The primary aim of the Data Analysis Core is to combine data from all contributing research projects and from the Genotyping Core and to use this information to address the three Program Specific Aims. To achieve this objective the following steps will be performed: 1. A system for data entry and data quality control will be developed and implemented. 2. Program scientists will be assisted in the statistical analysis and interpretation of the results within each of the individual components. 3. New statistical programs in the form of SAS macros will be written and tested. 4. To address Program Specific Aim #1, QTL mapping analysis will be performed on the 600 UM-HET3 mice in population I to identify loci that affect age-sensitive traits within each measurement domain. 5. A similar analysis will identify QTL that affect age-sensitive measures across multiple physiological and biochemical domains. 6. To address Program Specific Aim #2, correlations between age-sensitive traits within and across measurement domains will be studied both with and without adjusting for genetic effects. 7. To address Program Specific Aim #3, age-sensitive outcomes will be compared in two groups of mice (Population 2), one of which has been selected for alleles that are associated with extended life span. This application presents the statistical model that will be used for the QTL mapping as well as a power analysis based upon the currently available genetic map with a set of simulated phenotypes. The sample size-which is based upon budgetary limitation-is adequate to detect approximately 70% of a set of 50 hypothetical Qtls with heritability 0.2 after adjustments for multiple comparison artifacts. Several precautions are described to alleviate the problem of multiple comparisons and to control the overall alpha level.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program Projects (P01)
Project #
5P01AG016699-04
Application #
6593398
Study Section
Project Start
2002-06-01
Project End
2003-04-30
Budget Start
1998-10-01
Budget End
1999-09-30
Support Year
4
Fiscal Year
2002
Total Cost
$312,755
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
791277940
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Burke, David T; Kozloff, Kenneth M; Chen, Shu et al. (2012) Dissection of complex adult traits in a mouse synthetic population. Genome Res 22:1549-57
Chisa, Jennifer L; Burke, David T (2007) Mammalian mRNA splice-isoform selection is tightly controlled. Genetics 175:1079-87
Hanlon, Philip; Lorenz, William Andrew; Shao, Zhihong et al. (2006) Three-locus and four-locus QTL interactions influence mouse insulin-like growth factor-I. Physiol Genomics 26:46-54
Harper, James M; Salmon, Adam B; Chang, Yayi et al. (2006) Stress resistance and aging: influence of genes and nutrition. Mech Ageing Dev 127:687-94
Harper, James M; Durkee, Stephen J; Smith-Wheelock, Michael et al. (2005) Hyperglycemia, impaired glucose tolerance and elevated glycated hemoglobin levels in a long-lived mouse stock. Exp Gerontol 40:303-14
Volkman, Suzanne K; Galecki, Andrzej T; Burke, David T et al. (2004) Quantitative trait loci that modulate femoral mechanical properties in a genetically heterogeneous mouse population. J Bone Miner Res 19:1497-505
Harper, James M; Galecki, Andrzej T; Burke, David T et al. (2004) Body weight, hormones and T cell subsets as predictors of life span in genetically heterogeneous mice. Mech Ageing Dev 125:381-90
Wisser, Kathleen C; Schauerte, Joseph A; Burke, David T et al. (2004) Mapping tissue-specific genes correlated with age-dependent changes in protein stability and function. Arch Biochem Biophys 432:58-70
Harper, James M; Galecki, Andrzej T; Burke, David T et al. (2003) Quantitative trait loci for insulin-like growth factor I, leptin, thyroxine, and corticosterone in genetically heterogeneous mice. Physiol Genomics 15:44-51
Bennett-Baker, Pamela E; Wilkowski, Jodi; Burke, David T (2003) Age-associated activation of epigenetically repressed genes in the mouse. Genetics 165:2055-62

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