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White blood cell (WBC) count is a common clinical measure from complete blood count assays, and it varies widely among healthy individuals. Total WBC count and its constituent subtypes have been shown to be moderately heritable, with the heritability estimates varying across cell types. We studied 19,509 subjects from seven cohorts in a discovery analysis, and 11,823 subjects from ten cohorts for replication analyses, to determine genetic factors influencing variability within the normal hematological range for total WBC count and five WBC subtype measures. Cohort specific data was supplied by the CHARGE, HeamGen, and INGI consortia, as well as independent collaborative studies. We identified and replicated ten associations with total WBC count and five WBC subtypes at seven different genomic loci (total WBC count-6p21 in the HLA region, 17q21 near ORMDL3, and CSF3; neutrophil count-17q21; basophil count- 3p21 near RPN1 and C3orf27; lymphocyte count-6p21, 19p13 at EPS15L1; monocyte count-2q31 at ITGA4, 3q21, 8q24 an intergenic region, 9q31 near EDG2), including three previously reported associations and seven novel associations. To investigate functional relationships among variants contributing to variability in the six WBC traits, we utilized gene expression- and pathways-based analyses. We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. Gene expression data from whole blood was utilized to show that significant biological consequences can be extracted from our genome-wide analyses, with effect estimates for significant loci from the meta-analyses being highly corellated with the proximal gene expression. In addition, collaborative efforts between the groups contributing to this study and related studies conducted by the COGENT and RIKEN groups allowed for the examination of effect homogeneity for genome-wide significant associations across populations of diverse ancestral backgrounds.
Pubmed ID: 21738480 RIS Download
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The NIH Biowulf cluster is a GNU/Linux parallel processing system designed and built at the National Institutes of Health and managed by the Helix Systems Staff. The system is designed for large numbers of simultaneous jobs common in bioinformatics as well as large-scale distributed memory tasks such as molecular dynamics. Sponsor: This work was supported by the National Institutes of Health Intramural Research Program through the Center for Information Technology and the National Institute of Neurological Disorders and Stroke, and by the Internal National Institute of Standards and Technology Research Fund. Keywords: Software, Program, Processing, System, Simulatenous, Bioinformatics, Memory, Molecular, Dynamics,
View all literature mentionsA tool to examine relationships between genes in different disease associated loci. Given several genomic regions or SNPs associated with a particular phenotype or disease, GRAIL looks for similarities in the published scientific text among the associated genes. As input, users can upload either (1) SNPs that have emerged from a genome-wide association study or (2) genomic regions that have emerged from a linkage scan or are associated common or rare copy number variants. SNPs should be listed according to their rs#''s and must be listed in HapMap. Genomic Regions are specified by a user-defined identifier, the chromosome that it is located on, and the start and end base-pair positions for the region. Grail can take two sets of inputs - Query regions and Seed regions. Seed regions are definitely associated SNPs or genomic regions, and Query regions are those regions that the user is attempting to evaluate agains them. In many applications the two sets are identical. Based on textual relationships between genes, GRAIL assigns a p-value to each region suggesting its degree of functional connectivity, and picks the best candidate gene. GRAIL is developed by Soumya Raychaudhuri in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, and the Broad Institute. GRAIL is described in manuscript, currently in preparation.
View all literature mentionsA longitudinal, epidemiologic study to identify the common risk factors or characteristics that contribute to cardiovascular disease by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms or suffered a heart attack or stroke. Since that time the FHS has studied three generations of participants resulting in biological specimens and data from nearly 15,000 participants. Since 1994, two groups from minority populations, including related individuals have been added to the FHS. FHS welcomes proposals from outside investigators for data and biospecimens. The researchers recruited 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Since 1948, the subjects have continued to return to the study every two years for a detailed medical history, physical examination, and laboratory tests, and in 1971, the Study enrolled a second generation - 5,124 of the original participants'''' adult children and their spouses - to participate in similar examinations. In 1994, the need to establish a new study reflecting a more diverse community of Framingham was recognized, and the first Omni cohort of the Framingham Heart Study was enrolled. In April 2002 the Study entered a new phase, the enrollment of a third generation of participants, the grandchildren of the Original Cohort. In 2003, a second group of Omni participants was enrolled. Over the years, careful monitoring of the Framingham Study population has led to the identification of major CVD risk factors, as well as valuable information on the effects of these factors such as blood pressure, blood triglyceride and cholesterol levels, age, gender, and psychosocial issues. Risk factors for other physiological conditions such as dementia have been and continue to be investigated. In addition, the relationships between physical traits and genetic patterns are being studied. FHS clinical and research data is stored in the dbGaP and NHLBI Repository repositories and may be accessed by application. Please check the following repositories before applying for data through FHS. Investigators seeking data that is not available through dbGaP or BioLINCC or seeking biological specimens may submit a proposal through the FHS web-based research application. The FHS data repository may be accessed through this FHS website, under the For Researchers link, then Description of Data, in order to determine if and how the desired data is stored. Proposals may involve the use of existing data, the collection of new data, either directly from participants or from previously collected samples, images, or other materials (e.g., medical records). The FHS Repository also has biological specimens available for genetic and non-genetic research proposals. Specimens include urine, blood and blood products, as well as DNA.
View all literature mentionsA research program of the NIA which focuses on neuroscience, aging biology, and translational gerontology. The central focus of the program's research is understanding age-related changes in physiology and the ability to adapt to environmental stress, and using that understanding to develop insight about the pathophysiology of age-related diseases. The IRP webpage provides access to other NIH resources such as the Biological Biochemical Image Database, the Bioinformatics Portal, and the Baltimore Longitudinal Study of Aging.
View all literature mentionsSoftware application designed to facilitate meta-analysis of large datasets (such as several whole genome scans) in a convenient, rapid and memory efficient manner. (entry from Genetic Analysis Software)
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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