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• Genetic Effects on Gene Expression and Splicing during Human Neurogenesis

  Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n=85) and their sorted neuronal progeny (n=74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

  Study phs002493

• Whole-genome sequencing of normal Singaporean volunteers

  Sleep is associated with various health outcomes. Despite their growing adoption, the potential for consumer wearables to contribute sleep metrics to sleep-related biomedical research remains largely uncharacterized. Here we analyzed sleep tracking data, along with questionnaire responses and multi-modal phenotypic data generated from 482 normal volunteers. First, we compared wearable-derived and self-reported sleep metrics, particularly total sleep time (TST) and sleep efficiency (SE). We then identified demographic, socioeconomic and lifestyle factors associated with wearable-derived TST. Among others, male gender (β = -15.539, 95% confidence interval [CI] = -26.245 - -4.832, p = 0.005), older age (β = -0.493, CI = -0.941 - -0.044, p = 0.032) and manual labor (β = -26.856, CI = -49.715 - -3.997, p = 0.022) were associated with reduced TST, whereas alcohol consumption (β = 19.247, CI = 8.008 - 30.486, p = 8.54x10-04) was associated with increased TST. Multi-modal phenotypic data analysis showed that wearable-derived TST and SE were associated with various cardiovascular disease risk markers such as body mass index (β = -0.006, CI = -0.00 - -0.000, p = 0.040) and waist circumference (β = 0.001, CI = -0.015-0.017, p = 0.893), whereas self-reported measures were not. We then showed that insufficient sleep was associated with premature telomere attrition, using telomere length estimated using whole-genome sequencing (β = 1.275, CI = 0.187 - 2.363, p = 0.023) and quantitative PCR (β = 0.001, CI = 8.318x10-05 - 0.001, p = 0.028). Our study highlights the potential for sleep metrics from consumer wearables to provide novel insights into data generated from population cohort studies.

  Study EGAS00001004007

• ETMR_Nanostring

  Total RNA (100ng) from 21 ETMRs with C19MC structural alterations and 28 other PBTs was prepared with nCounter miRNA Sample Prep Kit according to standard protocol. miRNA expression profiling was conducted with human v1, v2, or v3 miRNA panel on nCounter miRNA expression platform (NanoString Technologies, Seattle, WA) according to manufacturer’s protocol. Signal normalization was done using nSolver Analysis and batch corrected using ComBat (Johnson et al. Biostatistics. 2007). 565 miRNAs overlapped between all three versions and was used for further analyses. Fold change and supervised t-test with FDR correction was calculated between the ETMRs and other PBTs.

  Dataset EGAD00010001701

• Whole-genome enzymatic DNA methylation sequencing and single-cell RNA sequencing data from nasal cells of COVID-19 patients

  Nasal swabs were collected from COVID-19 patients during the acute phase of infection as well as 3 and 12-months post-infection. Cells isolated from nasal swabs were subjected to whole-genome enzymatic DNA methylation sequencing (n=33) and scRNA-seq (n=24). For n=12 former COVID-19 pateints, follow-up samples were collected at 3 and 12 months post-infection and subjected to scRNA-seq.

  Dataset EGAD50000000398

• MGUS/SMM to MM WES

  This dataset contains raw sequencing reads for matched MGUS/SMM to MM patient samples, including normal germline controls. FASTQ files were generated on Illumina NextSeq 500 and HiSeq 4000 machines following exome capture using the Agilent Clinical Research Exome kit. DNA was extracted from CD138+CD38++ cells (representing MGUS/SMM/MM cells) and CD138-CD38- (representing normal cells) isolated from bone marrow. 10 patients are included with 3 samples each representing normal, MGUS/SMM, MM stages.

  Dataset EGAD00001004190

• Center for Technology Licensing at Cornell University for data associated with Nature Medicine 2024 paper

  For data access to plasma/tumor/normal samples from the manuscript "Ultrasensitive plasma-based monitoring of tumor burden using machine learning-guided signal enrichment" from Widman et al. Nature Medicine 2024

  Dac EGAC50000000207

• Meniere Disease Genomic Data Access Committee

  This Data Access Committee is responsible for reviewing and managing data access requests related to genomic variant files from whole exome sequencing of patients diagnosed with Meniere disease. This includes data from both familial and sporadic forms of the disease.

  Dac EGAC50000000708

• Transcriptome Analysis of Treg and Tfh cells

  Treg and Tfh cells (8 Samples)from the same donors was subject to RNA-seq (TruSeq Stranded Total RNA) processing. Single end fastq-files are supplied.

  Dataset EGAD00001007662

• Targeted Sequencing of Tumor-Normal pairs from advanced Melanoma

  Aligned BAM files from NextSeq500 tageted panel sequencing of 84 samples from matched tumour-normal pairs of 42 melanoma patients. The dataset consists of 30 non-responders and 12 responders to ICB.

  Dataset EGAD00001005778

• Clinical phenotype data

  Clinical data corresponding to the patients with ovarian cancer studied using scRNA-seq and bulk RNA-seq. Variables include molecular subtype, predicted immune phenotype, reviewing pathologist comment, final immune phenotype, histology characterization, and tumor stage.

  Dataset EGAD00001006973