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The Future of QTL Mapping  

2011-10-03 09:55:30|  分类: 数量遗传学 |  标签: |举报 |字号 订阅

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        New permutations of QTL mapping build upon the utility of the original premise: locus discovery by co-segregation of traits with markers. Now, however, the definition of a trait can be broadened beyond whole-organism phenotypes to phenotypes such as the amount of RNA transcript from a particular gene (expression or eQTL; Schadt et al., 2003) or the amount of protein produced from a particular gene (protein QTL or PQL; Damerval et al., 1994). QTL mapping works in these contexts because these phenotypes are polygenic, just like more traditional organismal phenotypes, such as yield in corn.

The Future of QTL Mapping - 喜欢吃桃子 - wangyufeng的博客
 
a) Quantitative trait locus (QTL) mapping requires parental strains (red and blue plots) that differ genetically for the trait, such as lines created by divergent artificial selection. b) The parental lines are crossed to create F1 individuals (not shown), which are then crossed among themselves to create an F2, or crossed to one of the parent lines to create backcross progeny. Both of these crosses produce individuals or strains that contain different fractions of the genome of each parental line. The phenotype for each of these recombinant individuals or lines is assessed, as is the genotype of markers that vary between the parental strains. c) Statistical techniques such as composite interval mapping evaluate the probability that a marker or an interval between two markers is associated with a QTL affecting the trait, while simultaneously controlling for the effects of other markers on the trait. The results of such an analysis are presented as a plot of the test statistic against the chromosomal map position, in recombination units (cM). Positions of the markers are shown as triangles. The horizontal line marks the significance threshold. Likelihood ratios above this line are formally significant, with the best estimate of QTL positions given by the chromosomal position corresponding to the highest significant likelihood ratio. Thus, the figure shows five possible QTL, with the best-supported QTL around 10 and 60 cM.

For example, transcript abundance is controlled not just by cis-acting sequences like the promoter, but also by potentially unlinked, trans-acting transcription factors. Similarly, protein abundance is controlled by "local" variation at the coding gene itself, and by "distant" variation mapping to other regions of the genome. Local variation is likely to be composed of cis variants controlling transcript levels (though the correlationprotein abundance is often quite low, so this may represent a minority of cases; see Foss et al., 2007). Other local mechanisms might include polymorphisms for the stability or regulation of the protein. In contrast, distant variation could include upstream regulation control regions. Beyond these examples, further extension of QTL analysis includes mapping the contribution of imprinting to size-related traits (Cheverud et al., 2008), and other adaptations of QTL mapping will no doubt follow. between transcript level and

Historically, the availability of adequately dense markers (genotypes) has been the limiting step for QTL analysis. However, high-throughput technologies and genomics have begun to overcome this barrier. Thus, the remaining limitations in QTL analysis are now predominantly at the level of phenotyping, although the use of genomic and proteomic data as phenotypes circumvents this challenge to some extent.

Genome-wide association studies (GWAS) are becoming increasingly popular in genetic research, and they are an excellent complement to QTL mapping. Whereas QTL contain many linked genes, which are then challenging to separate, GWAS produce many unlinked individual genes or even nucleotides, but these studies are riddled with large expected numbers of false positives. Though GWAS remain limited to organisms with genomic resources, combining the two techniques can make the most of both approaches and help provide the ultimate deliverable: individual genes or even nucleotides that contribute to the phenotype of interest.

Indeed, combining different QTL techniques and technologies has great promise. For example, Hubner and colleagues (2005) used data on gene expression in fat and kidney tissue from two previously generated, recombinant rat strains to study hypertension. Alternatively, samples adapted to different environments may be compared, or other populations of interest might be selected for expression analysis. This approach permits measurement of hundreds or even thousands of traits simultaneously. Differences in expression may be co-localized with phenotypic QTL that have been previously determined to create manageable lists of positional candidate genes (Wayne & McIntyre, 2002). Other interesting questions concerning gene regulation can be addressed by combining eQTL and QTL, such as the relative contributions of cis-regulatory elements versus trans-regulatory elements. Regarding hypertension, Hubner et al. (2005) identified 73 candidate genes deemed suitable for testing in human populations, and many of the most highly linked eQTL were regulated in cis. These integrated approaches will become more common, and they promise a deeper understanding of the genetic basis of complex traits, including disease (Hubner et al., 2006). Integrating phenotypic QTL with protein QTL can also give investigators a more direct link between genotype and phenotype via co-localization of candidate protein abundance with a phenotypic QTL (De Vienne et al., 1999). Still more kinds of data can be integrated with QTLmapping for a "total information" genomics approach (e.g., eQTL, proteomics, and SNPs) (Stylianou et al., 2008).

QTL studies have a long and rich history and have played important roles in gene cloning and characterization; however, there is still a great deal of work to be done. Existing data on model organisms need to be expanded to the point at which meta-analysis is feasible in order to document robust trends regarding genetic architecture. Data generated by lab-based QTL studies can also be used to direct and inform other efforts, such as population genomics, wherein a large number of molecular markers are scored in the attempt to identify targets of selection and thus genes underlying ecologically important traits (Stinchcombe & Hoekstra, 2008). Furthermore, QTL studies can inform functional genomics, in which the goal is to characterize allelic variation and how it influences the fitness and function of whole organisms. Thus, although the map between genotype and phenotype remains difficult to read, QTL analysis and a variety of associated innovations will likely continue to provide key landmarks.

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