Correlation analysis of IL-11 polymorphisms and Hirschsprung disease subtype susceptibility in Southern Chinese Children

Background Hirschsprung disease (HSCR) is a hereditary defect, which is characterized by the absence of enteric ganglia and is frequently concurrent with Hirschsprung-associated enterocolitis (HAEC). However, the pathogenesis for HSCR is complicated and remains unclear. Recent studies have shown that pro-inflammatory cytokines such as interleukin-11 (IL-11) are involved in the enteric nervous system's progress. It was found that IL-11 SNPs (rs8104023 and rs4252546) are associated with HSCR in the Korean population waiting for replication in an independent cohort. This study evaluated the relationship between IL-11 and the susceptibility of patients to HSCR by performing subphenotype interaction examination, HAEC pre-/post-surgical patient-only association analysis, and independence testing. Methods In this study, a cohort consisting of children from Southern China, comprising 1470 cases and 1473 controls, was chosen to examine the relationship between two polymorphisms (rs8104023 and rs4252546 in IL-11) and susceptibility to HSCR by replication research, subphenotype association analysis, and independence testing. Results The results showed that IL-11 gene polymorphisms (rs8104023 and rs4252546) are not associated with the risk of HSCR in the Chinese population. The results of both short-segment and long-segment (S-HSCR and L-HSCR) surgery (3.34 ≤ OR ≤ 4.05, 0.02 ≤ P ≤ 0.04) showed that single nucleotide polymorphisms (SNP) rs8104023 is associated with susceptibility to HAEC. Conclusions This study explored the relationship between genetic polymorphisms and susceptibility to HAEC in HSCR subtypes for the first time. These findings should be replicated in a larger and multicentre study.

Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests

Widespread availability of rich educational databases facilitates the use of conditioning strategies to estimate causal effects with nonexperimental data. With dozens, hundreds, or more potential predictors, variable selection can be useful for practical reasons related to communicating results and for statistical reasons related to improving the efficiency of estimators. Background knowledge should take precedence in deciding which variables to retain. However, with many potential predictors, theory may be weak, such that functional form relationships are likely to be unknown. In this article, I propose a nonparametric method for data-driven variable selection based on permutation testing with conditional random forest variable importance. The algorithm automatically handles nonlinear relationships and interactions in its naive implementation. Through a series of Monte Carlo simulation studies and a case study with Early Childhood Longitudinal Study–K data, I find that the method performs well across a variety of scenarios where other methods fail.

Nonparametric independence testing via mutual information

Summary We propose a test of independence of two multivariate random vectors, given a sample from the underlying population. Our approach is based on the estimation of mutual information, whose decomposition into joint and marginal entropies facilitates the use of recently developed efficient entropy estimators derived from nearest neighbour distances. The proposed critical values may be obtained by simulation in the case where an approximation to one marginal is available or by permuting the data otherwise. This facilitates size guarantees, and we provide local power analyses, uniformly over classes of densities whose mutual information satisfies a lower bound. Our ideas may be extended to provide new goodness-of-fit tests for normal linear models based on assessing the independence of our vector of covariates and an appropriately defined notion of an error vector. The theory is supported by numerical studies on both simulated and real data.