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• An_evaluation_of_different_strategies_for_large_scale_pooled_sequencing_study_design_

  Second generation sequencing technology has enabled the design of large-scale sequencing experiments in targeted disease-related regions in thousands of cases and controls. Cost implications dictate a pooled DNA sequencing design, evaluation of allele frequency differences based on the resequenced samples and large-scale targeted follow-up in further samples. To evaluate the feasibility of this approach and to assess different study design strategies, we have carried out a pilot study which tests the feasibility of PCR-based and pull-down DNA pooling for (a) SNP discovery and (b) allele frequency comparison purposes. All samples are from the HapMap or 1958 BC. The sample composition of the pool is listed below: Pool 5: 31 HapMap Individuals (NA12249, NA12156, NA12004, NA11831, NA12716, NA11832, NA11993, NA12057, NA11995, NA12006, NA12144, NA12802, NA12146, NA12005, NA12003, NA07000, NA12043, NA12044, NA11992, NA11881, NA11994, NA07345, NA12154, NA06994, NA06985, NA12239, NA07022, NA07034, NA12155, NA07056, NA06993); 19 1958 BC Individuals (WTCCC88214, WTCCC88215, WTCCC88216, WTCCC88217, WTCCC88222, WTCCC88233, WTCCC88240, WTCCC88241, WTCCC88242, WTCCC88247, WTCCC88249, WTCCC88262, WTCCC88264, WTCCC88278, WTCCC88294, WTCCC88298, WTCCC88302, WTCCC88305, WTCCC88321) Some pools were carried out in duplicate to assess reproducibility. We focused on 10 (PD), 7(PCR) chromosomal regions, ~2(PD), ~1.6 (PCR) Mb in total.

  Study EGAS00001000134

• H3Africa - An integrated approach to the identification of genetic determinants of susceptibility to trypanosomiasis

  The objective of the TrypanoGEN project is to discover genetic variants associated with different responses to sleeping sickness infection. The project was designed in two phases:Phase 1) Variant discovery; 222 samples were sequenced to approximately 10X coverage on an Illumina 2500 with 2x150bp reads, at the University of Liverpool Center for Genomic Research. In addition, 77 samples from Cameroon and Zambia were sequenced to approximately 30X Coverage at Baylor College of Medicine. The sequenced reads were mapped onto the 1000 genomes project human_g1k_v37_decoy reference genome using BWA. The SNP calling on all the samples was done using the genome analysis tool kit GATK v3.4. Samples were selected from seven populations where Trypanosomiasis is endemic: Nilo-Saharan speakers from North-West Uganda and Niger-Congo speakers from South-East Uganda, DRC, Ivory Coast, Guinea, Cameroon and Zambia. The samples included 182 controls, 97 cases, 17 latent infections and 3 suspect cases. Phase 2) Variants discovered in Phase 1 will be used in the development of the H3Africa SNP chip. This chip will then be used in a GWAS study to identify variants associated with trypanosomiasis.

  Study EGAS00001002602

• A Single-Cell and Spatial Atlas of Early Human Olfactory Development

  The human nasal region is a complex anatomical structure comprising neural crest- and placode-derived lineages, yet its developmental dynamics in humans remain poorly understood due to limited access to fetal tissue and challenges in resolving its spatial and cellular complexity. We built a single-cell and spatial transcriptomic atlas of the human fetal nasal region from 10 fetuses spanning post-conceptional weeks 7 to 12. Using snRNA-seq, we identify 34 cell types in the fetal nasal region and reconstruct lineage trajectories in the olfactory epithelium (OE). Spatial analyses uncover regionalized OE organization, dorsoventral gradients of olfactory receptors (ORs) and high-resolution OR expression. Combining snRNAseq and MERFISH, we reveal early onset of OR gene expression, with most olfactory sensory neurons (OSNs) following the one neuron-one receptor rule already during the first trimester. This work provides the first integrated molecular and spatial framework of early human nasal region and it provides an unprecedented molecular blueprint of the human olfactory system development. In addition, this study offers a foundational resource for embryologists and developmental biologists investigating sensory neurogenesis, epithelial patterning, and congenital diseases.

  Study EGAS50000001203

• Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)

  The Foundation Medicine adult cancer clinical dataset consists of 18,004 unique solid tumor samples that underwent genomic profiling on a single uniform platform as part of standard clinical care. The dataset is derived from the FoundationOne® genomic profiling assay version 2 that interrogates exonic regions of 287 cancer-related genes and selected introns from 19 genes known to undergo rearrangements in human cancer. Genomic DNA samples were sequenced to over 500x median coverage, and custom computational analyses identified all classes of genomic alterations (base substitutions, insertions and deletions, copy number alterations, and rearrangements). Since matched normal tissue was unavailable for analysis, these data underwent additional filtering to enrich for cancer-related events. The reported data includes genomic alterations that are known and suspected tumor drivers, as well as variants of unknown significance. To preserve patient anonymity, all known or suspected germline variants were removed from the data unless known to be associated with cancer development. The dataset contains genomic alteration profiles generated by FoundationOne version 2 testing for adult cancer patients (over 18 y.o.), and represents a vast diversity of tumor subtypes, including many rare diseases not profiled as part of large-scale profiling efforts. Cases are grouped into 16 broad disease categories containing tumors from 162 unique disease subtypes. Since specimens were profiled as part of clinical care, limited clinical parameters were available, including age, gender, tissue of origin, % of tumor nuclei, and diagnosis. Publication of this dataset is intended to allow the broad scientific community access to this unique cohort for use in scientific research projects of common and rare types of cancer, both for generating leads regarding causal mechanisms as well as cross-testing and confirming existing hypotheses. A pediatric cancer clinical dataset consisting of data from 1,215 patients under 18 y.o. is available separately at: FOUNDATION MEDICINE

  Study phs001179

• Transcriptome study of differential expression in schizophrenia

  Schizophrenia is a common and severe psychotic disorder. While some common SNPs and rare copy number variants have been identified as being significantly associated with disease risk, the biological mechanisms remain undefined. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines from a total of 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, and immunity. The observed differential expression of extended major histocompatibility complex region genes converges with the genetic evidence from schizophrenia genome-wide association studies, which find the same region to be the most significant schizophrenia susceptibility locus. Our analysis also provides novel candidate genes for further study to assess their potential contribution to schizophrenia.

  Study phs000775

• Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs drug target discovery in ankylosing spondylitis

  Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterised by enthesitis of the spine and sacroiliac joints. Genome-wide association studies have revealed >100 genetic associations but their functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We report differential disease signatures for specific genomic regions from individual -omic modalities and multi-omic integration, showing enrichment at genetically associated loci and evidence for altered monocyte function in AS. We link putative functional SNPs with cognate genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1. We also show how disease-specific functional genomic data can be integrated with GWAS evidence to enhance therapeutic target discovery. This study combines multiple epigenetic and transcriptional analysis with GWAS to identify cell types, candidate drug target genes and transcriptional regulation of likely pathogenic relevance.

  Study EGAS00001006233

• Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs drug target discovery in ankylosing spondylitis

  Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterised by enthesitis of the spine and sacroiliac joints. Genome-wide association studies have revealed >100 genetic associations but their functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We report differential disease signatures for specific genomic regions from individual -omic modalities and multi-omic integration, showing enrichment at genetically associated loci and evidence for altered monocyte function in AS. We link putative functional SNPs with cognate genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1. We also show how disease-specific functional genomic data can be integrated with GWAS evidence to enhance therapeutic target discovery. This study combines multiple epigenetic and transcriptional analysis with GWAS to identify cell types, candidate drug target genes and transcriptional regulation of likely pathogenic relevance.

  Study EGAS00001006945

• Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study - Maternal Glycemia and Birthweight GEI Study

  Low and high birth weight are not only major causes of neonatal morbidity and mortality, but epidemiological data have established an association between birth weight and later life risk of adult metabolic diseases. Fetal growth is determined by complex interactions between fetal genes and the maternal uterine environment. Subtle or overt variation in maternal glucose tolerance, which is, in part, genetically determined, is related to fetal size at birth. Moreover, new emerging data suggest that genetic variation in the fetus can impact maternal metabolism (e.g., blood pressure and glucose tolerance). Given the above, we are addressing the hypothesis that, during pregnancy, gene-environment interactions in the context of the maternal-fetal unit impact fetal size at birth and maternal metabolism. Genes that control fetal growth or maternal metabolism during pregnancy are largely unknown, so the first step to address our hypothesis will be to identify genetic variation that impacts fetal growth and maternal metabolism and to determine the interaction of that variation with the intrauterine and fetal environment. To accomplish this, we are performing genome wide association (GWA) mapping on a subset of ~37,000 DNA samples that were collected from mothers and their offspring as part of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. HAPO is a multicenter, international study in which high quality phenotypic data related to fetal growth and maternal glucose metabolism has been collected from 25,000 pregnant women of varied racial and socio-demographic backgrounds using standardized protocols that were uniform across centers. For these studies, we are genotyping 1,500 infants and their mothers of European descent, 1,250 Afro-Caribbean infants and mothers, 800 Hispanic (Mexican-American) infants and mothers, and 1200 Thai infants and mothers. Genotyping is being performed using the Illumina Human610 Quad (European ancestry participants), Human1M Duo (Afro-Caribbean and Hispanic participants), and Omni1-Quad_v1-0_B (Thai participants). The specific aims for the project are as follows: (1) To apply analytic approaches for conducting GWA mapping studies on quantitative phenotypes related to offspring size at birth (birth weight, ponderal index, head circumference and adiposity) allowing for other known influences such as gestational age, parity, and maternal weight gain. (2) To apply the above approaches to identify genetic variation that impacts maternal glucose tolerance at ~28 weeks of gestation (fasting glucose, glucose during an oral glucose tolerance test, and insulin sensitivity expressed as quantitative traits) allowing for other known influences such as maternal weight gain, parity and age. (3) To examine the interaction between maternal genes, the intrauterine environment, and fetal genes to identify interactions that modulate genetic regulation of size at birth and fetal genetic variation that impacts on maternal glucose tolerance. GWA mapping will provide initial evidence for association of specific SNPs with the quantitative traits outlined above. As low and high birth weight are not only major causes of neonatal morbidity and mortality but have also been associated with increased risk of metabolic diseases in adults, identification of genes that regulate fetal growth and maternal metabolism will provide novel information about the pathways that regulate these processes as well as important insight into susceptibility genes for chronic diseases like type 2 diabetes. The Version 1 (v1) dbGaP release will include data only from the Hispanic study participants. The Version 2 (v2) dbGaP release will include data from the Hispanic and European ancestry study participants. The Version 3 (v3) dbGaP release will include data from the Afro-Caribbean, Hispanic and European ancestry participants. The Version 4 (v4) dbGaP release will include data from all participants (i.e., Afro-Caribbean, Hispanic, European ancestry, and Thai participants). This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to maternal metabolism and birthweight through large-scale genome-wide association studies of infants and their mothers at multiple international sites. Genotyping was performed at the Broad Institute of MIT and Harvard, and at CIDR of Johns Hopkins University, GENEVA genotyping centers. Data cleaning and harmonization was performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000096

• Sanger sequencing of catalytic-domain encoding exons of tyrosine kinase genes from human endometrial tumor DNAs

  The purpose of the original study was to search for somatic mutations in the tyrosine kinome of serous and clear cell endometrial carcinomas (human). The study was conducted in two phases. Phase 1: A mutation discovery screen, in which ~577 exons encoding the catalytic domains of 86 tyrosine kinases were PCR-amplified and bidirectionally Sanger sequenced from 24 serous, 11 clear cell, and 5 mixed histology endometrial tumors. This was followed by alignment of sequence reads to the human reference sequence and subsequent nucleotide variant calling to identify potential somatic (tumor-specific) mutations. Potential somatic mutations were confirmed by re-amplification and sequencing of the relevant tumor DNA as well as matched non-tumor ("normal") DNA from the same case. Phase 2: A mutation prevalence screen, in which the non-catalytic regions two tyrosine kinase genes, TNK2 and DDR1, were PCR-amplified and sequenced from the 40 discovery screen tumors, and all coding exons of TNK2 and DDR1 were PCR-amplified and sequenced from another 10 clear cell, 21 serous, and 41 endometrioid endometrial tumors, in an effort to identify additional somatic mutations in each gene. Exons encoding the exonuclease domain of POLE were also sequenced to document somatic mutations.

  Study phs000841

• NextGen Consortium: GENESiPS Study: Identifying the Gene Networks of Insulin Resistance

  Variability in induced pluripotent stem cell (iPSC) lines remains a roadblock for disease modeling and regenerative medicine. Through linear mixed models we have described different sources of gene expression variability from RNA sequencing data in 317 human iPSC lines from 101 individuals. We found that ~50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele specific expression show that iPSCs retain a subject-specific gene expression pattern. Pathway enrichment and key driver analyses, based on predictive causal gene networks, found that Polycomb targets explain a significant part of the non-genetic variability present in iPSCs within and across individuals. These publically available iPSC lines and genetic datasets will be a resource to the scientific community and will open new avenues to reduce variability in iPSCs and improve their utility in disease modeling. SNP array data from individuals included in RNA-seq transcriptome profiling study of human induced pluripotent stem cells to characterize gene expression variation across individuals and within multiple iPSC lines from the same individual. Genotyping was performed on patient blood. Data availability: SNP-genotyping: dbGaP - current study RNA-seq counts: GEO - GSE79636 FASTQ files: SRA - SRP072417

  Study phs001139