Comparison of three methods for obtaining principal components from family data in genetic analysis of complex disease.
Three multivariate techniques used to derive principal components (PCs) from family data were compared for their ability to model family data and power to detect linkage. Using the simulated data from Genetic Analysis Workshop 12, the five quantitative traits were first adjusted for age, sex, and environmental factors 1 and 2. Then, standard PCs, PCs obtained from between-family covariance, and PCs obtained from within-family genetic covariance were derived and subjected to multivariate sib pair linkage analysis. The standard PCs obtained from the overall correlation matrix allowed identification of key features of the true genetic model more readily than did the other methods. For detection of linkage, standard PCs and PCs obtained from the between-family genetic covariance performed similarly in terms of both power and type 1 error, and both methods performed better than the PCs obtained from within-family genetic covariance.
Moser
K L
KL
Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, USA.
Jedrey
C M
CM
Conti
D
D
Schick
J H
JH
Gray-McGuire
C
C
Nath
S K
SK
Daley
D
D
Olson
J M
JM
D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D019655:Quantitative Trait, Heritable