PERIODICUM BIOLOGORUM UDC 57:61 VOL. 112, No 4, 395–402, 2010 CODEN PDBIAD ISSN 0031-5362

Overview

Quantitative Genetics and

Abstract KRUNOSLAV BR^I]-KOSTI] Today, evolution is a unifying concept in biology. A century and half ago, Laboratory for Evolutionary Genetics Darwin developed the theory of , and proposed it as the Department of Molecular Biology main mechanism of evolution. A quantitative approach to the study of evo- Ru|er Bo{kovi} Institute, Zagreb, Croatia E-mail: [email protected] lution required new theoretical developments in population and quantita- tive genetics. Here, I review the basic concepts of quantitative genetics neccessary to understand microevolutionary change. Natural selection is a consequence of differences in fitness (reproductive success) between individ- uals in a population. But natural selection is not equal to evolution. In or- der to achieve evolutionary change, variation in fitness must be heritable, i. e. it must be transmited by genes from parents to offspring. Besides fitness differences, individuals within a population often differ in many other characters (morphological, physiological and behavioural) which are also genetically transmited from generation to generation. It is crucial to distin- guish the process of selection which operates in an existing generation from the evolutionary change which is visible in the next generation. Most con- cepts of quantitative genetics centre around variances and covariances, and include the evolutionary potential of a population or heritability (ratio of additive genetic variance and phenotypic variance), the strength of selection on a particular trait (covariance of particular trait and fitness), the total strength of selection (phenotypic variance in fitness) and evolutionary re- sponse (phenotypic change in the next generation) which can be predicted by breeder’s equation.

HISTORICAL BACKGROUND On the 24th of November 1859, published his fa- mous and most influential book The Origin of (1). From that point on biology was not the same. In The Origin of Species he thor- oughly developed the theory of natural selection providing numerous empirical and experimental examples which support natural selection as the mechanism of evolution. Natural selection is a consequence of variation in fitness (reproductive success) between individuals in a pop- ulation. The existence of variation in fitness is a prerequisite but not sufficient for evolution to occur. Another essential requirement leading to evolutionary change is that fitness as a trait is inherited through genes from parents to offspring. Evolution can be viewed as the change of allele frequencies (classical population genetics), as the change of the mean phenotype and variance in a population (quantitative genetics), and as the rate of allele substitutions in a population (molecular popu- lation genetics). The first two aspects concern short-term evolution (microevolution), whereas the latter concerns long-term evolution (ma- Received January 29, 2010. croevolution). In this review I will describe the basic concepts of quan- titative genetics required to understand microevolutionary change. K. Br~i}-Kosti} Quantitative Genetics and Evolution

Quantitative genetics was developed as a solution to Y =(1–h2)M + h2X (1) the debate between two opposing views on evolution and the mechanism of inheritance (2). According to salta- Y is the average phenotypic value of offspring from par- tionism evolution was viewed as very fast and an abrupt ticular parent pair, X is the average phenotypic value of a parent pair (midparent value), M is the average pheno- process visible through the change of Mendelian (sim- typic value of parental population (population mean), ple) traits. Mendelian traits are determined by a single and h2 is heritability of a trait (slope of regression line). gene with large allelic effects on phenotype, and almost Heritability is the ratio of additive genetic variance and no environmental influence. Such phenotypes are quali- total phenotypic variance, and its meaning will be fully tative in nature (they are verbally described), and are described later. Phenotypic variances and covariances are suitable to standard genetic analysis. In contrast, gradu- essential in quantitative genetics, and before further con- alism assumed that evolutionary change is gradual, and sideration it is useful to define them. From elementary that it is a consequence of selection on quantitative traits. statistics we know that Such traits (body size, antipredator behaviour, fitness components etc.) are expressed as quantities in particu- 1 n =−=−∑ 2 22 Vxi()xx x () x lar units. At that time, both saltationists and gradualists n i =1 believed that Mendel’s particulate theory of inheritance can be applied only to simple traits. The inheritance of 1 n =−−=−∑ quantitative traits on the other hand, are beleived to fol- covxy (xxyy i )( i ) xyxy ( )( ) (2) n = low different laws connected with the action of fluids i 1 (blending inheritance). In his seminal work Fisher sho- where xi and yi are the phenotypic values of i-th individ- wed that inheritance and variation in quantitative traits ual, x and y are the mean population values for traits x can be explained by simultaneous segregation of many and y, and n is the number of individuals in a population. Mendelian factors (genes) (3). Consistant with this, quan- The variance of a trait x, Vx is a measure of variation in a titative (complex) traits are determined by many genes population, and represents the mean square deviation, or with very small allelic effects, but with substantial envi- mean square minus square of the mean. The covariance ronmental influence on phenotype. Since inheritance between traits x and y, covxy is a measure of the linear re- and variation in quantitative traits are based on the ag- lationship between two traits, and represents the mean gregate action of many loci, they are described by statisti- product deviation, or mean product minus product of the cal terms (means and variances) without information means. If variance and covariance are estimated from a about the individual effect of any given locus. Phenotypic population sample, then in the upper formulas instead of values for most quantitative traits are normaly distrib- 1/n we use 1/(n-1). uted around a mean, and in theoretical quantitative ge- netics normality is always assumed. Besides its origin in Decomposition of the phenotype the field of evolution, the important application of quan- Each individual in a population has its own pheno- titative genetics is primarily in the field of animal and typic value which can be measured. Phenotypic value plant breeding which greatly stimulated its development. can be decomposed into casual components (5, 6). First, Darwin introduced the verbal concepts of evolution, the phenotypic value P is the sum of the effects of a geno- and he noticed that two visible manifestations of evolu- type and environment. In the simplest case, when there is tion are genetic variation and evolutionary change. To no covariance between genotype and environment, P = describe evolution quantitatively, we must introduce the G + E, where G is a genotypic value and E is an environ- basic theoretical concepts of quantitative genetics (4, 5, mental deviation. Since environmental deviation is ran- 6) which are incorporated in quantitative genetic equa- dom, the mean environmental deviation in a large popu- tions. From these equations we will understand the rela- lation is equal to zero, and the mean phenotypic value is tionships between evolutionary change, genetic varia- equal to the mean genotypic value. Second, the geno- tion and the process of selection. typic value of a particular individual (or genotype) G is the sum of different genetic effects. One is transmission of genes between generations, and the other are allele in- CONCEPTS teractions (dominance and epistasis). According to this, we have that G = A + D + I, where A is a breeding value Genetic basis of quantitative trait (genic value or additive phenotype), D is a dominance If a quantitative trait is transmited by genes from par- deviation, and I is an interaction (epistatic) deviation. ents to offspring, then we expect that parents with higher Among the components of genotypic value, only the trait value will give offspring with higher trait value, and breeding value is essential for evolution since it measures vice versa. In contrast, if a trait is not genetically trans- the potential of a particular individual (or genotype) to mited, then the offspring of the parents with any trait change the population mean in the next generation. value will have similar (population mean) trait value. In order to understand the meaning of breeding value, The equation which connects the average trait value of we will consider a simple genetic model introduced by particular offspring with the average trait value of their Fisher (3). It assumes that genotypic value is affected by a parents (midparent value) is the regression equation single locus with two alleles. In such a situation there is

396 Period biol, Vol 112, No 4, 2010. Quantitative Genetics and Evolution K. Br~i}-Kosti} no effect of epistasis, and contribution of a single locus to its allele frequency. This means that a particular individ- genotypic value is G = A + D. The genotypic values for ual (or genotype) always has the same genotypic value, three genotypes are A1A1 (a), A1A2 (d) and A2A2 (-a), but has different breeding values in different popula- whereas allele frequencies are A1 (p) and A2 (q). Let us tions. Also, from the above example it is obvious that in assume that the A1 allele increases and A2 allele de- the case of complete dominance, the dominant homozy- creases the trait value. The genotypic value of a heterozy- gote and heterozygote have the same genotypic values, gote d reflects the degree of dominance. Toclearly distin- but different breeding values. It can be shown that the av- guish breeding value from genotypic value let us assume erage effects of alleles A1 and A2 expressed as deviations that in this particular example the A1 allele is dominant from the population mean are over the A2 allele. This means that both the »good« a [ ] homozygote (A1A1) and the heterozygote (A1A2) have 1 = q a +d (q – p) the same genotypic value equal to a. Before defining the a [ ] 2 =–p a + d(q – p) (3) breeding value, we should introduce the concept of the average effect of an allele (Figure 1). Let us assume we The corresponding breeding values of the three geno- perform two crosses. In the first, the male of A1A1 ho- types A1A1, A1A2 and A2A2 are mozygote mates with a random sample of females from a A =2a =2q[a + d(q – p)] population, whereas in the second, the A1A2 heterozy- 11 1 a a [ ] gote mates with a random sample of females from a pop- A12 = 1 + 2 =(q – p) a + d(q – p) ulation. The mean genotypic value in a population be- a [ ] fore the cross is M, after the homozygotic cross M’, and A22 =2 2 =–2p a + d(q – p) (4) after the heterozygotic cross M’’. The change of the pop- D In all of the above formulas, there is a common factor (in ulation mean in the next generation M’ = M’ – M is square brackets) which is called the average effect of allele larger than DM’’ = M’’ – M. The reason is obvious be- a a a a substitution . Its meaning is = 1 – 2 = a + d( q – p). cause the homozygote always gives a »good« allele (A1) After defining the breeding value, we can understand the to its progeny, whereas the heterozygote gives a »good« meaning of the dominance deviation. It is the difference be- allele (A1) only to half of its progeny. The change in the tween genotypic value and breeding value D = G – A.Ac- population mean as a cosequence of transmission of an cording to the above model, the dominance deviations of allele from a particular individual (or genotype) is called the three genotypes are the average effect of that allele. From the homozygotic a D 2 cross the average effect of allele A1 is 1 = M’, whereas D11 =–2q d the mean average effect of alleles from the heterozygotic a D D12 =2pqd cross is 12 = M’’. The breeding value of the individual (or genotype) A1A1 is A =2DM’, whereas the breeding 2 11 D22 =–2p d (5) value of the individual (or genotype) A1A2 is A12 = 2DM’’. It is twice the average effect of an allele since each It is important to note that breeding value and domi- individual (or genotype) has two alleles, but gives only nance deviation are statistical descriptions, and that both one to its progeny. are dependent on allele frequencies. In addition, the breeding value depends on the genotypic values a (ho- If we know the average effects of both alleles in the ge- mozygote) and d (heterozygote), whereas dominance notype, then the breeding value of this genotype is simply deviation depends only on d (heterozygote). If there is no the sum of the average effects of its alleles (5). It is impor- interaction (dominance) between alleles, then genotypic tant to note that genotypic value is a constant quantity value is equal to breeding value. (for particular environment), whereas the average effect of allele and the breeding value are relative quantities Partitioning the variance which are dependent on the referent population, i. e. on After decomposition of the phenotype, we can pro- ceed with the study of phenotypic variation and the vari- ation in its components. If we know the phenotypic val- ues of individuals in a population, we can estimate the phenotypic variance VP. Phenotypic variance is the total variance in a population, and as with phenotypic value, it can be partitioned into casual components (5, 6). A par- ticular theorem of random variables states that variance of the sum is equal to the sum of variances. Since phe- notypic value is the sum of its components, it follows that VP = VG + VE, where VG is genotypic variance (total ge- netic variance) and V is environmental variance. Simi- Figure 1. Average effect of allele – Two individuals (genotypes A1A1 E and A1A2) with the same genotypic value (a) have different poten- larly, genotypic variance can be further partitioned, and tial to change population mean genotypic value (M). The mean we have VG = VA +VD + VI. These components are the progeny genotypic value of the homozygotic cross is M’ and of variance of breeding value VA, the variance of dominance heterozygotic cross is M’’. For details see the text. deviation VD and the variance of epistatic deviation VI.

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Evolutionary significance lies with the variance of breed- we assume standardized fitness which is called relative ing value VA, and it is called the additive genetic variance. fitness. One type of is artificial selec- Additive genetic variance is part of the phenotypic vari- tion where the breeder wants to increase (or decrease) the ance caused by transmission of genes to the next genera- population mean of a particular quantitative trait. To tion. According to the above model, in the absence of epi- achieve this, the breeder in each generation selects indi- stasis, we have VG = VA + VD. It can be shown that addi- viduals with high (or low) trait value as parents for the tive genetic variance and dominance variance are equal to next generation. The final consequence of directional se- lection is fixation of the »best« allele of the locus, and the [ ]2 a2 VA =2pq a+d(q–p) =2pq stable equlibrium allele frequency is p = 1 and q =0(or p = 0 and q =1)(7). At the level of change of allele fre- V =(2pqd)2 (6) D quency the general selection equation is Both variances are dependent on 2pq which repre- pq[] p()() w−+ w q w − w sents the equilibrium heterozygosity, and is a measure of ∆p = 11 12 12 22 (8) genetic variation in classical population genetics. Addi- w tive variance depends on both genotypic values (homo- This equation describes the unit change of allele fre- zygotes and heterozygotes), whereas dominance varian- D ce depends only on the genotypic value of heterozygotes. quency p (between two successive generations) due to selection. The w11, w12 and w22 are the fitnesses of A1A1, Additive genetic variance is often expressed as he- A1A2 and A2A2 genotypes respectively, whereas w is the ritability, or the ratio of additive genetic variance and to- mean fitness of a population. Two important inferences tal phenotypic variance can be drawn from this equation. First, it is visible that V selection is based on fitness diferences between individu- h2 = A (7) V als (genotypes), and second, that evolutionary change is P faster when genetic variation in a population is larger, i. Heritability represents a proportion of total phenoty- e. when p and q tend to have intermediate values. pic variance attributable to the transmission of genes To derive an analogous equation which shows the from parents to offspring. It is a measure of the evolu- unit change of mean phenotype (between two successive tionary potential of a population. The reason for this lies generations) in a population due to selection, we must go in the fact that genes are inherited between generations, back to the regression equation. In its original sense, it whereas gene interactions are not. Gene interactions are relates the mean phenotype of particular offspring with properties of genotypes which are de novo formed in each the mean phenotype of their parents (midparent value). generation. Heritability is sometimes called narrow sen- It can also relate the mean phenotype of all individuals in se heritability to distinguish it from the so called broad the offspring generation (all offspring of all parents) and sense heritability (degree of genetic determination of a the mean phenotype of all parents (8, 9). Now we have trait) which is the ratio of genotypic variance and phe- notypic variance, H2 = V /V . =−22 + G P YhYhXttt+ 1 ()1 (9) Breeder’s equation where Yt+1 is the mean phenotype of offspring genera- As we mentioned earlier, two essential requirements tion, Yt is the mean phenotype of parent generation (all for evolutionary change to occur are the presence of ge- individuals) and Xt is the mean phenotype of the indi- netic variation in a population and the action of evolu- viduals selected as parents. Toobtain the breeder’s equa- tionary force, i. e. natural selection or genetic drift. We tion in its recognizable form, the upper equation must be will here consider Darwinian evolution by natural selec- rearranged tion. More precisely,we will deal with a particular type of YYhXY=+2() − natural selection called directional selection (Figure 2). tt+ 1 tt −=2 − Directional selection operates on fitness itself and its YYhXYtt+ 1 () tt (10) components (life history traits), as well as on other quan- RhS= 2 titative traits which are (in a particular environment) positively correlated with fitness. Under the term fitness In the breeder’s equation, S is the strength of selection called selection differential which is the difference be- tween the mean phenotype of selected individuals (par- ents) and the mean phenotype of the population before selection (all individuals) (5, 6). Heritability h2 is the ge- netic variation attributable to the additive effects of ge- nes. Finally, R is the evolutionary change called the re- sponse of the population to selection, or evolutionary Figure 2. Relationship between trait and fitness reflects the type of natural selection – On the left, there is a linear relationship between response. It represents the difference between the mean trait and fitness (directional selection), in the middle (stabilizing se- phenotype of offspring of the selected parents and the lection) and on the right (disruptive selection) the relationship be- mean phenotype of the population before selection (5, 6). tween trait and fitness is nonlinear (quadratic). The above description gives us one meaning of the selec-

398 Period biol, Vol 112, No 4, 2010. Quantitative Genetics and Evolution K. Br~i}-Kosti} tion differential. It does not tell us anything about the re- self, and the rate of change in population mean fitness. In lation of a particular trait and fitness. It is intuitive for ar- order to define selection differential for fitness, we can tificial selection (truncation selection) when individuals apply the same logic. We have are selected if their phenotypic value is equal or higher n n n ∑∑∑− 2 than the determined value (truncation point). It is also wwii() w wi wwi intuitive for strong natural selection which acts at the = i ===1 =−i 11i Sw level of viability, i. e. when the population experiences a n n n n n high mortality rate due to environmental factors. In this ∑ 2 ∑ wi wi scenario, the selection differential can be estimated as the = = S =−i 1 w i 1 =−ww22() (13) difference between the mean phenotype of survivors and w n n the mean phenotype of all individuals (before selection). SV= In most real situations natural selection is not so strong, wPw and is manifested through the action of both viability and The selection differential for fitness is equal to the fertility. When differences in fertility are more important phenotypic variance of fitness in a population. It is also for fitness, the above meaning of selection differential is known as the opportunity for selection (14). To express not so intuitive although it is correct. For such a scenario, the change of mean fitness between two successive gen- we will apply a different definition for selection differen- erations as a result of natural selection, we will apply the tial, and will arrive at a simple and intuitive expression breeder’s equation to fitness as a trait (5). Wehave there- which relates a particular trait with fitness. fore that

2 2 Fundamental theorem of natural Rw = h wSw = h wVPw selection Rw = VAw (14) Let us assume that selection on a trait x operates due This expression is called the fundamental theorem of to differences in fertility. Since the contribution to the natural selection, and was discovered by Fisher (4). The next generation will vary among individuals, the differ- change of mean fitness in a population due to natural se- ence between the phenotypic value of a particular indi- lection between two successive generations is equal to vidual and the population mean must be weighted by the the additive genetic variance of fitness in previous gener- fitness of this individual. Then, the selection differential ation. Fisher’s original statement was slightly different is defined as and somewhat obscure (4). n ∑ − wxii() x = Multivariate evolution S = i 1 (11) x n The breeder’s equation concerns selection of a single trait. In reality there are many traits which are under nat- where wi is the fitness of i-th individual, xi is the pheno- ural selection. Some of these traits are correlated. One typic value of i-th individual, x is the mean phenotypic can distinguish genetic correlation (correlation between value of a population, and n is the number of individuals breeding values) from phenotypic correlation (correla- in a population (5). The upper expression can be rear- tion between phenotypic values). There are two causes of ranged in the following way genetic correlations. The first and most common cause is ∑∑∑n n n ∑n pleiotropy which means that the same locus determines wxii wxi wxii wi two (or more) traits. The second is linkage disequilib- =−=−i ===111i i i = 1 (12) Sx x rium which is a consequence of selection for particular n n n n allelic combinations of two (or more) loci which affect =− = Swxx ()()covwx wx different traits. If two traits (x and y) are genetically cor- related and natural selection acts on trait x, we expect two It implies that selection differential operating on a consequences. One is a direct evolutionary response on a trait x is equal to the phenotypic covariance between trait trait x, and the second is an indirect (correlated) evolu- x and fitness (10, 11, 12). This is intuitive and logical tionary response on trait y. The selection differential of a conclusion since the primary target for natural selection particular trait measures the net effect of selection caused are fitness differences. It is important to note that both by different factors (direct and indirect). An important meanings of selection differential are mathematically question is how to distinguish the direct strength of se- equivalent (13), and that they only refer to different lection on a particular trait from indirect (correlated) points of view. The strength of selection is sometimes ex- strength of selection on the same trait. Based on Pear- pressed as the intensity of selection which is the stan- son’s regression theory, Lande and Arnold showed that dardized selection differential scaled by phenotypic stan- the partial regression coefficient of fitness on any trait dard deviation (5, 7) or by phenotypic variance (9). Selec- value is a measure of the strength of direct selection on tion intensity is useful for comparing the strength of se- this trait. This measure is called the selection gradient, lection between different traits. and is designated as b (15). Avoiding details, we will just Since the primary target of selection is fitness, it is im- summarize the most important equations concerning portant to describe the selection differential for fitness it- multivariate evolution, and their meanings (6,7,8,15).

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First, we will write the breeder’s equation on a single trait emphasizing that heritability is the ratio of additive vari- ance and phenotypic variance

–1 R=VAVP S (15) The multivariate analogue of the breeder’s equation can be written in matrix algebra as

− R  V cov V cov  1 S  x = Ax Axy Px Pxy x       (16) Ry  covAxyV Ay covPxy VPy  Sy  Figure 3. Relationship between breeding values of trait x and y –On the left, there is no covariance between two breeding values (each The left side term is called the vector of selection re- trait evolves independently); on the right, there is positive covariance sponses, and is designated as R. On the right side, the between two breeding values (correlated evolution). Spots represents first term is the matrix of genetic variances and covarian- hypothetical individual breeding values of traits x and y. ces, or G matrix; the second is the inverse of the matrix of phenotypic variances and covariances, or P–1 matrix; and third is the vector of selection diferentials, or S. Equation EVOLUTION IN THE REAL WORLD (16) can be written as In order to predict the evolutionary response accord- R = GP–1 S (17) ing to breeder’s equation, one should have an estimate of heritability. After many generations under directional se- Conveniently the product of the inverse of the P ma- lection, heritability is no longer constant since selection –1 trix (P ) and S is called the vector of selection gradients, erodes the additive variance and the heritability of a trait. and is designated as b. According to this, the multivariate For a certain number of generations (15–20 generations selection equation is often written as for a typical artificial selection experiment) there is no change in heritability, and the response is relatively con- R = G b (18) stant (short-term response) (5). For further prediction of If the covariance term in the G matrix is equal to zero, the response (long-term response), a new estimate of then the two traits evolve independently and evolution of heritability is required. For the prediction of a multiva- each trait can be described by the single trait breeder’s equa- riate evolutionary response it is necessary to have esti- tion (Figure 3). The multivariate breeder’s equation for two mates of genetic variances and covariances, as well as se- traits can be written by using the ordinary algebra as lection gradients. Empirical results from selection of a single trait imply that the breeder’s equation correctly b b Rx = VAx x + covAxy y predicts the response to selection. In contrast, the re- R = V b + cov b (19) sponses to selection on multiple traits are less consistent y Ay y Axy x with the current theory. A possible explanation for this is The selection gradient b for a particular trait is that the multivariate model assumes multivariate nor- mality which is often biologically inconsistent (9). Here VS− cov S b = Py x Pxy y we will briefly consider some examples of evolution in x VV − cov 2 Px Py Pxy (20) natural populations, as well as experimental evolution VS− cov S driven by artificial selection on quantitative traits. b = Px y Pxy x y − 2 VPxPyV cov Pxy We must again emphasize that selection diferential S measures the net effect of selection (all factors, i. e. direct and indirect), whereas the selection gradient b measures only the direct effect of selection on particular trait. In or- der to estimate b, we need to have estimates of selection diferentials (Sx and Sy), phenotypic variances (VPx and VPy), and phenotypic covariance covPxy. Natural selection can be visualized by individual fitness surface (selection surface) which represents the relationship between phe- notypic values and corresponding individual fitness (16). Hypothetical individual fitness surface for two traits is presented in Figure 4. The empirical shape of the fitness surface, i. e. local average slopes (linear or directional se- lection) and local average curvatures (nonlinear selec- tion – stabilizing or disruptive selection) can be studied Figure 4. Hypothetical individual fitness surface – The individual by the multiple regression approach in order to estimate fitness value in a population is a function of the phenotypic values for partial regression coefficients (selection gradients) (16). traits x and y.

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Evolution by natural selection selection differential for bill length was weak and nega- tive (statistically not significant) whereas the selection Lande and Arnold studied the effects of natural selec- gradient was strong and positive. The selection differen- tion on morphological characters in the pentatomid bug tial for bill length can be explained by correlated selec- Euschistus variolarius. They collected 94 bugs after a storm tion on other traits which had small and weak selection (39 survived and 55 died), and measured four morpho- gradients. The Grants also predicted the evolutionary re- logical traits on all bugs (15). From this data they esti- sponses for several traits (equation 16 or 19), and com- mated phenotypic variances and covariances according pared them with the observed responses (difference be- to equation 2. The selection differential for a particular tween the mean phenotypic value of the progeny of trait was estimated as the difference between the mean surviving birds and the mean phenotypic value before se- trait value of surviving bugs and the mean trait value of lection). The observed selection responses were in a good all bugs, whereas the selection gradient was estimated ac- agreement with theoretical predictions, especially for the cording to equation 20. They found no significant chan- first period (1976 – 1977) when natural selection was ge in thorax width as indicated by selection differential. stronger (18). In contrast, the selection gradient indicated strong direct selection for increased thorax width. The thorax width is highly positively correlated with another trait, wing length. Evolution by artificial selection The selection gradient for wing length indicated strong Finally, I will mention two examples of experimental direct selection for decreased wing length. Therefore, the phenotypic evolution driven by artificial selection. One is absence of significant change in thorax width represents the artificial evolution of behaviour in silver fox, a rare the net effect of a strong direct increase in thorax width naturally occuring variety of red fox Vulpes vulpes, and the and strong indirect (correlated) decrease in thorax width other is artificial evolution of oil content in the maize Zea due to strong direct selection for decreased wing length. mays. Selection experiments with silver foxes started in They also noticed that scutellum length decreased signif- 1959, and were led by the Russian geneticist Dmitry K. icantly as indicated by the selection differential while the Belyaev. In addition to its scientific purpose, the experi- selection gradient was not significant. From this, it can be ment was also motivated by the Russian fur industry concluded that a significant selection differential on scu- which prefered tame animals for routine handling. The- tellum lenght is a consequence of the correlated selection refore the target for selection was tameness, a behavioral on scutellum length due to direct selection on other traits trait which is normally present in juvenile animals. In (thorax width and/or wing length). the original (wild) population, the vast majority of foxes Grant and Grant studied the evolution of morpholog- showed fear and/or aggresion towards humans, whereas ical traits in Darwin’s medium ground finch Geospiza a small fraction was more or less tolerant to humans. fortis from the Galapagos islands (17, 18). Several species These tolerant animals were selected as parents of the of Galapagos finches have been the research subject of next generation. The fox behaviour was quantified ac- cording to the degree of tameness, and in each genera- Charles Darwin, and inspired him in the development of tion animals with higher degree of tameness were further his ideas about evolutionary change. The normal diet of selected. The response to selection was very strong in the Galapagos finches includes various types of seeds, from early generations, and after several decades of selection large hard seeds to small soft seeds. The type of available the end product was a unique population of domesti- seeds is determined by the amount of rainfall. The ability cated foxes which are fundamentally different in many of a bird to handle a particular type of seed depends on aspects from their wild counterparts. They are devoted, the characteristics of its bill. Two periods, 1976 – 1977 affectionate and capable of forming strong social bonds and 1984 – 1986, were characterized by a severe drought, with people. In addition to large changes in behaviour, and the Grants measured the survival of G. fortis over the domesticated foxes evolved many new characteristics, these periods. During the first period only 15% of birds i. e. piebald coat colour, rolled tails, floppy ears and survived, and the survivors were larger birds. During the changes in reproductive cycle accompanied by changes second period 32% of birds survived, and the surviving in hormone levels. It follows that direct selection on a be- birds were slightly smaller. The selection differential for havioural trait caused correlated selection responses to body weight during the first period was larger than the various morphological, physiological and life history traits selection gradient, although both tended to increase body (19, 20). The fur industry was not satisfied with these re- weight. The difference between them represents the stren- sults since the fur of domesticated foxes was in many as- ght of correlated selection since body weight is positively pects different from the fur of wild silver foxes, and was correlated with several other morphological traits which commercially useless. Most important were the scientific were directly selected to increase their phenotypic values. implications of the results. This experiment showed that Interesting behaviour during the first period showed se- the process of domestication could be much faster than lection for bill width. The selection differential tended to was previously thought with obvious implications to the increase the bill width whereas the selection gradient domestication of dogs. tended to decrease it. This can be explained by correlated selection which tended to increase bill width due to di- The last example includes the selection of a trait of ag- rect selection on other morphological traits (bill depth, ronomic importance, the oil content in maize kernels. wing length and weight). During the second period the This experiment started in 1896, and is the longest selec-

Period biol, Vol 112, No 4, 2010. 401 K. Br~i}-Kosti} Quantitative Genetics and Evolution tion experiment ever performed. Geneticists from the bioinformatics etc.). Simultaneous achievements in all of University of Illinois continuously selected maize to these approaches paint a more complete picture of reality. change its oil content in both directions, i. e. to increase Acknowledgments: I thank Ignacija Vla{i} and Vedran and to decrease the oil level in kernels. The base popula- Dunjko for the help in preparation of figures. This work is tion had about 5% oil in the kernel, and after more than a supported by the Croatian Ministry of Science, Education century of selection the high oil-producing line has and Sport (grant 098-0982913-2867). about 20% of oil in kernels, whereas the low oil-produc- ing line has almost none (21). Subsequent analysis has shown that the number of quantitative trait loci (QTLs) REFERENCES which can account for half of the divergence between 1. 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