By Derek A. Roff
The impetus for this booklet arose out of my prior ebook, The Evolution of lifestyles Histories (Roff, 1992). In that ebook I awarded a unmarried bankruptcy on quanti tative genetic concept. despite the fact that, because the e-book used to be eager about the evolution of existence histories and features hooked up to this, the presence of quantitative genetic version was once an underlying subject all through. a lot of the point of interest was once put on optimality thought, for it's this method that has confirmed to be tremendous profitable within the research of existence historical past version. yet quantitative genetics can't be ig nored, simply because there are a few questions for which optimality ways are irrelevant; for instance, even supposing optimality modeling can deal with the ques tion of the upkeep of phenotypic edition, it can't say something approximately genetic version, on which additional evolution essentially relies. the current publication is, hence, a traditional extension of the 1st. i've got approached the matter no longer from the viewpoint of an animal or plant breeder yet from that of 1 attracted to realizing the evolution of quantitative characteristics in wild populations. the topic is huge with a substantial physique of concept: I mostly current the assumptions underlying the research and the consequences, giving the suitable references for these drawn to the intervening arithmetic. My curiosity is in what quantitative genetics tells me approximately evolutionary techniques; accordingly, i've got targeting components of analysis such a lot proper to box studies.
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Additional info for Evolutionary Quantitative Genetics
The concept of genetic correlation is discussed in detail in Chapter 3; at present, it is sufficient simply to note that like all correlation coefficients it lies between 0 and 1. Because it is generally not known, a minimum estimate of h~ is made by assuming that rA = 1. 3. There is no fully satisfactory method of placing confidence limits on h~: Riska et aI. (1989) suggested the use of the bootstrap method, whereas Simons and Roff (1994) estimated the upper and lower 95% confidence bounds by substituting the upper and lower bounds of the regression slope.
For the mean offspring on mid-parent regression and full-sib methods, the number of families, from top curve to bottom, is 25, 50, and 100. For the half-sib design, the number of sires is constant at 25, and the number of dams per sire from top curve to bottom is 2, 3,4, and 5. parent. The latter is particularly likely in mammals where maternal effects are common (see Chapter 7 for a detailed discussion of sex-related effects). It follows from Eq. 16) that the slope of the regression of the mean offspring value on one parent is equal to one-half of the heritability.
5 ........ '" •............. 9. Percentage of the phenotypic variability explained by the average QTL in the simulation model of Beavis (1994). Solid lines show results for a model in which there are 40 QTLs and dotted lines the results for a model in which there are 10 QTLs. The numbers on the right show the total amount of phenotypic variation actually determined by the QTLs (total, per QTL). (2) How large a sample is required to quantify the effect of the detected QTL? , 1993; Beavis, 1994; Jansen, 1994).