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  • As has been pointed out in previous

  • modules, most traits of interest in forest trees are quantitatively

  • inherited. That is, they are controlled by many genes,

  • each of which controls a modest amount of the variance

  • in that trait. A gene that controls some portion of the genetic

  • variance of a phenotypic trait is called a quantitative trait locus,

  • or QTL. It is possible to

  • identify QTL and their relative location in the genome by

  • placing them on genetic maps. This is done by demonstrating

  • a statistically significant association between the

  • quantitative trait phenotype and one or more genetic

  • markers already located on a map. QTL mapping

  • is a powerful tool for elucidating the genetic architecture of

  • complex traits and provides a clearly defined approach for marker-assisted

  • selection in applied breeding or natural environment settings.

  • We will identify the key elements of

  • QTL mapping and give some examples of how it has been done

  • in forest trees. Additionally, we will reflect on

  • some of the limitations of the QTL approach using pedigree crosses.

  • Most of the elements required

  • to identify and map QTL are those previously noted for

  • construction of genetic maps themselves. The factors that distinguish

  • QTL mapping from genetic mapping are 1)

  • the need for high quality phenotypes for all the progeny being genotyped

  • and 2) a different set of analytical tools.

  • Let’s quickly review the points noted here.

  • Experimental Populations: For the most part,

  • pedigreed crosses such as those needed for genetic mapping are needed.

  • For outcrossing trees, ideally, this

  • means a three generation intercross, though virtually any

  • cross can be used, including open-pollinated crosses and

  • two-generation pseudo-testcrosses.

  • You may improve your chances of detecting QTL by making the crosses

  • to ensure segregation of the traits of interest in the F2 progeny.

  • The size of the full sib family used is

  • important. Studies have shown that 500 or more progeny

  • are required to avoid bias in the number of QTL identified

  • and the proportion of variation those QTL explain.

  • Informative markers: The best

  • markers are multi-allelic, co-dominant markers that

  • could potentially tag as many as four alleles

  • in the QTL. Generally, these will be

  • the same set of markers used in making your genetic map.

  • The Map: A good framework map

  • with enough markers (say, 75-100)

  • to completely cover the genome is desirable.

  • Phenotypes: High quality measurement of the

  • traits of interest are essential. One way to dramatically

  • improve phenotypic trait estimates is to clonally replicate the progeny

  • in the study trials. In effect, this results

  • in an increase in trait heritability. We will leave

  • comments on the last two points, analytical tools and verification,

  • for later. Suffice it to say that what we seek are

  • associations between differences in phenotypic means

  • of genotypic classes, evaluated one locus at a time.

  • The subsequent effort to accurately locate significant

  • markers is the focus of most of the more

  • sophisticated software.

  • The figure at right illustrates

  • the concept of what constitutes a QTL, as might be

  • found in a three-generation intercross. Note

  • that thedeck is stacked”, so to speak, in the grandparent

  • generation. That is, crosses are made between parents that are

  • contrasting phenotypically. For this example we see that

  • grandparents that are homozygous for upper case alleles at

  • markers across a given linkage group appear to be associated with small

  • stature and trees homozygous for lower case alleles

  • seem to be of large stature. The F1 offspring of this

  • cross are intermediate in size, suggesting that the locus that

  • appears to be affecting tree size must be exhibiting additive gene

  • action (i.e. heterozygotes are intermediate

  • to either homozygote). In the segregating F2

  • generation, one can test mean tree size of all three marker genotypic

  • classes to determine if they vary from one another.

  • In this case, only locus B appears to show a relationship

  • between tree size and genotypic class. The logical

  • interpretation is that the locus affecting tree size must

  • reside near the B marker. How close are they to one another?

  • They may be only a few thousand base pairs apart,

  • or they may be a few million base

  • pairs from one another. We can’t tell the difference at this point.

  • Remember from the genetic mapping module that mapping precision

  • is largely a function of how many meiotic events you have to look at.

  • As you will see, the confidence interval around

  • the location of a QTL is generally quite large.

  • Now image testing the genotypic classes of 75

  • markers spread across the genome for this same trait.

  • You may find no significant associations

  • or many. Whatever your result, if you have done the

  • experiment correctly, you can have some confidence that the result is a

  • reflection of the real situation for that one specific pedigreed cross.

  • This cartoon is a little more

  • illustrative of realistic data that come from QTL studies.

  • Again, the basic concepts of QTL mapping are shown here.

  • For simplicity, this is illustrated using F2 offspring

  • derived from the intermating of two inbred lines.

  • In terms of markers, only three genotypes are possible,

  • shown here as AA, AB,

  • and BB. Markers can be widely interspersed,

  • since recombination will be rare in a single generation.

  • Frequently, QTL studies are done with framework maps with

  • markers spaced 10-30 cM apart. Offspring

  • are grouped by genotype and their phenotypes are examined for

  • a significant difference among group means, such as using

  • ANOVA. In this case,

  • the AB (heterozygous) genotype is intermediate between the

  • two parental homozygotes, implying the QTL exhibits

  • additive gene action. The distribution of phenotypes

  • for the array of individuals with a given genotype clearly

  • suggests that the effect of that particular QTL on that phenotype

  • is relatively small, and that many other factors may be

  • influencing the trait.

  • Let’s do a quick overview of

  • QTL mapping. The idea is to find a statistically

  • meaningful association between genetic markers and phenotypic traits,

  • and to place the resultant QTL on a genetic map.

  • This is done using one full-sib family at a time.

  • To find an association, both the QTL locus

  • and the marker must be heterozygous in the cross chosen.

  • Imagine a trait that has a heritability of 0.5,

  • and it is controlled by ten genes, each of equal influence.

  • That is, each gene or QTL, accounts

  • for 5% of the total phenotypic variance for that trait since

  • half the variance is caused by non-genetic or rather,

  • environmental factors. It may be that the

  • cross you are using is homozygous for seven of the ten QTL.

  • In that case, you would only detect three of them,

  • assuming the power of your experiment was sufficient.

  • Identifying and locating those QTL

  • that are heterozygous in your cross depends on several things.

  • Certainly, marker density is important, but not nearly

  • so much as the number of progeny sampled for several reasons we have

  • articulated previously. Early studies conducted with

  • relatively few progeny (say, under 100) were shown to

  • overestimate the size of the QTL effect and to underestimate

  • the number of QTL. As you might imagine, this

  • problem increases as the size of the QTL effect decreases.

  • While breeders had visions of identifying major genes with

  • large effects, the reality is that we have found most trait

  • effects to be very small (<5%).

  • QTL detection and estimation of effect size is also a

  • function of a number of interactions between the QTL and other loci

  • (i.e. epistatic effects) and environmental

  • conditions. Finally, we note there are a

  • few different analytical approaches to QTL mapping. We will spend the next

  • several slides discussing each of these approaches.

  • The simplest analytical approach to QTL

  • detection is the single-marker method, which, as the name implies,

  • is a statistical test of the association between phenotype

  • and genotype class one marker at a time.

  • If you have 75 markers distributed over 12 linkage groups,

  • you perform 75 different calculations. This

  • can be done using simple t-tests, or with very

  • sophisticated analysis of variance models that seek to

  • partition experimental variances as much as possible

  • (i.e. remove non-genetic sources of variance).

  • A statistically significant result

  • is evidence that a QTL has a map location somewhere near the marker,

  • though neither the distance to the marker nor the size of the QTL

  • effect can be estimated well. It is not necessary

  • to have a genetic map to use this approach, but having one greatly

  • increases the amount of information available to you. There

  • are other drawbacks to this approach. It does not differentiate

  • between one and multiple QTL when they exist on the same

  • linkage group. This may result in overestimating the size of the

  • QTL effect. Conversely, the magnitude of

  • QTL effect may be underestimated due to increasing,

  • but unknown, recombination between marker and QTL.

  • That is, the further the QTL is removed from the marker,

  • the lower the estimated effect of the QTL.

  • As noted, single marker testing is relatively simple