A fine-scale genetic map of the Japanese population.

Genetic maps are fundamental resources for linkage and association studies. A fine-scale genetic map can be constructed by inferring historical recombination events from the genome-wide structure of linkage disequilibrium-a non-random association of alleles among loci-by using population-scale sequencing data. We constructed a fine-scale genetic map and identified recombination hotspots from 10 092 551 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.

Functional variants in a TTTG microsatellite on 15q26.1 cause familial nonautoimmune thyroid abnormalities.

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.

Long-term clinical observation of patients with heterozygous KIF1A variants.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Pathological variants in genes associated with disorders of sex development and central causes of hypogonadism in a whole-genome reference panel of 8380 Japanese individuals.

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.

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.