The University of Osaka · Medicine
Professor Tatsuhiko Naito's research lab specializes in computational genetics and bioinformatics, focusing on advanced statistical and deep learning methods for genomic data analysis. The lab develops innovative algorithms for HLA imputation and genotype imputation, with particular emphasis on improving accuracy for rare and low-frequency alleles across diverse populations. Their work addresses challenges in fine-mapping disease-associated variants in complex genomic regions such as the major histocompatibility complex (MHC), contributing significantly to the understanding of genetic predispositions to autoimmune and neurological diseases. The lab also pioneers machine learning approaches for predicting the functional impact of genetic variants, especially those affecting splicing mechanisms.
Figures are computed from collected data and may differ slightly.
Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accurac
Variations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of v
This study demonstrates the genome-wide evidence for a genetic predisposition to PA susceptibility in the cross-ancestry cohorts and its significant contribution to the genetic background of hypertension. The strongest association with the <i>WNT2B</i> variants reinforces the implication of the Wnt/β-catenin pathway in the PA pathogenesis.
Our study highlights the shared and distinct genetic features of the MHC region in patients with PD across ethnicities. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing-related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence-based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Pa
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibili
Our 2-sample Mendelian randomization study revealed that PA had increased risks of CAD, CHF, and stroke. These findings highlight that early and active screening of PA is critical to prevent future cardiovascular events.
Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an an
Our results suggest that the efficacy of zonisamide in PD patients is associated with glutamate-related synaptic modulation and p53-mediated dopaminergic neural loss. Their transcriptomic differences could be captured before treatment, which would lead to the realisation of future personalised treatment.
Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.
Abstract Conventional HLA imputation methods drop their performance for infrequent alleles, which reduces reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed DEEP * HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels ( n = 1,118 and 5,112), DEEP * HLA achieved the highest accuracies in both datasets (0.987 and 0.976) especially for low-frequency and rar
Mosaic chromosomal alterations (mCAs) in blood, a form of clonal hematopoiesis, have been linked to various diseases, but their role in Alzheimer's disease (AD) remains unclear. We analyzed blood whole-genome sequencing (WGS) data from 24,049 individuals in the Alzheimer's Disease Sequencing Project and found that autosomal mCAs were significantly associated with increased AD risk (odds ratio = 1.27; <i>P</i> = 1.3 × 10<sup>-5</sup>). This association varied by ancestry, mCA subtype, APOE ε4 all
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