There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at

Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5m genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N≤71,225 European ancestry, N=12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N=29,136). We identified association between systolic or diastolic blood pressure and common variants in 8 regions near the CYP17A1 (P=7×10−24), CYP1A2 (P=1×10−23), FGF5 (P=1×10−21), SH2B3 (P=3×10−18), MTHFR (P=2×10−13), c10orf107 (P=1×10−9), ZNF652 (P=5×10−9) and PLCD3 (P=1×10−8) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.

We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

Hematopoiesis is a carefully controlled process that is regulated by complex networks of transcription factors that are, in part, controlled by signals resulting from ligand binding to cell-surface receptors. To further understand hematopoiesis, we have compared gene expression profiles of human erythroblasts, megakaryocytes, B cells, cytotoxic and helper T cells, natural killer cells, granulocytes, and monocytes using whole genome microarrays. A bioinformatics analysis of these data was performed focusing on transcription factors, immunoglobulin superfamily members, and lineage-specific transcripts. We observed that the numbers of lineage-specific genes varies by 2 orders of magnitude, ranging from 5 for cytotoxic T cells to 878 for granulocytes. In addition, we have identified novel coexpression patterns for key transcription factors involved in hematopoiesis (eg, GATA3-GFI1 and GATA2-KLF1). This study represents the most comprehensive analysis of gene expression in hematopoietic cells to date and has identified genes that play key roles in lineage commitment and cell function. The data, which are freely accessible, will be invaluable for future studies on hematopoiesis and the role of specific genes and will also aid the understanding of the recent genome-wide association studies. (Blood. 2009;113:e1-e9) IntroductionThe hematopoietic system represents one of the best-studied cellular differentiation processes in mammals. The differentiation of the hematopoietic stem cell (HSC) into the blood cell lineages, which is depicted as a stepwise process, generates diverse types of cells that perform many different functions. Historical observations of the blood, made in the late 18th century using some of the first microscopes, revealed that blood is composed of a heterogeneous population of cells that are distinct in number, morphology, and function. Since these early studies, the application of both technologic and methodologic advances to the investigation of blood has led to an ever-increasing understanding of the nature and function of the different types of blood cells. For example, the use of monoclonal antibodies (mAbs) and the designation of the cluster of differentiation (CD) markers, of which there are now more than 300, 1 allows hematologists to assign detailed phenotypes to malignant blood cells, which form the basis of decisions on therapeutic intervention.The value of the current understanding of the hematopoietic system to patient care is perhaps best illustrated in the field of malignancy where gene and protein expression profiles permit rapid and routine patient stratification. It is now possible to stratify patients with leukemia and lymphoma with unprecedented accuracy using gene expression profiles. Signature gene expression profiles may be used for diagnosis and predicting disease prognosis. In addition to studies in patients, gene expression profiles are available for a wide range of healthy tissue types. However, many of these resources, although broad in tissue coverage, are limited in the nu...

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Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups. Recently, it has even been observed that large but frozen pre-trained language models (PLMs) with prompt learning outperform smaller but fine-tuned models. However, as with many recent NLP trends, the performance of even the largest PLMs such as GPT-3 do not perform well on specialized domains (e.g. medical text), and the common practice to achieve State of the Art (SoTA) results still consists of pre-training and fine-tuning the PLMs on downstream tasks. The reliance on fine-tuning large PLMs is problematic in clinical settings where data is often held in non-GPU environments, and more resource efficient methods of training specialized domain models is crucial. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared with more traditional fine-tuning methods. Results are partially in line with the prompt learning literature, with prompt learning able to match or improve on traditional fine-tuning with substantially fewer trainable parameters and requiring less training data. We argue that prompt learning therefore provides lower computational resource costs applicable to clinical settings, that can serve as an alternative to fine-tuning ever increasing in size PLMs.

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