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In genomic studies, univariate analysis is commonly used to discover susceptible variants. It applies univariate regression for each variant and tests the significance of the regression coefficient or slope parameter. This strategy, however, may miss signals that are jointly detectable with other variants. Multivariate analysis is another popular approach, which tests grouped variants with a predefined group, e.g., based on a gene, pathway, or physical location. However, the power will be diminished if the modeling assumption is not suited to the data. Therefore, data-adaptive testing that relies on fewer modeling assumptions is preferable. Possible approaches include a data-adaptive test proposed by Ueki (2021), which applies to various data-adaptive regression models using a generalization of Yanai's generalized coefficient of determination. While several regression models are possible choices for the data-adaptive test, this paper focuses on the fused lasso that can count for the effect of adjacent variants and investigates its performance through comparison with other existing tests. Simulation studies demonstrate that the test using fused lasso has a high power compared to the existing tests including the univariate regression test, saturated regression test, SKAT (sequence kernel association test), burden test, SKAT-O (optimized sequence kernel association test), and the tests using lasso, ridge, and elastic net when assuming a similar effect of adjacent variants. [ABSTRACT FROM AUTHOR]

The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the ideal sample as the combination of the observed data and the missing/counterfactual data for regression models. The robustness of a causal inference can be quantified by the probability of a robust inference for internal validity in regression, i.e., the PIVR, which is the probability of rejecting the null hypothesis again for the ideal sample provided the same null hypothesis has been already rejected for the observed sample. Drawing on the relationship between the PIVR and the mean counterfactual outcomes, we formalize a conceptual framework of quantifying the robustness of a regression-based causal inference based on a joint distribution about the mean counterfactual outcomes, holding the observed sample fixed. Interpretatively, the PIVR is the statistical power of the null hypothesis significance testing that is thought to be built on the ideal sample. We demonstrate the conceptual framework of quantifying the robustness of a regression-based causal inference with an empirical example. [ABSTRACT FROM AUTHOR]