Summary. We introduce a path following algorithm for L 1 -regularized generalized linear models. The L 1 -regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L 1 -norm of the coefficients, in a manner that is less greedy than forward selection-backward deletion. The generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor-corrector method of convex optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths; we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate the implementation with several simulated and real data sets.

We propose using a variant of logistic regression (LR) with (L)_(2)-regularization to fit gene-gene and gene-environment interaction models. Studies have shown that many common diseases are influenced by interaction of certain genes. LR models with quadratic penalization not only correctly characterizes the influential genes along with their interaction structures but also yields additional benefits in handling high-dimensional, discrete factors with a binary response. We illustrate the advantages of using an (L)_(2)-regularization scheme and compare its performance with that of "multifactor dimensionality reduction" and "FlexTree," 2 recent tools for identifying gene-gene interactions. Through simulated and real data sets, we demonstrate that our method outperforms other methods in the identification of the interaction structures as well as prediction accuracy. In addition, we validate the significance of the factors selected through bootstrap analyses.

Although averaging is a simple technique, it plays an important role in reducing variance. We use this essential property of averaging in regression of the DNA microarray data, which poses the challenge of having far more features than samples. In this paper, we introduce a two-step procedure that combines (1) hierarchical clustering and (2) Lasso. By averaging the genes within the clusters obtained from hierarchical clustering, we define supergenes and use them to fit regression models, thereby attaining concise interpretation and accuracy. Our methods are supported with theoretical justifications and demonstrated on simulated and real data sets.

Mutations in five PARK genes (SNCA, PARKIN, DJ-1, PINK1, and LRRK2) are well-established genetic causes of Parkinson disease (PD). Recently, G2385R substitution in LRRK2 has been determined as a susceptibility allele in Asian PD. The objective of this study is to determine the frequency of mutations in these PARK genes in a Korean early-onset Parkinson disease (EOPD) cohort. The authors sequenced 35 exons in SNCA, PARKIN, DJ-1, PINK1, and LRRK2 in 72 unrelated EOPD (age-at-onset ≤50) recruited from ten movement disorders clinics in South Korea. Gene dosage change of Neurogenetics

Preliminary results from this study suggest that an injection of Botox resulted in relatively satisfactory clinical effects, although there was only a short-term follow-up. It is suggested that the use of botulinum toxin type A for contouring of the lower face can be established as a simple, predictable, alternative facial contouring procedure without a prolonged recovery time.