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• WES Elucidation of Pathogenetic Mechanisms for Immune System Disorders and NIAID Pilot of Genetic Incidental Finding Management

  Background: - There are many types of immune disorders. These range from rare immune deficiencies to allergies to autoimmune disease like rheumatoid arthritis. Genes are the instructions our body uses to work and develop. A new technology called whole exome sequencing may help find the cause of these disorders. Whole exome sequencing is a way to look at many genes at once for errors. Researchers hope to find new gene changes that lead to immune disorders. Additionally, researchers are interested in finding the best way to manage unexpected but important findings by whole exome sequencing. Objectives: - To better understand genetic causes of immune system disorders. Also, to better understand people s thoughts and feelings about immune system disorders and new genomic testing. Eligibility: - People ages 0 100 with an immune disorder or a relative with an immune disorder. People must be at least 2 to be evaluated at the NIH clinical center. People must be at least 12 to do the survey/interview portion of the study. Design: Participants will have their genes sequenced. They may be asked for a new sample of blood. If participants cannot come for a study visit, they can have a blood sample collected by their local lab or doctor and sent by mail. Researchers may or may not find the cause of the participant s immune disorder. Participants will learn that information. Some participants may be asked to return to NIH to get results and have more tests. Researchers may share information with other studies. The data will be anonymous. For the survey part of the study, participants will answer questions about their or their relative s immune disorder. They will also answer about their thoughts and feelings about genomic testing. Some participants will be asked for a brief interview to ask more about the survey topics. There may be more follow-up after several months. From clinicaltrials.gov

  Study phs001561

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Phase I Study and Cell-Free DNA Analysis of T-DM1 and Metronomic Temozolomide for Secondary Prevention of HER2-Positive Breast Cancer Brain Metastases

  This study is a phase I trial combining ado-trastuzumab emtansine (T-DM1) with Temozolomide (TMZ) for the prevention of additional brain metastases after previous occurrence and local treatment in HER2+ breast cancer patients. Twelve patients were enrolled in this study. Data includes whole exome sequencing (WES) from patients' cerebrospinal fluid (CSF) and plasma samples. The CSF was collected from each patient at trial entry and after the initial two treatment cycles of TMZ and T-DM1 treatment. Blood was collected through a standard peripheral venipuncture at the following four time points: 1) upon trial entry, 2) after two treatment cycles, 3) the treatment cycle prior to the last follow-up, and 4) most recent follow-up. Whole exome libraries were captured by the SureSelect Human All Exon V8 Kit (Agilent, Santa Clara, CA, USA) and sequenced by NovaSeq 6000 S2 Sequencer in paired-end sequencing mode (Illumina, San Diego, CA, USA). Gene mutations in DNMT3A, ELP1, NOTCH4, KMT2B, CRLF2, and KCNJ18 were observed in patients' CSF.

  Study phs003165

• Circulating, Cell-Free DNA Methylation Patterns Indicate Cellular Sources of Allograft Injury after Liver Transplant

  Post-transplant complications reduce allograft and recipient survival. Current approaches for detecting allograft injury non-invasively are limited and do not differentiate between cellular mechanisms. Here, we monitor cellular damages after liver transplants from cell-free DNA (cfDNA) fragments released from dying cells into the circulation. We analyzed longitudinal blood samples collected from patients at different time points after transplant. Sequence-based methylation of cfDNA fragments were mapped to an atlas of cell-type-specific DNA methylation patterns derived from 476 methylomes of purified cells. For liver cell types, DNA methylation patterns and multi-omic data integration show distinct enrichment in open chromatin and regulatory regions functionally important for the respective cell types. We find that multi-tissue cellular damage post-transplant recovers in patients without allograft injury during the first post-operative week. However, sustained elevation of hepatocyte and biliary epithelial cfDNA within the first month indicates early-onset allograft injury. Further, cfDNA composition differentiates amongst causes of allograft injury indicating the potential for non-invasive monitoring and timely intervention.

  Study phs003610

• Philippine Ayta possess the highest level of Denisovan ancestry in the world

  Multiple lines of evidence show that modern humans interbred with archaic Denisovans. Here, we report an account of shared demographic history between Australasians and Denisovans distinctively in Island Southeast Asia. Our analyses are based on ~2.3 million genotypes from 118 ethnic groups of the Philippines including 25 diverse self-identified Negrito populations, along with high-coverage genomes of AustraloPapuans and Ayta Magbukon Negritos. We show that Ayta Magbukon possess the highest level of Denisovan ancestry in the world – ~30-40% greater than that of Australians and Papuans – consistent with an independent admixture event into Negritos from Denisovans. Together with the recently described Homo luzonensis, we suggest that there were multiple archaic species that inhabited the Philippines prior to the arrival of modern humans, and that these archaic groups may have been genetically related. Altogether, our findings unveil a complex intertwined history of modern and archaic humans in the Asia-Pacific region, where distinct Islander Denisovan populations differentially admixed with incoming Australasians across multiple locations and at various points in time.

  Study EGAS00001005407

• Philippine Ayta possess the highest level of Denisovan ancestry in the world

  Multiple lines of evidence show that modern humans interbred with archaic Denisovans. Here, we report an account of shared demographic history between Australasians and Denisovans distinctively in Island Southeast Asia. Our analyses are based on ~2.3 million genotypes from 118 ethnic groups of the Philippines including 25 diverse self-identified Negrito populations, along with high-coverage genomes of AustraloPapuans and Ayta Magbukon Negritos. We show that Ayta Magbukon possess the highest level of Denisovan ancestry in the world – ~30-40% greater than that of Australians and Papuans – consistent with an independent admixture event into Negritos from Denisovans. Together with the recently described Homo luzonensis, we suggest that there were multiple archaic species that inhabited the Philippines prior to the arrival of modern humans, and that these archaic groups may have been genetically related. Altogether, our findings unveil a complex intertwined history of modern and archaic humans in the Asia-Pacific region, where distinct Islander Denisovan populations differentially admixed with incoming Australasians across multiple locations and at various points in time.

  Study EGAS00001005408

• Human CD4 Memory T Cell Activation Time Course

  We performed a high-resolution time course RNA-seq experiment on CD4+ memory T cells during activation with anti-CD3/CD28 beads. We performed this in 24 adult individuals of European ancestry with no autoimmune diseases. We used 1 million cells as starting material, per time point (up to 8 time points per individual from 0 to 72 hours). We identified widespread dynamic allele-specific expression (dynASE) across the genome, i.e. ASE that changes with time after activation, which reflects cell state-dependent genetic regulatory effects. DynASE events were enriched in risk loci for multiple autoimmune diseases. This highlights the importance of studying multiple cellular states in order to elucidate the mechanisms of autoimmune disease. It also provides a successful framework to do it in other cellular systems relevant for other traits. We also showed an unprecedented amount of regulatory variation for HLA genes. HLA-DQB1 has 3 distinct regulatory haplotypes with expression profiles that could come from 3 different genes. We performed ATAC-seq in replicate for CD4+ memory T cells stimulated for 72 hours from another healthy individual. These data, together with genetic fine-mapping, CRISPR editing, EMSA and luciferase assays, helped us find a SNP in the promoter of HLA-DQB1 that causes at least part of the large cell state-dependent change in expression, at the RNA and protein levels.

  Study phs002259

• Genetic Epidemiology Network of Arteriopathy (GENOA)

  The Genetic Epidemiology Network of Arteriopathy (GENOA) is one of four networks in the NHLBI Family-Blood Pressure Program (FBPP). GENOA's long-term objective is to elucidate the genetics of target organ complications of hypertension, including both atherosclerotic and arteriolosclerotic complications involving the heart, brain, kidneys, and peripheral arteries. The longitudinal GENOA Study recruited European-American and African-American sibships with at least 2 individuals with clinically diagnosed essential hypertension before age 60 years. All other members of the sibship were invited to participate regardless of their hypertension status. Participants were diagnosed with hypertension if they had either 1) a previous clinical diagnosis of hypertension by a physician with current anti-hypertensive treatment, or 2) an average systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg based on the second and third readings at the time of their clinic visit. The Family Blood Pressure Program (FBPP), GENOA's parent program, is an unprecedented collaboration to identify genes influencing blood pressure (BP) levels, hypertension, and its target-organ damage. This program has conducted over 21,000 physical examinations, assembled a shared database of several hundred BP and hypertension-related phenotypic measurements, completed genome-wide linkage analyses for BP, hypertension, and hypertension associated risk factors and complications, and published over 130 manuscripts on program findings. The FBPP emerged from what was initially funded as four independent networks of investigators (HyperGEN, GenNet, SAPPHIRe and GENOA) competing to identify genetic determinants of hypertension in multiple ethnic groups. Realizing the greater likelihood of success through collaboration, the investigators created a single confederation with program-wide and network-specific goals.

  Study phs001238

• NHLBI TOPMed: Genetic Epidemiology Network of Arteriopathy (GENOA)

  The Genetic Epidemiology Network of Arteriopathy (GENOA) is one of four networks in the NHLBI Family-Blood Pressure Program (FBPP). GENOA's long-term objective is to elucidate the genetics of target organ complications of hypertension, including both atherosclerotic and arteriolosclerotic complications involving the heart, brain, kidneys, and peripheral arteries. The longitudinal GENOA Study recruited European-American and African-American sibships with at least 2 individuals with clinically diagnosed essential hypertension before age 60 years. All other members of the sibship were invited to participate regardless of their hypertension status. Participants were diagnosed with hypertension if they had either 1) a previous clinical diagnosis of hypertension by a physician with current anti-hypertensive treatment, or 2) an average systolic blood pressure = 140 mm Hg or diastolic blood pressure = 90 mm Hg based on the second and third readings at the time of their clinic visit. Only participants of the African-American Cohort were sequenced through TOPMed. The Family Blood Pressure Program (FBPP), GENOA's parent program, is an unprecedented collaboration to identify genes influencing blood pressure (BP) levels, hypertension, and its target-organ damage. This program has conducted over 21,000 physical examinations, assembled a shared database of several hundred BP and hypertension-related phenotypic measurements, completed genome-wide linkage analyses for BP, hypertension, and hypertension associated risk factors and complications, and published over 130 manuscripts on program findings. The FBPP emerged from what was initially funded as four independent networks of investigators (HyperGEN, GenNet, SAPPHIRe and GENOA) competing to identify genetic determinants of hypertension in multiple ethnic groups. Realizing the greater likelihood of success through collaboration, the investigators created a single confederation with program-wide and network-specific goals. Comprehensive phenotypic data for GENOA study participants are available through dbGaP phs001238.

  Study phs001345

• The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective longitudinal and ethnically diverse cohort

  The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective longitudinal and ethnically diverse cohort and biobank that we established. To date, approximately 28,000 participants have enrolled in the study, with over 13,000 having completed their initial visit. The project is aimed at identifying novel molecular signatures with diagnostic, prognostic and therapeutic value, and at developing AI-based predictive models for disease onset and progression. A unique feature of the HPP is its deep and longitudinal profiling that includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests, continuous glucose and sleep monitoring, imaging modalities and molecular profiling of the transcriptome, genetics, gut, vaginal and oral microbiome, metabolome and immune system. For several of these modalities, the HPP is the first or largest cohort of its kind. Our analyses of this data provide novel insights into the variation of phenotypes with age and ethnicity, highlighting the importance of having ethnically diverse cohorts and the need for establishing age-dependent norms. We demonstrate how the HPP can be used to unravel personalized molecular signatures of disease by comparison to patient-specific and disease-specific matched healthy controls. Leveraging the extensive dietary and lifestyle HPP data, we systematically identify associations between lifestyle factors and health outcomes. Finally, we present a multi-modal foundation AI model that is trained using self-supervised learning on diet and CGM data and outperforms existing methods in predicting future onset of disease. We extend this AI framework to integrate all data modalities of each subject as a continuous sequence of diverse medical events, creating a digital twin that can simulate interventions and predict health trajectories and outcomes.

  Study EGAS00001008040