The NHLBI Family Blood Pressure Program is made up of four cooperating networks whose overall objective is to localize and characterize genes contributing to variation in blood pressure levels and hypertension status. The four networks were originally separately funded and competitive, but two critical realizations have led to full cooperation and collaboration. First, the oligogenic nature of blood pressure control dictates that large samples are necessary to achieve adequate statistical power for genomic linkage and association analyses. Second, linkage intervals are broad and contain large numbers of genes, so that success in identifying genes and mutations requires the effort of multiple laboratories freely sharing information. This coordination extends far beyond phenotyping and genotyping and is best exemplified by the Program's creation of a pooled data set and agreements about coordinated publications. During the initial funding period, the Program surpassed its original recruitment goals, carried out multiple genome-wide linkage and association analyses and created an interim pooled data set consisting of phenotype and genotype data from more than 10,000 individuals. In this renewal application, the Program proposes five specific aims to be carried out by all four networks.
These aims can be grouped according to two complementary themes: First, these applicants will create and analyze a database of blood pressure- related phenotype and genotype data from all FBPP participants (Aim 1). Within linked regions, they will identify allelic variation within positional candidate genes and evaluate the relationship of these polymorphisms with blood pressure levels and hypertension status (Aims 2 and 3). Second, they will use quantitative measures of target organ damage to identify genes that influence susceptibility to develop hypertensive heart and kidney diseases (Aims 4 and 5). In addition to the Program specific aims, each network proposes specific aims to be carried out by that network alone, based on unique aspects of their population and interests and expertise of the investigators. The Family Blood Pressure Program represents the most determined multidisciplinary approach to the genetics of hypertension ever assembled. The resulting synthesis of ideas and amassed data permits rigorous hypothesis testing not otherwise possible and will hasten understanding of the previously elusive genetic variation responsible for disease risk.
| Bloss, Cinnamon S; Jeste, Dilip V; Schork, Nicholas J (2011) Genomics for disease treatment and prevention. Psychiatr Clin North Am 34:147-66 |
| Lanktree, Matthew B; Hegele, Robert A; Schork, Nicholas J et al. (2010) Extremes of unexplained variation as a phenotype: an efficient approach for genome-wide association studies of cardiovascular disease. Circ Cardiovasc Genet 3:215-21 |
| Dixit, Anshuman; Torkamani, Ali; Schork, Nicholas J et al. (2009) Computational modeling of structurally conserved cancer mutations in the RET and MET kinases: the impact on protein structure, dynamics, and stability. Biophys J 96:858-74 |
| Libiger, Ondrej; Nievergelt, Caroline M; Schork, Nicholas J (2009) Comparison of genetic distance measures using human SNP genotype data. Hum Biol 81:389-406 |
| Torkamani, Ali; Topol, Eric J; Schork, Nicholas J (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92:265-72 |
| Torkamani, Ali; Schork, Nicholas J (2008) Predicting functional regulatory polymorphisms. Bioinformatics 24:1787-92 |
| Malo, Nathalie; Libiger, Ondrej; Schork, Nicholas J (2008) Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am J Hum Genet 82:375-85 |
| Torkamani, Ali; Kannan, Natarajan; Taylor, Susan S et al. (2008) Congenital disease SNPs target lineage specific structural elements in protein kinases. Proc Natl Acad Sci U S A 105:9011-6 |
| Schork, Nicholas J; Wessel, Jennifer; Malo, Nathalie (2008) DNA sequence-based phenotypic association analysis. Adv Genet 60:195-217 |
| Torkamani, Ali; Schork, Nicholas J (2008) Prediction of cancer driver mutations in protein kinases. Cancer Res 68:1675-82 |
Showing the most recent 10 out of 26 publications
