Masao Ueki

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

BackgroundGenome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS) however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed.ResultsWe adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS) for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units) technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium) data.ConclusionsBased on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

Immunohistopathological analysis of frizzled-4-positive immature melanocytes from hair follicles of patients with Rhododenol-induced leukoderma

A recent study in July 2013 reported that repeated application of racemic RS-4-(4-hydroxyphenyl)-2-butanol (rhododendrol; trade name: Rhododenol [RD]), a melanin synthesis inhibitor used in topical skin-whitening cosmetics, induced cutaneous depigmentation. Approximately 16,000 consumers developed skin depigmentation (RD-induced leukoderma) on their face, neck, and upper limbs [1]. Mechanisms underlying this condition have been investigated by performing biochemical, cytological, and immunological studies. Then, not only cytotoxic effects on melanocytes but also subsequent immune reactions have come out to be contributing to the expression of RD-induced leukoderma [2]. Repigmentation of vitiligo vulgaris frequently occurs from hair follicles, indicating the presence of inactivemelanocytes that may induce repigmentation [3]. In 2002, Nishimura et al. identified melanocyte stem cells (MSCs) in the lower permanent portion of mouse hair follicles by using Dct-lacZ transgenic mice [4]. Thus, immature melanocytes such as MSCs may play a role in the repigmentation of vitiligo vulgaris. Repigmentation of RDinduced leukoderma sometimes occurs in the same manner as that of vitiligo vulgaris (Fig. 1). We histologically investigated the survival rate of immature melanocytes or MSCs in hair follicles of patients with RD-induced leukoderma. Skin biopsies were performed for patients (n =25) with RD-induced leukoderma who visited Yamagata University Hospital. Two samples, i.e., from affected (depigmented) area and non-depigmented area close to the affected area (perilesion), were obtained from each patient. Informed consent was obtained from each patient, and study protocols were approved by the Ethics Committee of the Yamagata University Faculty of Medicine. Skin samples lacking hair follicles were excluded from the study. Thus, the study included 10 and 9 samples from the affected and perilesional areas, respectively. To

Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods

We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin B6 responsiveness by selected phenotype variables. We analyzed the existing data from our intervention study with 17 persons. First, we focused on signs and biomarkers that have been identified as candidates for vitamin B6 responsiveness indicators. Second, we conducted hypothesis testing among these selected variables and their combinations. Finally, we further investigated the results by conducting cluster analyses with two different algorithms, affinity propagation and k-medoids. Statistically significant variables for vitamin B6 responsiveness, including combination of hypersensitivity to sound and clumsiness, and plasma glutamine level, were included. As an a priori variable, the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS) scores was also included. The affinity propagation analysis showed good classification of three potential vitamin B6-responsive persons with ASD. The k-medoids analysis also showed good classification. To our knowledge, this is the first study to attempt to identify subgroup of persons with ASD who show specific treatment responsiveness using selected phenotype variables. We applied machine learning methods to further investigate these variables’ ability to identify this subgroup of ASD, even when only a small sample size was available.