Artificial selection. Long before Darwin and Wallace, farmers and breeders were using the idea of selection to cause major changes in the features of their plants. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework. A simulation tool was developed for estimating the power to detect artificial selection acting directly on single loci. The simulation tool should be.
Power Selection Artificial The of
Finally, mixed-breeds are a combination of multiple breeds, where their parents were not purebreds. There are too many possible combinations to count! In purebreds, since there is only one lineage, these mistakes are often more apparent and can make purebred dogs prone to certain diseases. An example of artificial selection - Genetically modified organisms.
Recently we have started to artificially select traits at a molecular level where we mix DNA from different plant or animal species to make genetically modified organisms GMOs. To genetically modify an organism, genetic information or, the blueprint of the organism is added or removed, or replaced by the information from another organism that has a trait we desire.
If you could identify the genetic information that coded for drought resistance from another plant, then you could insert that into the blueprints of your corn species to make it more resistant to drought!
Cartoon showing how drought-sensitive corn is bred with drought-resistant corn to produce drought-resistant offspring. GMOs are used in agriculture to help crops become more resistant to drought, cold, salinity, pests and diseases. This is advantageous for us because it allows us to feed our growing population by doing agriculture in places that are usually less than ideal or not possible. With more areas to do agriculture, we have larger agricultural production to feed ourselves.
Common misconceptions about evolution. Evolution is not the same as adaptation or natural selection. Imagine a scenario where one trait might be highly advantageous in one environment, but highly detrimental in another. A good example of this is the fur color of mice. In the forest, it will be more likely that mice take on a darker color to match the earth. Can beneficial traits arise in more than one area by accident? When multiple environments favor the existence of a trait, these beneficial traits can pop up through mutation and spread throughout their individual populations completely independently.
Evolutionary biologists call this convergent evolution. In the lactose tolerance example, this is exactly what happened. A population in Europe evolved the ability to digest lactose as an adult independently from an African population.
Both populations had begun farming dairy, and both traits arose around the same time. Cartoon showing a cow in Europe and a cow in Africa. In this post, we examine how artificial selection shaped the dog genome during the early domestication process.
One example of artificial selection that Darwin drew upon was the domestication of dogs — a process that has recently been greatly informed by genomics comparisons between dogs and their closest wild relatives, wolves. The domestic dog has the distinction of being the only known animal to be domesticated by humans prior to the advent of agriculture.
But when we compare the dray-horse and race-horse, the dromedary and camel, the various breeds of sheep fitted either for cultivated land or mountain pasture, with the wool of one breed good for one purpose, and that of another breed for another purpose; when we compare the many breeds of dogs, each good for man in very different ways… We cannot suppose that all the breeds were suddenly produced as perfect and as useful as we now see them; indeed, in several cases, we know that this has not been their history.
In this sense he may be said to make for himself useful breeds. Note that Darwin is careful to point out that the variation itself is due to heredity: This point was important for Darwin to make, since he would later argue that natural selection also acts on that same heritable variation over time in a cumulative way. Darwin erroneously, as we will soon see suspected the latter, perhaps in part because of the dramatic morphological differences between dog breeds.
When we attempt to estimate the amount of structural difference between the domestic races of the same species, we are soon involved in doubt, from not knowing whether they have descended from one or several parent-species.
This point, if it could be cleared up, would be interesting; if, for instance, it could be shown that the grey-hound, bloodhound, terrier, spaniel, and bull-dog, which we all know propagate their kind so truly, were the offspring of any single species, then such facts would have great weight in making us doubt about the immutability of the many very closely allied and natural species—for instance, of the many foxes—inhabiting different quarters of the world.
I do not believe, as we shall presently see, that all our dogs have descended from any one wild species; but, in the case of some other domestic races, there is presumptive, or even strong, evidence in favour of this view…. The whole subject must, I think, remain vague; nevertheless, I may, without here entering on any details, state that, from geographical and other considerations, I think it highly probable that our domestic dogs have descended from several wild species.
As it turns out, Darwin was wrong on this point—we now know that all dog breeds are derived from only one wild species, the gray wolf Canis lupis. Genome sequencing studies place dogs and gray wolves as extremely close relatives, which is hardly surprising, since they remain fully capable of interbreeding.
Beyond establishing wolves as the closest wild relatives to dogs, genome comparisons are also beginning to reveal how human artificial selection brought dogs into being. Teasing out the genetic basis for the domestication process has become increasingly possible now that the dog genome has been completely sequenced published in This complete sequence allows for detailed comparisons between dogs and gray wolves, as well as comparisons between dog breeds.
Both studies shed light on how artificial selection shaped dogs over their shared history with humans. In this study, we wanted to estimate the power to detect selection at single loci as a function of effective population sizes, number of parallel populations, number of generations since onset of selection, selection intensity, and the initial genetic distance between populations. Two different methods for detecting selection were considered: For both methods, the power to detect selection was estimated by simulating a single, bi-allelic locus both in the absence and presence of selection.
The simulation program was written in Python v2. The code may be found in Additional files: The parameter values were chosen to be relevant for populations of Atlantic salmon in Norway, the focus of our own research, but should match a wide range of aquaculture species. With some exceptions, the Atlantic salmon breeding programmes share the following features: The breeding programmes were once established from different sets of Norwegian rivers, with some overlap between the different sets [ 7 ].
F ST values between wild Norwegian populations have been found to lie around 0. These results were backed up by our own data on four wild populations genotyped for 12 microsatellite loci and 13 wild populations genotyped for SNP loci unpublished.
On this background, our default simulated data set consisted of 10 closed farm populations low-F ST outlier approach or 10 closed farm populations and 10 wild populations high-F ST outlier approach , each population having an effective population size of Specifically, we assumed that directional selection is only occurring in the breeding programmes and that this selection is leading to convergent evolution among different breeding strains.
In an evolutionary context we are thus interested in detecting low-F ST outlier loci, that will appear as low-F ST outliers when only different breeding strains are being studied, but as high-F ST outlier loci when a pool of breeding strains are compared with a pool of wild populations where no selection is occurring. From now on these different approaches will be referred to as Low- and High-F ST outlier approaches, respectively.
Parameter values Ne, number of populations, and start F ST were altered one at a time in order to assess the impact of the parameter on experimental power. Two different approaches for the detection of outlier loci were investigated. The first approach was based on the detection of loci displaying lower-than-expected under a null hypothesis of no selection F ST values between farmed strains.
The second approach was based on the detection of loci displaying higher-than-expected values of F ST between a pool of farmed populations and a pool of wild populations. For both approaches, a single bi-allelic locus was simulated with and without selection.
In each of iterations, a single overall allele frequency was first drawn randomly from a uniform distribution between 0 and 1. N pop populations, each consisting of N e animals with a single diploid locus, were then formed. Half of the individuals were designated as males, the other half as females.
Genotypes were assigned randomly to individual animals, given the overall allele frequency. Next, random mating was simulated in each population for a number of generations, until the F ST value between populations reached the wanted level for initial F ST F ST 0.
Following this initial phase, random mating with alternative hypothesis or without null hypothesis selection was applied for N gen generations; selection was applied by defining different fitness values for the different genotypes assuming no dominance.
At the end of each iteration, F ST between populations [ 13 ] was calculated. This process was iterated times without selection in order to generate a distribution of F ST under the null hypothesis, and times with selection in order to generate a distribution of F ST under the alternative hypothesis.
Finally, the power to detect outlier loci was calculated. The power was defined as the fraction of the F ST -distribution generated under the alternative hypothesis i. The Python code can be found in Additional file 1. The populations were then split into two sets of equal size, representing farmed and wild populations. For the farmed populations, random mating with alternative hypothesis or without null hypothesis selection was simulated for N gen generations.
For the wild populations, random mating without selection was simulated for N gen generations, but the size of each population was first increased to in order to minimise the effect of drift in wild populations. At the end of each iteration, the populations were merged into one farmed and one wild 'metapopulation' and F ST between these metapopulations was calculated. The Python code can be found in Additional file 2. Comparison between low-F ST and high-F ST outliers approaches for detecting non-neural loci in domesticated populations.
Effective population size is Number of populations is Number of generations is Initial F ST is 0. Number of iterations is The power to detect high-F ST outliers rapidly increased, and was large for moderate and large selection coefficients, when the effective population size, number of populations and number of generation passed reached 40, 5, and 10, respectively.
Power to detect high-F ST outliers. Default parameter values are: The power to detect low-F ST outliers was not affected by increasing effective population size, or by the initial F ST.
Power to detect low-F ST outliers. This study was undertaken as a preparatory step preceding an empirical study seeking to identify markers for loci under artificial selection in Norwegian Atlantic salmon breeding programmes. Our motivation for identifying such loci was fourfold: The motivations and the approaches to identify non-neutral loci presented here may also apply to other aquaculture species [ 14 ], many of which might escape and interact with their wild counterparts e.
Evolution: Natural selection and human selection article
Slow though the process of selection may be, if feeble man can do much by his powers of artificial selection, I can see no limit to the amount of change, to the. Artificial selection, also called "selective breeding”, is where humans select for . The distribution of citations of Academic papers is in a power law distribution (a. Artificial selection is a process of genetic modification of farm animal species that . Given the intrinsic power of this method to assess genetic pleiotropy, it might.