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Quantitative geneticists have become interested in the heritability of transcription and detection of expression quantitative trait loci (eQTLs). Linkage mapping methods have identified major-effect eQTLs for some transcripts and have shown that regulatory polymorphisms in cis and in trans affect expression. It is also clear that these mapping strategies(More)
A population-based latent variable approach is proposed for association mapping of quantitative trait loci (QTL), using multiple closely linked genetic markers within a small candidate region in the genome. By incorporating QTL as latent variables into a penetrance model, the QTL are flexible to characterize either alleles at putative trait loci or(More)
Experiments using microarrays abound in genomic research, yet one factor remains in question. Without replication, how much stock can we put into the findings of microarray experiments? In addition, there is a growing desire to integrate microarray data with other molecular databases. To accomplish this in a scientifically acceptable manner, we must be able(More)
Because common complex diseases are affected by multiple genes and environmental factors, it is essential to investigate gene-gene and/or gene-environment interactions to understand genetic architecture of complex diseases. After the great success of large scale genome-wide association (GWA) studies using the high density single nucleotide polymorphism(More)
The International HapMap Project aims to generate detailed human genome variation maps by densely genotyping single-nucleotide polymorphisms (SNPs) in CEPH, Chinese, Japanese, and Yoruba samples. This will undoubtedly become an important facility for genetic studies of diseases and complex traits in the four populations. To address how the genetic(More)
BACKGROUND The current genome-wide association (GWA) analysis mainly focuses on the single genetic variant, which may not reveal some the genetic variants that have small individual effects but large joint effects. Considering the multiple SNPs jointly in Genome-wide association (GWA) analysis can increase power. When multiple SNPs are jointly considered,(More)
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