Biallelic expansions and a motif change in RFC1 are a common cause of cerebellar ataxia, neuropathy, and vestibular areflexia syndrome. Molecular diagnosis relies on a complicated combination of repeat primed PCR and Southern blotting. We developed a whole genome sequencing based method for RFC1 repeat detection. The combination of sequence motifs and allele length analysis in 29,478 individuals showed that 92.5% of samples have no expanded allele or have expansions of known benign motifs on one allele. In total, 103 samples were classified as biallelic carriers of pathogenic expansions and the frequency of the most common pathogenic allele AAGGG was estimated as 3.9%. Our RFC1 classification method was validated by molecular diagnostic methods achieving sensitivity and specificity of 100% and 97.4%, respectively. Additionally, we catalogued 8 rare repeat motifs, further elucidating the high sequence complexity within the RFC1 locus. In this study we report a new method to identify patients carrying RFC1 pathogenic repeat expansions from WGS data, reducing the need for the inefficient molecular workflow currently used for clinical diagnoses. This method correctly identifies recessive pathogenic repeat expansions in RFC1 and allows screening of existing large scale WGS datasets of unsolved cases. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. In addition, RS is grateful to the Medical Research Council for funding this work (MRC UK MR/J004758/1, G0802760, G1001253). J.V. holds a fellowship from the Health Education England Genomics Education Programme. H.H. is grateful to the Medical Research Council (UK), the Wellcome Trust Synaptopathies Award, Ataxia UK, Rosetrees Trust, Brain Research UK, University College London Official Development Assistance and Low and Middle Income Country award, The Multiple System Atrophy Trust, Muscular Dystrophy UK, and Muscular Dystrophy Association. Andrea Cortese was supported by the Medical Research Council (MR/T001712/1), Fondazione Cariplo (grant n. 2019-1836), the Inherited Neuropathy Consortium, and Fondazione Regionale per la Ricerca Biomedica (Regione Lombardia, project ID 1751723). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics comittee of UCL Queen Square Institute of Neurology and the NHNN, London, UK gave ethical approval for this work. The National Genomic Research Library v5.1, Genomics England. approved this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Research on the de-identified patient data used in this publication can be carried out in the Genomics England Research Environment subject to a collaborative agreement that adheres to patient led governance. All interested readers will be able to access the data in the same manner that the authors accessed the data. For more information about accessing the data, interested readers may contact research-network{at}genomicsengland.co.uk or access the relevant information on the Genomics England website: .

The study investigates the use of volatile organic compounds (VOCs) in exhaled breath as a non-invasive diagnostic tool for lung cancer (LC). Employing a novel micro gas chromatography- micro photoionisation detector (μGC-μPID) system, we aimed to identify and validate VOCs that could differentiate between LC patients and those with benign pulmonary diseases. The cross-sectional study included 106 participants, categorized into 85 LC patients and 21 benign controls, based on computed tomography and histological assessments. Participants provided breath samples following a standardized protocol, and the μGC-μPID system, known for its rapid point-of-care capabilities and low detection limits, was utilized for rapid and sensitive online VOC analysis. Through a meticulous process of data analysis, including principal component analysis, single-factor hypothesis testing, orthogonal partial least squares discriminant analysis and various tests of machine learning algorithms, including random forest, k-nearest neighbor, logistic regression, XGBoost, and support vector machine, we finally identified six potential VOC biomarkers, with diagnostic models incorporating these markers achieving high sensitivity (0.95-1.00) and specificity (0.84-0.88), and areas under the receiver operating characteristic curve ranging from 0.79 to 0.91. Moreover, these models were also extended favourably to the recurrence and metastasis of pulmonary cancer and oesophageal cancer. The study demonstrates the potential of μGC-μPID as a point-of-care tool for LC differential diagnosis, highlighting the need for further validation in larger, multi-centric cohorts to refine the VOC biomarker panel and establish a robust diagnostic framework for clinical application. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was not supported by any grants. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abstract Background: Despite its revolutionary impact, cancer genomics alone provides little information on tumor phenotype or functional state, which are governed by epigenetic mechanisms, notably methylation of regulatory regions. Tumor and host epigenetic methylation signatures reflect not only tumor phenotype, such as histology, prognosis, protein expression, and functional sub-type, but also that of the tumor microenvironment and the patient, including immune status, therapy-related adverse events, comorbidities, and disease location. Epigenetic markers also provide more sensitive and precise measures of tumor burden, opening up applications for longitudinal therapy response and monitoring. Here we report the initial validation of GuardantINFINITY, a liquid biopsy assay combining genomic information from >800 genes with characterization of the blood-quiet regulatory methylome, both at single-molecule sensitivity from a single tube of peripheral blood. Methods: Analytical performance was assessed using 594 cancer patient cfDNA, cell line, and cancer-free donor samples at 5-30ng cfDNA input. Results: Reportable ranges established for SNVs were ≥0.04% variant allele fraction (VAF), ≥0.04% for indels, ≥0.06% for fusions, ≥2.12 copies for amplifications (CNAs), <1.7 copies for copy loss. Observed 95% limits of detection (LoD) were 0.282% for SNVs across all genes (0.2% for oncogenic hotspots), 0.397% for non-homopolymeric indels, 0.05% for fusions, 2.5 copies for CNAs, 16.3% VAF or 1.84 copies for gene deletions, 7.3 copies for viral (HPV, EBV) detection, and 0.06% for MSI-H. For promoter and sample-level methylation, LoDs were 0.06% and 0.05% tumor fraction, respectively. cfDNA cancer samples demonstrated 100% accuracy for SNVs and Indels above 0.5% VAF and 100% for CNAs and fusions across the reportable range. The analytical false positive rate per base was 6.84e-6 for SNVs, 3.42e-6 for indels, and 0 for CNAs and fusions, with positive predictive values of 97.5% for SNVs, 98% for indels, and 100% for CNAs above 2.5 copies and all tested fusions. Conclusions: GuardantINFINITY is a patient-care-ready liquid biopsy capable of integrated genomic and epigenomic analysis of all solid tumors at single-molecule sensitivity. In addition to traditional genotyping compatible with Guardant360 for more content, the technology’s demonstrated LoD showed the potential for ultra-sensitive ctDNA detection for MRD and recurrence surveillance, tumor fraction quantitation for therapy monitoring, oncogenic virus detection, immunogenotyping, epigenotyping, and tumor phenotype characterization, representing a new standard in biomarker discovery. Citation Format: Tingting Jiang, Indira Wu, Yvonne Kim, Nageswara Alla, Giao Tran, Dustin Ma, Forum Shah, Jun Zhao, Sai Chen, Sante Gnerre, Melis Hazar, Hao Wang, Catalin Barbacioru, Karen Ryall, Ankit Jambusaria, Anupam Chakravarthy, Anthony Zunino, Theresa Pham, Farsheed Ghadiri, Evan Diehl, Benjamin Morck, Arancha Sanchez, Rochelle Dayan, XianXian Liu, Jeffrey Werbin, Jill Lai, Brett Kennedy, Ross Eppler, Justin Odegaard, Han-Yu Chuang, Helmy Eltoukhy. Analytical validation of a robust integrated genomic and epigenomic liquid biopsy for biomarker discovery, therapy selection, and response monitoring [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6601.

Objective The effects of preoperative respiratory muscle training (RMT) on postoperative complications in patients surgically treated for myasthenia gravis (MG) remain unclear. The present study therefore evaluated the effects of preoperative moderate-to-intense RMT and aerobic exercise, when added to respiratory physiotherapy, on respiratory vital capacity, exercise capacity, and duration of hospital stay in patients with MG. Methods Eighty patients with MG scheduled for extended thymectomy were randomly divided into two groups. The 40 subjects in the study group (SG) received preoperative moderate-to-intense RMT and aerobic exercise in addition to respiratory physiotherapy, whereas the 40 subjects in the control group (CG) received only chest physiotherapy. Respiratory vital capacity (as determined by VC, FVC, FEV1, FEV1/FVC, and PEF) and exercise capacity (as determined by the 6-min walk test [6 MWT]) were measured pre- and postoperatively and before discharge. The duration of hospital stay and activity of daily living (ADL) were also determined. Results Demographic and surgical characteristics, along with preoperative vital capacity and exercise capacity, were similar in the two groups. In the CG, VC ( p = 0.001), FVC ( p = 0.001), FEV1 ( p = 0.002), PEF ( p = 0.004), and 6MWT ( p = 0.041) were significantly lower postoperatively than preoperatively, whereas the FEV1/FVC ratio did not differ significantly. Postoperative VC ( p = 0.012), FVC ( p = 0.030), FEV1 ( p = 0.014), and PEF ( p = 0.035) were significantly higher in the SG than in the CG, although 6MWT results did not differ. ADL on postoperative day 5 was significantly higher in the SG than in the CG ( p = 0.001). Conclusion RMT and aerobic exercise can have positive effects on postoperative respiratory vital capacity and daily life activity, and would enhance recovery after surgery in MG patients.

Background Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

Background: Circulating tumor DNA (ctDNA) level and the change in level at a subsequent time point (e.g. on-treatment change from baseline or postoperative changes through time) are promising tools for predicting patient prognosis and response to therapy. Existing methods use somatic variant allele frequencies to quantify circulating tumor fractions (cTF). Their performance can be limited by the number of detectable somatic alterations and the associated limit of detection (LoD), as well as interference from copy number variation and non-tumor alterations, such as clonal hematopoiesis. Here, we describe the LoD, precision and limit of quantitation (LoQ) of cTF level and change using GuardantINFINITY, a next generation sequencing panel covering over 800 genes with genome-wide methylation detection. Method: The cTF of a single sample is estimated from methylation signals across targeted regions of the GuardantINFINITY methylation panel, calibrated using internal training data. cTF change compares two or more samples from the same patient to identify patient-specific methylated regions, and compare the methylation signals of the paired regions. LoQ of cTF level and change were assessed in experimental titrations of advanced colorectal, breast, and lung cancer patient samples and cell line samples into cancer-free backgrounds at different target levels between 0.1%-0.5% cTF. LoD is defined as the lowest cTF level where >95% replicates were detected to have tumor-derived methylation signals. LoQ of cTF level or change is defined as the lowest cTF where the coefficient of variation (CV) across replicates is less than 30%. Accuracy of methylation based cTF compared to cTFs calculated from maximum VAF of somatic mutations was assessed on 1,400 clinical samples of colorectal and lung cancer patients (N=189, 372, 252 and 463 for stage I to IV). Results: Experimental titrations of cancer samples demonstrated a single-sample LoD of 0.05% cTF (lowest dilution level) and quantitative precision down to a LoQ of below 0.1%, compared to the LoQ of 0.3% estimated by somatic mutations. In paired clinical titration samples, the LoQ of methylation ctDNA level change was also below 0.1%, compared to the LoQ of ctDNA level change estimated by somatic mutations at 0.3-0.5%. In the 1,400 clinical samples, 64% had at least one somatic mutation detected, 90% had ct-DNA detected with methylation and 96% of these ct-DNA detected samples had cTF above the defined methylation LoQ. Among patients with both methylation and genomic signals identified, the methylation method quantified a similar cTF to those that were calculated using maximum somatic driver mutations (Pearson r=0.83). Conclusion: Methylome sequencing using GuardantINFINITY enables accurate and precise quantification of ctDNA level and change with a liquid-only approach, offering longitudinal ctDNA monitoring for more patients than previous methods. Citation Format: Sai Chen, Katie Quinn, Che-Yu Lee, Jun Zhao, Kyle Chang, Tingting Jiang, Shile Zhang, Carin Espenschied, Sara Wienke, Thereasa Rich, Indira Wu, Yvonne Kim, Xianxian Liu, Nageswara Alla, Dustin Ma, Giao Tran, Han-Yu Chuang. A method for quantifying circulating tumor DNA level and molecular response using methylome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3123.

Background: Liquid biopsy offers a rapid and non-invasive alternative to tissue biopsy for identifying biomarkers. More recently, its application has broadened to include assessment of early response to therapy (i.e. molecular response) and in the early-stage settings, detection of minimal residual disease (MRD) and early disease recurrence1. While circulating tumor fraction (cTF) estimated by somatic mutations is well associated with the tumor progression and prognosis, interference can occur from clonal hematopoiesis of indeterminate potential (CHIP), and for cell-free DNA (cfDNA) samples that lack detectable somatic mutations, somatic tumor fraction cannot be estimated. In this analysis, we demonstrate that epigenomic signatures accurately measure cTF using orthogonal analytes to somatic mutations and enable cTF estimation even in cases without detectable tumor driver variants. Methods: To capture tumor-associated methylated cfDNA, we designed a custom assay of a broad genomic panel (15.2 Mb) targeting unmethylated regions in plasma cfDNA from healthy individuals. We profiled plasma samples from cancer patients with this panel, and utilized machine learning to integrate methylation signals into an estimate of cTF. We benchmarked the accuracy of methylation cTFs on real plasma samples, as well as in-vitro and in-silico titration datasets. Both titration data sets were generated by mixing cfDNA from patients with colorectal cancer (CRC) into the plasma from cancer-free donors, either via titration of CRC cfDNA into cfDNA from cancer-free donors for the in-vitro data, or via computationally mixing reads from CRC patients with those from cancer-free donors for the in-silico data. Results: Our methylation cTF quantified a similar cTF to those derived from well-calibrated genomic tumor driver mutations; across the 670 stage I-IV CRC samples, a strong correlation (Pearson r=0.85) was observed between methylation logit(cTF) and genomic logit(cTF). The methylation cTF was capable of quantifying low cTFs: it quantified a cTF over 0.1% in >99% of the 270 in-vitro and 1,000 in-silico titration samples with true cTFs >0.1%. In contrast, when applied to 2,037 cancer-free samples, less than 5% of the samples resulted in estimated cTFs of >0.1%. Our methylation cTF was more robust than genomic cTF on the 62 in vitro titration samples with true cTFs between 0.3-1%, with a five fold lower coefficient of variation across methylation cTFs compared to genomic cTFs. Conclusions: cTFs from methylated cfDNA may overcome the current limitations of somatic mutation based methods. Our methylation approach is capable of accurately detecting cTFs in tumor-driver positive and negative cases. As we estimate tumor-negative cases to be 30-50% of patients with stage I-III cancer and 15-20% of patients with stage IV cancer, our methylation approach may hold promise for providing better evaluation for patient care and management. Citation Format: William W. Greenwald, Yupeng He, Sai Chen, Tingting Jiang, Anton Valouev, Jun Min, Catalin Barbacioru, Daniel P. Gaile, Dustin Ma, Yvonne Kim, Giao Tran, Indira Wu, Ariel Jaimovich, Victoria Raymond, Rebecca J. Nagy, Han-Yu Chuang. Accurate epigenomic estimates of circulating tumor fraction in large-scale clinical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3758.

Introduction: The ability to detect genome-wide epigenetic changes, such as DNA methylation, has expanded translational applications in oncology settings. Because these changes occur early in carcinogenesis, they can be used for early cancer detection when genomic technologies fall short due to lower sensitivity, and in the early and late-stage cancer setting for minimal residual disease detection, disease monitoring and therapy selection. In this analysis, we demonstrated our detection of differential methylations that classify cancer from healthy normals, as well as the quantification of promoter methylation, using a highly sensitive targeted assay that simultaneously captures both genomic alterations and methylation signatures in cell-free DNA (cfDNA). Methods: Methylation signals were profiled with a broad genomic panel (15.2 Mb) targeting regions that are unmethylated in plasma cfDNA from cancer-free donors. The panel covers the promoter regions of 925 out of 1,217 known tumor suppressor genes (TSGs) (e.g. TP53, APC, RB1, PTEN), homologous recombination and repair (HRR) genes (e.g. ATM, BRCA1/2, CDK12, RAD51C/D). We applied our genomic and epigenomic assay on cfDNA from 1,968 colorectal cancer (CRC) patients, 480 patients with other 6 common cancers, and 2,037 cancer-free donors. To test the sensitivity of our epigenomic assay, we generated an in-silico dataset by computationally mixing reads from the cancer patients with those from cancer-free donors at a low tumor fraction (TF) of 0.1%. Results: Among 62 clinically relevant TSG and HRR genes, 56 (90%) were differentially methylated (Wilcoxon p<0.05) between the 2,448 cancer vs. 2,037 cancer-free donor samples. In the in-silico dataset, 51 of the 56 differentially methylated genes remained statistically significant (Wilcoxon p<0.05) at 0.1% TF. In the 1,968 CRC patients, we observed a significant association (Fisher’s p<1e-05) between MLH1 promoter methylation and microsatellite instability (MSI-H): 70% of MSI-H samples has MLH1 promoter methylation above a predefined “high methylation” threshold, while only 7% of microsatellite-stable samples has MLH1 promoter methylation above this threshold. Our result is consistent with previous studies that 54-100% of CRC patients with MSI-H tumors harbor MLH1 promoter methylation. Conclusion: We demonstrate that our assay can accurately detect cancer-driven DNA methylation across the genome in clinical plasma samples. Detection of differential methylation in cancerous versus non-cancerous tissues could allow for early cancer detection, leading to better survival and prediction of recurrence prior to imaging. The highly sensitive detection of promoter methylation shown with TSG, HRR, and MLH1 genes may provide orthogonal information to oncologists in therapeutic selection with high confidence. Citation Format: Sai Chen, Shile Zhang, Tingting Jiang, Jennifer Yen, Yupeng He, Ariel Jaimovich, Yvonne Kim, Dustin Ma, Giao Tran, Daniel P. Gaile, Rebecca J. Nagy, Elena Helman, Han-Yu Chuang. Detection of tumor-associated gene inactivation in clinical blood draws via cell-free DNA methylation profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3763.

Background: To elucidate the mechanisms of thymic epithelial tumor (TET) canceration by characterizing genomic mutations and signaling pathway alterations. Methods: Primary tumor and blood samples were collected from 21 patients diagnosed with TETs (thymoma and thymic cancer), 15 of whom were screened by nucleic acid extraction and whole exon sequencing. Bioinformatics was used to comprehensively analyze the sequencing data for these samples, including gene mutation information and the difference of tumor mutation burden (TMB) between thymoma and thymic carcinoma groups. We performed signaling pathway and functional enrichment analysis using the WebGestalt 2017 toolkit. Results: ZNF429 (36%) was the gene with the highest mutation frequency in thymic carcinoma. Mutations in BAP1 (14%), ABI1 (7%), BCL9L (7%), and CHEK2 (7%) were exclusively detected in thymic carcinoma, whereas ZNF721 mutations (14%) and PABPC1 (14%) were found exclusively in thymoma. The mean TMB values for thymic carcinoma and thymoma were 0.722 and 0.663 mutations per megabase (Mb), respectively, and these differences were not statistically significant. The ErbB signaling pathway was enriched in the thymoma and intersection groups, and pathways of central carbon metabolism in cancer, longevity regulating and MAPK signaling were only found in the thymoma group, while pathways in cancer ( hsa05200) was found in the thymoma and thymic carcinoma groups. Conclusions: Multiple differences in somatic genes and pathways have been identified. Our findings provide insights into differences between thymoma and thymic carcinoma that could aid in designing personalized clinical therapeutic strategies.

Objective An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined. Design To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling. Results We identified 13 loci that reached genome-wide significance (p<5×10 −8 ) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer. Conclusion Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.

Background The authors aimed to create a novel model to predict lymphatic metastasis in thymic epithelial tumors. Methods Data of 1018 patients were collected from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. To construct a nomogram, the least absolute shrinkage and selection operator (LASSO) regression model was used to select candidate features of the training cohort from 2004 to 2013. A simple model called the Lymphatic Node Metastasis Risk Scoring System (LNMRS) was constructed to predict lymphatic metastasis. Using patients from 2014 to 2015 as the validation cohort, the predictive performance of the model was determined by receiver operating characteristic (ROC) curves. Results The LASSO regression model showed that age, extension, and histology type were significantly associated with lymph node metastasis, which were used to construct the nomogram. Through analysis of the area under the curve (AUC), the nomogram achieved a AUC value of 0.80 (95 % confidence interval [Cl] 0.75–0.85) in the training cohort and 0.82 (95 % Cl 0.70–0.93) in the validation cohort, and had closed calibration curves. Based on the nomogram, the authors constructed the LNMRS model, which had an AUC of 0.80 (95 % Cl 0.75–0.85) in the training cohort and 0.82 (95% Cl 0.70–0.93) in the validation cohort. The ROC curves indicated that the LNMRS had excellent predictive performance for lymph node metastasis. Conclusion This study established a nomogram for predicting lymph node metastasis. The LNMRS model, constructed to predict lymphatic involvement of patients, was more convenient than the nomogram.

Additional file 7: Table S6. Simulation results for expression-linked repeat expansions with short motifs.

Additional file 3: Table S2. Repeats with long motifs and expression-linked repeats which were used for simulation.

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

Repeat expansions are responsible for over 40 monogenic disorders, and undoubtedly more pathogenic repeat expansions remain to be discovered. Existing methods for detecting repeat expansions in short-read sequencing data require predefined repeat catalogs. Recent discoveries emphasize the need for methods that do not require pre-specified candidate repeats. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide repeat expansion detection. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference repeat expansions not discoverable via existing methods.

SummaryWe describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci.Availability and implementationExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/.Contactmeberle@illumina.com

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1 . In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10 −8 , bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

SummaryMetabolic dysregulation in multiple tissues alters glucose homeostasis and influences risk for type 2 diabetes (T2D). To identify pathways and tissues influencing T2D-relevant glycemic traits (fasting glucose [FG], fasting insulin [FI], two-hour glucose [2hGlu] and glycated hemoglobin [HbA1c]), we investigated associations of exome-array variants in up to 144,060 individuals without diabetes of multiple ancestries. Single-variant analyses identified novel associations at 21 coding variants in 18 novel loci, whilst gene-based tests revealed signals at two genes, TF (HbA1c) and G6PC (FG, FI). Pathway and tissue enrichment analyses of trait-associated transcripts confirmed the importance of liver and kidney for FI and pancreatic islets for FG regulation, implicated adipose tissue in FI and the gut in 2hGlu, and suggested a role for the non-endocrine pancreas in glucose homeostasis. Functional studies demonstrated that a novel FG/FI association at the liver-enriched G6PC transcript was driven by multiple rare loss-of-function variants. The FG/HbA1c-associated, islet-specific G6PC2 transcript also contained multiple rare functional variants, including two alleles within the same codon with divergent effects on glucose levels. Our findings highlight the value of integrating genomic and functional data to maximize biological inference.Highlights23 novel coding variant associations (single-point and gene-based) for glycemic traits51 effector transcripts highlighted different pathway/tissue signatures for each traitThe exocrine pancreas and gut influence fasting and 2h glucose, respectivelyMultiple variants in liver-enriched G6PC and islet-specific G6PC2 influence glycemia

Every author has erroneously been assigned to the affiliation "62". The affiliation 62 belongs to the author Graham Casey.