Influence of sex and diet on quantitative trait loci
for HDL cholesterol levels in an SM/J by
NZB/BlNJ intercross population
Article available at jlr.org

Ron Korstanje, Renhua Li, Timothy Howard, Peter Kelmenson, Jan Marshall,
Beverly Paigen, and Gary Churchill

The Jackson Laboratory, 600 Main St., Bar Harbor, ME 04609; and Wake Forest University, Winston-Salem, NC


Abstract

To investigate the dependence of HDL quantitative trait loci (QTL) on sex and diet, we generated a large intercross population of mice from parental strains SM/J and NZB/BlNJ. We measured HDL levels in progeny fed a chow diet and measured them again after 6, 12, and 16 weeks of feeding a high-fat, high-cholesterol diet. QTL analysis was performed on the 260 female and 253 male F2 progeny. A total of 13 significant QTL were found. Four QTL were specific to female mice: Hdlq23 (Chr 6, 26 cM), Hdlq26 (Chr 10, 70 cM), Hdlq27 (Chr 15, 48 cM), and Hdlq32 (Chr 19, 40 cM). One significant QTL was specific to male mice: Hdlq29 (Chr 17, 36 cM). In addition, several QTL were found to have effects that were dependent on diet. Sex- and diet-dependent effects were characterized using a linear model-based genome scan method that avoids the potential pitfalls of subdivided data analysis. The dependence of QTL effects on sex suggests an important role for the sex hormones in HDL regulation. We recommend that sex should be explicitly accounted for in future studies in the genetics of HDL regulation in both mice and humans.—Korstanje, R., R. Li, T. Howard, P. Kelmenson, J. Marshall, B. Paigen, and G. Churchill. Influence of sex and diet on quantitative trait loci for HDL cholesterol levels in an SM/J by NZB/BlNJ intercross population. J. Lipid Res. 2004. 45: 881–888.

Statistical analyses

Genome scans: We performed genomewide scans and multiple QTL modeling using the method of Sen and Churchill (20). For simple genome scans, this approach is similar to the interval mapping procedure of Lander and Botstein (21) but is based on an imputation algorithm. An advantage of the imputation approach is that it can be generalized to incorporate covariates, such as sex, as well as include multiple QTL in genome scan analyses. Logarithm of the odds (LOD) ratio scores for genome scans were computed at 2 cM intervals across the genome, and significance was determined by permutation testing (22). Significant and suggestive QTL meet or exceed the 95% and 90% genomewide thresholds, respectively. We ran multiple scans on each phenotype, and the usual suggestive threshold [37%, as reviewed in ref. (23)] was deemed too liberal. The software package used in this study was PSEUDOMARKER release version 1.0 (24).

We carried out single-locus genome scans to detect QTL with main effects. These scans included sex as an additive covariate to account for overall differences in HDL levels between the sexes. We then carried out a second set of single-locus scans that included an interaction between sex and the putative QTL at each locus. These scans allow the QTL effect to differ between the two sexes and can be used to identify sex-specific QTL. The difference in LOD scores between these two scans constitutes a test for sex by QTL interaction. This secondary test is carried out with no adjustment for multiple testing, and the threshold, based on the usual chi-square distribution of the likelihood ratio, is 2.0 on the LOD scale.

Lastly, we subdivided the population by sex and scanned male and female data separately for the main effect QTL. We carried out simultaneous genome scans of all pairs of markers in the search for epistatic interactions. The search strategy is described by Sen and Churchill (20) and Sugiyama et al. (25). We failed to detect any significant QTL by QTL interactions using stringent genomewide adjusted criteria. For some of the QTL detected in the single-locus genome scans, the shape of the LOD curve suggested that there might be two or more QTL present. In order to resolve multiple-linked QTL, we carried out multi-locus scans on a per chromosome basis. The change in the LOD score between one QTL and two QTL models was used as a test statistic. In one case, we also tested two QTL versus three QTL models. Thresholds for these chromosome-wide searches were established by running simulations.

   

Allele effects

 

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