bayesian and Inverse Bayesian Inference under Ambiguous Prediction-Error Attribution

In non-stationary environments, prediction errors may signal environmental change or transient outliers, and adaptive systems must track such changes without overreacting to outliers. We distinguish surprise reduction, which updates beliefs to fit observations, from surprise nullification, which weakens constraints imposed by the predictive structure, and formalize both within Bayesian and inverse Bayesian (BIB) inference. Belief and likelihood updates are derived from variational objectives sharing a nullification strength, determined endogenously by minimizing surprise under the candidate post-update predictive distribution. In the Gaussian case, nullification expands belief and likelihood variances by a common factor relative to standard Bayesian updating, leaving the ratio unchanged. BIB thus defers attribution of the prediction error, committing to neither latent-state change nor observation-process uncertainty. The nullification strength is carried over as a candidate and is maintained or released according to the predictive surprise of the next observation. In a mean estimation task with outliers and changepoints, no scanned parameter setting of a Sage-Husa-type adaptive Kalman filter, fixed-strength BIB variant, or belief-forgetting-only variant outperforms BIB in both changepoint tracking and post-outlier stability. An oracle-informed reduced Bayesian model tracks changepoints better but is less stable after outliers. Although BIB maintains no explicit hypotheses about changepoints or outliers, it generates event-dependent dynamics. The learning rate increases after changepoints, whereas after outliers, nullification is released, and this increase is suppressed. Deferring attribution and letting subsequent observations differentiate the responses may constitute a principle of adaptive inference in non-stationary environments.

stat.ME

Generalized Ridge Refitting for the Lasso and Prediction Improvement Bounds

We study a class of Lasso based estimators obtained by applying a quadratic correction on the Lasso equicorrelation set. The penalty matrix determines both the magnitude and geometry of the correction and contains, among other cases, the isotropic Lasso--Ridge correction, least squares refitting, Gram proportional interpolation between the Lasso and least squares, and coordinate specific penalties. We first derive a closed form representation and isolate the positive gain component of the resulting prediction improvement. We then control the remaining stochastic linear term in expectation by localizing the random signed equicorrelation model around a deterministic reference support. This yields a finite sample expectation bound that explicitly accounts for the randomness induced by Lasso model selection. The resulting decomposition provides a unified framework for understanding when Lasso based quadratic corrections can improve prediction.

stat.ME

Discretization in covariate-adaptive randomization: gains and losses

Covariate-adaptive randomization(CAR) is widely implemented in clinical trials to balance prognostic covariates across treatment arms. Continuous covariates are often discretized into strata in practice, yet their consequences are not clearly understood. This paper provides a comprehensive study of the impact of discretization on both the CAR design process and the inferential results thereafter. We establish the asymptotic properties of both imbalance measures and treatment effect estimators under discretized and non-discretized settings. Practical recommendations are given on when and how discretization should be employed. We show that discretization in design is generally recommended, as it enhances robustness against model misspecification. However, if the true model is known, the most efficient strategy is to balance covariates according to that model in the design. The theoretical results are corroborated by extensive simulation studies and an empirical application to a diabetes trial dataset. Together, the results clarify the gains and losses of discretization in CAR and pave the way for learning impact of discretization to other designs and beyond.

stat.ME