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• Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging

  In this study we characterised the consequences of DNMT3A- and TET2-mutations in human clonal haematopoiesis. We screened 195 bone marrow samples for mutations by targeted DNA sequencing, and sequenced 99 paired peripheral blood samples to compare mutation detection between bone marrow and blood. We performed single-cell analysis with TARGET-seq+ on 13 samples (4 controls and 9 clonal haematopoiesis samples), which combines single-cell genotyping for targeted mutations with whole transcriptome sequencing on FACS sorted cells. Single-cell genotyping and transcriptomes were sequenced separately and linked by common single-cell identifiers.

  Study EGAS00001007358

• HipSci genotyping microarray for embryonic stem cell control lines

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we use commercially available embryonic stem cell lines as control samples against which to compare HipSci's iPS cell lines. We performed genotyping analysis using the Infinium HumanExome BeadChip.

  Study EGAS00001001730

• GWAS of SJS/TEN in Thai population

  This study looks at the Phenytoin-Induced SJS/TEN development based on SNP genotyping data of 1965 individuals from Thailand. Their normal DNAs were genotyped on Illumina HumanOmniExpressExome-8 v1.2 array.

  Study EGAS00001008316

• MuTHER_adipose_tissue_small_RNA_expression

  We investigate the naturally occurring variation of small RNA expression in conjunction with genetic and gene expression variation. We shall assess the variation in subabdominal fat tissue small RNA levels in 168 unrelated MuTHER individuals. Using gene expression data from the same samples, we can survey how much the small RNA expression contributes to the variance in gene expression profiles. We can also take advantage of the high density genotyping data on these samples to identify genetic associations of small RNA expression levels, and the downstream effect of the genetic variants in small RNA sequence. Previous small RNA sequencing efforts have also extended the repertoire of small RNAs, and we hope to potentially find additional small RNAs active in adipose tissue biology. Altogether, the project should provide an exciting view into small RNA biology in humans, and lead to development of new methodology for analysis of molecular phenotype data.

  Study EGAS00001000212

• APCDR AGV Project: The African Genome Variation Project(dense array genotyping data)

  The African Genome Variation Project (AGVP) aims to provide a global resource to understand population diversity and help design, implement and interpret genomic studies in sub-Saharan Africa (SSA), and worldwide. The AGVP represents dense genotyping data on 1,481 individuals from 18 ethno-linguistic groups and low coverage whole genome sequencing data on 320 individuals from 7 ethno-linguistic groups in SSA.

  Study EGAS00001000959

• InsPIRE islets

  We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors and 26 FAC sorted beta-cells.

  Study EGAS00001003997

• The study provides comprehensive access to the set of EGA studies which may be useful as controls.

  The subjects studies in these datasets were selected due to being healthy control individuals or otherwise provide well characterised phenotype or exclude certain disease phenotype categories such that they may be useful as control population, such as to derive allele frequencies from a known reference population.

  Study EGAS00001000646

• Pancreatic Cancer NGS WGS data

  Whole genome sequencing data for Pancreatic Cancer Samples exploring the tandem duplicator phenotype

  Study EGAS50000001078

• Single-cell transcriptional mapping reveals genetic and hierarchy-based determinants of aberrant differentiation in AML

  Therapeutic targeting of acute myeloid leukemia (AML) has in part been hampered by profound cell state heterogeneity among leukemia cells. To develop a more precise understanding of leukemia cell states in AML, we constructed a reference atlas of human hematopoiesis from 263,519 single cell transcriptomes spanning 55 cellular states. Using this atlas, we mapped over 1.2 million single-cell transcriptomes spanning 318 myeloid leukemia samples. This uncovered twelve recurrent patterns of aberrant differentiation in AML and additional leukemia cell states involving lymphoid and erythroid progenitors. Systematic mapping of genotype-to-phenotype associations revealed specific differentiation landscapes associated with >45 genetic drivers. Importantly, AML samples sharing common genetic driver alterations can have distinct cellular origins which inform the cellular identity of their disease-sustaining leukemia stem cell (LSC). Further, distinct LSC-driven hierarchies can co-exist within individual patients. Thus, precise mapping of malignant cell states provides insights into leukemogenesis, refines disease classification, and informs therapy development.

  Study EGAS50000000918

• Systematic comparative analysis of single-nucleotide variants detection methods from single-cell RNA sequencing data

  Systematic interrogation of single nucleotide variations (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single cell level. While SNV detection from abundant single cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Here, we performed a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, using both simulation and scRNA-seq datasets, and identified multiple elements influencing their performance. Our study provided the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

  Study EGAS00001003883