? ? The PROGENI network is a collection of four family pharmacogenetic studies which hope to exploit the gene-finding power that arises from measuring the phenotypic expression of genes in several controlled environments. The goal is to discover genes for response using both candidate gene and genomic approaches in the context of family designs. Large numbers of families of multiple ethnicities and wide geographical dispersion were collected, phenotyped before and after intervention. Selected candidate genes were examined and genome-wide linkage panels are being completed. This renewal period is devoted to analysis and follow-up genotyping. Although the interventions and primary outcomes differ from study to study, the PROGENI network component studies have important key aspects in common, most importantly, the need for efficient statistical analysis methods for gene finding for intervention response phenotypes. The PROGENI Data Coordinating Center provides both administrative and scientific support to the entire network, including study website, data pooling, quality control, Analysis Workshops, and development and evaluation of novel statistical methods of analysis for all studies. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01HL088655-01
Application #
7252979
Study Section
Special Emphasis Panel (ZHL1-CSR-K (F1))
Program Officer
Jaquish, Cashell E
Project Start
2007-07-16
Project End
2009-05-31
Budget Start
2007-07-16
Budget End
2008-05-31
Support Year
1
Fiscal Year
2007
Total Cost
$343,494
Indirect Cost
Name
Washington University
Department
Genetics
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
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Borecki, Ingrid B; Province, Michael A (2008) Genetic and genomic discovery using family studies. Circulation 118:1057-63
Province, Michael A; Borecki, Ingrid B (2008) Gathering the gold dust: methods for assessing the aggregate impact of small effect genes in genomic scans. Pac Symp Biocomput :190-200