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Aim: This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes., Methods: This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC)., Result: Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732])., Conclusion: Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.

Purpose: The purpose of this study was to investigate biologically meaningful endotypes of open-angle glaucoma (OAG) by applying unsupervised machine learning to plasma metabolites., Methods: This retrospective longitudinal cohort study enrolled consecutive patients aged ≥20 years with OAG at Tohoku University Hospital from January 2017 to January 2020. OAG was confirmed based on comprehensive ophthalmic examinations. Among the 523 patients with OAG with available clinical metabolomic data, 173 patients were longitudinally followed up for ≥2 years, with available data from ≥5 reliable visual field (VF) tests without glaucoma surgery. We collected fasting blood samples and clinical data at enrollment and nuclear magnetic resonance spectroscopy to profile 45 plasma metabolites in a targeted approach. After computing a distance matrix of preprocessed metabolites with Pearson distance, gap statistics determined the optimal number of OAG endotypes. Its risk factors, clinical presentations, metabolomic profiles, and progression rate of sector-based VF loss were compared across endotypes., Results: Five distinct OAG endotypes were identified. The highest-risk endotype (endotype B) showed a significant faster progression of central VF loss (P = 0.007). Compared with patients with other endotypes, those with endotype B were more likely to have a high prevalence of dyslipidemia, cold extremities, oxidative stress, and low OAG genetic risk scores. Pathway analysis of metabolomic profiles implicated altered fatty acid and ketone body metabolism in this endotype, with 34 differentially enriched pathways (false discovery rate [FDR] < 0.05)., Conclusions: Integrated metabolomic profiles identified five distinct etiologic endotypes of OAG, suggesting pathological mechanisms related with a high-risk group of central vision loss progression in the Japanese population.

Persistent thrombocytopenia is caused by various diseases, including immune thrombocytopenia, inherited thrombocytopenia, and inherited bone marrow failure syndromes. Considering the large number of genes responsible for inherited disorders, comprehensive genetic analysis is required to diagnose monogenic disorders. In this study, we enrolled 53 pediatric patients with persistent thrombocytopenia exhibiting visually small or normal-sized platelets. We performed whole-exome sequencing, including 56 genes responsible for inherited thrombocytopenia, and evaluated clinical parameters according to disease type. Among 53 patients, 12 patients (22.6%) were diagnosed with monogenic disorders. Nine patients had a family history of thrombocytopenia. Pathogenic or novel variants of genes responsible for inherited thrombocytopenia were identified in three and six patients, respectively. The variants in genes for inherited thrombocytopenia with large or giant platelets were unexpectedly identified in six patients. Pathogenic variants in genes for inherited bone marrow failure syndromes with systemic features were identified in three patients with atypical symptoms. Since the definitive diagnostic methods for immune thrombocytopenia are limited, and a substantial number of patients with inherited thrombocytopenia are at a high risk of developing malignancies, comprehensive genetic analysis is indispensable for selecting appropriate therapies, avoidance of unnecessary treatments for immune thrombocytopenia, and long-term follow-up of patients with inherited thrombocytopenia.

Aim: Genome-wide association studies (GWAS) of postpartum depression (PPD) based on accumulated cohorts with multiple ethnic backgrounds have failed to identify significantly associated loci. Herein, we conducted a GWAS of Japanese perinatal women along with detailed confounding information to uncover PPD-associated loci., Methods: The first and second cohorts (n = 9260 and n = 8582 perinatal women enrolled in the Tohoku Medical Megabank Project) and the third cohort (n = 997), recruited at Nagoya University, underwent genotyping. Of them, 1421, 1264, and 225 were classified as PPD based on the Edinburgh Postnatal Depression Scale 1 month after delivery. The most influential confounding factors of genetic liability to PPD were selected, and logistic regression analyses were performed to evaluate genetic associations with PPD after adjusting for confounders., Results: A meta-analysis of GWAS results from the three cohorts identified significant associations between PPD and the following loci (P < 5 × 10 -8 ) by integrating the number of deliveries and the number of family members living together as the most influential confounders: rs377546683 at DAB1, rs11940752 near UGT8, rs141172317, rs117928019, rs76631412, rs118131805 at DOCK2, rs188907279 near ZNF572, rs504378, rs690150, rs491868, rs689917, rs474978, rs690118, rs690253 near DIRAS2, rs1435984417 at ZNF618, rs57705782 near PTPRM, and rs185293917 near PDGFB. Pathway analyses indicated that SNPs suggestively associated with PPD were mostly over-represented in categories including long-term depression, GnRH signaling, glutamatergic synapse, oxytocin signaling, and Rap1 signaling., Conclusion: The current GWAS study identified eight loci significantly associated with PPD, which may clarify the genetic structure underlying its pathogenesis., (© 2024 The Author(s). Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

Aims/hypothesis: Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time., Methods: Cohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2D kmeans ) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA 1c , HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2D RF15 ). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications., Results: T2D RF15 demonstrated a 94% accuracy for predicting T2D kmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2D RF15 showed an accuracy of 82.9% for detecting T2D kmeans , also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan-Meier analysis, the diabetes clusters of T2D RF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5-18.3) years, similarly to the subtypes using T2D kmeans . The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2D RF15 , after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2D kmeans ., Conclusions/interpretation: The new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations., (© 2024. The Author(s).)

Purpose: To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals., Design: Cross-sectional analysis., Participants: Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study)., Methods: We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements., Main Outcome Measure: Proportion of patients with POAG after stratification according to the GRS., Results: We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35-8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79-9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r = 0.08: P = 4.0 × 10 -4 ) and vertical cup-to-disc ratio (r = 0.11; P = 4.0 × 10 -6 )., Conclusions: The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Next-generation sequencing (NGS) has become widely available and is routinely used in basic research and clinical practice. The reference genome sequence is an essential resource for NGS analysis, and several population-specific reference genomes have recently been constructed to provide a choice to deal with the vast genetic diversity of human samples. However, resources supporting population-specific references are insufficient, and it is burdensome to perform analysis using these reference genomes. Here, we constructed a set of resources to support NGS analysis using the Japanese reference genome, JG. We created resources for variant calling, variant-effect prediction, gene and repeat element annotations, read mappability, and RNA-seq analysis. We also provide a resource for reference coordinate conversion for further annotation enrichment. We then provide a variant calling protocol with JG. Our resources provide a guide to prepare sufficient resources for the use of population-specific reference genomes and can facilitate the migration of reference genomes.

Conotruncal heart defects are severe congenital malformations of the outflow tract, including truncus arteriosus (TA) and double-outlet right ventricle (DORV). TA is a severe congenital heart disease (CHD) in which the main arterial outflow tract of the heart fails to separate. We recently reported TMEM260 (NM&#95;017799.4), c.1617del (p.Trp539Cysfs*9), as a major cause of TA in the Japanese population (TMEM260 Keio-Tohoku variant) comparable to the prevalence of the 22q11.2 deletion syndrome, which accounts for 12%-35% of TA. However, no other major causes of TA have not been identified. Here, we report a family that included a TA patient and a DORV patient, harboring the compound heterozygous variants of TMEM260, a 7066-bp deletion encompassing exons 6-7 and c.1393C > T, p.(Gln465*). The allele frequency of the 7066-bp deletion was particularly high in the Japanese population (0.17%). Based on the allele frequency of this deletion and c.1617del (0.36%) in the Japanese population, TMEM260 variants might be associated with more than half of the Japanese patients with TA. This study showed that TMEM260 pathogenic variants might be the most common cause of TA in the Japanese population and could explain the wide spectrum of phenotypes associated with TMEM260-related CHD, including DORV, demonstrating the usefulness of genetic testing in Japanese patients with TA., (© 2024 The Author(s). American Journal of Medical Genetics Part A published by Wiley Periodicals LLC.)

KIF1A-related disorders (KRDs) encompass recessive and dominant variants with wide clinical variability. Recent genetic investigations have expanded the clinical phenotypes of heterozygous KIF1A variants. However, there have been a few long-term observational studies of patients with heterozygous KIF1A variants. A retrospective chart review of consecutive patients diagnosed with spastic paraplegia at Miyagi Children's Hospital from 2016 to 2020 identified six patients with heterozygous KIF1A variants. To understand the long-term changes in clinical symptoms, we examined these patients in terms of their characteristics, clinical symptoms, results of electrophysiological and neuroimaging studies, and genetic testing. The median follow-up period was 30 years (4-44 years). This long-term observational study showed that early developmental delay and equinus gait, or unsteady gait, are the first signs of disease onset, appearing with the commencement of independent walking. In addition, later age-related progression was observed in spastic paraplegia, and the appearance of axonal neuropathy and reduced visual acuity were characteristic features of the late disease phenotype. Brain imaging showed age-related progression of cerebellar atrophy and the appearance of hyperintensity of optic radiation on T2WI and FLAIR imaging. Long-term follow-up revealed a pattern of steady progression and a variety of clinical symptoms, including spastic paraplegia, peripheral neuropathy, reduced visual acuity, and some degree of cerebellar ataxia. Clinical variability between patients was observed to some extent, and therefore, further studies are required to determine the phenotype-genotype correlation., (© 2024 Wiley Periodicals LLC.)

Objective: To investigate the inter-relationships among genetic risk, healthy lifestyle adherence, and hyperuricaemia susceptibility., Methods: This prospective cohort study was conducted with 7,241 hyperuricaemia-free individuals aged ≥ 20 years from the Tohoku Medical Megabank Community-based cohort study. A comprehensive lifestyle score included body mass index, smoking, drinking, and physical activity, and a polygenic risk score (PRS) was constructed based on uric acid loci from a previous genome-wide association study meta-analysis. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and hyperuricaemia incidence and calculate the area under the receiver operating characteristic curve (AUROC). Hyperuricaemia was defined as a uric acid level ≥7.0 mg/dl or a self-reported history of hyperuricaemia., Results: Of the 7,241 adults (80.7% females; mean [SD] age: 57.7 [12.6] years), 217 (3.0%) developed hyperuricaemia during 3.5 years of follow-up. Genetic risk correlated with hyperuricaemia development (P for interaction = 0.287), and lifestyle risks were independently associated. Those with a high genetic risk and poor lifestyle had the highest risk (odds ratio: 5.34; 95% confidence interval [CI]: 2.61-12.10). Although not statistically significant, incorporating the PRS in the model with lifestyle information improved predictive ability (AUROC = 0.771, 95% CI: 0.736-0.806 for lifestyle; AUROC = 0.785, 95% CI: 0.751-0.819 for lifestyle and PRS; p = 0.07)., Conclusion: : A healthy lifestyle to prevent hyperuricaemia, irrespective of genetic risk, may mitigate the genetic risk. Genetic risk may complement lifestyle factors in identifying individuals at a heightened hyperuricaemia risk., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

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 10 092 551 bi-allelic high-quality autosomal markers segregating among 150 unrelated Japanese individuals whose genotypes were determined by high-coverage (30×) whole-genome sequencing, and the genotype quality was carefully controlled by using their parents' and offspring's genotypes. The pedigree information was also utilized for haplotype phasing. The resulting genome-wide recombination rate profiles were concordant with those of the 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., (© 2024 The Authors. Clinical Genetics published by John Wiley & Sons Ltd.)

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/m 2 ) to five clusters based on obesity-related factors. Subsequently, participants from each cluster and those with a BMI <25 kg/m 2 were combined. A GWAS was conducted for each cluster. Several previously identified obesity-related genes were verified in Step-1. Of the genes detected in Step-1, unique obesity-related genes were detected separately for each cluster in Step-2. Our novel findings suggest that a smaller sample size with increased homogeneity may provide insights into the genetic architecture of obesity., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Shinichi Kuriyama reports financial support was provided by 10.13039/501100009619Japan Agency for Medical Research and Development (AMED) and 10.13039/501100001700Ministry of Education, Culture, Sports, Science and Technology (MEXT). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

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.

No study, to our knowledge, has constructed a polygenic risk score based on clinical blood pressure and investigated the association of genetic and lifestyle risks with home hypertension. We examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. In a cross-sectional study of 7027 Japanese individuals aged ≥20 years, we developed a lifestyle score based on body mass index, alcohol consumption, physical activity, and sodium-to-potassium ratio, categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score was constructed with the target data (n = 1405) using publicly available genome-wide association study summary statistics from BioBank Japan. Using the test data (n = 5622), we evaluated polygenic risk score performance and examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertension and home hypertension were defined as blood pressure measured at a community-support center ≥140/90 mmHg or at home ≥135/85 mmHg, respectively, or self-reported treatment for hypertension. In the test data, 2294 and 2322 participants had hypertension and home hypertension, respectively. Both polygenic risk and lifestyle scores were independently associated with hypertension and home hypertension. Compared with those of participants with low genetic risk and an ideal lifestyle, the odds ratios for hypertension and home hypertension in the low genetic risk and poor lifestyle group were 1.94 (95% confidence interval, 1.34-2.80) and 2.15 (1.60-2.90), respectively. In summary, lifestyle is important to prevent hypertension; nevertheless, participants with high genetic risk should carefully monitor their blood pressure despite a healthy lifestyle., (© 2024. The Author(s).)

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., (© 2024 The Authors.)

Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (n T2D  = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R 2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Insufficient thyroid hormone production in newborns is referred to as congenital hypothyroidism. Multinodular goiter (MNG), characterized by an enlarged thyroid gland with multiple nodules, is usually seen in adults and is recognized as a separate disorder from congenital hypothyroidism. Here we performed a linkage analysis of a family with both nongoitrous congenital hypothyroidism and MNG and identified a signal at 15q26.1. Follow-up analyses with whole-genome sequencing and genetic screening in congenital hypothyroidism and MNG cohorts showed that changes in a noncoding TTTG microsatellite on 15q26.1 were frequently observed in congenital hypothyroidism (137 in 989) and MNG (3 in 33) compared with controls (3 in 38,722). Characterization of the noncoding variants with epigenomic data and in vitro experiments suggested that the microsatellite is located in a thyroid-specific transcriptional repressor, and its activity is disrupted by the variants. Collectively, we presented genetic evidence linking nongoitrous congenital hypothyroidism and MNG, providing unique insights into thyroid abnormalities., (© 2024. The Author(s).)

Truncus Arteriosus (TA) is a congenital heart disease characterized by a single common blood vessel emerging from the right and left ventricles instead of the main pulmonary artery and aorta. TA accounts for 4% of all critical congenital heart diseases. The most common cause of TA is 22q11.2 deletion syndrome, accounting for 12-35% of all TA cases. However, no major causes of TA other than 22q11.2 deletion have been reported. We performed whole-genome sequencing of 11 Japanese patients having TA without 22q11.2 deletion. Among five patients, we identified pathogenic variants in TMEM260; the biallelic loss-of-function variants of which have recently been associated with structural heart defects and renal anomalies syndrome (SHDRA). In one patient, we identified a de novo pathogenic variant in GATA6, and in another patient, we identified a de novo probably pathogenic variant in NOTCH1. Notably, we identified a prevalent variant in TMEM260 (ENST00000261556.6), c.1617del (p.Trp539Cysfs*9), in 8/22 alleles among the 11 patients. The c.1617del variant was estimated to occur approximately 23 kiloyears ago. Based on the allele frequency of the c.1617del variant in the Japanese population (0.36%), approximately 26% of Japanese patients afflicted with TA could harbor homozygous c.1617del variants. This study highlights TMEM260, especially c.1617del, as a major genetic cause of TA in the Japanese population., (© 2024. The Author(s).)

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.

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., (© 2024. The Author(s).)

Objectives: Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate., Materials and Methods: Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence., Results: Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence., Conclusions: Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a public database for plasma metabolome and proteome analyses and has been continuously updated. The current update will significantly expand the scale of the data (metabolome, genome, transcriptome, and metagenome). In addition, the user interface and backend server implementations were rewritten to improve the connectivity between the items stored in jMorp. This paper provides an overview of the new version of the jMorp., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Aim: The influence of family history of diabetes, probably reflecting genetic and lifestyle factors, on the association of combined genetic and lifestyle risks with diabetes is unknown. We examined these associations., Methods: This cross-sectional study included 9,681 participants in the Tohoku Medical Megabank Community-based Cohort Study. A lifestyle score, which was categorized into ideal, intermediate, and poor lifestyles, was given. Family history was obtained through a self-reported questionnaire. A polygenic risk score (PRS) was constructed in the target data (n=1,936) using publicly available genome-wide association study summary statistics from BioBank Japan. For test data (n=7,745), we evaluated PRS performance and examined the associations of combined family history and genetic and lifestyle risks with diabetes. Diabetes was defined as non-fasting blood glucose ≥ 200 mmHg, HbA1c ≥ 6.5%, and/or self-reported diabetes treatment., Results: In test data, 467 (6.0%) participants had diabetes. Compared with a low genetic risk and an ideal lifestyle without a family history, the odds ratio (OR) was 3.73 (95% confidence interval [CI], 1.92-7.00) for a lower genetic risk and a poor lifestyle without a family history. Family history was significantly associated with diabetes (OR, 3.58 [95% CI, 1.73-6.98]), even in those with a low genetic risk and an ideal lifestyle. Even among participants who had an ideal lifestyle without a family history, a high genetic risk was associated with diabetes (OR, 2.49 [95% CI, 1.65-3.85]). Adding PRS to family history and conventional lifestyle risk factors improved the prediction ability for diabetes., Conclusions: Our findings support the notion that a healthy lifestyle is important to prevent diabetes regardless of genetic risk.

Bipolar disorder (BD) is a global medical issue, afflicting around 1% of the population with manic and depressive episodes. Despite various genetic studies, the genetic architecture and pathogenesis of BD have not been fully resolved. Besides germline variants, postzygotic mosaic variants are proposed as new candidate mechanisms contributing to BD. Here, we performed extensive deep exome sequencing (DES, ~300×) and validation experiments to investigate the roles of mosaic variants in BD with 235 BD cases (194 probands of trios and 41 single cases) and 39 controls. We found an enrichment of developmental disorder (DD) genes in the genes hit by deleterious mosaic variants in BD (P = 0.000552), including a ClinVar-registered pathogenic variant in ARID2. An enrichment of deleterious mosaic variants was also observed for autism spectrum disorder (ASD) genes (P = 0.000428). The proteins coded by the DD/ASD genes with non-synonymous mosaic variants in BD form more protein-protein interaction than expected, suggesting molecular mechanisms shared with DD/ASD but restricted to a subset of cells in BD. We also found significant enrichment of mitochondrial heteroplasmic variants, another class of mosaic variants, in mitochondrial tRNA genes in BD (P = 0.0102). Among them, recurrent m.3243 A > G variants known as causal for mitochondrial diseases were found in two unrelated BD probands with allele fractions of 5-12%, lower than in mitochondrial diseases. Despite the limitation of using peripheral tissues, our DES investigation supports the possible contribution of deleterious mosaic variants in the nuclear genome responsible for severer phenotypes, such as DD/ASD, to the risk of BD and further demonstrates that the same paradigm can be applied to the mitochondrial genome. These results, as well as the enrichment of heteroplasmic mitochondrial tRNA variants in BD, add a new piece to the understanding of the genetic architecture of BD and provide general insights into the pathological roles of mosaic variants in human diseases., (© 2023. The Author(s).)

Myosin-binding protein C1 (MYBPC1) encodes myosin-binding protein C, slow type (sMyBP-C), an accessory protein that regulates actomyosin cross-linking, stabilizes thick filaments, and modulates contractility in muscle sarcomeres and has recently been linked to myopathy with tremor. The clinical features of MYBPC1 mutations manifesting in early childhood bear some similarities to those of spinal muscular atrophy (SMA), such as hypotonia, involuntary movement of the tongue and limbs, and delayed motor development. The development of novel therapies for SMA has necessitated the importance of differentiating SMA from other diseases in the early infancy period. We report the characteristic tongue movements of MYBPC1 mutations, along with other clinical findings, such as positive deep tendon reflexes and normal peripheral nerve conduction velocity testing, which could help in considering other diseases as differential diagnoses., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Introduction: Perinatal 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)., Methods: Nine 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 Discussion: In 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Ono, Warita, Shoji, Nakagawa, Usukura, Yu, Takahashi, Ichiji, Sugita, Kobayashi, Kikuchi, Kimura, Hamaie, Hino, Kunii, Murakami, Ishikuro, Obara, Nakamura, Nagami, Takai, Ogishima, Sugawara, Hoshiai, Saito, Tamiya, Fuse, Fujii, Nakayama, Kuriyama, Yamamoto, Yaegashi, Homma and Tomita.)

Introduction: Paget's disease of bone (PDB) is a skeletal disorder characterized by disorganized bone remodeling due to abnormal osteoclasts. Tumor necrosis factor receptor superfamily member 11A (TNFRSF11A) gene encodes the receptor activator of nuclear factor kappa B (RANK), which has a critical role in osteoclast function. There are five types of rare PDB and related osteolytic disorders due to TNFRSF11A tandem duplication variants so far, including familial expansile osteolysis (84dup18), expansile skeletal hyperphosphatasia (84dup15), early-onset familial PDB (77dup27), juvenile PDB (87dup15), and panostotic expansile bone disease (90dup12)., Materials and Methods: We reviewed a Japanese family with PDB, and performed whole-genome sequencing to identify a causative variant., Results: This family had bone symptoms, hyperphosphatasia, hearing loss, tooth loss, and ocular manifestations such as angioid streaks or early-onset glaucoma. We identified a novel duplication variant of TNFRSF11A (72dup27). Angioid streaks were recognized in Juvenile Paget's disease due to loss-of-function variants in the gene TNFRSF11B, and thought to be specific for this disease. However, the novel recognition of angioid streaks in our family raised the possibility of occurrence even in bone disorders due to TNFRSF11A duplication variants and the association of RANKL-RANK signal pathway as the pathogenesis. Glaucoma has conversely not been reported in any case of Paget's disease. It is not certain whether glaucoma is coincidental or specific for PDB with 72dup27., Conclusion: Our new findings might suggest a broad spectrum of phenotypes in bone disorders with TNFRSF11A duplication variants., (© 2022. The Japanese Society Bone and Mineral Research.)

Chronic kidney disease (CKD) is a syndrome characterized by a gradual loss of kidney function with decreased estimated glomerular filtration rate (eGFR), which may be accompanied by an increase in the urine albumin-to-creatinine ratio (UACR). Although trans-ethnic genome-wide association studies (GWASs) have been conducted for kidney-related traits, there have been few analyses in the Japanese population, especially for the UACR trait. In this study, we conducted a GWAS to identify loci related to multiple kidney-related traits in Japanese individuals. First, to detect loci associated with CKD, eGFR, and UACR, we performed separate GWASs with the following two datasets: 475 cases of CKD diagnosed at seven university hospitals and 3471 healthy subjects (dataset 1) and 3664 cases of CKD-suspected individuals with eGFR <60 ml/min/1.73 m 2 or urinary protein ≥ 1+ and 5952 healthy subjects (dataset 2). Second, we performed a meta-analysis between these two datasets and detected the following associated loci: 10 loci for CKD, 9 loci for eGFR, and 22 loci for UACR. Among the loci detected, 22 have never been reported previously. Half of the significant loci for CKD were shared with those for eGFR, whereas most of the loci associated with UACR were different from those associated with CKD or eGFR. The GWAS of the Japanese population identified novel genetic components that were not previously detected. The results also suggest that the group primarily characterized by increased UACR possessed genetically different features from the group characterized by decreased eGFR., (© 2022. The Author(s), under exclusive licence to The Japan Society of Human Genetics.)

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., (© 2022. The Author(s).)

Disorders of sex development (DSD) comprises a congenital condition in which chromosomal, gonadal, or anatomical sex development is atypical. In this study, we screened for pathogenic variants in 32 genes associated with DSDs and central causes of hypogonadism (CHG) in a whole-genome reference panel including 8380 Japanese individuals constructed by Tohoku Medical Megabank Organization. Candidate pathogenic (P) or likely pathogenic (LP) variants were extracted from the ClinVar, InterVar, and Human Gene Mutation databases. Ninety-one candidate pathological variants were found in 25 genes; 28 novel candidate variants were identified. Nearly 1 in 40 (either ClinVar or InterVar P or LP) to 157 (both ClinVar and InterVar P or LP) individuals were found to be carriers of recessive DSD and CHG alleles. In these data, genes implicated in gonadal dysfunction did not show loss-of-function variants, with a relatively high tendency of intolerance for haploinsufficiency based on pLI and Episcore, both of which can be used for estimating haploinsufficiency. We report the types and frequencies of causative variants for DSD and CHG in the general Japanese population. This study furthers our understanding of the genetic causes and helps to refine genetic counseling of DSD and CHG., (© 2022. The Author(s).)

Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale., (© 2022. The Author(s).)

Objective: Human papillomavirus subtypes are predictive indicators of cervical intraepithelial neoplasia (CIN) progression. While colposcopy is also an essential part of cervical cancer prevention, its accuracy and reproducibility are limited because of subjective evaluation. This study aimed to develop an artificial intelligence (AI) algorithm that can accurately detect the optimal lesion associated with prognosis using colposcopic images of CIN2 patients by utilizing objective AI diagnosis., Methods: We identified colposcopic findings associated with the prognosis of patients with CIN2. We developed a convolutional neural network that can automatically detect the rate of high-grade lesions in the uterovaginal area in 12 segments. We finally evaluated the detection accuracy of our AI algorithm compared with the scores by multiple gynecologic oncologists., Results: High-grade lesion occupancy in the uterovaginal area detected by senior colposcopists was significantly correlated with the prognosis of patients with CIN2. The detection rate for high-grade lesions in 12 segments of the uterovaginal area by the AI system was 62.1% for recall, and the overall correct response rate was 89.7%. Moreover, the percentage of high-grade lesions detected by the AI system was significantly correlated with the rate detected by multiple gynecologic senior oncologists (r=0.61)., Conclusion: Our novel AI algorithm can accurately determine high-grade lesions associated with prognosis on colposcopic images, and these results provide an insight into the additional utility of colposcopy for the management of patients with CIN2., Competing Interests: No potential conflict of interest relevant to this article was reported., (© 2022. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology, and Japan Society of Gynecologic Oncology.)

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology., Competing Interests: Declaration of interests G.C.-P. is currently an employee of 23andMe Inc. M.J.C. is the Chief Scientist for Genomics England, a UK Government company. B.M. Psaty serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. G. Thorleifsson, A.H., D.F.G., H. Holm, U.T., and K.S. are employees of deCODE/Amgen Inc. V.S. has received honoraria for consultations from Novo Nordisk and Sanofi and has an ongoing research collaboration with Bayer Ltd. M. McCarthy has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. M. McCarthy and A. Mahajan are employees of Genentech and holders of Roche stock. M.S. receives funding from Pfizer Inc. for a project unrelated to this work. M.E.K. is employed by SYNLAB MVZ Mannheim GmbH. W.M. has received grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants from Astrazeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital GmbH , grants from bestbion dx GmbH, grants from Boehringer Ingelheim Pharma GmbH Co KG, grants from Immundiagnostik GmbH, grants from Merck Chemicals GmbH, grants from MSD Sharp and Dohme GmbH, grants from Novartis Pharma GmbH, grants from Olink Proteomics, and other from Synlab Holding Deutschland GmbH, all outside the submitted work. A.V.K. has served as a consultant to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color Genomics; received speaking fees from Illumina and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research, and reports a patent related to a genetic risk predictor (20190017119). S. Kathiresan is an employee of Verve Therapeutics and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape; and he reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). D.K. accepts consulting fees from Regeneron Pharmaceuticals. D.O.M.-K. is a part-time clinical research consultant for Metabolon, Inc. D. Saleheen has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. P.N. reports investigator-initated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a scientific advisory board member of Esperion Therapeutics, geneXwell, and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. The spouse of C.J.W. is employed by Regeneron., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Paucity of interlobular bile ducts (PILBD) is a heterogeneous disorder classified into two categories, syndromic and non-syndromic bile duct paucity. Syndromic PILBD is characterized by the presence of clinical manifestations of Alagille syndrome. Non-syndromic PILBD is caused by multiple diseases, such as metabolic and genetic disorders, infectious diseases, and inflammatory and immune disorders. We evaluated a family with a dominantly inherited PILBD, who presented with cholestasis at 1-2 months of age but spontaneously improved by 1 year of age. Next-generation sequencing analysis revealed a heterozygous CACYBP/SIP p.E177Q pathogenic variant. Calcyclin-binding protein and Siah1 interacting protein (CACYBP/SIP) form a ubiquitin ligase complex and induce proteasomal degradation of non-phosphorylated β-catenin. Immunohistochemical analysis revealed a slight decrease in CACYBP and β-catenin levels in the liver of patients in early infancy, which almost normalized by 13 months of age. The CACYBP/SIP p.E177Q pathogenic variant may form a more active or stable ubiquitin ligase complex that enhances the degradation of β-catenin and delays the maturation of intrahepatic bile ducts. Our findings indicate that accurate regulation of the β-catenin concentration is essential for the development of intrahepatic bile ducts and CACYBP/SIP pathogenic variant is a novel cause of PILDB., (© 2022. The Author(s), under exclusive licence to The Japan Society of Human Genetics.)

Cerebellar ataxia, mental retardation, and disequilibrium syndrome 4 (CAMRQ4) is early onset neuromotor disorder and intellectual disabilities caused by variants of ATP8A2. We report sibling cases and systematically analyze previous literature to increase our understanding of CAMRQ4. Japanese siblings presented with athetotic movements at 1 and 2 months of age. They also had ptosis, ophthalmoplegia, feeding difficulty, hypotonia, and severely delayed development. One patient had retinal degeneration and optic atrophy. Flattening of the auditory brainstem responses and areflexia developed. At the last follow-up, neither patient could sit or achieve head control, although some nonverbal communication was preserved. Whole exome sequencing revealed compound heterozygous variants of ATP8A2: NM&#95;016529.6:c.[1741C>T];[2158C>T] p.[(Arg581*)];[(Arg720*)]. The p.(Arg581*) variant has been reported, while the variant p.(Arg720*) was novel. The symptoms did not progress in the early period of development, which makes it difficult to distinguish from dyskinetic cerebral palsy, particularly in solitary cases. However, visual and hearing impairments associated with involuntary movements and severe developmental delay may be a clue to suspect CAMRQ4.