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Three-locus and four-locus QTL interactions influence mouse insulin-like growth factor-I

¹ Department of Mathematics, University of Michigan, Ann Arbor, Michigan
² Institute of Gerontology, University of Michigan, Ann Arbor, Michigan
³ Department of Pathology, University of Michigan, Ann Arbor, Michigan
⁴ Geriatrics Center, University of Michigan, Ann Arbor, Michigan
⁵ Ann Arbor Veterans Affairs Medical Center, University of Michigan, Ann Arbor, Michigan
⁶ Department of Human Genetics, University of Michigan, Ann Arbor, Michigan

A previous analysis of serum insulin-like growth factor I (IGF-I) levels in a mouse population (n = 961) derived from a cross of (BALB/cJ x C57BL/6J) F₁ females and (C3H/HeJ x DBA/2J) F₁ males documented quantitative trait loci (QTL) on chromosomes 1, 10, and 17. We employed a newly developed, random walk-based method to search for three- and four-way allelic combinations that might influence IGF-I levels through nonadditive (conditional or epistatic) interactions among 185 genotyped biallelic loci and with significance defined by experiment-wide permutation (P < 0.05). We documented a three-locus combination in which an epistatic interaction between QTL on paternal-derived chromosomes 5 and 18 had an opposite effect on the phenotype based on the allele inherited at a third locus on maternal-derived chromosome 17. The search also revealed three four-locus combinations that influence IGF-I levels through nonadditive genetic interactions. In two cases, the four-allele combinations were associated with animals having high levels of IGF-I, and, in the third case, a four-allele combination was associated with animals having low IGF-I levels. The multiple-locus genome scan algorithm revealed new IGF-I QTL on chromosomes 2, 4, 5, 7, 8, and 12 that had not been detected in the single-locus genome search and showed that levels of this hormone can be regulated by complex, nonadditive interactions among multiple loci. The analysis method can detect multilocus interactions in a genome scan experiment and may provide new ways to explore the genetic architecture of complex physiological phenotypes.

quantitative trait loci; epistasis; gene interactions

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