Review article
In Search of New Targets for Plasma High-Density Lipoprotein Cholesterol Levels: Promise of Human–Mouse Comparative Genomics

https://doi.org/10.1016/j.tcm.2006.04.003Get rights and content

Many lines of evidence suggest that raising plasma high-density lipoprotein cholesterol (HDL-C) levels may inhibit, perhaps even reverse, atherosclerosis. Quantitative trait locus (QTL) analysis has been performed in both humans and mice. So far, ∼40 high-density lipoprotein (HDL)-regulating QTLs have been identified in each species. To compare human and mouse HDL-C QTLs, we generate human–mouse comparative chromosome maps based on homologous genes in humans and mice. The comparative maps reveal that most human and mouse HDL-C QTLs are concordant, which suggests that identifying the underlying QTL genes in mice will facilitate identifying their homologs in humans. The maps also help to narrow QTLs by mouse–human homologous QTL comparison. By using a combination of classic genetic approaches and newer bioinformatics tools (including comparative genomics as highlighted in this study), identifying new drug targets for plasma HDL-C levels holds more promise than ever.

Section snippets

QTL Analysis

QTL analysis is a means of finding novel genes that regulate complex traits. It has been used to identify genetic loci that influence atherosclerosis (Wang et al. 2005a) and plasma levels of HDL-C (Wang and Paigen, 2002, Wang and Paigen, 2005a), LDL cholesterol, and triglycerides (Wang and Paigen 2005b) in humans and mice. For several reasons, identifying the genes that underlie QTLs in laboratory mice is much easier than identifying those that underlie human QTLs. First, although human

Mouse HDL-C QTLs

So far, 19 studies identifying mouse HDL-C QTLs have been reported (Table 1). They involve 23 different mouse crosses, 10 that use C57BL/6 as one parent and seven that use one of the four wild strains (CASA, CAST, PERA, and SPRET) as one parent. Although these crosses reveal 111 significant or suggestive QTLs (each represented by a bar on the left of the chromosomes in Figure 1), many of them are at the same chromosomal locations and are likely caused by the same genes. Only ∼40 of these QTLs

Human HDL-C QTLs

So far, 30 studies on human HDL-C QTLs, involving 28 human populations, have been reported (Table 2). They reveal 67 significant or suggestive QTLs, but only ∼38 are unique (Table 2, Figure 2). Because the 95% CIs of human HDL-C QTLs are not reported, we used 1.5-logarithm of the odds ratio (LOD) drop intervals as their estimates and found that the size of a human QTL is 21 ± 4 Mb (mean ± SEM, n=10). Assuming there are 30,000 genes in the 3.08-Gb human genome, each QTL would contain an average

Narrowing QTL With the Use of Mouse–Human Comparative Genomics

To make a detailed mouse–human homology comparison, we generated a mouse–human homology data set based on mouse–human homologous gene comparison. First, we used Ensembl MartView data mining to export all the mouse genes in Mouse Genome Assembly NCBIm33. We filtered the resulting 28,594 mouse genes through the multispecies comparison option and determined that only 17,943 were homologous to human genes (the Ensembl homologous prediction method is explained within the Web site help files at //www.ensembl.org/info/data/index.html

Identifying Genes Underlying HDL-C QTLs

Not only does the mouse–human comparative map (Figure 1) help narrow the mouse HDL-C QTLs, but it helps us prioritize those whose genes we should identify first—those detected in multiple crosses with homologous human QTLs. Such QTLs will be most effectively narrowed with haplotype analysis (DiPetrillo et al., 2005, Manenti et al., 2004, Park et al., 2003, Wang et al., 2004, Wang and Paigen, 2005b) and will most likely facilitate identifying the underlying orthologous human genes.

After a QTL is

Conclusions

Atherosclerosis is a multifactorial disease and should be treated as such. The strong evidence that small increases in HDL-C levels are associated with significantly reduced risks of developing CHD and a lack of powerful HDL-raising drugs has triggered considerable interest and effort in identifying such drugs. The increasing number of bioinformatics tools and resources, especially genome sequences and single nucleotide polymorphisms in humans and multiple mouse strains, has greatly accelerated

Acknowledgments

This work was supported by grants from National Institutes of Health (HL74086, HL77796, HL66611) and from the American Heart Association (AHA 0430381N). The authors thank Jennifer L. Torrance and Raymond Lambert for helping to prepare the manuscript.

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    Present address: Novartis Institutes for Biomedical Research, Cambridge, MA 02139.

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