The genomes of all organisms are susceptible to continuous deleterious mutations. The rate (U) at which deleterious mutations occur in the genome per generation and the distribution of their effects have important implications for the improvement of human health and agricultural production, for the conservation of endangered species and for the testing of numerous theories in population genetics. However, little is known due to the limitation and tremendous cost of the previous estimation method (mutation-accumulation), and it has been argued that this is one of the most glaring gaps in the broad field of genetics. We propose to narrow this gap by accomplishing the following specific aims. We will 1) develop efficient experimental designs and powerful statistical methods to estimate U and the distribution parameters of deleterious mutation effects; 2) develop statistical methods to discriminate between the two long- standing rival hypotheses concerning the maintenance of genetic variability and inbreeding depression-namely, the dominance and overdominance hypotheses; 3) investigate the statistical properties of the methods developed/to be developed and test their robustness under a range of biologically plausible conditions and experimental designs; 4) investigate the optimal experimental design for mutation-accumulation experiments; 5) develop a use-friendly computer software package for empiricists to apply any of the estimation methods, whatever complex statistical and computational techniques are involved. The results from the proposed work will provide powerful and efficient experimental designs and statistical methods for estimating U, and the distribution of mutation effects in a broad range of taxa, and will provide useful and powerful tools for empiricists to characterize deleterious genomic mutations.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM060402-03
Application #
6526188
Study Section
Genetics Study Section (GEN)
Program Officer
Eckstrand, Irene A
Project Start
2000-08-01
Project End
2006-07-31
Budget Start
2002-08-01
Budget End
2004-07-31
Support Year
3
Fiscal Year
2002
Total Cost
$176,250
Indirect Cost
Name
Creighton University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
Omaha
State
NE
Country
United States
Zip Code
68178
Liu, Pengyuan; Lu, Yan; Recker, Robert R et al. (2010) ALOX12 gene is associated with the onset of natural menopause in white women. Menopause 17:152-6
Chen, Xiang-Ding; Xiao, Peng; Lei, Shu-Feng et al. (2010) Gene expression profiling in monocytes and SNP association suggest the importance of the STAT1 gene for osteoporosis in both Chinese and Caucasians. J Bone Miner Res 25:339-55
Liu, Pengyuan; Lu, Yan; Recker, Robert R et al. (2010) Association analyses suggest multiple interaction effects of the methylenetetrahydrofolate reductase polymorphisms on timing of menarche and natural menopause in white women. Menopause 17:185-90
Lu, Yan; Liu, Pengyuan; Recker, Robert R et al. (2010) TNFRSF11A and TNFSF11 are associated with age at menarche and natural menopause in white women. Menopause 17:1048-54
Chen, Yuan; Shen, Hui; Yang, Fang et al. (2009) Choice of study phenotype in osteoporosis genetic research. J Bone Miner Metab 27:121-6
Lei, Shufeng; Deng, Feiyan; Xiao, Peng et al. (2009) Bivariate whole-genome linkage scan for bone geometry and total body fat mass. J Genet Genomics 36:89-97
Zhang, Zhi-Xin; Lei, Shu-Feng; Deng, Fei-Yan et al. (2009) Bivariate genome-wide linkage analysis for traits BMD and AAM: effect of menopause on linkage signals. Maturitas 62:16-20
He, Li-Na; Recker, Robert R; Deng, Hong-Wen et al. (2009) A polymorphism of apolipoprotein E (APOE) gene is associated with age at natural menopause in Caucasian females. Maturitas 62:37-41
Yan, H; Liu, Y-J; Zhou, Q et al. (2009) Comparison of whole genome linkage scans in premenopausal and postmenopausal women: no bone-loss-specific QTLs were implicated. Osteoporos Int 20:771-7
Guo, Yanfang; Xiao, Peng; Lei, Shufeng et al. (2008) How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes. Acta Biochim Biophys Sin (Shanghai) 40:426-36

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