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• Evolution of Chromatin Architecture and Transcriptional Regulation in Mammals

  Changes in gene expression are a critical force driving the evolution of form and function. As a result, the mechanisms by which gene expression evolves have become a subject of intense interest. A number of investigators have measured evolutionary changes in gene expression at the chromatin, mRNA, and protein levels, leading to new insights about how species evolve. However, studies of different stages in gene expression have not always been in agreement. For example, the extremely rapid rates of evolutionary changes that have been widely observed at enhancers appear to conflict with the slower rates observed in mRNA abundance. We recently completed a major comparative study in primates showing that this disparity between enhancer and mRNA evolution, in part, reflects extensive compensation at enhancers that jointly determine transcription at target genes. Likewise, related findings have demonstrated that post-transcriptional changes buffer protein abundance to relatively more common differences in mRNA expression. Together, these recent findings demonstrate that evolutionary changes affecting multiple stages of transcriptional regulation often have interdependent effects on gene expression. Here we propose to determine how stages early during transcriptional regulation work in concert, either to conserve gene expression through compensatory changes across stages, or, in rare cases, to change mRNA in ways that alter organism phenotypes. Our central hypothesis is that interactions between stages are common, especially at long evolutionary time-scales. To test this hypothesis we propose an ambitious plan to collect rich genomic data profiling distinct stages of gene expression in two cell types and three tissues from nine mammalian species. We have focused our study on several early rate-limiting steps in mRNA production, using molecular assays selected to provide orthogonal sources of information about chromatin architecture (Hi-C/Hi-ChIP), accessibility (ATAC-seq), transcription (PRO-seq), and mRNA levels (RNA-seq). This project will produce the largest resource of genomic data uniformly collected across mammals to date. We will integrate genomic data using a suite of new computational tools, which together will provide a new understanding of how distinct regulatory stages work in concert during regulatory evolution.

  Study phs002146

• National Institute on Aging (NIA) SardiNIA Study

  Sardinia is the second largest island in the Mediterranean and constitutes a genetically isolated founder population. This population has aided in the identification of genes involved in several Mendelian disorders and is attractive for genetic studies due to its organization in long-established settlements that developed from an initial group of founder settlers (~1,000) thousands of years ago. The current study has recruited 6,148 Sardinians aged 14 and older, from a cluster of four towns in the Lanusei Valley in the Ogliastra region of the province of Nuoro. This sample corresponds to approximately 62% of the population eligible in the area for recruitment. Information collected during enrollment allowed the individuals to be organized into 711 complex pedigrees, each up to five generations deep, with an average kinship coefficient of 0.1628. All volunteers have been characterized for 98 quantitative traits. Traits include anthropomorphic measures, plasma and serum markers (including cholesterol and other markers of cardiovascular disease), and personality traits (using the five-factor model). The main goal of the study is to examine phenotypic similarities between relatives that yield information on the overall contributions of genes to trait variability. Data given here provide p-values for 98 traits studied in 1,412 individuals, based on genotyping with the Affymetrix 500K chip and imputed markers using the HapMap population as a reference (N=2,259,179). Sharing this genome assessment data at high level of resolution for a variety of quantitative traits will be useful for other groups to validate newly observed associations and to investigate possible pleiotropic effects. For ulterior information, and in particular for any individual genotype information, a formal request should be addressed to the Steering Committee, c/o David Schlessinger, Chief, Laboratory of Genetics, National Institute on Aging, National Institutes of Health, NIH Biomedical Research Center, 251 Bayview Blvd., Suite 100 Baltimore, MD 21224 USA and should include IRB approval of a detailed proposal, along with a signed statement guaranteeing that no attempt will be made to identify any individual participant.

  Study phs000338

• NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (Ischemic Stroke Genetic Study, ISGS)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. The Ischemic Stroke Genetics Study (ISGS) is a study of newly onset cases (~600) with ischemic stroke (a stroke due to sudden interruption of blood flow to a part of the brain) compared with sex- and age-matched non-stroke participants. The study was conducted to determine the genes and their variants that contribute to an individual's risk of developing an ischemic stroke. The coordination of the recruitment and flow of the samples occurred at the Mayo Clinic, Jacksonville, FL, under the direction of James F. Meschia, MD. The University of Virginia (Stephen S. Rich, PhD) served as the analytic site for the genetic data. All GWAS data on ISGS participants have been deposited into dbGaP. As part of the NHLBI Exome Sequencing Project, DNA from a subset of ISGS participants will undergo exome sequencing. For the NHLBI ESP, a subset of 92 individuals with lacunar (small vessel) or atherosclerotic (large vessel) TOAST subtypes were selected from among all ISGS participants, excluding those individuals with TOAST subtypes of stroke of other etiology or of stroke with undetermined etiology. All 92 samples pass initial quality control metrics and 89 samples completed exome sequencing. A total of 75 participants with appropriate consent and variant calls had their genetic and phenotypic data deposited into dbGaP.

  Study phs000546

• Gabriella Miller Kids First (GMKF) Pediatric Research Program in Susceptibility to Ewing Sarcoma Based on Germline Risk and Familial History of Cancer

  Ewing sarcoma (EWS) is a deadly bone cancer that occurs in children and adolescents. Mounting evidence suggests that a genetic predisposition exists for this pediatric cancer, although the specific genetic contribution has yet to be identified. EWS has never been linked to a specific cancer predisposition syndrome, although several case reports have been published that describe siblings and cousins with EWS. Furthermore, neuroectodermal tumors appear to occur more commonly in families with EWS. The two consistent epidemiology findings in EWS include a very strong Caucasian predilection and increased rates of hernia in EWS patients and their family members. Finally, the role of genetic microsatellite repeats in EWS tumorigenesis has been recently described, and these GGAA microsatellites are polymorphic in repeat size and location across the genome. The study goals of this Kids First project include (1) To identify cancer predisposition genes in EWS trios increasing disease risk, (2) To identify genome-wide GGAA microsatellite repeats in EWS trios increasing disease risk, and (3) To identity de novo mutation and structural variant rates in EWS trios reflecting underlying DNA repair defects that increase disease risk. As part of the Kids First Common Fund initiative, this study proposal will further elucidate the genetic contribution to pediatric cancer development. Around 375 of these trios were selected for whole genome sequencing as part of the Gabriella Miller Kids First fund. The EWS trios have been collected as part of the Children's Oncology Group's AEPI10N5 Study ("Genetic Epidemiology of Ewing Sarcoma"), and each trio has associated phenotypic data including a detailed family history. We will interrogate the sequence data using our genomic analysis pipeline at the University of Utah and the Utah Science Technology and Research initiative's (USTAR) Center for Genetic Discovery. We will look for the genetic contribution to EWS and the sequence data with be shared in a repository designated by the Kids First Common Fund. The WGS of these ~375 EWS trios will help us to understand the genetic origins of a deadly childhood cancer and may lead to novel strategies for prevention and treatment.

  Study phs001228

• DNA-Sequencing of Baseline Plasma From the Phase III Alliance A031201 Trial

  Liquid biopsy assays tailored for metastatic castration-resistant prostate cancer (mCRPC) patients are needed to identify molecular alterations and predict survival in the context of androgen receptor (AR) inhibitors. We developed a novel plasma cell-free DNA (cfDNA) sequencing assay to detect circulating tumor DNA (ctDNA) and alterations in key mCRPC driver genes, and tested associations with clinical features and survival outcomes in a phase III trial. cfDNA was isolated from evaluable baseline plasma specimens of untreated mCRPC patients (n=790) enrolled in the Alliance A031201 randomized phase III trial (NCT01949337) prior to enzalutamide therapy with or without abiraterone acetate and prednisone. A customized targeted cfDNA sequencing assay was used to detect AR and non-AR alterations in mCRPC. For this, DNA-seq libraries were constructed from cfDNA using Takara ThruPLEX Tag-Seq or Plasma-Seq kits, and subjected to hybridization enrichment using a custom-designed Agilent SureSelect bait panel. Post-capture DNA-seq libraries were pooled and sequenced on an Illumina HiSeq 2500 using 2x150 bp settings. ctDNA positivity was defined as >20% ctDNA using an aneuploidy-based estimate derived from ichorCNA (Group 1) or a novel definition of ≤20% ctDNA, but with detection of any alteration in AR, or gain of MYC and/or MYCN (Group 2). cfDNA sequencing data was obtained from 776 patients. There were 480/776 (62%) ctDNA-positive patients, with 324 (42%) in Group 1 and an additional 156 (20%) in Group 2 with AR or MYC/MYCN alterations detected. ctDNA-positive patients in Groups 1 and 2 had similarly poor median overall survival (OS) of 25.4 months (95% CI: 21.8-29.0) and 29.7 months (95% CI: 27.3-35.5), respectively, as compared to ctDNA-negative patients who had a median OS of 49.1 months (95% CI: 42.3-53.5). Relative to ctDNA-negative patients, OS was inferior in ctDNA-positive patients in Group 1 (HR 2.3, 95% CI: 1.9-2.9, P AR inhibitors in a contemporary phase III trial.

  Study phs003325

• Automated machine-learning approach for next generation profiling of sequence alterations, mutation burden, microsatellite instability, and structural variants in human cancers

  Sequence and structural alterations together with tumor mutation burden (TMB) and microsatellite instability (MSI) have been identified as biomarkers for the determination of response to targeted and immune checkpoint inhibitor therapies. However, widespread clinical adoption of these biomarkers has historically been limited due to barriers such as evidence of clinical utility and reimbursement. We have developed 2.2 Mb targeted NGS system and an automated machine-learning analysis approach (PGDx elio™ tissue complete, ETC) that has been FDA cleared for examination of 500+ cancer-related genes and 68 mononucleotide repeats for identification of sequence and structural alterations, TMB, and MSI in solid cancers in a clinical setting. We designed and trained this approach using sequence data from 4,174 cancers and >124,000 in silico alterations and evaluated the methodology in >2,550 tumor or non-cancerous normal samples. Independent analyses of ETC sequence changes in 440 formalin fixed paraffin embedded (FFPE) tumor or cell line samples using MSK-IMPACT™, FoundationOne®, and ddPCR revealed a positive percent agreement (PPA) >97% with high sensitivity as low as 3% mutant allele fraction. We observed high concordance between panel-wide and whole-exome TMB for 307 pan-cancer FFPE tumors (Pearson r=0.95, p < 0.0001) using samples with ≥20% tumor cellularity. Comparison of the mutation context and repeat-based MSI approach in ETC with a multiplex MSI PCR assay in 223 samples revealed a PPA of 99% and negative predictive agreement (NPA) >99%. We confirmed the accuracy and precision of TMB and MSI measurements across three independent laboratories (CV of <5% and average PPA >99%, respectively). Finally, evaluation of amplifications and translocations against DNA and RNA-based approaches exhibited >98% NPA and PPA of 86% and 82% respectively. These results demonstrate high analytical performance for determination of sequence and structural changes, TMB, and MSI using a targeted NGS panel and provide a scalable and facile approach for evaluating these biomarkers in a clinical laboratory.

  Study EGAS00001005556

• Identifying transcriptional programs underlying anti-EGFR small molecule response and resistance with TraCe-seq

  Genetic and non-genetic heterogeneity within cancer cell populations represents a major challenge to anti-cancer therapies. We currently lack robust methods to determine how pre-existing and adaptive features affect cellular responses to therapies. Here, by conducting clonal fitness mapping and transcriptional characterization using expressed barcodes and single-cell RNA-sequencing, we have developed TraCe-seq, a method that captures at clonal resolution the origin, fate, and differential early adaptive transcriptional programs of cells in a complex population in response to distinct treatments. We used TraCe-seq to benchmark how next-generation dual EGFR inhibitors-degraders compare to standard EGFR kinase inhibitors in EGFR-mutant lung cancer cells. To QC the TraCe-seq strategy, single-cell RNA-seq libraries were generated from a variety of human cancer cell lines transduced with the TraCe-seq library to validate the TraCe-seq strategy. Specifically, 5 different cell lines (PC9, MCF-10A, MDA-MB-231, NCI-H358, and NCI-H1373) were each transduced with a unique TraCe-seq barcode. The transduced cells were selected with puromycin only, dissociated to single cell suspensions, and then mixed together. The complex mixture of the 5 cell lines was profiled by 10X scRNA-seq. Furthermore, transduced NCI-H1373 cells were sorted by FACS to enrich for the top 50% of eGFP positive cells, and sorted cells were cultured briefly and used to construct scRNA-seq libraries and profiled by 10x scRNA-seq. To carry out the full TraCe-seq experiment, ~600 PC9 cells carrying unique TraCe-seq barcodes were expanded over 12 doublings to establish the barcoded population. A subset of the barcoded PC9 population was used to generate scRNA-seq libraries and profiled by 10x scRNA-seq prior to treatment to establish a baseline transcription profile for each barcoded clone. The rest of the cells were then treated for four days with 1 µM erlotinib, 1 µM GNE-069, or 1 µM GNE-104 respectively. scRNA-seq libraries were then generated form the treated cells and profiled by 10x scRNA-seq.

  Study EGAS00001005405

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• African Demographic History Study Using Illumina 1M Array Data

  These data were used as part of a study to characterize genomic diversity in Africa, population substructure, and evolutionary history.

  Study phs001780

• African Demographic History Study Based on WGS Data

  These data were used as part of a study to characterize genomic diversity in Africa, population substructure, and evolutionary history.

  Study phs003096