Extended Multipoint Identity-By-Descent
Analysis of Human Quantitative Traits:
Efficiency, Power, and Modeling Considerations
Schork NJ
Department of Medicine, University of Michigan,
Ann Arbor 48109-0500.
American Journal of Human Genetics, 53(6):1306-1319 (Dec 1993)
Abstract
Goldgar introduced a novel marker-based method for partitioning the
variation of a quantitative trait into specific chromosomal regions. Unlike
traditional linkage mapping methods, Goldgar's method does not require the
estimation of statistical quantities characterizing each locus thought to
influence the trait under scrutiny (e.g., allele frequencies, penetrances, etc.).
Goldgar's method is thus more flexible and less model dependent than
many traditional marker-based genetic analysis techniques. Unfortunately,
however, many of the properties of Goldgar's method have not been
investigated. In this paper, the utility of an extended version of Goldgar's
approach is studied in settings in which sibships are taken as the sampling
unit of interest. The extensions discussed resolve around the incorporation
of a wider variety of effects and factors into Goldgar's basic model. Analytic
studies pertaining to power, sample-size requirements, and estimation
procedures for the proposed extended version of Goldgar's method are
described. Hypothesis-testing strategies are also discussed. The results of
the analytic studies indicate that, although an extended sib-pair version of
Goldgar's variance-partitioning approach to modeling the chromosomal
determinants of a quantitative trait will be useful only for traits with high
heritabilities or when fine-scale genetic maps can be employed. Goldgar's
technique as a whole has promise, as it can be made relatively robust
statistically, refined through some simple and intuitive extensions, and can
be easily adapted to work with more complex sampling units. Further
extensions of Goldgar's methods are proposed, and areas in need of
additional research are discussed.
Variance component linkage analysis