Biology subjects

Obara, T.

Publications and source records attributed to Obara, T..

4 recordsLinked to original sources

bioRxiv2023

A fine-scale genetic map of the Japanese population

Genetic maps are fundamental resources for linkage and association studies. A fine-scale genetic map can be constructed by inferring historical recombination events from the genome-wide structure of linkage disequilibrium--a non-random association of alleles among loci--by using population-scale sequencing data. We constructed a fine-scale genetic map and identified recombination hotspots from 10,092,573 bi-allelic high-quality autosomal markers segregating among 150 unrelated Japanese individuals. These individuals genotypes were determined by high-coverage (30x) whole-genome sequencing, and the genotype quality was carefully controlled by using their parents and offsprings genotypes. The pedigree information was also utilized for haplotype phasing. The resulting genome-wide recombination rate profiles were concordant with those of the HapMap worldwide population on a broad scale, and the resolution was much improved. We identified 9487 recombination hotspots and confirmed the enrichment of previously known motifs in the hotspots. Moreover, we demonstrated that the Japanese genetic map improved the haplotype phasing and genotype imputation accuracy for the Japanese population. The construction of a population-specific genetic map will help make genetics research more accurate.

genetics↗

bioRxiv2022

Deep embedded clustering by relevant scales and genome-wide association study in autism

The etiology of autism spectrum disorders (ASD) remains unclear. Stratifying patients with ASD may help to identify genetically homogeneous subgroups. Using a deep embedded clustering algorithm, we conducted cluster analyses of Simons Foundation Powering Autism Research for Knowledge (SPARK) datasets and performed genome-wide association studies (GWAS) of the clusters. We observed no significant associations in the conventional GWAS comparing all patients to all controls. However, in the GWAS, comparing patients divided into clusters with similar phenotypes to controls (cluster-based GWAS), we identified 90 chromosomal loci that satisfied the P < 5.0 x 10-8, several of which were located within or near previously reported candidate genes for ASD. Our findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies.

genomics↗

bioRxiv2019

Clustering by phenotype and genome-wide association study in autism

BackgroundAutism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability. MethodsWe conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a dataset of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC dataset of 712 probands and 354 controls in the replication stage. ResultsIn the preliminary study, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P<5.0x10-8. Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs. controls in the replication cohort. ConclusionsThese findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts.

neuroscience↗

bioRxiv2019

Management of family relationship informationfor a three-generation cohort study

A system for inputting and storing family information, named \"BirThree Enrollment,\" was developed to promote a birth and three-generation cohort study (BirThree Cohort Study), and this system was operated successfully. In the study, it was necessary to satisfy many operational demands. Input information is overwritten and changed continuously. Complex kinship information must be quickly and accurately input and corrected, and information on those families not yet recruited must be retrieved. For these purposes, many devices are needed, from an input interface to the internal data structure. In the field of genetic statistics, a simple standard expressive form is used for describing family structure. This form has sufficient information for genetics; however, we developed this form further for our purposes in conducting the BirThree Cohort Study. To provide information about family roles as required in the BirThree Cohort Study, we expanded the data structure, and constructed the system that is able to be used for the daily operation. In our system, family pedigree information is stored along with initial clinical information, and enabled the input of all self-reported information to the data base. Operators are able to input this family information before the day is out. As a result, when recruitment is completed, family information will be completed concurrently. Therefore, it is possible to immediately know a certain persons family structure. By using our system, data correction was improved dramatically, and the system was operated successfully. This study is the first report of the method for storing three generations of family data.

epidemiology↗