With the advent of modern genetics, scientist often try to investigate the under

Question

With the advent of modern genetics, scientist often try to investigate the underlying portions of the genome that influence quantitative traits. These portions are called Quantitative Trait Loci or QTLs. The way this is done is using QTL mapping, which is a suite of different computational techniques that uses a genetic ‘map’ to identify portions of the genome responsible for any given trait. One of the first QTLs identified was the one responsible for color in Monkey flowers. The above is the clade which contains multiple species of monkey flower, primarily organized by hummingbird pollinated or bee pollinated.

QTL Mapping results are often displayed as a LOD score. A LOD score measures the likelihood that a QTL near your marker is responsible for the trait in question. LOD scores increase the closer the association is between marker and QTL. Make sure you can explain exactly what a peak means in terms of finding a QTL.

Explanation / Answer

Thesimplest methodforQTLmappingis analysis ofvariance (ANOVA, sometimes called “marker regression”) at the marker loci. At each typed marker, one splits the backcross progeny into two groups, according to their genotypes at the marker, and compares the phenotype distributions of the two groups. For example, in Fig. 3A, we see that the individuals with genotype AA at marker D1M30 have somewhat higher phenotype values than those with genotype AB at that marker, indicating that the marker is linked to a QTL. In contrast, when the individuals are split according to their genotype at marker D2M99 (Fig. 3B), the phenotype distributions are approximately the same; this marker does not appear to be linked to a QTL. BOX:The Case ofa Single QTL Suppose that the mice with QTL genotype AA have average phenotype , while the mice with QTL genotype AB have average phenotype . The QTL thus has effect . Consider a marker locus that is a recombination fractionaway from the QTL. Of the individuals with marker genotype AA, a fraction of them will have QTL genotype AA, while the remainder will have QTL genotype AB, and so these individuals have average phenotype . Similarly, the individuals with marker genotype AB have average phenotype . Thus, the difference between the phenotype averages for the two marker genotype groups is . Note that when are unlinked, , so that the marker and QTL , and the two marker genotype groups will have the same phenotype average. When , so that the marker and QTLare linked, (provided that that the QTL really does have an effect, ). Thus, a nonzero difference between the marker genotype groups indicates linkage between the marker and a QTL. Note that the difference between the phenotype averages for the two marker genotype groups will always be smaller (in absolute value) than the true QTL effect,, unless there is complete linkage between the marker and the QTL( ). Geneticists often measure the effect of a QTL as the proportion of the phenotypic variance that is attributable to the QTL. The variance induced by a QTL is the variance in the trait that would be observed if there were no environmental variation nor measurement error, and no other QTLs. For a backcross and a QTL with an effect(the difference in the phenotype averages, described above), this variance is , and so the proportion of the phenotypic variance attributable to the QTL is , where variance in the backcross generation. is the total phenotypic The assessment of the strength of evidence for the presence of a QTL will be described in detail below. Briey, in a backcross, one may calculate a t-statisticto compare the averages of the two marker genotype groups. For other types of crosses (such as the intercross), where there are more than two possible genotypes, one uses a more general form of ANOVA, which provides a so-called F-statistic. These are both equivalent to the LOD score statistic (described below). The chief advantage of analysis of variance at the marker loci is its simplicity. In addition, a genetic map for the markers is not required, and the method may be easily extended to account for multiple loci. A further advantage is the easy inclusion of covariates, such as sex, treatment, or an environment effect. Many phenotypes show marked sex differences, and these must be accounted for in QTL mapping. In addition, one may apply a treatment to some individuals but not others, or raise some individuals in one environment and others in a different environment. The ANOVA approach for QTL mapping has three important weaknesses. First, we do not receive separate estimates of QTL location and QTL effect. QTL location is indicated only by looking at which markers give the greatest differences between genotype group averages, and the apparent QTL effect at a marker will be smaller than the true QTL effect as a result of recombination between the marker and the QTL. Second, we must discard individuals whose genotypes are missing at the marker. Third, when the markers are widely spaced, the QTL may be quite far from all markers, and so the power for QTL detection will decrease.

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