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• DAC_TWINS

  DAC related to monozygotic twins discordant for ALS

  Dac EGAC50000000517

• Rare germline sequence variants, copy number variations, expression alterations, methylation variations and disease susceptibility in familial melanoma

  Although a number of high-risk melanoma genes have been identified, they account for melanoma risk in less than 40% of melanoma-prone families. The major goals of our research are to identify novel high-penetrance genes for familial melanoma in melanoma-prone families without known mutations and to identify modifier factors, including genetic and epigenetic, in melanoma-prone families with and without known major mutations. We are using whole exome sequencing to identify additional high-risk susceptibility genes and using targeted sequencing to follow up on the top genes in additional family members and in population-based melanoma cases and controls. We are also using RNASeq to investigate genome-wide allele-specific expression and eQTLs in these families to identify genes with expression alteration, which will provide additional information for gene discovery. In addition, we also conducted genome-wide copy number variation (CNV), methylation, and miRNA expression profiling analyses in our melanoma families with the goal of identifying genetic and epigenetic factors as disease modifiers.

  Study phs001177

• A Pilot Trial of Complement Inhibition Using Eculizumab to Overcome Platelet Transfusion Refractoriness in HLA Allo-Immunized Patients

  Platelet transfusion can be a life-saving procedure in preventing or treating serious bleeding in patients who have low and/or dysfunctional platelets. Heavily transfused patients frequently develop human leukocyte antigen (HLA) allo-immunization resulting in platelet transfusion refractoriness and a high risk for life-threatening thrombocytopenia. Data suggest complement activation leading to the destruction of platelets bound by HLA allo-antibodies may play a pathophysiologic role in platelet refractoriness. We conducted a pilot trial to investigate the use of eculizumab to treat platelet transfusion refractoriness in allo-immunized patients with severe thrombocytopenia. We hypothesized that when we treated patients having platelet refractoriness with eculizumab, platelet counts would increase to higher numbers after platelet transfusions, decreasing the risk of bleeding complications associated with having a low platelet count. The response of the treatment was assessed by the corrected platelet count increment (CCI) 10 - 60 min and 18 - 24 h post transfusion, and any requirement for subsequent platelet transfusions following eculizumab. Reference 1 (PMID: 32086819) contains the main results for this trial.

  Study phs003212

• Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors

  Large-cell neuroendocrine lung carcinomas (LCNEC) have similarities with other lung cancers, but their precise relationship has remained unclear. We conducted comprehensive genomic (n=60) and transcriptomic (n=69) analyses of 75 LCNECs and identified two molecular subgroups: “type I LCNECs” with bi-allelic TP53 and STK11/KEAP1 alterations (37%), and “type II LCNECs” enriched for bi-allelic inactivation of TP53 and RB1 (42%). Despite sharing genomic alterations with adenocarcinomas and squamous cell carcinomas, no transcriptional relationship was found; instead LCNECs form distinct transcriptional subgroups with closest similarity to SCLC. While type I LCNECs and SCLCs exhibit a neuroendocrine profile with ASCL1high/DLL3high/NOTCHlow, type II LCNECs bear TP53 and RB1 alterations and differ from most SCLC tumors with reduced neuroendocrine markers, a pattern of ASCL1low/DLL3low/NOTCHhigh and upregulation of immune-related pathways. In conclusion, LCNECs are comprised of two molecularly defined subgroups, and distinguishing them from SCLC may allow for stratified targeted treatments for high-grade neuroendocrine lung tumors.

  Study EGAS00001000708

• Whole-genome sequencing of rare disease patients in a national healthcare system

  Most patients with rare diseases do not receive a molecular diagnosis and the aetiologicalvariants and mediating genes for more than half such disorders remain to be discovered. Weimplemented whole-genome sequencing (WGS) in a national healthcare system to streamlinediagnosis and to discover unknown aetiological variants, in the coding and non-coding regionsof the genome. In a pilot study for the 100,000 Genomes Project, we generated WGS data for13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to1,138 of the 7,065 patients with detailed phenotypic data. We identified 95 Mendelianassociations between genes and rare diseases, of which 11 have been discovered since 2015and at least 79 are confirmed aetiological. Using WGS of UK Biobank1, we showed that rarealleles can explain the presence of some individuals in the tails of a quantitative red blood cell(RBC) trait. Finally, we reported 4 novel non-coding variants which cause disease through thedisruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates asynergy by using WGS for diagnosis and aetiological discovery in routine healthcare.

  Study EGAS00001004364

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): Digital Health Solutions for COVID-19: COVID Community Action and Research Engagement (COVID-CARE)

  Vibrent Health will expand the Vibrent Digital Health Solutions Platform (DHSP) implementation to additional populations among diverse user groups for additional validation of the technology's performance, usability, and reliability in refinement of analytics generating predictive algorithms for infection and that support individual, organizational, community and societal-level decision-making in the COVID-19 pandemic response. This includes the aims to innovate a technology that can differentiate between COVID-19 and flu (or other respiratory illness); develop and test a novel WIFI-based contact tracing tool using a University’s enterprise WIFI system and software release of augmented contact tracing capabilities in Vibrent's DHSP; and develop a full technical integration approach and strategy to support data exchange between this solution and the NCI data hub.DOI: https://rapids.ll.mit.edu/10.57895/ravs-1b57

  Study phs002533

• Yale Center for Mendelian Genomics (YCMG)

  From 2012-2021, Yale University was home to one of four national centers created by the National Institutes of Health (NIH) to study the genetics of rare inherited diseases. Researchers at Yale University, University of Washington, the Broad Institute and a center operated jointly by Baylor University and Johns Hopkins University analyzed the genomes of thousands of patients who suffer from more than 6,000 rare Mendelian disorders affecting more than 25 million individuals in United States. The Centers for Mendelian Genomics (CMG) applied next-generation sequencing and computational approaches to discover the genes and variants that underlie Mendelian disorders. The discovery of new genes that cause Mendelian conditions expanded our understanding about their biology to facilitate their diagnosis, and potentially indicate new treatments. The CMG provided free exome sequencing and analysis to collaborating investigators for qualified phenotypes.

  Study phs000744

• Multimodal epigenetic sequencing analysis of cell-free DNA identifies biomarkers for ALS diagnosis and progression

  The role of the circulating epigenome in ALS remains understudied. We aim to develop diagnostic and prognostic blood-based biomarkers for Amyotrophic Lateral Sclerosis (ALS) by profiling methylome signatures in circulating cell-free DNA (cfDNA) in 20 sporadic ALS cases, 10 C9orf72-associated ALS cases, 10 asymptomatic C9orf72 expansion carriers, and 21 healthy controls using targeted enzymatic methyl-sequencing (EM-seq) of ~4 million CpG loci. We detected numerous differentially methylated genes, including several implicated in ALS risk and pathobiology, and integrated multiple epigenetic features with our Multimodal Epigenetic Sequencing Analysis (MESA) algorithm, achieving an ROC AUC of 0.91 and detecting ~70% of ALS cases at ~100% specificity. Methylation at a subset of loci also correlated with clinical progression and cerebrospinal-fluid neurofilament levels, supporting the potential of cfDNA-based biomarkers for ALS diagnosis and monitoring.

  Study EGAS50000001267

• scRNAseq of colonic organoids derived from biopsies taken from healthy human individuals treated with IL22

  scRNAseq dataset of colonic organoids derived from epithelium from biopsies taken from three healthy human individuals. The organoids have either been grown in standard conditions (control) or treated with IL22 (treated). Includes 6 samples in total, one control from each individual (ctrl1, ctrl2, ctrl3) and one treated from each (treat1, treat2, treat3). The samples have been multiplexed using the antibody hashing technique. The 6 samples have been pooled into the one organoids sample. In order to analyse the raw files, they have to be demultiplexed first. Information necessary for demultiplexing, as well as which files belong to which sample, can be found in the map_file.csv, attached to each sample. Dataset includes raw Fastq files and processed csv count matrices. Fastq files are divided into HTO (hashtag) and RNA (transcriptome) files. HTO has one index (I1) and two read (R1, R2) files and RNA has two index (I1, I2) and two read (R1, R2) files. The fastq files are for the pooled (organoids) sample and need to be demultiplexed. Count matrices contain comma-separated values with cell barcodes as column names and gene names as row names. Since count matrices have been created after the demultiplexing step, there’s one matrix for each of the 6 individual samples. scRNA-seq data from human colon organoids was analysed in the same manner as for the Colitis dataset, apart from the following changes. Data was generated with the Cell Hashing technique, which uses oligo-tagged antibodies against surface proteins to barcode single cells. This allows for samples to be multiplexed together and run in a single experiment. The data was demultiplexed using the HTODemux() function from Seurat (Hao et al., 2021).

  Dataset EGAD00001010168

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. It seems plausible that rates of implantation would improve if we had a better understanding of molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to identify candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Dataset EGAD00001005044