Genetic etiology of truncus arteriosus excluding 22q11.2 deletion syndrome and identification of c.1617del, a prevalent variant in TMEM260, in the Japanese population

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.

Treatment of ZC4H2 Variant-Associated Spastic Paraplegia with Selective Dorsal Rhizotomy and Intensive Postoperative Rehabilitation: A Case Report

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.

Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women

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.

Case Report: Identification of a CARD8 variant in all three patients with PFAPA syndrome complicated with Kawasaki disease

Background

Periodic 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 presentation

Herein, 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.

Conclusions

We 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.

Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment

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.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS)

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 used 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, 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.

jMorp: Japanese Multi-Omics Reference Panel update report 2023

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.

Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study

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.

Deep exome sequencing identifies enrichment of deleterious mosaic variants in neurodevelopmental disorder genes and mitochondrial tRNA regions in bipolar disorder

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.

Establishment of the early prediction models of low-birth-weight reveals influential genetic and environmental factors: a prospective cohort study

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.

A Case Series of Patients With MYBPC1 Gene Variants Featuring Undulating Tongue Movements as Myogenic Tremor

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.

Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability

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.

Familial Paget's disease of bone with ocular manifestations and a novel TNFRSF11A duplication variant (72dup27)

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.

Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank

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 m² 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.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

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.

Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology

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.

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.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

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.

Heterozygous calcyclin-binding protein/Siah1-interacting protein (CACYBP/SIP) gene pathogenic variant linked to a dominant family with paucity of interlobular bile duct

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.

Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis

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.

High-grade lesion occupancy in the uterovaginal area was significantly correlated with CIN2 patients’ prognosis. The number of high-grade lesions in 12 segments detected by an artificial intelligence (AI)-based system was comparable to that detected by senior colposcopists. The overall correct response rate of the AI algorithm for detecting high-grade lesions was 89.7%.

A patient with early-onset SMAX3 and a novel variant of ATP7A

OBJECTIVE

To describe clinical and genetic studies on a patient with early-onset spinal muscular atrophyX3 (SMAX3) with novel variant of ATP7A.

METHODS

Clinical, neurophysiological, neuroimaging and pathological examinations were performed. Whole exome sequencing was applied to search genetic bases of this patient.

RESULTS

The patient had gait abnormality from early infantile period. Muscle imaging at 42 years old showed predominant involvement of proximal muscles as compared to the distal muscles. The patient had a novel variant of ATP7A, which was the fourth genotype of ATP7A exhibited as SMAX3. Contrary to previous reports of distal motor neuropathy, the clinical and neuroimaging findings in this case revealed dominant involvement in the proximal portion of the extremities and trunk, which is similar to patients with type III SMA.

CONCLUSION

The dominant involvement of proximal motor system in this patient may expand the phenotypic variability of SMAX3. We need to be aware of this disorder in differential diagnosis of patients with type III SMA-like phenotype.

The longest reported sibling survivors of a severe form of congenital myasthenic syndrome with the ALG14 pathogenic variant

Congenital myasthenic syndromes (CMS) is a group of diseases that causes abnormalities at the neuromuscular junction owing to genetic anomalies. The pathogenic variant in ALG14 results in a severe pathological form of CMS causing end-plate acetylcholine receptor deficiency. Here, we report the cases of two siblings with CMS associated with a novel variant in ALG14. Immediately after birth, they showed hypotonia and multiple joint contractures with low Apgar scores. Ptosis, low-set ears, and high-arched palate were noted. Deep tendon reflexes were symmetrical. They showed worsening swallowing and respiratory problems; hence, nasal feeding and tracheotomy were performed. Cranial magnetic resonance imaging scans revealed delayed myelination and cerebral atrophy. Exome sequencing indicated that the siblings had novel compound heterozygous missense variants, c.590T>G (p.Val197Gly) and c.433G>A (p.Gly145Arg), in exon 4 of ALG14. Repetitive nerve stimulation test showed an abnormal decrease in compound muscle action potential. After treatment with pyridostigmine, the time off the respirator increased. Their epileptic seizures were well controlled by anti-epileptic drugs. Their clinical course is stable even now at the ages of 5 and 2 years, making them the longest reported survivors of a severe form of CMS with the ALG14 variant thus far.

Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions

We 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., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automatically removes irrelevant predictors (including variants and GxE combinations), and the model is able to simultaneously incorporate multiple environmental variables. Simulation studies to evaluate the proposed method in comparison with other modeling approaches demonstrate its superior performance under the presence of GxE interaction effects. We illustrate the usefulness of our prediction model through application to real GWAS data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

The power of genetic diversity in genome-wide association studies of lipids

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

A cross-population atlas of genetic associations for 220 human phenotypes

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n_total = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

Identification and Validation of Combination Plasma Biomarker of Afamin, Fibronectin and Sex Hormone-Binding Globulin to Predict Pre-eclampsia

The purpose of the present study was to identify a plasma protein biomarker able to predict pre-eclampsia (PE). Comprehensive quantitative proteomics using mass spectrometry with sequential window acquisition of all theoretical fragment ion spectra (SWATH-MS) was applied to plasma samples of 7 PE and 14 healthy pregnant women (for PE subjects, plasma samples were taken before onset of PE), and 11 proteins were selected as candidates potentially able to differentiate the two groups. Plasmas collected at gestational weeks 14-24 from 36 PE and 120 healthy pregnant women (for PE subjects, plasma samples were taken before onset of PE) were used to conduct selected reaction monitoring quantification analysis, optimize protein combinations and conduct internal validation, which consisted of 30 iterations of 10-fold cross-validation using multivariate logistic regression and receiver operating characteristic (ROC) analysis. The combination of afamin, fibronectin, and sex-hormone-binding globulin was selected as the best candidate. The 3-protein combination predictive model (predictive equation and cut-off value) generated using the internal validation subjects was successfully validated in another group of validation subjects (36 PE and 54 healthy (for PE subjects, plasma samples were taken before onset of PE)) and showed good predictive performance, with the area under the curve (AUC) 0.835 and odds ratio 13.43. In conclusion, we newly identified a 3-protein combination biomarker and established a predictive equation and cut-off value that can predict the onset of PE based on analysis of plasma samples collected during gestational weeks 14-24.

Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project

In order to assess the long-term impact of the Great East Japan Earthquake on the oral health of disaster victims and to evaluate gene-environmental interactions in the development of major oral diseases and oral-systemic associations, the oral part of two large-scale genome cohort studies by the Tohoku Medical Megabank Organization (ToMMo), including the Community-based cohort (CommCohort) study and the Birth and Three-Generation cohort (BirThree) study, have been conducted. The study population comprised 32,185 subjects, including 16,886 participants in the CommCohort study and 15,299 participants in the BirThree cohort study, recruited from 2013 to 2017. The oral studies consist of a questionnaire regarding oral hygiene behavior, clinical examinations by dentists, and oral plaque and saliva sampling for microbiome analyses, which were carried out at seven community support centers in Miyagi prefecture. The median age of all participants was 55.0 years, and 66.1% of participants were women. Almost all participants reported that they brushed their teeth more than once a day. The median number of present teeth was 27.0, and the decayed, missing and filled tooth number was 16.0, with a significant difference according to age and sex. The median periodontal pocket and clinical attachment level was 2.48 mm and 4.00 mm, respectively. Periodontal parameters increased significantly according to age, except for the accumulation of dental calculus. The oral part of these extensive cross-sectional studies provides a unique and important platform for future studies on oral health and diseases that elicit through interactions with systemic diseases, lifestyles, life events and genetic backgrounds, and contributes to researches clarifying the long-term effects of disasters on oral health.

Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs

Ethnic-specific SNP arrays are becoming more important to increase the power of genome-wide association studies in diverse population. In the Tohoku Medical Megabank Project, we have been developing a series of Japonica Arrays (JPA) for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population. Here, we designed a novel version of the SNP array for the Japanese population, called Japonica Array NEO (JPA NEO), comprising a total of 666,883 markers. Among them, 654,246 tag SNPs of autosomes and X chromosome were selected from an expanded reference panel of 3,552 Japanese, 3.5KJPNv2, using pairwise r² of linkage disequilibrium measures. Additionally, 28,298 markers were included for the evaluation of previously identified disease risk markers from the literature and databases, and those present in the Japanese population were extracted using the reference panel. Through genotyping 286 Japanese samples, we found that the imputation quality r² and INFO score in the minor allele frequency bin >2.5–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. From these results, JPA NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores.

Body mass index and colorectal cancer risk: A Mendelian randomization study

Traditional observational studies have reported a positive association between higher body mass index (BMI) and the risk of colorectal cancer (CRC). However, evidence from other approaches to pursue the causal relationship between BMI and CRC is sparse. A two-sample Mendelian randomization (MR) study was undertaken using 68 single nucleotide polymorphisms (SNPs) from the Japanese genome-wide association study (GWAS) and 654 SNPs from the GWAS catalogue for BMI as sets of instrumental variables. For the analysis of SNP-BMI associations, we undertook a meta-analysis with 36 303 participants in the Japanese Consortium of Genetic Epidemiology studies (J-CGE), comprising normal populations. For the analysis of SNP-CRC associations, we utilized 7636 CRC cases and 37 141 controls from five studies in Japan, and undertook a meta-analysis. Mendelian randomization analysis of inverse-variance weighted method indicated that a one-unit (kg/m2 ) increase in genetically predicted BMI was associated with an odds ratio of 1.13 (95% confidence interval, 1.06-1.20; P value <.001) for CRC using the set of 68 SNPs, and an odds ratio of 1.07 (1.03-1.11, 0.001) for CRC using the set of 654 SNPs. Sensitivity analyses robustly showed increased odds ratios for CRC for every one-unit increase in genetically predicted BMI. Our MR analyses strongly support the evidence that higher BMI influences the risk of CRC. Although Asians are generally leaner than Europeans and North Americans, avoiding higher BMI seems to be important for the prevention of CRC in Asian populations.

Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries

Primary open-angle glaucoma (POAG), is a heritable common cause of blindness world-wide. To identify risk loci, we conduct a large multi-ethnic meta-analysis of genome-wide association studies on a total of 34,179 cases and 349,321 controls, identifying 44 previously unreported risk loci and confirming 83 loci that were previously known. The majority of loci have broadly consistent effects across European, Asian and African ancestries. Cross-ancestry data improve fine-mapping of causal variants for several loci. Integration of multiple lines of genetic evidence support the functional relevance of the identified POAG risk loci and highlight potential contributions of several genes to POAG pathogenesis, including SVEP1, RERE, VCAM1, ZNF638, CLIC5, SLC2A12, YAP1, MXRA5, and SMAD6. Several drug compounds targeting POAG risk genes may be potential glaucoma therapeutic candidates.

ALOX12 mutation in a family with dominantly inherited bleeding diathesis

The arachidonic acid (AA) cascade plays a significant role in platelet aggregation. AA released from membrane phospholipids is metabolized by cyclooxygenase (COX) pathway to thromboxane A₂ (TXA₂) or by 12S-lipoxygenase (ALOX12) to 12-hydroperoxyeicosatetraenoic acid (12-HPETE). In contrast to a well-known role of the COX pathway in platelet aggregation, the role of ALOX12 is not well understood. Platelets of ALOX12-deficient mice exhibit increased sensitivity for ADP-induced aggregation. However, recent evidence strongly suggests a significant role of ALOX12 in platelet aggregation and calcium signaling. 12-HPETE potentiates thrombin- and thromboxane-induced platelet aggregation, and calcium signaling. Inhibition experiments of ALOX12 demonstrated decreased platelet aggregation and calcium signaling in stimulated platelets. We studied a family with a dominantly inherited bleeding diathesis using next-generation sequencing analysis. Platelet aggregation studies revealed that the proband's platelets had defective aggregation responses to ADP, TXA₂ mimetic U46619, collagen, and AA, normal affinity of TXA₂ receptor for U46619, and normal induction of GTPase activity upon stimulation with U46619. However, the production of inositol 1,4,5-triphosphate (IP₃) was only increased up to 30% of the control upon U46619 stimulation, suggesting a defect in phospholipase C-β2 (PLCB2) activation downstream from TXA₂ receptors. Affected family members had no mutation of PLCB2, but had a heterozygous c.1946A > G (p.Tyr649Cys) mutation of ALOX12. ALOX12 activity in platelets from the affected members was decreased to 25-35% of the control. Our data strongly suggested that a heterozygous c.1946A > G ALOX12 mutation was a disease-causing mutation; however, further experiments are required to confirm the pathogenesis of ALOX12 mutation in platelet aggregation.

Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks

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.

Construction and integration of three de novo Japanese human genome assemblies toward a population-specific reference

The complete human genome sequence is used as a reference for next-generation sequencing analyses. However, some ethnic ancestries are under-represented in the reference genome (e.g., GRCh37) due to its bias toward European and African ancestries. Here, we perform de novo assembly of three Japanese male genomes using > 100× Pacific Biosciences long reads and Bionano Genomics optical maps per sample. We integrate the genomes using the major allele for consensus and anchor the scaffolds using genetic and radiation hybrid maps to reconstruct each chromosome. The resulting genome sequence, JG1, is contiguous, accurate, and carries the Japanese major allele at most loci. We adopt JG1 as the reference for confirmatory exome re-analyses of seven rare-disease Japanese families and find that re-analysis using JG1 reduces total candidate variant calls versus GRCh37 while retaining disease-causing variants. These results suggest that integrating multiple genomes from a single population can aid genome analyses of that population.

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

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.

Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population

We performed a metabolome genome-wide association study for the Japanese population in the prospective cohort study of Tohoku Medical Megabank. By combining whole-genome sequencing and nontarget metabolome analyses, we identified a large number of novel associations between genetic variants and plasma metabolites. Of the identified metabolite-associated genes, approximately half have already been shown to be involved in various diseases. We identified metabolite-associated genes involved in the metabolism of xenobiotics, some of which are from intestinal microorganisms, indicating that the identified genetic variants also markedly influence the interaction between the host and symbiotic bacteria. We also identified five associations that appeared to be female-specific. A number of rare variants that influence metabolite levels were also found, and combinations of common and rare variants influenced the metabolite levels more profoundly. These results support our contention that metabolic phenotyping provides important insights into how genetic and environmental factors provoke human diseases.

A genotype imputation method for de-identified haplotype reference information by using recurrent neural network

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.

Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario.

Clustering by phenotype and genome-wide association study in autism

Autism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability. We conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a data set of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC data set of 712 probands and 354 controls in the replication stage. In the preliminary study, which was conducted in conventional GWAS design, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P < 5.0 × 10^-8. Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA, and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs controls in the replication cohort. These findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts.

Abstract 3486: Body mass index and colorectal cancer risk in Japanese populations: a Mendelian randomization study

Background: Prospective cohort studies have shown a positive association between body mass index (BMI) and the risk of colorectal cancer (CRC). However, limitations inherent in traditional observational studies, such as reverse causality and residual confounding, might explain the association. To overcome these limitations, Mendelian randomization (MR) studies of the BMI-CRC association have been conducted in European and U.S. groups, but the association remains to be clarified in East Asians. Purpose: We performed MR analyses to investigate the causal association between BMI and CRC in Japanese populations.

Methods: Our study design consisted of 4 steps. (1) Based on a previous Genome-Wide Association Study in Japanese populations, we selected 68 BMI-associated single nucleotide polymorphisms (SNPs), which explained about 2.0% of the BMI variance, as instruments. (2) We examined the associations between 68 SNPs and BMI among general Japanese populations in the Japanese Consortium of Genetic Epidemiology studies (N=36,253). (3) We performed a fixed-effect meta-analysis to investigate associations between 68 SNPs and CRC using individual-level data and publicly available summary-statistic data of Japanese populations (cases=7,473, controls=33,322). (4) Finally, we used the inverse-variance weighted (IVW) method to calculate MR estimates. Several sensitivity analyses were applied to assess robustness or horizontal pleiotropy using weighted median, weighted mode, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier (PRESSO) methods.

Results: In the main analysis using the IVW method, a one-unit increase in BMI was associated with an odds ratio of 1.12 (95% confidence interval [CI]: 1.06-1.20) for CRC. Sensitivity analyses consistently showed increased odds ratios for CRC per one-unit increase in BMI. The odds ratios for weighted median, weighted mode, and MR-Egger regression were 1.16 (95% CI: 1.06-1.27), 1.14 (95% CI: 1.05-1.24), and 1.10(95% CI: 0.98-1.23), respectively. The MR-Egger intercept P-value was 0.63. No outlier was detected using the MR-PRESSO method.

Conclusions: Our MR analyses provide evidence that BMI is positively associated with CRC in Japanese populations. Our findings seem to suggest that MR estimates for the BMI-CRC association may be consistent across different ethnicities.

Citation Format: Shiori Suzuki, Atsushi Goto, Masahiro Nakatochi, Akira Narita, Taiki Yamaji, Norie Sawada, Ryoko Katagiri, Tsuyoshi Hachiya, Yoichi Sutoh, Isao Oze, Yuriko Koyanagi, Yumiko Kasugai, Hidemi Ito, Hiroaki Ikezaki, Keitaro Tanaka, Takashi Tamura, Haruo Mikami, Toshiro Takezaki, Sadao Suzuki, Nagato Kuriyama, Kiyonori Kuriki, Yoshikuni Kita, Kokichi Arisawa, Kenji Takeuchi, Kozo Tanno, Atsushi Shimizu, Gen Tamiya, Atsushi Hozawa, Kengo Kinoshita, Kenji Wakai, Makoto Sasaki, Masayuki Yamamoto, Keitaro Matsuo, Shoichiro Tsugane, Motoki Iwasaki. Body mass index and colorectal cancer risk in Japanese populations: a Mendelian randomization study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3486.

Artificial intelligence powered statistical genetics in biobanks

Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been established and released their vast amount of data in several countries. These genetic and epidemiological resources are expected to allow investigators to disentangle genetic and environmental components conferring common complex diseases. There are, however, two major challenges to statistical genetics for this goal: small sample size-high dimensionality and multilayered-heterogenous endophenotypes. Rather counterintuitively, biobank data generally have small sample size relative to their data dimensionality consisting of genomic variation, lifestyle questionnaire, and sometimes their interaction. This is a widely acknowledged difficulty in data analysis, so-called "p»n problem" in statistics or "curse of dimensionality" in machine-learning field. On the other hand, we have too many measurements of individual health status, which are endophenotypes, such as health check-up data, images, psychological test scores in addition to metabolomics and proteomics data. These endophenotypes are rich but not so tractable because of their worsen dimensionality, and substantial correlation, sometimes confusing causation among them. We have tried to overcome the problems inherent to biobank data, using statistical machine-learning and deep-learning technologies.

Endogenization and excision of human herpesvirus 6 in human genomes

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.

Genome-wide association study identifies new loci for albuminuria in the Japanese population

BACKGROUND

Urinary albumin excretion (UAE) is a risk factor for cardiovascular diseases, metabolic syndrome, chronic kidney disease, etc. Only a few genome-wide association studies (GWAS) for UAE have been conducted in the European population, but not in the Asian population. Here we conducted GWAS and identified several candidate genes harboring single nucleotide polymorphisms (SNPs) responsible for UAE in the Japanese population.

METHODS

We conducted GWAS for UAE in 7805 individuals of Asian ancestry from health-survey data collected by Tohoku Medical Megabank Organization (ToMMo) and Iwate Tohoku Medical Megabank Organization (IMM). The SNP genotype data were obtained with a SNP microarray. After imputation using a haplotype panel consisting of 2000 genome sequencing, 4,962,728 SNP markers were used for the GWAS.

RESULTS

Eighteen SNPs at 14 loci (GRM7, EXOC1/NMU, LPA, STEAP1B/RAPGEF5, SEMA3D, PRKAG2, TRIQK, SERTM1, TPT1-AS1, OR5AU1, TSHR, FMN1/RYR3, COPRS, and BRD1) were associated with UAE in the Japanese individuals. A locus with particularly strong associations was observed on TSHR, chromosome 14 [rs116622332 (p = 3.99 × 10^-10)].

CONCLUSION

In this study, we successfully identified UAE-associated variant loci in the Japanese population. Further study is required to confirm this association.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

Outlier detection for questionnaire data in biobanks

BACKGROUND

Biobanks increasingly collect, process and store omics with more conventional epidemiologic information necessitating considerable effort in data cleaning. An efficient outlier detection method that reduces manual labour is highly desirable.

METHOD

We develop an unsupervised machine-learning method for outlier detection, namely kurPCA, that uses principal component analysis combined with kurtosis to ascertain the existence of outliers. In addition, we propose a novel regression adjustment approach to improve detection, namely the regression adjustment for data by systematic missing patterns (RAMP).

RESULT

Application to epidemiological record data in a large-scale biobank (Tohoku Medical Megabank Organization, Japan) shows that a combination of kurPCA and RAMP effectively detects known errors or inconsistent patterns.

CONCLUSIONS

We confirm through the results of the simulation and the application that our methods showed good performance. The proposed methods are useful for many practical analysis scenarios.

Study Profile of the Tohoku Medical Megabank Community-Based Cohort Study

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.

Maternal Baseline Characteristics and Perinatal Outcomes: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study

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.