Complex Genetic Architecture Revealed by Analysis of High-Density Lipoprotein Cholesterol in Chromosome Substitution Strains and F^sub 2^ Crosses

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Intercrosses between inbred lines provide a traditional approach to analysis of polygenic inheritance in model organisms. Chromosome substitution strains (CSSs) have been developed as an alternative to accelerate the pace of gene identification in quantitative trait mapping. We compared a classical intercross and three CSS intercrosses to examine the genetic architecture underlying plasma high-density lipoprotein cholesterol (HDL) levels in the C57BL/6J (B) and A/J (A) mouse strains. The B × A intercross revealed significant quantitative trait loci (QTL) for HDL on chromosomes 1, 4, 8, 15, 17, 18, and 19. A CSS survey revealed that many have significantly different HDL levels compared to the background strain B, including chromosomes with no significant QTL in the intercross and, in some cases (CSS-1, CSS-17), effects that are opposite to those observed in the B × A intercross population. Intercrosses between B and three CSSs (CSS-3, CSS-11, and CSS-8) revealed significant QTL but with some unexpected differences from the B × A intercross. Our inability to predict the results of CSS intercrosses suggests that additional complexity will be revealed by further crosses and that the CSS mapping strategy should be viewed as a complement to, rather than a replacement for, classical intercross mapping.

THE genomewide distribution of allelic variation in an intercross population, as in human populations, provides great potential for epistatic interactions that can mask or enhance the impact of allelic variation at multiple loci. This complexity may hinder our ability to detect the genes and alleles that are most important. One approach to circumvent this possibility is to reduce the number of potential interactions by reducing the amount of segregating variation in a cross. Chromosome substitution strain (CSS, also known as consomic) panels achieve this goal by restricting variation to a single chromosome (Nadeau et al. 2000; Cowley et al. 2004; Fernandes et al. 2004; Bevova et al. 2006). In these panels each strain is derived from a "background" inbred strain except a single chromosome that is derived from a distinct "donor" inbred strain. Consequently any genetic variation seen among CSSs is presumably due to allelic variation on the donor chromosome. It is possible that some differences between the CSSs may be due to recent mutations (Cook et al. 2006). However, it is estimated that only 3% of polymorphisms among strains have arisen within the past 100 years. Thus, most of the differences between inbred strains, and specifically for this report between C57BL/6J (B) and A/J (A), are ancestral and differences among consomic strains due to recent mutations are expected to be quite rare (Frazer et al. 2004).

Ischemic cardiovascular disease (ICD) is the leading cause of morbidity and mortality in developed nations. Major risk factors for ICD are high levels of low-density lipoprotein cholesterol (LDL) and low levels of high-density lipoprotein cholesterol (HDL) in plasma (Fruchart and Duriez 2002). High levels of plasma HDL provide protection against heart disease as shown in both human (Gordon et al. 1989; Wilson et al. 1994; Nissen et al. 2003) and animal studies (Badimon et al. 1990; Rubin et al. 1991; Plump et al. 1994; Sugano et al. 1998; Okamoto et al. 2000; Rittershaus et al. 2000). A successful route toward identifying genes that affect quantitative phenotypes such as HDL levels is through the use of inbred mouse strains and quantitative trait locus (QTL) analysis. A combination of genetic tools and techniques is helping to identify these QTL genes (Korstanje and Paigen 2002; Dipetrillo et al. 2005; Hillebrandt et al. 2005;Wang et al. 2005). A total of 22 mapping crosses have identified .130 loci for HDL and these localize to 37 unique QTL (Wang and Paigen 2005). Only a small number of gene-by-gene interactions have been reported for HDL (Ishimori et al. 2004), in comparison to other traits such as obesity (Cheverud et al. …