In recent years, there has been a focus to understand the etiology of complex diseases which do not follow simple Mendelian, single-locus segregation. Complex diseases are assumed to result from more than one locus and/or environmental factors, or merely exhibit continuous (i.e.quantitative) variation. Quantitative traits have been studied extensively in plant and animal genetics. With the advent of new tools and methods, comprehensive approaches to identify the candidate genes underlying quantitative traits for humans are available. Testing the contribution of candidate genes to quantitative trait variation will become commonplace as more genes are identified. We consider two likelihood-based statistical strategies for testing and quantifying the effect of candidate locus genotypes on a quantitative trait with randomly ascertained pedigree or family data. The first strategy estimates, and then tests the equality of, mean phenotype values association with each genotype. This strategy can be referred to as the """"""""mean effects"""""""" strategy. The second strategy estimates and tests a variance component parameter associated with identity-by-state information gathered from alleles at the candidate locus. This strategy tests whether or not allelic variation and co-variation at the locus in question among related individuals explains variation and co-variation in the phenotype of interest among those individuals. This second strategy can be referred as the """"""""variance component"""""""" strategy, and forms the basis of most linkage analysis strategies for quantitative traits. Both strategies can be framed in the context of linear models which can accommodate the effects of other factors (e.g. gender, age, sex, etc.) on the phenotype of interest. We consider the use of these models with sib-pair data and bi-allelic loci, and describe analytic derivations that compare and contrast their power and efficiency. Our results suggest that the mean effects model is superior to the variance component model in the sib-pair, di-allelic locus setting. ?

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR003655-14
Application #
6206072
Study Section
Project Start
1999-09-01
Project End
2000-08-31
Budget Start
1998-10-01
Budget End
1999-09-30
Support Year
14
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Case Western Reserve University
Department
Type
DUNS #
077758407
City
Cleveland
State
OH
Country
United States
Zip Code
44106
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