Analysis Of Quantitative Traits
Characterization of the genetic architecture of quantitative traits is typically carried out using one of two different study designs. The first approach starts with the quantitative trait of interest (such as height or blood pressure) and attempts to draw inferences about the underlying genetics from looking at the degree of trait resemblance among related subjects. This approach is sometimes referred to as a top-down or unmeasured genotype strategy because the inheritance pattern of the trait is the focus and no genetic variations are actually measured. The top-down approach is often the first step taken to determine whether there is evidence for a genetic component.
Heritability (the likelihood that the trait will be passed on to offspring) and segregation analysis are examples of statistical analyses that use a top-down approach. With the bottom-up or measured genotype approach, candidate QTLs are measured and then used to draw inferences about which genes might play a role in the genetic architecture of a quantitative trait. Prior to the availability of technologies for measuring QTLs, the top-down approach was very common. However, it is now inexpensive and efficient to measure many QTLs, making the bottom-up strategy a common study design. Linkage analysis and association analysis are two general statistical approaches that utilize the bottom-up study design.
The definition and characterization of quantitative traits is changing very rapidly. New technologies such as DNA microarrays and protein mass spectrometry are making it possible to quantitatively measure the expression levels of thousands of genes simultaneously. These new measures make it possible to study gene expression at both the RNA level and the protein level as a quantitative trait. These new quantitative traits open the door for understanding the hierarchy of the relationship between QTL variation and variation in quantitative traits at both the biochemical and physiological level.
Jason H. Moore
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Hartl, D. L., and A. G. Clark. Principle of Population Genetics. Sunderland, MA: Sinauer Associates, 1997.