Because of its paramount importance in many biological and biomedical aspects, epistasis, expressed as the suppression or enhancement of a gene by the effect of an unrelated gene, has received a resurgence of interest in recent years. One of the most powerful analytical approaches for detecting epistasis is based on the genetic mapping of interacting quantitative trait loci (QTL) that often present long chromosomal segments. Current high-throughput technologies for genotyping single nucleotide polymorphisms (SNPs) to construct the haplotype map or HapMap for the entire human genome are shaping our prospects into the role of epistasis. In this article, we have developed a new statistical model for refining QTL structure into individual nucleotides and estimating and testing epistasis between different DNA nucleotides throughout the HapMap. This model detects quantitative trait nucleotides (QTN) for complex diseases. It is founded on the SNP-based haplotype blocking theory, constructed within the context of maximum likelihood and implemented with the EM algorithm. The model provides a quantitative framework for testing the additive × additive, additive × dominance, dominance × additive and dominance × dominance interaction effects between different QTN sequences from haplotype blocks. The model was used to detect sequence-sequence interactions between two candidate genes, BAR-1 and BAR-2, for human obesity in 155 subjects sampled from a natural population. This model will have many implications for the detection of specific DNA sequence variants that interactively contribute to the genetic architecture of complex diseases.
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