The Haseman & Elston (1972) sibling-pair regression method
has
been used to detect and estimate
the variance contribution to observed values of a quantitative trait
by allelic variation in specific
candidate genes. The procedure was developed under a model with a
single biallelic trait locus. This
assumption does not hold for several known systems. In this paper
we prove that for candidate gene
analysis the Haseman–Elston procedure extends to the case of
multiple trait loci, each possibly
having more than two alleles. Simulation experiments comparing single-locus
to two-locus models
show that fitting the extended regression equations maintains nominal
significance levels, but the
power to detect linkage to trait variation is not improved by including
additional loci. These results
indicate that the original proposal is statistically robust to
violations of the underlying genetic
model. Practical issues associated with quantifying the relative
variance contribution by individual
loci are also discussed. Applications of the extended regression
equations to lipoprotein(a) and high
density lipoprotein cholesterol are given for illustration.