Use of multivariate linkage analysis for dissection of a complex cognitive trait.
Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.
Marlow
Angela J
AJ
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
Fisher
Simon E
SE
Francks
Clyde
C
MacPhie
I Laurence
IL
Cherny
Stacey S
SS
Richardson
Alex J
AJ
Talcott
Joel B
JB
Stein
John F
JF
Monaco
Anthony P
AP
Cardon
Lon R
LR
D000704:Analysis of Variance; D002874:Chromosome Mapping; D002887:Chromosomes, Human, Pair 18; D002896:Chromosomes, Human, Pair 6; D003072:Cognition Disorders / Q000235:genetics*; D004410:Dyslexia / Q000235:genetics*; D006801:Humans; D015999:Multivariate Analysis; D040641:Quantitative Trait Loci