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The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35–74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women’s health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

Background: Various disease prediction models have been developed, capitalizing on the wide use of electronic health records, but environmental factors that are important in the development of noncommunicable diseases are rarely included in the prediction models. Hypertensive disorders of pregnancy are leading causes of maternal morbidity and mortality and are known to cause several serious complications later in life. Objective: This study aims to develop early hypertensive disorders of pregnancy prediction models using comprehensive environmental factors based on self-report questionnaires in early pregnancy. Study Design: We developed machine learning and artificial intelligence models for the early prediction of hypertensive disorders of pregnancy using early pregnancy data from approximately 23,000 pregnancies in the Tohoku Medical Megabank Birth and Three Generation Cohort Study. We clarified the important features for prediction based on regression coefficients or Gini coefficients of the interpretable artificial intelligence models (i.e., logistic regression, random forest and XGBoost models) among our developed models. Results: The performance of the early hypertensive disorders of pregnancy prediction models reached an area under the receiver operating characteristic curve of 0.93, demonstrating that the early hypertensive disorders of pregnancy prediction models developed in this study retain sufficient performance in hypertensive disorders of pregnancy prediction. Among the early prediction models, the best performing model was based on self-reported questionnaire data in early pregnancy (mean of 20.2 gestational weeks at filling) which consist of comprehensive lifestyles. The interpretation of the models reveals that both eating habits were dominantly important for prediction. Conclusion: We have developed high-performance models for early hypertensive disorders of pregnancy prediction using large-scale cohort data from the Tohoku Medical Megabank project. Our study clearly revealed that the use of comprehensive lifestyles from self-report questionnaires led us to predict hypertensive disorders of pregnancy risk at the early stages of pregnancy, which will aid early intervention to reduce the risk of hypertensive disorders of pregnancy.

Whether all obesity-related variants contribute to the onset of obesity or one or a few variants cause obesity in genetically heterogeneous populations remains obscure. Here, we investigated the genetic architecture of obesity by clustering the Japanese and British populations with obesity using obesity-related factors. In Step-1, we conducted a genome-wide association study (GWAS) with body mass index (BMI) as the outcome for eligible participants. In Step-2, we assigned participants with obesity (BMI ≥25 kg/m2) to five clusters based on obesity-related factors. Subsequently, participants from each cluster and those with a BMI

Abstract Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection.

Abstract Background Low birth weight (LBW) is a leading cause of neonatal morbidity and mortality, and increases various disease risks across life stages. Prediction models of LBW have been developed before, but have limitations including small sample sizes, absence of genetic factors and no stratification of neonate into preterm and term birth groups. In this study, we challenged the development of early prediction models of LBW based on environmental and genetic factors in preterm and term birth groups, and clarified influential variables for LBW prediction. Methods We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birth groups, we trained AI-based models using genetic and environmental factors of lifestyles. We then clarified influential environmental and genetic factors for predicting LBW in the term and preterm groups. Results We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively. We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related to toll-like receptor regulations and infection reactions are influential genetic factors for prediction in the preterm LBW model. Conclusions We developed precise early prediction models of LBW based on lifestyle factors in the term birth group and genetic factors in the preterm birth group. Because of its accuracy and generalisability, our prediction model could contribute to risk assessment of LBW in the early stage of pregnancy and control LBW risk in the term birth group. Our prediction model could also contribute to precise prediction of LBW based on genetic factors in the preterm birth group. We then identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which are major targets for understanding the LBW to address serious burdens on newborns' health throughout life.

BackgroundPeriodic fever, aphthous stomatitis, pharyngitis, and cervical adenitis (PFAPA syndrome), and Kawasaki disease (KD) are both considered to be disorders of the innate immune system, and the potential role of inflammasome activation in the immunopathogenesis of both diseases has been previously described.Case presentationHerein, we report the clinical courses of three patients who presented a rare combination of PFAPA syndrome and KD. Two patients who presented KD later developed the PFAPA syndrome, of whom one developed recurrent KD 2 years after the initial diagnosis. The third patient developed KD one year after the onset of PFAPA syndrome. The presence of both of these conditions within individual patients, combined with the knowledge that inflammasome activation is involved in both PFAPA syndrome and KD, suggests a shared background of inflammatory dysregulation. To elucidate the mechanism underlying shared inflammatory dysregulation, we investigated the roles of Nod-like receptors (NLRs) and their downstream inflammasome-related genes. All the patients had a frameshift variant in CARD8 (CARD8-FS). A previous study demonstrated a higher frequency of CARD8-FS, whose product loses CARD8 activity and activates the NLRP3 inflammasome, in patients with the PFAPA syndrome. Additionally, the NLRP3 inflammasome is known to be activated in patients with KD. Together, these results suggest that the CARD8-FS variant may also be essential in KD pathogenesis. As such, we analyzed the CARD8 variants among patients with KD. However, we found no difference in the variant frequency between patients with KD and the general Japanese population.ConclusionsWe report the clinical courses of three patients with a rare combination of PFAPA syndrome and KD. All the patients had the CARD8-FS variant. However, we could not find a difference in the variant frequency between patients with KD and the general Japanese population. As the frequency of KD is much higher than that of PFAPA among Japanese patients, and the cause of KD is multifactorial, it is possible that only a small portion of patients with KD harbor CARD8-FS as a causative gene.

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Long-read sequencing on activated T-cells from a sample of 333 Japanese individuals (representing 111 parent-offspring trios) provides a useful reference dataset of structural variation in the Japanese population.

Sexual development disorders: Variant prevalence in the Japanese population Researchers in Japan have reported the frequency of mutations related to atypical sexual development in the Japanese population. A team led by Junichi Sugawara of Tohoku University, Sendai, analyzed whole-genome data collected by the Tohoku Medical Megabank Organization from 8380 healthy Japanese people. They screened the genomes for variations in 32 genes linked with central hypogonadism (CHG) and disorders of sex development (DSD), and used bioinformatics to predict the effect of the variants. This revealed 91 potentially pathogenic variants in 25 of the genes, including 28 new variants. The team also estimated the frequency of unaffected carriers of these variants. The analysis provides an overview of the prevalence of these variants in the general Japanese population and also offers valuable data for genetic diagnosis and counseling related to DSD and CHG.

IntroductionPerinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV).MethodsNine HRV indicators (features) and sleep–wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep–wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated—shallow sleep, deep sleep, and the two types of wake conditions—was also tested.Results and DiscussionIn the test for predicting three types of sleep–wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82–0.88) and accuracy (0.78–0.81). The test using four types of sleep–wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep–wake conditions. Among the seven features, “the number of interval differences of successive RR intervals greater than 50 ms (NN50)” and “the proportion dividing NN50 by the total number of RR intervals (pNN50)” were useful to predict sleep–wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.

Background: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study was launched in 2013 to evaluate the complex interactions of genetic and environmental factors in multifactorial diseases. The present study describes the maternal baseline profile and perinatal data of participating mothers and infants. Methods: Expectant mothers living in Miyagi Prefecture were recruited from obstetric facilities or affiliated centers between 2013 and 2017. Three sets of self-administered questionnaires were collected, and the medical records were reviewed to obtain precise information about each antenatal visit and each delivery. Biospecimens, including blood, urine, umbilical cord blood, and breast milk, were collected for the study biobank. The baseline maternal sociodemographic characteristics, results of screening tests, and obstetric outcomes were analyzed according to the maternal age group. Results: A total of 23,406 pregnancies involving 23,730 fetuses resulted in 23,143 live births. Younger maternal participants had a tendency toward a higher incidence of threatened abortion and threatened premature labor, while older age groups exhibited a significantly higher rate of low lying placenta, placenta previa, gestational diabetes, and hypertensive disorders of pregnancy. Conclusions: The present study clearly shows the distribution of maternal baseline characteristics and the range of perinatal outcomes according to maternal age group. This cohort study can provide strategic information for creating breakthroughs in the pathophysiology of perinatal, developmental, and noncommunicable diseases by collaborative data visiting or sharing.

Databases: Integrating megadata for disease research A database integrating 1.3 trillion genome cohort data entries from 157,191 individuals in Japan will facilitate research into the gene-environment interactions underlying common diseases. The Tohoku Medical Megabank integrated database called dbTMM was developed by Soichi Ogishima, Masayuki Yamamoto and colleagues at Tohoku University in Japan. It incorporates the genome, metabolome, proteome, clinical, sociodemographic, lifestyle and environmental data from 84,073 adults, and 73,529 pregnant women and their families, including children. Blood and urine samples were collected from participants and analysed, then obtained genome/multiomics data were stored in dbTMM. Users can stratify the entire population into smaller populations based on multiple data variables, including whole genome variants, to search for statistically significant differences that might warrant further research. The dbTMM is expected to help clarify the genes and gene-environment interactions underlying common diseases and improve disease risk prediction.

Abstract Alzheimer’s disease (AD) has no cure, but early detection and risk prediction could allow earlier intervention. Genetic risk factors may differ between ethnic populations. To discover novel susceptibility loci of AD in the Japanese population, we conducted a genome-wide association study (GWAS) with 3962 AD cases and 4074 controls. Out of 4,852,957 genetic markers that passed stringent quality control filters, 134 in nine loci, including APOE and SORL1, were convincingly associated with AD. Lead SNPs located in seven novel loci were genotyped in an independent Japanese AD case–control cohort. The novel locus FAM47E reached genome-wide significance in a meta-analysis of association results. This is the first report associating the FAM47E locus with AD in the Japanese population. A trans-ethnic meta-analysis combining the results of the Japanese data sets with summary statistics from stage 1 data of the International Genomics of Alzheimer’s Project identified an additional novel susceptibility locus in OR2B2. Our data highlight the importance of performing GWAS in non-European populations.

Primary open-angle glaucoma (POAG) is highly heritable, yet not well understood from a genetic perspective. Here, the authors perform a meta-analysis of genome-wide association studies in 34,179 POAG cases, identifying 44 previously unreported risk loci and mapping effects across multiple ethnicities.

Background: We established a community-based cohort study to assess the long-term impact of the Great East Japan Earthquake on disaster victims and gene-environment interactions on the incidence of major diseases, such as cancer and cardiovascular diseases. Methods: We asked participants to join our cohort in the health check-up settings and assessment center based settings. Inclusion criteria were aged 20 years or over and living in Miyagi or Iwate Prefecture. We obtained information on lifestyle, effect of disaster, blood, and urine information (Type 1 survey), and some detailed measurements (Type 2 survey), such as carotid echography and calcaneal ultrasound bone mineral density. All participants agreed to measure genome information and to distribute their information widely. Results: As a result, 87,865 gave their informed consent to join our study. Participation rate at health check-up site was about 70%. The participants in the Type 1 survey were more likely to have psychological distress than those in the Type 2 survey, and women were more likely to have psychological distress than men. Additionally, coastal residents were more likely to have higher degrees of psychological distress than inland residents, regardless of sex. Conclusion: This cohort comprised a large sample size and it contains information on the natural disaster, genome information, and metabolome information. This cohort also had several detailed measurements. Using this cohort enabled us to clarify the long-term effect of the disaster and also to establish personalized prevention based on genome, metabolome, and other omics information.

Bone dysplasia: Estimating carrier frequency with bioinformatics A bioinformatics approach helps estimate carrier frequency of a rare inherited bone disease which causes abnormalities in skeletal shape and structure. Autosomal recessive bone dysplasias affect bone and cartilage development and result from inheriting two mutated genes, one from each parent. Junichi Sugawara, Tohoku University, Sendai, Japan, and colleagues used mutation databases and a bioinformatics tool for variant interpretation to detect 198 pathogenic variants in 54 genes associated with autosomal recessive bone dysplasia in a whole-genome reference panel of 3,552 general Japanese individuals (3.5KJPNv2). They then estimated the frequency of people in the sample carrying bone dysplasia mutations and the expected proportion in whom the disorder could manifest, which compared well with reported incidence rates in the general population. These findings could prove useful for calculating the risk of bone dysplasia in the future children of carrier parents.

Abstract Skin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation. Since UV photography equipment is not usually available in general practice, technologies emphasizing pigmentation in color photo images are desired for daily care. We propose a new method using conditional generative adversarial networks, named UV-photo Net, to generate synthetic UV images from color photo images. Evaluations using color and UV photo image pairs taken by a UV photography system demonstrated that pigment spots were well reproduced in synthetic UV images by UV-photo Net, and some of the reproduced pigment spots were difficult to be recognized in color photo images. In the pigment spot detection analysis, the rate of pigment spot areas in cheek regions for synthetic UV images was highly correlated with the rate for UV photo images (Pearson’s correlation coefficient 0.92). We also demonstrated that UV-photo Net was effective for floating up pigment spots for photo images taken by a smartphone camera. UV-photo Net enables an easy assessment of pigmentation from color photo images and will promote self-care of skin damages and early signs of skin cancers for preventive medicine.

Human reference genomes are typically constructed from few individuals, and are biased towards European and African genomes. Here, the authors assemble three Japanese genomes to create a population-specific reference genome. They then demonstrate improved variant calling from exome sequencing with this reference genome.

Abstract The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted P value = 2.2 × 10−4, and raw P value = 3.1 × 10−9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time (P value = 1.2 × 10−3), which was further strengthened by the other two components (P value = 9.7 × 10−5). Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors.

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including “happy,” as a positive emotion and “anxiety,” “sad,” “frustrated,” as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

AbstractWe propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene–environment (GxE) interaction effects—i.e.

Technologies for acquiring explainable features from medical images need further development. Here, the authors report a deep learning based automated acquisition of explainable features from pathology images, and show a higher accuracy of their method as compared to pathologist based diagnosis of prostate cancer recurrence.

Introduction: Mutations in genes encoding nucleoporins (NUPs; components of nuclear pore complexes [NPCs]), such as NUP93, have been reported to cause steroid-resistant nephrotic syndrome (SRNS) or focal segmental glomerulosclerosis (FSGS), which often progresses to end-stage renal disease (ESRD) in childhood. The expression of NUP93 in renal or extrarenal tissues, and the mechanism by which NUP93 mutations cause this renal phenotype, remain unclear. Methods: The expression of NUP93 in normal control kidney and in a patient with FSGS carrying NUP93 mutations was examined by immunofluorescence analysis. The expression of NUP93 in blood cells was analyzed by Western blot analysis. Results: Immunofluorescence analysis detected NUP93 expression in nuclei of all glomerular and tubulointerstitial cells in human kidneys. Whole-exome sequencing identified a compound heterozygous NUP93 mutation comprising a novel missense mutation p.Arg525Trp, and a previously reported mutation, p.Tyr629Cys, in a patient with FSGS that developed ESRD at the age of 6 years. In the patient’s kidney, the intensity of NUP93 immunofluorescence was significantly decreased in the nuclei of both glomerular and extraglomerular cells. The expression of CD2-associated protein (CD2AP) and nephrin in the patient’s podocytes was relatively intact. The amount of NUP93 protein was not significantly altered in the peripheral blood mononuclear cells of the patient. Conclusion: NUP93 is expressed in the nuclei of all the cell types of the human kidney. Altered NUP93 expression in glomerular cells as well as extraglomerular cells by NUP93 mutations may underlie the pathogenic mechanism of SRNS or FSGS. Keywords: focal segmental glomerulosclerosis, nuclear pore complex, nucleoporin, NUP93, steroid-resistant nephrotic syndrome

Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals' privacy.

Sequences homologous to human herpesvirus 6 (HHV-6) are integrated within the nuclear genome of about 1% of humans, but it is not clear how this came about. It is also uncertain whether integrated HHV-6 can reactivate into an infectious virus. HHV-6 integrates into telomeres, and this has recently been associated with polymorphisms affecting MOV10L1. MOV10L1 is located on the subtelomere of chromosome 22q (chr22q) and is required to make PIWI-interacting RNAs (piRNAs). As piRNAs block germline integration of transposons, piRNA-mediated repression of HHV-6 integration has been proposed to explain this association. In vitro, recombination of the HHV-6 genome along its terminal direct repeats (DRs) leads to excision from the telomere and viral reactivation, but the expected "solo-DR scar" has not been described in vivo. Here we screened for integrated HHV-6 in 7,485 Japanese subjects using whole-genome sequencing (WGS). Integrated HHV-6 was associated with polymorphisms on chr22q. However, in contrast to prior work, we find that the reported MOV10L1 polymorphism is physically linked to an ancient endogenous HHV-6A variant integrated into the telomere of chr22q in East Asians. Unexpectedly, an HHV-6B variant has also endogenized in chr22q; two endogenous HHV-6 variants at this locus thus account for 72% of all integrated HHV-6 in Japan. We also report human genomes carrying only one portion of the HHV-6B genome, a solo-DR, supporting in vivo excision and possible viral reactivation. Together these results explain the recently-reported association between integrated HHV-6 and MOV10L1/piRNAs, suggest potential exaptation of HHV-6 in its coevolution with human chr22q, and clarify the evolution and risk of reactivation of the only intact (non-retro)viral genome known to be present in human germlines.

Abstract 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.

Abstract Background Genotype imputation from single-nucleotide polymorphism (SNP) genotype data using a haplotype reference panel consisting of thousands of unrelated individuals from populations of interest can help to identify strongly associated variants in genome-wide association studies. The Tohoku Medical Megabank (TMM) project was established to support the development of precision medicine, together with the whole-genome sequencing of 1070 human genomes from individuals in the Miyagi region (Northeast Japan) and the construction of the 1070 Japanese genome reference panel (1KJPN). Here, we investigated the performance of 1KJPN for genotype imputation of Japanese samples not included in the TMM project and compared it with other population reference panels. Results We found that the 1KJPN population was more similar to other Japanese populations, Nagahama (south-central Japan) and Aki (Shikoku Island), than to East Asian populations in the 1000 Genomes Project other than JPT, suggesting that the large-scale collection (more than 1000) of Japanese genomes from the Miyagi region covered many of the genetic variations of Japanese in mainland Japan. Moreover, 1KJPN outperformed the phase 3 reference panel of the 1000 Genomes Project (1KGPp3) for Japanese samples, and IKJPN showed similar imputation rates for the TMM and other Japanese samples for SNPs with minor allele frequencies (MAFs) higher than 1%. Conclusions 1KJPN covered most of the variants found in the samples from areas of the Japanese mainland outside the Miyagi region, implying 1KJPN is representative of the Japanese population’s genomes. 1KJPN and successive reference panels are useful genome reference panels for the mainland Japanese population. Importantly, the addition of whole genome sequences not included in the 1KJPN panel improved imputation efficiencies for SNPs with MAFs under 1% for samples from most regions of the Japanese archipelago.

Abstract Background With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. The present paper reports on our plans, current attempts, and accomplishments in addressing security problems involved in data sharing to ensure donor privacy while promoting scientific advancement. Methods Biospecimens and data have been collected in prospective cohort studies with the comprehensive agreement. The sample size of 150,000 participants was required for multiple researches including genome-wide screening of gene by environment interactions, haplotype phasing, and parametric linkage analysis. Results We established the T ohoku M edical M egabank (TMM) data sharing policy: a privacy protection rule that requires physical, personnel, and technological safeguards against privacy violation regarding the use and sharing of data. The proposed policy refers to that of NCBI and that of the Sanger Institute. The proposed policy classifies shared data according to the strength of re-identification risks. Local committees organized by TMM evaluate re-identification risk and assign a security category to a dataset. Every dataset is stored in an assigned segment of a supercomputer in accordance with its security category. A security manager should be designated to handle all security problems at individual data use locations. The proposed policy requires closed networks and IP-VPN remote connections. Conclusion The mission of the biobank is to distribute biological resources most productively. This mission motivated us to collect biospecimens and health data and simultaneously analyze genome/omics data in-house. The biobank also has the mission of improving the quality and quantity of the contents of the biobank. This motivated us to request users to share the results of their research as feedback to the biobank. The TMM data sharing policy has tackled every security problem originating with the missions. We believe our current implementation to be the best way to protect privacy in data sharing.

Gene-environment (GxE) interaction is one potential explanation for the missing heritability problem. A popular approach to genome-wide environment interaction studies (GWEIS) is based on regression models involving interactions between genetic variants and environment variables. Unfortunately, GWEIS encounters systematically inflated (or deflated) test statistics more frequently than a marginal association study. The problematic behavior may occur due to poor specification of the null model (i.e. the model without genetic effect) in GWEIS. Improved null model specification may resolve the problem, but the investigation requires many time-consuming analyses of genome-wide scans, e.g. by trying out several transformations of the phenotype. It is therefore helpful if we can predict such problematic behavior beforehand. We present a simple closed-form formula to assess problematic behavior of GWEIS under the null hypothesis of no genetic effects. It requires only phenotype, environment variables, and covariates, enabling quick identification of systematic test statistic inflation or deflation. Applied to real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our formula identified problematic studies from among hundreds GWEIS considering each metabolite as the environment variable in GxE interaction. Our formula is useful to quickly identify problematic GWEIS without requiring a genome-wide scan.

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87−0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible.

TATA-box binding protein-associated factor 1 (TAF1), the largest subunit of the transcription factor IID complex, plays an important role in the RNA polymerase II-mediated gene transcription pathway regulating the transcription of a large number of genes related to cell division. The neuron-specific isoform of the TAF1 gene (N-TAF1) may have an essential role in neurons through transcriptional regulation of many neuron-specific genes. The present study reports the preparation and properties of a monoclonal antibody directed against N-TAF1. The monoclonal antibody, 3A-11F, specifically recognized N-TAF1 protein with no reactivity to TAF1 protein, as evidenced by immunocytochemistry and immunoprecipitation using cultured cells expressing recombinant N-TAF1 or TAF1 protein. Immunohistochemistry using 3A-11F showed that N-TAF1-imunoreactivity was detected in the nuclear region of neurons in the rat brain. The 3A-11F monoclonal antibody promises to be a useful tool for determining the expression pattern and biological function of N-TAF1 in the brain.

BACKGROUND: Stevens-Johnson syndrome (SJS) is an acute inflammatory vesiculobullous reaction of the skin and mucosa, often including the ocular surface, and toxic epidermal necrolysis (TEN) occurs with its progression. Although SJS/TEN is thought to be initiated by certain types of medication coupled with possible infection. In the present study we examined the multiplicative interaction(s) between HLA-A*0206 and 7 Toll-like receptor 3 (TLR3) Single-nucleotide polymorphisms (SNPs) in patients with SJS/TEN. PRINCIPAL FINDINGS: We analyzed the genotypes for HLA-A and 7 TLR3 SNPs in 110 Japanese SJS/TEN patients with severe ocular complications and 206 healthy volunteers to examine the interactions between the two loci. We found that HLA-A*0206 exhibited a high odds ratio for SJS/TEN (carrier frequency: OR = 5.1; gene frequency: OR = 4.0) and that there was a strong association with TLR3 rs.5743312T/T SNP (OR = 7.4), TLR3 rs.3775296T/T SNP (OR = 5.8), TLR3 rs.6822014G/G SNP (OR = 4.8), TLR3 rs.3775290A/A SNP (OR = 2.9), TLR3 rs.7668666A/A SNP (OR = 2.7), TLR3 rs.4861699G/G SNP (OR = 2.3), and TLR3 rs.11732384G/G SNP (OR = 1.9). There was strong linkage disequilibrium (LD) between rs.3775296 and rs.5743312 and between rs.7668666 and rs.3775290. The results of interaction analysis showed that the pair, HLA-A*0206 and TLR3 SNP rs3775296T/T, which exhibited strong LD with TLR3 rs.5743312, exerted more than additive effects (OR = 47.7). The other pairs, HLA-A*0206 and TLR3 rs.3775290A/A SNP (OR = 11.4) which was in strong LD with TLR3 rs7668666A/A SNP, and TLR3 rs4861699G/G SNP (OR = 7.6) revealed additive effects. Moreover, the combination HLA-A*0206 and TLR3 rs3775296T/T was stronger than the TLR3 rs6822014G/G and TLR3 rs3775290A/A pair, which reflected the interactions within the TLR3 gene alone. SIGNIFICANCE: By interaction analysis, HLA-A*0206 and TLR3 SNP rs3775296T/T, which were in strong LD with TLR3 SNP rs5743312T/T, manifested more than additive effects that were stronger than the interactions within the TLR3 gene alone. Therefore, multiplicative interactions of HLA-A and TLR3 gene might be required for the onset of SJS/TEN with ocular complications.

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.

Selective dorsal rhizotomy (SDR) has been used to treat children with spastic cerebral palsy (CP), and its beneficial effect on quality of life and ambulation has been confirmed in long-term follow-up studies. However, the role of SDR in the treatment of spasticity in patients with hereditary spastic paraplegia (HSP) and related disorders is not well-established. Here, we report the first patient with the ZC4H2 variant who underwent SDR to treat spastic paraplegia. Abnormal gait was discovered during a regular checkup at the age of 3 years and 9 months, and she was diagnosed with spastic paraplegia. She was heterozygous for the ZC4H2 variant and underwent SDR at the age of 5 years and 11 months, which alleviated the spasticity. The patient underwent inpatient postoperative rehabilitation for 4 months and continued outpatient physiotherapy after discharge. The Gross Motor Function Measure-88 score and maximum walking speed decreased transiently 1 month postoperatively, but gradually recovered, and continuously improved 6 months postoperatively. SDR and postoperative intensive rehabilitation were effective in improving motor and walking functions up to 6 months after surgery, although long-term follow-up is needed to draw conclusions. [ABSTRACT FROM AUTHOR]

Cervical cancer is the second most common cancer in women, and the role of human papillomavirus (HPV) testing in its etiology is becoming increasingly important. We aimed to evaluate the performance of an artificial intelligence (AI)-assisted colposcopy system in detecting high-risk human papillomavirus (HR-HPV) subtypes in cervical intraepithelial neoplasia (CIN) 2 patients. We conducted a post-hoc analysis of a previous observational study that developed an AI algorithm for colposcopic images of patients with CIN2. Out of 78 patients with HR-HPVs (HPV 16, 18, 31, 33, 35, 45, 52 and 58), 60 (76.9%) had positive AI colposcopy results. The accuracy, sensitivity and specificity of the AI-assisted colposcopy system in detecting HR-HPVs (16, 18, 31, 33, 35, 45, 52 and 58) were 0.689, 0.769 and 0.537, respectively. This study is the first to focus on using AI to detect high-risk subtypes. The AI algorithm accurately detects the characteristics of HR-HPVs. It can be used to detect high-risk CIN types in patients with cervical dysplasia based only on colposcopic imaging findings and is potentially a valuable tool for follow-up. [ABSTRACT FROM AUTHOR]