The first Japanese MDPL case

Mandibular hypoplasia, deafness, progeroid features and lipodystrophy (MDPL) syndrome is a rare autosomal dominant disorder caused by heterozygous POLD1 mutations. To date, 13 patients affected by POLD1 mutation-caused MDPL have been described. We report a clinically undiagnosed 11-year-old male who noted joint contractures at 6 years of age. Targeted exome sequencing identified a known POLD1 mutation [NM_002691.3:c.1812_1814del, p.(Ser605del)] that diagnosed him as the first Japanese/East Asian MDPL case

ターゲットエクソーム解析および染色体マイクロアレイ解析を用いた母斑性基底細胞癌症候群の遺伝子診断

Background: Nevoid basal cell carcinoma syndrome (NBCCS) is an autosomal dominant disorder mainly caused by heterozygous mutations of PTCH1. In addition to characteristic clinical features, detection of a mutation in causative genes is reliable for the diagnosis of NBCCS; however, no mutations have been identified in some patients using conventional methods. Objective: To improve the method for the molecular diagnosis of NBCCS. Methods: We performed targeted exome sequencing (TES) analysis using a multi-gene panel, including PTCH1, PTCH2, SUFU, and other sonic hedgehog signaling pathway-related genes, based on next-generation sequencing (NGS) technology in 8 cases in whom possible causative mutations were not detected by previously performed conventional analysis and 2 recent cases of NBCCS. Subsequent analysis of gross deletion within or around PTCH1 detected by TES was performed using chromosomal microarray (CMA). Results: Through TES analysis, specific single nucleotide variants or small indels of PTCH1 causing inferred amino acid changes were identified in 2 novel cases and 2 undiagnosed cases, whereas gross deletions within or around PTCH1, which are validated by CMA, were found in 3 undiagnosed cases. However, no mutations were detected even by TES in 3 cases. Among 3 cases with gross deletions of PTCH1, deletions containing the entire PTCH1 and additional neighboring genes were detected in 2 cases, one of which exhibited atypical clinical features, such as severe mental retardation, likely associated with genes located within the 4.3 Mb deleted region, especially. Conclusion: TES-based simultaneous evaluation of sequences and copy number status in all targeted coding exons by NGS is likely to be more useful for the molecular diagnosis of NBCCS than conventional methods. CMA is recommended as a subsequent analysis for validation and detailedmapping of deleted regions, which may explain the atypical clinical features of NBCCS cases

播磨灘および大阪湾のイガイ中のノニルフェノール

Nonylphenol (NP) having an endocrine disrupter effect is used in the form of a poly(oxy ethylene)nonylphenyl ether (NPnEO) as a non-ion surfactant, ethoxy straight chain is decomposed by the microbial degradation in environment, then NPnEO is converted to NP through the action. Consequently it is concerned about the effect of NP on wildlife. Blue Mussel has the tolerance, which is strong in pollution. In this study, seawater and Blue Mussel were collected in Harimanada and Osaka Bay enclosed coastal seas. We investigated the distribution and seasonal movement of NP concentration. The result of the study showed that the concentrations of NP in seawater were gradually increasing from August. Moreover, the concentrations in Blue Mussel were the highest in almost all points in August. These results indicate that the water environment where Blue Mussel lives influenced them. The water temperature of 25℃ demonstrated most efficient filtration capability in August and dissolved oxygen (DO) concentration was low in August and September, so the Blue Mussel took in sea water so much. NP concentration in Blue Mussel was varied in the short term. This was suggested that Blue Mussel could become the important indicator of NP concentration in seawater

A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females

Personalized approaches to prevention based on genetic risk models have been anticipated, and many models for the prediction of individual breast cancer risk have been developed. However, few studies have evaluated personalized risk using both genetic and environmental factors. We developed a risk model using genetic and environmental risk factors using 1319 breast cancer cases and 2094 controls from three case–control studies in Japan. Risk groups were defined based on the number of risk alleles for 14 breast cancer susceptibility loci, namely low (0–10 alleles), moderate (11–16) and high (17+). Environmental risk factors were collected using a self-administered questionnaire and implemented with harmonization. Odds ratio (OR) and C-statistics, calculated using a logistic regression model, were used to evaluate breast cancer susceptibility and model performance. Respective breast cancer ORs in the moderate- and high-risk groups were 1.69 (95% confidence interval, 1.39–2.04) and 3.27 (2.46–4.34) compared with the low-risk group. The C-statistic for the environmental model of 0.616 (0.596–0.636) was significantly improved by combination with the genetic model, to 0.659 (0.640–0.678). This combined genetic and environmental risk model may be suitable for the stratification of individuals by breast cancer risk. New approaches to breast cancer prevention using the model are warranted

Bromodomain protein BRD8 regulates cell cycle progression in colorectal cancer cells through a TIP60-independent regulation of the pre-RC complex

Summary: Bromodomain-containing protein 8 (BRD8) is a subunit of the NuA4/TIP60-histone acetyltransferase complex. Although BRD8 has been considered to act as a co-activator of the complex, its biological role remains to be elucidated. Here, we uncovered that BRD8 accumulates in colorectal cancer cells through the inhibition of ubiquitin-dependent protein degradation by the interaction with MRG domain binding protein. Transcriptome analysis coupled with genome-wide mapping of BRD8-binding sites disclosed that BRD8 transactivates a set of genes independently of TIP60, and that BRD8 regulates the expression of multiple subunits of the pre-replicative complex in concert with the activator protein-1. Depletion of BRD8 induced cell-cycle arrest at the G1 phase and suppressed cell proliferation. We have also shown that the bromodomain of BRD8 is indispensable for not only the interaction with histone H4 or transcriptional regulation but also its own protein stability. These findings highlight the importance of bromodomain as a therapeutic target

Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS, and impact on esophageal cancer risk in Japanese

<p><span>An East Asian-specific variant on <em>aldehyde</em> <em>dehydrogenase</em> <em>2</em> (<em>ALDH2</em> rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study (GWAS) meta-analysis in up to 175,672 Japanese individuals to uncover additional loci associated with alcohol consumption in an rs671-dependent manner. Three loci (<em>GCKR</em>, <em>KLB</em>, and <em>ADH1B</em>)</span> <span>satisfied the genome-wide significance threshold in wild-type homozygotes (GG), whereas six loci (<em>GCKR</em>, <em>ADH1B</em>, <em>ALDH1B1</em>, <em>ALDH1A1</em>, <em>ALDH2</em>, and <em>GOT2</em>) did so in heterozygotes (GA). Of these, five loci showed genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Subsequent polygenic risk scoring depicted interactions highlighted by stratified GWAS. Further, most discovered loci showed significant effects on risk of esophageal cancer, a representative alcohol-related disease, and multiple other phenotypes. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.</span></p><p>No special software are required to open the files.</p><p>Funding provided by: Takeda Science Foundation<br>Crossref Funder Registry ID: https://ror.org/02y123g31<br>Award Number: </p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP16ck0106095</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP19ck0106266</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105001</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105002</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105003</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP16H06277</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP26253041</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP20K10463</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP19KK0418</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP20K10471</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105004</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP16ek0109070h0003</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP18kk0205008h0003</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP18kk0205001s0703</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP19ek0109283h0003</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP19ek0109348h0002</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP21gm4010006</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22km0405211</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22ek0410075</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22km0405217</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22ek0109594</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 2022-A-20</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 17015018</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 221S0001</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 17015018</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 221S0001</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 16H06277</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 22H04923</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP17K07255</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP17KT0125</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 22H00476</p><p>Funding provided by: Japan Science and Technology Agency<br>Crossref Funder Registry ID: https://ror.org/00097mb19<br>Award Number: JPMJMS2021</p><p>Funding provided by: Japan Science and Technology Agency<br>Crossref Funder Registry ID: https://ror.org/00097mb19<br>Award Number: JPMJMS2024</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 28-A-19</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 31-A-18</p><p>Funding provided by: Yamagiwa Yoshida Memorial UICC International Cancer Study Grants<br>Award Number: </p><p>Funding provided by: Kobayashi Foundation for Cancer Research<br>Crossref Funder Registry ID: https://ror.org/02qjq5342<br>Award Number: </p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 23-A-31</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 26-A-2</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 29-A-4</p><p>Funding provided by: Princess Takamatsu Cancer Research Fund<br>Crossref Funder Registry ID: https://ror.org/00q3q5393<br>Award Number: </p><p>Funding provided by: Osake-no-Kagaku Foundation*<br>Crossref Funder Registry ID: <br>Award Number: </p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 22H03350</p><p class="MsoNormal"><strong><span>Summary statistics for drinking amount and ever drinking four types of analysis, ALDH2 rs671 GG only, GA only, All and interaction with GA.</span></strong></p> <p class="MsoNormal"><span>These summary statistics were obtained from the analyses described below.</span></p> <p class="MsoNormal"><strong><span>Study subjects and genotyping</span></strong></p> <p class="MsoNormal"><span>We performed a genome-wide meta-analysis based on the Japanese Consortium of Genetic Epidemiology studies (J-CGE) (Suzuki S et al. Cancer Sci 2021), the Nagahama Study (Funada S et al. J Urol 2018), and the BBJ Study (Hirata et al. J Epidemiol 2017<em>, </em>Nagai A et al. J Epidemiol 2017). The J-CGE consisted of the following Japanese population-based and hospital-based studies: the HERPACC Study (Hamajima N et al. Asian Pac J Cancer Prev 2001), the J-MICC Study (Hamajima N et al. Asian Pac J Cancer Prev 2007, Wakai et al. J Epidemiol 2011), the JPHC Study (Tsugane S et al. Jpn J Clin Oncol 2014), and the TMM Study (Hozawa A et al. J Epidemiol 2021). Individual study descriptions and an overview of the characteristics of the study populations are provided in the Supplementary Information and Supplementary Table 1.</span></p> <p class="MsoNormal"><strong><span>Quality control and genotype imputation</span></strong></p> <p class="MsoNormal"><span>Quality control for samples and SNPs was performed based on study-specific criteria (Supplemental Table 2). Genotype data in each study were imputed separately based on the 1000 Genomes Project reference panel (Phase 3, all ethnicities) (The 1000 Genomes Project Consortium Nature 2015). Phasing was performed with the use of SHAPEIT (v2) (Delaneau O et al. Nat Methos 2013) and Eagle (Loh PR et al. Nat Genet 2016), and imputation was performed using minimac3 (Das S et al. Nat Genet 2016), minimac4, or IMPUTE (v2) (Howie BN et al. PLoS Genet 2009). Information on the study-specific genotyping, imputation, quality control, and analysis tools is provided in Supplementary Table 2. After genotype imputation, further quality control was applied to each study. SNPs with an imputation quality of r<sup>2</sup> < 0.3 for minimac3 or minimac4, info < 0.4 for IMPUTE2 or an MAF of <0.01 were excluded. </span></p> <p class="MsoNormal"><strong><span>Association analysis of SNPs with daily alcohol intake and drinking status</span></strong></p> <p class="MsoNormal"><span>Association analysis of SNPs with daily alcohol intake and drinking status was performed on three different subject groups: the entire population, subjects with the rs671 GG genotype only, and subjects with the rs671 GA genotype only. Because the number of drinkers with the rs671 AA genotype was too small (Supplementary Table 3), association analysis in subjects with the rs671 AA genotype only was not conducted. Daily alcohol intake was base-2 log-transformed (log<sub>2</sub> (grammes/day + 1)). The association of daily alcohol intake with SNP allele dose for each study was assessed by linear regression analysis with adjustment for age, age<sup>2</sup>, sex, and the first 10 principal components. For the BBJ Study, the affection status of 47 diseases was further added as covariates. The association of drinking status with SNP allele dose for each study was assessed by logistic regression analysis with adjustment for age, age<sup>2</sup>, sex, the first 10 principal components, and disease affection status of 47 diseases (for the BBJ Study). The effect sizes and standard errors estimated in the association analysis were used in the subsequent meta-analysis. The association analysis was conducted using EPACTS (http://genome.sph.umich.edu/wiki/EPACTS), SNPTEST (Marchini J et al. Nat Genet 2007), or PLINK2 (Chang CC et al. GigaScience 2015).</span></p> <p class="MsoNormal"><span>Association analysis, including interaction terms, was performed to evaluate the differential effects of each SNP on daily alcohol intake and drinking status between the GG and GA genotypes of rs671. In the interaction analysis for daily alcohol intake, the linear regression models were fit as the formula described in the Materials and Methods section. The GG genotype is coded as 0, and the GA genotype is coded as 1. Carriers of the AA genotype were excluded from the analysis. x<sub>snp</sub> is the imputed genotype coded as [0,2] for each SNP. c<sub>k</sub> is a covariate composed of age, age<sup>2</sup>, sex, the first 10 principal components, and 47 disease affection statuses (for the BBJ Study). The effect sizes of the interaction term, ß <sub>interaction</sub>, and its standard errors estimated in the association analysis were used in the subsequent meta-analysis. In the interaction analysis for drinking status, the logistic regression model was fit as the formula described in the Materials and Methods section. Other variables and procedures are as above. The association analysis, including the interaction term, was conducted using PLINK2 (Chang CC et al. GigaScience 2015). In this study, we employed rs671 genotypes directly extracted from SNP genotyping data, and no imputed data were used. With respect to concerns regarding genotype error, we further genotyped rs671 using TaqMan Assays with the 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in all HERPACC samples in this study (<em>n</em> = 4,958). Results confirmed a 99.96% (<em>n</em> = 4,956) match of rs671 genotypes between the SNP microarray- and TaqMan-based data. The BBJ Study, the biggest data source in this study, also guaranteed a 100% concordance of rs671 genotyping between the SNP microarray and their in-house whole-genome sequencing (WGS) data (<em>n</em> = 2,798) in their previous study (Matoba N et al. Nat Hum Behav 2020). All the other cohorts, accounting for 20% of the data in this study, also used the <em>Illumina</em> genotyping platform (Supplementary Table 2), indicating that we can be assured of the accuracy of rs671 genotypes in these studies.</span></p> <p class="MsoNormal"><span>To identify studies with inflated GWAS significance, which can result from population stratification, we computed the intercept from LDSC (Bulik-Sullivan BK et al. Nat Genet 2015). Before the meta-analysis, all study-specific results in the association analysis were corrected by multiplying the standard error of the effect size by the value of intercept from LDSC if the intercept of that study was greater than 1. </span></p> <p class="MsoNormal"><strong><span>Meta-analysis</span></strong></p> <p class="MsoNormal"><span>The meta-analysis was performed with all Japanese subjects in the six cohorts (Supplementary Table 1). The results of association analyses for each SNP across the studies were combined with METAL software (Willer CJ et al. Bioinformatics 2010) by the fixed-effects inverse-variance-weighted method. Heterogeneity of effect sizes was assessed by <em>I</em><sup>2</sup> and Cochran's <em>Q </em>statistic. The meta-analysis included SNPs for which genotype data were available from at least three studies with a total sample size of at least 20,000 individuals for unstratified GWAS or interaction GWAS or 10,000 individuals for rs671-stratified GWAS. The genome-wide significance level α was set to a <em>P</em> value <5 × 10<sup>–8</sup>. <em>P</em>-values with <1.0×10<sup>−300</sup> was calculated with Rmpfr of the R package. To assess the inflation of the test statistics for the meta-analysis, we computed the genomic inflation factor, l, and intercept from LDSC (Freedman ML et al. Nat Genet 2004).</span></p&gt

Genome-wide association meta-analysis identifies GP2 gene risk variants for pancreatic cancer

Previous genome-wide association studies have identified risk loci for pancreatic cancer but were centered on individuals of European ancestry. Here the authors identify GP2 gene variants associated with pancreatic cancer susceptibility in populations of East Asian ancestry