Association studies of QTL for multi-allele markers by mixed models.
In this paper, we extend association study methods of both Fan et al. [Hum Hered 2002;53:130-145], in which a quantitative trait locus (QTL) and a multi-allele marker are considered for trio families, and Fan and Xiong [Biostatistics 2003, in press], in which a QTL and a bi-allelic marker are considered for nuclear families. The objective is to build mixed models for association study between a QTL and a multi-allelic marker for nuclear families with any number of offspring. Two types of nuclear family data are considered: the first is genetic data of offspring from at least one heterozygous parents, and the second is genetic data of offspring of nuclear family. (1) For the data of offspring from at least one heterozygous parents, we assume that at least one parent is heterozygous at the marker locus, and we may infer clearly the transmission of parental marker alleles to the offspring. We show that it can be used in association study in the presence of linkage. The theoretical basis is the difference between the conditional mean of trait value given an allele is transmitted and the conditional mean of trait value given the allele is not transmitted from a heterozygous parent. To build valid models, we calculate the variance covariance structure of trait values of offspring. Besides, the reduction of the number of parameters is discussed under an assumption of tight linkage between the trait locus and the marker. (2) For the data of offspring of nuclear family, we show that it can be used in general association study. In this case, the theoretical basis is the difference between the conditional mean of trait values given an allele is transmitted from a parent and the population mean. Then, we calculate variance-covariance structure of trait values of offspring. (3) Based on the theoretical analysis, mixed models are built for each type of the data, and related test statistics are proposed for association study. By power calculation and comparison, we show that, in some instances, the proposed test statistics have higher power than that by collapsing alleles to be new ones. The proposed models are used to analyze chromosomes 4 and chromosome 16 data of the Oxford asthma data, Genetic Analysis Workshop 12.
Fan
Ruzong
R
Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA. rfan@stat.tamu.edu
Jung
Jeesun
J
D001249:Asthma / Q000235:genetics; D002874:Chromosome Mapping; D002885:Chromosomes, Human, Pair 16; D002894:Chromosomes, Human, Pair 4; D003627:Data Interpretation, Statistical; D005260:Female; D005819:Genetic Markers; D006579:Heterozygote; D006801:Humans; D008297:Male; D008957:Models, Genetic; D010375:Pedigree; D040641:Quantitative Trait Loci; D012107:Research Design / Q000706:statistics & numerical data
D005819:Genetic Markers