Evaluate How Can the Random Distribution of Alleles Result in a Predictable Ratio

Directional Selection

Directional selection on the nuances of the molar surface would require developmental constraints evolved by stabilizing selection on other dimensions of tooth shape and interaction.

From: Encyclopedia of Evolutionary Biology , 2016

Antagonistic Interspecific Coevolution

M. Neiman , P. Fields , in Encyclopedia of Evolutionary Biology, 2016

Positive Directional Option: Artillery Race Coevolution

Nether directional pick, relative fitness increases as the value of a trait increases (positive directional option) or decreases (negative directional selection). Dawkins and Krebs (1979) argued that reciprocal positive directional choice exerted by coevolving hosts and parasites could lead to a state of affairs where hosts continually go more resistant to parasitism while parasites reply by condign more than virulent or evolving new mechanisms of evading host immunity.

Unlike negative frequency-dependent option, this so-called 'Arms Race coevolution' does not generate a rare reward per se. Instead, host resistance and parasite 'virulence' are inherent properties of the individual genotype and exercise not depend on the frequency of the other genotypes. In this scenario, repeated selective sweeps favoring resistant hosts and virulent parasites volition lead to the evolution of reciprocal host and parasite adaptations that, once overcome by a counter-adaptation, volition non once more incur resistance/virulence (Figure i(b); Woolhouse et al., 2002; Burdon et al., 2013). Some other important distinction between Blood-red Queen and Arms Race coevolution is that the latter, in favoring traits that counter adaptations in the biological antagonist, has a high potential for evolutionary innovation (eastward.g., Kerns et al., 2008; reviewed in Daugherty and Malik, 2012).

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Natural Selection*

Yard.E. Holsinger , in Encyclopedia of Genetics, 2001

Directional Selection

Directional selection occurs when individuals homozygous for i allele accept a fettle greater than that of individuals with other genotypes and individuals homozygous for the other allele have a fitness less than that of individuals with other genotypes. At equilibrium the population will be composed entirely of individuals that are homozygous for the allele associated with the highest probability of survival. The rate at which the population approaches this equilibrium depends on whether the favored allele is ascendant, partially dominant, or recessive with respect to survival probability. An allele is ascendant with respect to survival probability if heterozygotes have the same survival probability every bit homozygotes for the favored allele, and it is recessive if heterozygotes take the same survival probability as homozygotes for the disfavored allele. An allele is partially dominant with respect to survival probability if heterozygotes are intermediate betwixt the two homozygotes in survival probability. This pattern of option is referred to as directional selection because one of the ii alleles is always increasing in frequency and the other is ever decreasing in frequency.

When a dominant favored allele is rare well-nigh individuals carrying information technology are heterozygous, and the large fitness difference between heterozygotes and disfavored homozygotes causes rapid changes in allele frequency. When the favored allele becomes mutual most individuals carrying the disfavored allele are heterozygous, and the small fitness difference between favored homozygotes and heterozygotes causes allele frequencies to change much more slowly (Figure 1). For the aforementioned reason changes in allele frequency occur slowly when an allele with recessive fitness effects is rare and much more than rapidly when it is common. A deleterious recessive allele may be found in different frequencies in isolated populations even if it has the aforementioned fitness effect in every population, because natural option is relatively inefficient when recessive alleles go rare, allowing the frequency to fluctuate randomly every bit a result of genetic drift.

Figure 1. Dynamics of directional option.

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Ecological Genetics

Beate Nürnberger , in Encyclopedia of Biodiversity (2nd Edition), 2013

Glossary

Directional choice

Natural choice that favors phenotypes that differ from the electric current mean phenotype of a population in ane direction (e.thousand., those that are larger than the current mean).

Disruptive option

Natural selection that favors phenotypes that deviate in either direction from the current population hateful.

Potency

Phenotypic effect of a item heterozygous combination of ii alleles at a single locus that deviates from the mean of the two homozygous phenotypes.

Epistasis

Phenotypic effects of detail combinations of alleles at two or more than loci that differ from the sum of additive effects of these alleles (i.east., the analog of dominance for more than one locus).

Gene flow

The influx of genetic variants into a local gene pool as effect of immigration of individuals or gametes (i.e., pollen).

Genetic drift

Stochastic change in allele frequencies at a given locus from generation t to generation t+1 due to the chance events that touch the number of offspring produced per parent in generation t. The consequence of genetic drift on allele frequencies and, past extension, on population mean phenotypes increases with decreasing population size.

Genetic linkage map

For a given taxon, a map of genetic marking loci that are arrayed based on the recombination rates among them. Recombination rates are estimated from crossing-over events in controlled crosses. Given a sufficiently high marker density, the resulting linkage groups correspond to chromosomes.

Polymorphism

The co-existence of two or more distinct variants in a population either in the form of alleles for a given locus or as distinct phenotypes.

Stabilizing selection

Natural selection against phenotypes that deviate from the current population mean.

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Mating Systems

Michael D. Brood , Janice Moore , in Animal Behavior (2d Edition), 2016

The Furnishings of Sexual Selection on the Heritability of Traits

Strong directional pick usually exhausts additive genetic variance for a trait in three to five generations. (In this context, this means traits governed by polygenic inheritance, or quantitative trait loci; come across Affiliate 3 on genetics.) This means that the proportion of variation in the phenotype due to genetic variation, or heritability, approaches goose egg. Subsequently that, there can be no further response to selection because the remaining phenotypic variation is from either environmental or nonadditive genetic variation. In theory, sexual pick on a trait such as antler size should rapidly eliminate the additive genetic variance for the trait. In other words, the trait volition be genetically fixed. In practice, many traits that seem to be under strong sexual selection still have considerable heritability. ²¹

Key Term

Directional pick causes 1 grade of a trait in a population, over generations, to be favored (see Effigy 11.3).

There are a number of possible explanations for why choice does non eliminate all of the additive genetic variance for traits involved in mate selection. They include the post-obit:

1.

Sexual selection is stiff only nether extreme environmental weather condition in which survivorship is depression. Variance is maintained during periods of relaxed option.

2.

Interactions with other traits (due east.yard., linkage effects, viability effects) limit sexual selection before the condiment variation is exhausted.

3.

Mate pick relies on many factors, rather than one trait. When selection acts on multiple traits, they limit each other's development so that variation remains for each of the traits.

4.

Counterbalancing selection for factors like protection from predators maintains additive genetic variance past limiting the elaborateness of a signal. ^22,23 It is hard to overemphasize the complexity of mate choice and the need to consider multiple factors involved in any mate pick decision.

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Production SYSTEMS AND AGRONOMY | Rubber

B.Southward. Jalani , O. Ramli , in Encyclopedia of Applied Plant Sciences, 2003

Narrow Genetic Base

The rubber breeding material used in Asia has a very narrow genetic base, having originated from seeds collected by Sir Henry Wickham in a very small area of Brazil. The initial convenance base of operations came from nearly 2000 seedlings from the Wickham collection imported into Asia in 1877. Most of these seedlings went to Sri Lanka, with much smaller numbers going to Malaya and Republic of indonesia. There were some other introductions to Southeast Asia in later years merely due to their poor yield they have not been used in subsequent breeding programs. Hence, rubber convenance in Asia (and in Africa) is based on express germplasm.

The intensive employ of directional selection for loftier yield, phenotypic assortative mating, and the extensive use of clonal propagation in the early on breeding programs have aggravated the trouble of the narrow genetic base of operations. As a result, some undesirable features have go apparent which tin can be illustrated by the following:

ane.

Most of the clones bred to date in Malaysia (and other Southeast Asian countries) can be traced to vii "chief clones." Some inbreeding has therefore featured in the early breeding programs. Some of these master clones are themselves related, and so inbreeding may be even greater.

ii.

There is a diminishing return obtained from breeding effort. In that location was a substantial yield increase in the early phases of convenance, but this has macerated in later phases (see below).

three.

Genetic erosion seems to accept occurred every bit materials selected in one region may be establish to be less adaptable in another. For example, Southeast Asian selections are susceptible to Southward American leafage blight (SALB) which could be due to lack of resistance in the original Wickham collection or subsequent loss through genetic erosion following selection in the absenteeism of the disease.

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Transposable Elements and Insecticide Resistance

Wayne G. Rostant , ... David J. Hosken , in Advances in Genetics, 2012

B Why are TEs so important?

Insecticide resistance results from very strong, persistent directional selection. TE-mediated changes in regulation tin atomic number 82 to massive and rapid changes in expression, responses that are potentially highly adaptive when an organism is faced with a major, pervasive, and novel mortality agent in the environment, similar an insecticide. A useful contrast which illustrates this point is the essential absence of TEs involved in natural xenobiotic resistance—if nosotros consider that mutational changes in plant allelochemicals are unlikely to bring nigh massive changes in mode of action or in toxicity, then mutational change associated with allelochemical resistance may be acquired more than slowly equally a consequence of the accumulation of minor changes in structural genes ( Li et al., 2007).

Application of insecticide tends to favor insecticide resistance, involving unmarried genes of major upshot rather than polygenic resistance (ffrench-Abiding et al., 2004), and it has been institute that about resistant field strains show monogenic resistance (Roush and McKenzie, 1987). Where resistance genes are already involved in essential functions, as is oftentimes the instance for metabolic enzymes, it is advantageous to maintain the quality of mRNA to allow wild-blazon office to be retained and instead regulate factor expression. TE insertion inside regulatory regions of genes which confer resistance frequently results in upregulation, that is, increase in the quantity of mRNA. This may be because many TEs accept built-in enhancer sequences related to their transposition (Zhang and Saier, 2009) that take been co-opted past the host, merely some other possibility is that such spacing may move genes further from existing regulatory sequences (Schlenke and Begun, 2004).

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Selection Intensity

W.M. Hill , in Brenner's Encyclopedia of Genetics (Second Edition), 2013

Pick intensity

Selection intensity is a measure out of the strength of directional selection applied in a option experiment or breeding program to modify a quantitative trait. If selection is skilful on individual operation, the choice practical can be described by the selection differential ( Southward), which is the difference betwixt the mean performance of the selected individuals and that of the population every bit a whole. The selection differential is measured in units of the trait, for example, grams of body weight or number of offspring built-in in a litter of mice or pigs. Formally, the selection intensity, usually denoted i, equals the selection differential measured in phenotypic standard deviations (σ _P  =   √V _P), that is, i  = Southward/σ _P. Therefore, the magnitude of the choice intensity so divers does not depend on the variability of the trait.

The selection intensity is a useful measure out because its value can be predicted in an artificial selection program from knowledge of the proportion of individuals selected and the distribution of the trait. For example, with truncation choice, in which the highest-performing individuals for the trait are selected, i is a elementary office of proportion selected (p) for any distribution. For the normal distribution, tables of i are bachelor, or information technology can exist computed as i  = z/p, where z is the ordinate of the standardized normal at the truncation indicate respective to p. For example,

$\begin{array}{l}p0.8\mathrm{0.v}0.20.10.050.010.001\\ i0.3500.798\mathrm{ane.400}\mathrm{i.755}\mathrm{ii.063}\mathrm{two.665}3.367\end{array}$

Notation that, every bit the proportion selected becomes very small, i equals approximately ane.2 –0.7log(p). Therefore, for example, the total selection intensity from two generations of option with p  =   0.one tin can greatly exceed that for i generation with p  =   0.01 and, because Mendelian segregation produces new assortment each generation, the consequent selection response from 2 generations with weak selection would be expected to be greater than i with intense pick.

Selection intensity also depends somewhat on the size of the population. For a given proportion selected, the intensity becomes slightly less equally the population size becomes smaller. Values can be computed using social club statistics (i.e., expected values of ranked observations) and are also tabulated (Falconer and Mackay). For instance, if N individuals are selected from Chiliad recorded, and N  = 1000/10

$\begin{array}{l}N/M1/\mathrm{ten}2/205/\mathrm{l}\mathrm{ten}/10020/200\mathrm{0.ane}M\left(M\to \infty \right)\\ i1.5391.6381.7051.730\mathrm{ane.742}\mathrm{one.755}\end{array}$

In these calculations for finite numbers, observations are causeless to be uncorrelated. The intensity is a bit further reduced, by a predictable but less hands computed amount in a small population in which animals are related because the family members resemble each other and phenotypes are correlated.

For traits that are non normally distributed, the pick intensities may differ quite substantially from those given above. The notable case is where selection is on some all-or-none character: if information technology is desirable and has an incidence say q, once the proportion selected is less than q, selecting e'er smaller proportions practise not increment the choice intensity.

The pick intensity can also be computed if, for example, there is no simple truncation choice, including, for instance, where individuals near the middle or extremes of the distribution are favored.

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Using Signatures of Directional Selection to Guide Discovery

John C. Crabbe , in Molecular-Genetic and Statistical Techniques for Behavioral and Neural Inquiry, 2018

Genetic and Genomic Consequences of Selection: Across Cistron Lists

Recent studies have used the potent genetic differences produced past directional selection every bit starting material. They have applied what might be thought of every bit informatics-driven filters to try to focus on individual genes whose manipulation will affect the selected trait. A clear case comes from Zhifeng Zhou and David Goldman's work with P and nonpreferring (NP) rats. ³⁶ P rats have been bred for many generations to have high preference for 10% alcohol versus water, and the NP rat line was bred for low preference. They first sequenced the exomes of six private rats from each selected line, finding >120,000 single nucleotide polymorphisms (SNPs). About xx% of these SNPs were homozygous and consistently different between P and NP rats, suggesting that they represented the signature of response to selection pressure level for high versus low drinking. Alternatively, given the relatively pocket-size population sizes necessarily involved in producing the selected lines, these SNPs could represent adventitious inbreeding. To distinguish betwixt these alternatives, they mapped the differential SNPs and institute numerous relatively large haplotype blocks. About chiliad such blocks of SNPs could be mapped to known functional genes.

Clearly, it was necessary to further reduce the number of potential targets. Two of the segregating SNPs revealed terminate codons in known genes, and 31 others showed exomic sequence differences predicted to adversely impact protein function. At this point, Zhou and Goldman turned back to behavior. They used an F2 segregating population of P and NP rats obtained by intercrossing inbred variants of P and NP lines. They tested hundreds of F2 rats for alcohol preference and performed a standard QTL linkage analysis to identify four variants in 3 genomic regions linked to preference variation. Comparing the QTL results to the exome sequencing SNP patterns consistently identified a stop codon in the Grm2 cistron (Grm2 ∗407) (see Fig. eleven.2): this gene encodes the metabotropic glutamate receptor ii, and the terminate codon variant in P rats leads to widely deficient receptor office. With a articulate target cistron of interest, these investigators amassed a wealth of testify that consistently implicated the mGluR2 receptor in P rats, including absence of receptor expression and impaired mGluR-mediated synaptic depression. They demonstrated elevated alcohol consumption in mice with a null mutation for Grm2. Overall, this paper shows how the ability of directional selection to influence the genome tin can yield fruitful results. Three additional possible genes of interest were discussed in the paper, but these have not all the same been validated.

Figure 11.two. Exome sequencing of preferring (P) and nonpreferring (NP) rats identified the mGluR2 gene as important for ethanol preference drinking. SNPs are shown for chromosomes 8, ix, 10, and 11.

From Zhou Z, Karlsson C, Liang T, et al. Loss of metabotropic glutamate receptor 2 escalates alcohol consumption. Proc Natl Acad Sci United states. 2013;110:16963–16968 with permission.

A similar conceptual approach was also practical to ethanol preference drinking, likewise in rats. These investigators took advantage of the other rat preference lines selected at Indiana Academy, the Loftier Alcohol Drinking and Low Alcohol Drinking rats. ³⁷ HAD and LAD rats were bred for high versus depression preference in the same way P and NP rats were. ³⁸ This population offered three major advantages from a genetic perspective. First, the lines were derived from a genetically heterogeneous intercross of eight inbred rat strains and thus presented loftier genetic diversity at the outset. ³⁹ Rats from this segregating population were also available to serve every bit controls. 2d, two contained populations of HAD rats, and two of LAD rats, were developed. Considering laboratory populations are always relatively pocket-size (from a population genetic perspective), many differences between divergently selected lines emerge during the course of selection due to accidental fixation of gene variants (i.e., loss of genetic variation, or inbreeding). The occurrence of identical gene or chromosomal regional fixation during selection in ii completely contained pairs of lines, merely non in the nonselected controls, is thus relatively unlikely; this improves the detection of signatures of option versus noise. ^one,2,29

Many details of the genomics analyses in this experiment differed from the analysis of P versus NP rats. Signatures of pick in the HAD/LAD populations were institute for nearly thousand genes, and most were located within a single gene. Within those genes, very few (four) were institute in exonic regions: most were in promoter or intronic regions (see Fig. 11.3). Functional overrepresentation analyses suggested an important office for genes involved with synaptic manual, memory, and reward pathways and included those coding for several ion channels and excitatory neurotransmitter receptors. ³⁷ Unlike the NIAAA group, these investigators did not pursue individual candidate genes and attempt to provide further confirmatory evidence.

Figure 11.3. Signatures of selective breeding in genome of Loftier and Low Alcohol Drinking rat lines. Chromosome 7 is depicted for Slc17a8, a vesicular glutamate transporter (encounter inset). Allele frequencies for HAD (cherry-red) and LAD (dark-green) are plotted versus chromosomal position. Excessive differentiation in genomic architecture between lines across both replicates, termed signatures of selection (SS), are plotted in blood-red based on intraclass correlation values (θ), plotted in bluish.

Adapted from Lo CL, Lossie AC, Liang T, et al. High resolution genomic scans reveal genetic compages controlling alcohol preference in bidirectionally selected rat model. PLoS Genet. 2016;12:e1006178 with permission.

Our group has besides taken advantage of the existence of replicated selectively bred lines. ^forty A rampage has been divers past the National Establish on Alcohol Abuse and Alcoholism of the US National Institutes of Health as a period of temporally focused drinking that leads to a blood ethanol concentration (BEC)   >   80   mg%, or 0.8 mg/mL. ⁴¹ Rampage drinking is a risk for development of an alcohol utilise disorder, and most booze abusers binge drink. Binge drinking is also a strong predictor of medical diagnosis and has deleterious wellness consequences. ⁴² Prevalence of binge drinking is increasing in the United States, ^43,44 and it is highly prevalent in both veterans and active military duty personnel. Alcohol use disorder is comorbid with many other psychiatric conditions: in these populations, posttraumatic stress disorder is too a frequent diagnosis. ⁴⁵ To develop an beast model of binge-like drinking, we explored several alternatives with the goal of achieving a uncomplicated behavioral assay for binge-like drinking in the mouse. Following earlier work in the surface area, ⁴⁶ we developed the drinking in the dark (DID) analysis, where mice swallow enough alcohol in 2–4   h to reach intoxicating BECs. ⁴⁷ The basic paradigm we used was to substitute 20% ethanol for water for a limited flow each day, during the early hours of the circadian night cycle, as this is when rodents eat much of their daily food and fluid. We take determined the optimal time afterwards "lights off" to offset access, ⁴⁷ and the optimal elapsing of access to upshot in elevated blood booze levels. ⁴⁸ Consumption of the ethanol solutions remains relatively consistent across 12   days. When we examined panels of multiple inbred strains in the DID process, we found that the trait was reliable upon retest and significantly heritable. ^49,50

We later selectively bred high DID (High Drinking in the Night [HDID]-1 and HDID-ii) mouse lines for high BECs after a 4-h DID session; these mice drink to the point of behavioral intoxication and reach blood levels that average most 200   mg/mL [Refs.48,51; see Fig. 11.4]. Behavioral label of HDID mice has revealed that HDID mice showroom behavioral impairment afterwards drinking, withdrawal afterwards a single binge drinking session, and escalate their intake in response to consecration of successive cycles of dependence. ^48,50 Notably, HDID mice do not showroom contradistinct tastant preference or alcohol clearance rates. ^52,53 1 articulate limitation of the DID model is that ethanol is not offered as a choice versus h2o, and when it is, ethanol intake and blood alcohol levels are somewhat lower. ^48,53 This selection has one or two unusual features. The first is that we bred for a pharmacological endpoint (blood alcohol level afterward drinking) rather than for increased intake. As Fig. eleven.4 shows, animals did nonetheless testify elevated drinking across generations, which was expected. Withal, they achieve college blood levels by patterning their drinking differently. HDID-1 mice prove larger (longer) bouts of sustained drinking, while HDID-two mice show more frequent, smaller bouts. ⁵⁴ 2nd, given that the HS/Npt foundation population shows very low blood levels (and intake: encounter data at Generations S0 and S14 in Fig. 11.four), we elected not to develop parallel lines for low blood levels after DID.

Figure 11.iv. Upper panel: Response to unidirectional selective breeding for blood ethanol concentration (BEC) in mice. High Drinking in the Night (HDID) mice were offered 20% ethanol in place of water for 4   h starting 3   h into their circadian night session. Each data point represents the mean   ±   standard error BEC at the finish of drinking for that generation'south population of near 100–150 mice (approximately half males and half females). HDID-1 mice (closed symbols) take been selected for 37 generations. The gap between S28 and S29 for HDID-ane represents ii generations where option was relaxed. Pick of HDID-2 mice (open symbols) was initiated ii   years later and is at the 31st selected generation. Response lines are plotted versus selected generation. Lower panel: Consumption in 1000 ethanol/kg body weight is too shown, although choice was based entirely on BEC. HS/Npt data are shown at the outset (generation S0) and for 14th generations later on. This genetically segregating population served as the foundation population for both lines and has never been directionally selected. For details, see Refs.48,51.

In an initial attempt to explore the genomic structure of response to intense selection, we compared patterns of gene expression in ventral striatum tissue from 48 naïve, male mice from all three genotypes. ^forty We compared HDID-1 mice from the 22nd selected generation, HDID-2 mice from the 15th selected generation, and HS/Npt unselected controls. Using Illumina WG 8.2 arrays, nosotros analyzed SNP variation in 3683 markers: analyses were carried out marker by mark and with Weighted Gene Coexpression Network Assay [WGCNA: Refs.55,56]. For both QTL analyses and network analyses, we predicted that genetic variability beyond animals would be greatest in the unselected HS mice, which we found to be true. We predicted that the HDID-2 mice would show differences from HS, and that the HDID-one mice would show even larger changes given their greater response to choice at that betoken. Of the more 9000 transcripts (more than 7000 unique genes) surveyed, there were more genes differentially expressed between HDID-1 and HS than HDID-ii and HS, and 94 transcripts differed from HS in both lines, with the aforementioned directionality.

One interesting finding from this written report is shown in Fig. xi.5. The WGCNA identified 21 modules, each representing a number of coexpressed genes. Of these, four modules were strikingly and consistently affected by option. In some instances, the coherence of the module was increased past increased selection (i.e., HDID-1   >   HDID-two   >   HS), while for others, the reverse was true (see Fig. 11.5). The overall signature of selection indicated that intramodule coherence was more meaningfully responsive to pick (i.e., consistent across the two replicates) than the specific differences in expression of private genes. ⁴⁰

Effigy 11.5. Multidimensional scaling plots of the coexpression networks in (A) heterogeneous stock (HS/Npt), (B) High Drinking in the Dark (HDID)-2, and (C) HDID-1 datasets. For visual clarity, only the four modules near consistently affected past selection ("black," "magenta," "dark reddish," and "greenish") are depicted. Each dot represents a transcript, with colors corresponding to module assignments. The distances between points correspond to network adjacency. The figure illustrates (1) the modularity of the networks, with similar colors clustered together, and (2) the effect of option on the network construction, with HDID-2 and HDID-1 successively diverging more from the original HS/Npt network structure. In particular, the "dark-ruddy" module appears have become more dispersed, while the "magenta" module appears to accept get more compacted in the pick networks.

From Iancu OD, Overbeck D, Darakjian P, et al. High Drinking in the Dark selected lines and brain gene coexpression networks. Alcohol Clin Exp Res. 2013;37:1295–1303 with permission.

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Quantitative Genetic Variation, Comparing Patterns of

K. McGuigan , J.D. Aguirre , in Encyclopedia of Evolutionary Biology, 2016

Abstruse

Heritable phenotypic variation determines how a population's mean phenotype evolves under directional pick. The additive genetic covariance matrix, Yard, summarizes the genetic variation among individuals (due to individual'southward carrying unlike alleles), and can exist used to predict (or possibly reconstruct) phenotypic responses to directional selection. Moreover, G itself is subject to evolution, and by comparison Thou amongst naturally or experimentally evolving taxa nosotros tin empathize how choice and drift drive changes in frequencies of alleles that decide phenotypes. Here, we present some unremarkably used tools for comparing matrices in order to understand the evolution of this of import evolutionary parameter.

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Progeny Testing

G.J.G. Rosa , in Brenner's Encyclopedia of Genetics (Second Edition), 2013

Selection Based on Progeny Performance

For quantitative traits, the expected response (R ) per generation of directional selection is given by

$R=\rho \times i\times \sqrt{{\sigma }_A^2}$

where ρ is the accurateness of option, which is defined as the correlation betwixt the true breeding values (TBVs) and the estimated breeding values (EBVs) of the option candidates; i is the option intensity, which is equal to the mean superiority of the selected individuals expressed in terms of phenotypic standard deviations; and σ _A ² is the additive genetic variance of the trait being directly selected for.

If option is based on a single phenotypic record on own performance of the choice candidates, the selection accurateness is ${\rho }_{OP}=\sqrt{h^2}$ , where h ² is the heritability of the trait of interest. Alternatively, if pick is based on the average performance of north offspring of each pick candidate, and then the selection accuracy is shown to be ${\rho }_{PT}=\sqrt{n\times h^{\mathrm{ii}}/[4+(n-1)h^2]}$ .

The ratio between the 'progeny test' $({\rho }_{PT})$ and the 'ain performance' $({\rho }_{OP})$ accuracies is equal to $v={\rho }_{PT}/{\rho }_{OP}=\sqrt{n/[4+(\mathrm{due\; north}-\mathrm{ane})h^2]}$ . Selection based on progeny testing will be more constructive than phenotypic choice if v  >   1, which occurs merely if $n>(\mathrm{four}-h^{\mathrm{ii}})/(1-h^2)$ . Hence, it is seen that progeny testing is more effective for lowly heritable traits, but that fifty-fifty in such cases at least five progeny are necessary for progeny test to be more efficient than phenotypic selection.

For reasons of economical profitability, progeny testing protocols are usually practical to selection of males in animate being breeding programs. First, males can be mated with a large number of females to produce a big number of offspring needed for analysis, particularly with the use of artificial insemination. Second, in many species, generation intervals for males are shorter than those for females.

The principal drawback of progeny testing is a substantial increase in fourth dimension and associated cost needed for animal evaluation. To be evaluated for most traits of economic importance, the progeny has to reach maturity, thus adding at least 1 generation to the fourth dimension required for a circular of selection (upwards to eight years in some species). Genetic progress per unit of time tin can be improved with progeny examination only if the resulting increase in generation interval is compensated with satisfactory increments in prediction accurateness. To obtain such high accuracy of choice, large populations of offspring take to be produced and maintained, thus making this approach feasible mostly to large-scale breeders.

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