. In silico genetics: identification of a functional element regulating H2-Ealpha gene expression. Science. 2004 Oct 22;306(5696):690-5. PubMed.


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  1. The identification of quantitative trait loci (QTL) controlling various phenotypic traits within the mouse, especially those relating to human disease, is a major focus of numerous academic and industrial laboratories around the world. The completion of the first draft of the mouse genome (Waterston, 2002) provides unique tools to help uncover the genes responsible for the QTLs. One of these tools is the increasingly dense map of single nucleotide polymorphisms (SNPs) between the various inbred mouse strains (Wade, 2002; Wiltshire, 2003). These maps have revealed that in the mouse genome, these SNPs are arranged in blocks (called haplotypes) of either high or low diversity, presumably due to the relatively small pool of founders for all common inbred strains. Information about the mouse haplotype blocks has greatly aided the analysis of QTLs, enabling researchers to focus on polymorphic candidate genes that may be responsible for phenotypic trait differences between two inbred strains.

    By contrast, the current manuscript (Liao, 2004) seeks to utilize the dense SNP map and phenotypic traits on multiple (i.e., >8) inbred mouse strains in an effort to identify QTL genes through "in silico" methods. Simply put, the in silico methods seek to correlate the phenotypes (i.e., a specific trait) and genotypes (the SNP map) in multiple inbred strains to map genes responsible for the phenotypic differences. The authors use this approach to predict the location of several major histocompatibility complex (MHC) phenotypes to genes within the MHC locus on mouse chromosome 17. In addition, the authors use [the approach] to identify a genetic locus controlling histocompatibility 2, class II antigen E α (H2-Ea) gene expression in the mouse lung. Perhaps somewhat predictably, the authors identified a locus within the H2-Eα gene itself (in the first intron) that controls its expression and further identified SNPs within transcription factor binding sites that appear to regulate transcription factor binding and activity.

    While this manuscript is a "proof of concept" for utilizing the in silico methods to map phenotypic traits in the mouse, there are a number of issues that limit the utility of this approach to various mouse models of human disease. First, both detailed phenotypic data and an extremely dense SNP map on a relatively large number of strains (40 to 50) are likely required in order to efficiently map the QTLs of interest. Second, increasing evidence suggests that the QTLs regulating mouse models of common human diseases (i.e., obesity, high blood pressure, diabetes) are due to multiple genes of relatively modest effect, and it remains unclear whether the in silico approach has the statistical power to identify such loci. Indeed, the loci identified thus far appear to be relatively simple loci regulating very specific phenotypes. Finally, for those human disease models which require genetic engineering in order to observe the specific phenotypes (i.e., Alzheimer disease, amyotrophic lateral sclerosis, etc.), it remains unlikely that a sufficient number of congenic lines in 40-50 inbred strains could be generated in order to perform the in silico analysis in a timely manner. That being said, with the continued development of the mouse phenome database, which is providing detailed phenotypic data on multiple inbred strains, and the increasingly dense SNP maps, the current manuscript suggests that an in silico approach to the identification of QTLs may be quite useful in specific instances.

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