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Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology
Author: Rodney Mauricio
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"370 | MAY 2001 | VOLUME 2 www.nature.com/reviews/genetics REVIEWS The world has recently secured the first complete genetic blueprint of a plant. The mouse-ear cress, Arabidopsis thaliana, a small mustard plant, has recently joined the increasingly less exclusive club of organisms for which every gene has been sequenced 1?6 . In the next three years, the complete sequence of a plant of worldwide agricultural impor- tance, rice (Oryza sativa), will be available 7 . Undoubtedly, the dawning of the genomics age will have a great impact on agriculture, human health and molecular genetics. This wealth of genetic informa- tion is beginning to have just as profound an impact on the study of evolutionary biology, particularly on understanding one of the most enduring problems in evolution and molecular biology ? the genetic basis of complex traits. The complexity of these phenotypic traits, particularly of those involved in adaptation, probably arises from segregation of alleles at many interacting loci (quantitative trait loci, or QTL), the effects of which are sensitive to the environment 8?10 .Recent and continuing advances in molecular genetics and statistical techniques make it possible to identify the chromosomal regions where these QTL are located. Ultimately, an understanding of adaptive evolution will require detailed know- ledge of the genetic changes that accompany evolutionary change. A needle in a haystack Modern quantitative genetics was born from the fusion of Mendelism and biometry, the mathematical theory that surrounds the science of heredity (BOX 1). The conceptual basis for the genetic dissection of com- plex traits is relatively straightforward. At its most basic level, QTL mapping simply involves finding an association between a genetic marker and a phenotype that one can measure (FIG. 1). For example, if all the tall plants among 500 individual corn plants of varying height have a particular allele of a genetic marker, then there is a very high probability that a QTL for plant tallness is associated with this marker in this popula- tion of plants. QTL mapping in both plants and animals involves just a few basic steps 11 . The primary requirement is for two parental strains that have differences between them in the alleles that affect variation in a trait. The parents need not be different in the mean phenotypic value of the trait as different allelic combinations can yield the same phenotypic mean. A polymorphic genetic map allows the two strains to be distinguished genetically. The more detailed the linkage map (that is, the greater the number of markers), the better the mapping resolution. The parental alleles are then shuf- fled by creating a large mapping population, in which the phenotype and the multilocus genotype of each individual are measured. MAPPING QUANTITATIVE TRAIT LOCI IN PLANTS: USES AND CAVEATS FOR EVOLUTIONARY BIOLOGY Rodney Mauricio Gregor Mendel was either clever or lucky enough to study traits of simple inheritance in his pea plants; however, many plant characters of interest to modern geneticists are decidedly complex. Understanding the genetic basis of such complex, or quantitative, traits requires a combination of modern molecular genetic techniques and powerful statistical methods. These approaches have begun to give us insight into understanding the evolution of complex traits both in crops and in wild plants. Department of Genetics, Life Sciences Building, University of Georgia, Athens, Georgia 30602- 7223, USA. e-mail: mauricio@uga.edu � 2001 Macmillan Magazines Ltd NATURE REVIEWS | GENETICS VOLUME 2 | MAY 2001 | 371 REVIEWS In practice, several crossing schemes are used to generate this mapping population 12 . In all of these, the parents are mated to generate an F 1 population. In one approach, recombinant inbred lines can be created by selfing (self-fertilizing) each of the F 1 progeny for several (usually eight) generations (FIG. 1) 13 . In an ?F 2 design?, the mapping population is gen- erated by mating the F 1 progeny to each other (FIG. 2). In a ?backcross design?, the mapping population is generated by crossing the F 1 progeny to either, or both, of the parents (FIG. 3). Several variations on these crossing schemes have been designed to maxi- mize the shuffling of parental alleles 12,14 . Once all individuals in the mapping population are scored for phenotype and multilocus genotype, the actual QTL mapping can begin. The statistical tools at the foun- dations of QTL mapping have been used for many years (BOX 2). In fact, in 1923, Karl Sax mapped a QTL for seed size in the bean, Phaseolus vulgaris, by statis- tically associating it with a Mendelian locus for seed pigmentation 15 . Today, we generally have much more detailed genetic maps available. For example, Arabidopsis thaliana has 1,262 genetic markers, which consist of restriction frag- ment length polymorphisms (RFLPs) and single nucleotide polymorphisms (SNPs) that vary between the two parental lines of the most commonly used set of recombinant inbred lines (FIG. 1) 16,17 . Cereon Genomics has identified and made available a collection of 28,117 SNPs, which include 15,674 insertion/deletion poly- morphisms that are polymorphic between the two par- ents of those same recombinant inbred lines. Ultimately, QTL analysis yields a statistical descrip- tion of the genes that underlie the phenotypes of interest. The ?statistical fog? is not completely lifted, but we can see the shadows of those genes. Box 1 | Quantitative genetics reborn ?? evolution is essentially a statistical problem,? W. F. R. Weldon (1893) The early history of evolutionary genetics focused on understanding complex traits, particularly those relating to humans, such as intelligence, temper and ?artistic faculty? 99 . Without the benefit of Mendel?s ideas, Francis Galton and the mathematician, Karl Pearson, established that useful predictions of the evolutionary trajectory of complex traits could be made without recourse to an explicit understanding of inheritance 99 . This area of ?quantitative genetics? rested on the statistical properties of the MULTIVARIATE NORMAL DISTRIBUTION 8,18 . With the rediscovery of Mendel?s theory of heredity in 1900, a conflict arose between this ?biometrical? school of quantitative genetics and the discrete genetics of the Mendelian school 99 . By 1910, it had been shown that continuous phenotypic variation could result from the action of the environment on the segregation of many Mendelian loci 99,100 . By 1918, Ronald Fisher convincingly reconciled the discrete inheritance of Mendelism with the biometrical approach 101 . Modern evolutionary quantitative genetics is largely based on the same statistical foundations that were laid by Pearson and Fisher 102?108 . For most of the twentieth century, quantitative genetics had a crucial role in both agriculture and evolutionary biology, but never seemed fully embraced by modern molecular genetics. There was (and perhaps still is) a widespread perception that quantitative genetics essentially ignored genetics, blanketing actual genes in what has been called a ?statistical fog? 109 . Mapping quantitative trait loci (QTL) ? the single or many genes that underlie quantitative phenotypes ? might represent an important part of the final synthesis of molecular and quantitative genetics. By working from the phenotype to the genotype, QTL mapping uses statistical techniques to localize chromosomal regions that might contain genes contributing to phenotypic variation in a complex trait of interest. Working from the gene to the phenotype, molecular geneticists might be able to meet quantitative geneticists at some genetic Promontory Point. F 1 a b c F 8 Parent 1 Parent 2 � � F 1 . . . Figure 1 | Principles of mapping quantitative trait loci. The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here for a | the density of hairs (trichomes) that occur on a plant leaf. Inbred parents that differ in the density of trichomes are crossed to form an F 1 population with intermediate trichome density. b | An F 1 individual is selfed to form a population of F 2 individuals. c | Each F 2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). Each RIL is homozygous for a section of a parental chromosome. The RILs are scored for several genetic markers, as well as for the trichome density phenotype. In c, the arrow marks a section of chromosome that derives from the parent with low trichome density. The leaves of all individuals that have inherited that section of chromosome from the parent with low trichome density also have low trichome density, indicating that this chromosomal region probably contains a QTL for this trait. MULTIVARIATE NORMAL DISTRIBUTION The central limit theorem assures a normal (bell-shaped) distribution for a variable that is the summation of many independent, random inputs. This applies to single or multiple variables. � 2001 Macmillan Magazines Ltd 372 | MAY 2001 | VOLUME 2 www.nature.com/reviews/genetics REVIEWS a ?straw man? ? these assumptions cannot be entirely correct as there are only a finite number of genes available in a genome. (Conversely, one of the most important traits to an evolutionary biologist is ?fitness? ? a very complex trait that is almost certainly con- trolled by many genes). On a practical level, once five or more loci contribute to a trait, it might be easier to model the evolution (or perhaps even function) of that trait with a mathematical abstraction rather than attempting to explicitly take into account each indi- vidual locus. Nevertheless, QTL mapping might indi- cate to evolutionary biologists the direction in which to proceed. Genetics of adaptation. One of the most enduring con- troversies in evolutionary biology is the genetic basis of adaptation 19,20 . R. A. Fisher concluded that mutations in genes of very small effect were responsible for adaptive evolution 21 . H. Allen Orr and Jerry Coyne stated that ?? the neo-Darwinian view has ? triumphed, and the genetic basis of adaptation now receives little atten- tion. Indeed, the question is considered so dead that few know the evidence responsible for its demise? 20 . Orr and Coyne re-examined the evidence for this Fisherian view and argued that both the theoretical and empirical basis for it were weak 20 . They encouraged evolutionary biologists to re-examine this research question by the ?genetic analysis of adaptive differences between natural populations or species? 20 . Plant evolutionary biologists have embraced this challenge and there are many examples of the use of QTL analysis to determine the genetic basis of traits that are thought to be of adaptive value. Several QTL have been identified for seed size, fruit size and seed number in A. thaliana 22 . Seed size is a very important adaptive trait in that large seeds often have higher fitness than small seeds 23 . Variation in seed size in A. thaliana was found to be determined by 11 QTL with relatively small additive effects. Seven of the seed size QTL localized to the same position as seed weight QTL, indicating possi- ble pleiotropic actions of the underlying genes. So, QTL analysis can provide information about the mode of gene action. QTL analysis has also been used to understand the genetic correlations among traits in natural popula- tions. Floral traits, such as petal length and width, have long been used as an example of positively, genetically correlated traits 24 . In a study of floral mor- phology in A. thaliana, 18 QTL were found 25 . As in the study of seed size, 11 floral trait QTL were associ- ated with more than one floral trait. So, the tight morphological integration of the flower is possibly due to pleiotropic gene action, or to tight linkage of loci 25 . The timing of flowering is another trait of impor- tance to plants, as earlier reproduction often translates into higher fitness 26 . Three research groups have inde- pendently examined flowering time in A. thaliana using three unique crosses. A cross between the late- flowering strain, HM, and the early-flowering strain, WS, revealed that two unlinked QTL affect flowering Genetics of evolution QTL mapping is beginning to be embraced by evolu- tionary biologists as a means to answer various basic questions. Heritable phenotypic variation is the raw material of evolution, producing adaptations and organismal diversity. A comprehensive understand- ing of these evolutionary mechanisms will be enhanced by elucidating the molecular genetic basis of quantitative traits. Several important evolutionary models are based on the key assumptions of quantitative genetics. These models assume that the complex phenotype of a trait is caused by the simultaneous segregation of a very large number of genes, each of which has a small, additive effect on the phenotype and interacts with other genes and with the environment 18 . QTL analysis allows us to test these assumptions. At one level, this is abc def ghi jkl Stigma (inserted)Corolla (open) Anthers (inserted) Nectar guides (brushy) Nectar guides (flat) Anthers (exserted) Corolla (tubular) Stigma (exserted) Figure 2 | F 2 design: genetic mapping in monkeyflowers. a | Mimulus lewisii and c | Mimulus cardinalis were crossed to produce b | a fertile F 1 progeny. The F 1 was self- pollinated to produce d?l | various F 2 progeny. The parent species differ in floral characteristics, including petal colour, COROLLA size and shape, presence or absence of NECTAR GUIDES, nectar volume, and concentration and position of ANTHERS and STIGMA. These floral characteristics are important in maintaining pollinator-induced reproductive isolation between these two species in the wild. (Adapted with permission from REF. 40 � (1999) National Academy of Sciences, USA.) COROLLA Collectively, the petals of a flower. NECTAR GUIDES Markings on the petals of flowers, often in contrasting colours or visible only in ultraviolet wavelengths, thought to act as directional beacons for pollinators, especially bees. ANTHER The pollen-bearing part of the male floral structure (stamen). � 2001 Macmillan Magazines Ltd NATURE REVIEWS | GENETICS VOLUME 2 | MAY 2001 | 373 REVIEWS Genetics of speciation. Extending QTL analysis to other plant systems is particularly important in addressing a second fundamental question in evolutionary biology: Are the genes that are variable in a population the same as those that cause divergence between populations and species? 35 As in the case of adaptive traits, there has been some controversy as to the role of the principal genes in speciation 36,37 . Recent QTL analyses use crosses between (necessarily) closely related species. By creating such hybrids, or by looking in natural HYBRID ZONES, the origin of species can be investigated at the genetic level. Toby Bradshaw, Douglas Schemske and their col- leagues pioneered this approach with their study of spe- ciation in monkeyflowers (Mimulus) 38?40 . They crossed two species with contrasting floral traits. Mimulus lewisii is a bumble-bee-pollinated flower with pink petals, contrasting yellow nectar guides, a wide corolla opening, concentrated nectar, and inserted anthers and stigma (FIG. 2a). Mimulus cardinalis is pollinated by hum- mingbirds and has red petals, a narrow, tubular corolla, copious nectar and exserted anthers and stigma (FIG. 2c) 38 . The two species grow and flower together, but hybrids are not commonly observed in nature. However, they are completely interfertile when artificial- ly mated (FIG. 2b). So, these two species are reproductive- ly isolated owing to different preferences of the pollina- time on chromosome 5 (REF. 27). In a cross between the Columbia and Landsberg strains, two QTL, one on chromosome 1 and another on chromosome 2, were found 28 . Finally, a cross between the early-flowering strain, Li-5, and the late-flowering strain, Naantali, revealed at least seven QTL, with one QTL on chromo- some 4 accounting for over 50% of the variation in flowering time 29 . The other ?minor? QTL were found on each of the five chromosomes of A. thaliana 29 . As this example illustrates, the QTL found for any particular character might be different depending on the parents used in the original cross. Although A. thaliana has been a popular plant for studying the genetics of adaptation using QTL analysis, the power of the approach is best illustrated by consid- ering studies of plants that are decidedly not annual weeds. QTL analysis has been successfully applied to studying the genetic basis of various important traits in forest trees, including pine, eucalyptus and poplar. QTL have been mapped for seedling height, leaf area and frost tolerance in the high altitude Eucalyptus nitens 30,31 and for frost hardiness and timing of bud set in natural populations of Scots pine (Pinus sylvestris) 32 . In studies of poplars, cottonwoods and aspens (Populus spp.), researchers have found QTL for bud set, bud flush, growth, form, PHENOLOGY and leaf shape 33,34 . STIGMA The region, usually the apex, of the gynoecium that receives pollen grains and on which the pollen germinates. The gynoecium is the seed-bearing organ of flowering plants, consisting of the stigma, style and ovary. STYLAR TUBE In the genus Iris, the styles (tubular columns of tissue arising from the top of the ovary) look like flower petals and are tightly appressed to the top of the actual petals, forming this tube between the petal and the style. PHENOLOGY The timing of periodic biological phenomena that are usually correlated with climactic conditions. HYBRID ZONE A region of reproduction between individuals of different species, usually occurring where the ranges of the species come together. ab cd Figure 3 | Backcross design: genetic mapping in the Louisiana irises. a | Iris fulva (swamp iris) and d | Iris brevicaulis (blue iris) were crossed to produce an F 1 . An F 1 backcrossed to Iris brevicaulis produced hybrids such as b |, and an F 1 backcrossed to Iris fulva produced hybrids such as c |. The parental species differ in floral characteristics, including petal and sepal colour, presence or absence of nectar guides, degree of petal reflexivity (petals of I. fulva curl down and inward, whereas those of I. brevicaulis angle upward), and anther position (the anthers of I. fulva extend out of the STYLAR TUBE, those of I. brevicaulis are inside the stylar tube). Backcross genotypes show a wide range of variation in these traits. These floral characteristics, as well as others involved in the reproductive isolation of these species, are now being analysed using quantitative trait loci mapping methods. (Courtesy of Amy Bouck, Department of Genetics, University of Georgia, USA.) � 2001 Macmillan Magazines Ltd 374 | MAY 2001 | VOLUME 2 www.nature.com/reviews/genetics REVIEWS Box 2 | Quantitative trait loci mapping methods Several techniques exist for mapping quantitative trait loci (QTL) in the population; I have used the example of trichome density in leaves described in the figure to illustrate these methods. a | In the regression technique, the phenotype is correlated with each marker genotype 110 (the middle panel represents the differential migration of DNA on a gel). In this case, a single marker ?A? is scored. Individuals that are homozygous for the A allele have high trichome density, individuals that are homozygous for the ?a? allele have low trichome density, and heterozygotes have intermediate trichome density. A linear regression of trichome density on the number of A alleles shows a significant relationship between the marker and the phenotype, which indicates that a QTL for trichome density is probably linked to that marker. The simple regression method described is of limited use in localizing the chromosomal segment that contains a QTL. The method underestimates the effect of the QTL. The further the QTL is from the marker, the weaker the effect. The interval mapping method uses a pair or two pairs of flanking markers at a time 83,111 . b | In this approach, the QTL is located within a chromosomal interval, defined by the flanking markers. The technique involves scoring a large number of markers, as illustrated in the top panel, and then assessing the probability that an interval between two markers is associated with a QTL that affects the trait of interest. The results of the analysis are plotted as a LIKELIHOOD-RATIO TEST STATISTIC against the chromosomal map position, measured in recombination units (Morgans). The dotted line represents a significance threshold above which a likelihood-ratio test provides a statistically significant fit to a model of the data. The best estimate of the location of the QTL is given by the chromosomal location that corresponds to the highest significant likelihood ratio. Although the interval mapping method was an important advance, it too is statistically biased. In particular, QTL outside the interval under consideration can affect the ability to find a QTL within it 112 .In addition, false identification of a QTL can arise if other QTL are linked to the interval of interest (the false ?ghost peak? on the right) 112 . c | A third method, known as composite interval mapping, combines the interval mapping technique with multiple regression analysis 81,85,112 . Composite interval mapping assesses the probability that an interval between two markers is associated with a QTL that affects the trait of interest, as well as controlling for the effects of other markers on the trait (thus providing more accurate results). The results of the analysis are plotted as in b. In this method, the test statistic is independent of QTL in other regions of the chromosomes (although it is still biased if the QTL is in the interval immediately adjacent to the interval of interest) 85 . Zhao-Bang Zeng and his colleagues have extended this method to a multiple interval mapping technique. Multiple interval mapping combines multiple QTL mapping analysis with the analysis of genetic architecture by using an algorithm to search for number, positions, effects and interactions of significant QTL 113,114 . Several computer programs that carry out these mapping methods are freely available 12,115 . Unfortunately, most of these programs lack an easy-to-use interface and are not of commercial quality, something desperately needed for most users. 1 4 3 2 6 5 8 7 9 a 123456789 AA AA aa Aa Aa AA Aa aa aa A a AA Aa Genotype aa T richome density Z 123456789 EG T e st statistic A B C D F ?z ?A ?b ?c ?D ?e ?F ?G ?z ?A ?B ?C ?d ?e ?f ?G ?z ?a ?B ?C ?D ?E ?F ?G ?z ?A/a ?B ?c ?d ?e ?F ?G ?Z ?A/a ?b ?c ?D ?E ?f ?g ?z ?A ?B ?C ?D ?E ?f ?g ?D ?E ?F ?G ?z ?A/a ?b ?C ?z ?a ?B ?C ?d ?E ?F ?g ?Z ?a ?b ?c ?d ?e ?f ?G ZE Map position G T est statistic A B C D F b c Ghost peak LIKELIHOOD-RATIO TEST STATISTIC A maximum-likelihood method of hypothesis testing. The likelihood-ratio test statistic is twice the natural logarithm of the ratio of the maximum likelihood that the data fit the alternative hypothesis to the maximum likelihood that the data fit the null hypothesis. � 2001 Macmillan Magazines Ltd NATURE REVIEWS | GENETICS VOLUME 2 | MAY 2001 | 375 REVIEWS studying the common weed sunflower, Helianthus annuus, which is postulated to have colonized Texas by acquiring advantageous alleles from the locally adapted, Helianthus debilis 45?47 . Michael Arnold and his col- leagues are studying hybrids of a cross of Iris brevicaulis (FIG. 3d), which prefers dry, sunny habitats, and Iris fulva (FIG. 3a), which prefers wet, shady habitats 43,48 .By map- ping QTL in the parents that control the ecological traits that are responsible for habitat preference and by exam- ining hybrids, it might be possible to show explicitly that INTROGRESSIVE HYBRIDIZATION is an important evolutionary mechanism in plants. Field of dreams QTL mapping has enormous practical potential for agriculture. Although classical quantitative genetics and selective breeding have been enormously successful in agriculture, the process of breeding a crop plant with a particular desirable trait is a combination of luck, hard work, time and money. A detailed knowledge of the genes that affect traits such as yield, DEHISCENCE and resistance, could facilitate and speed the development of improved crops 49,50 . Gardeners accustomed to planting varieties of tomato know that they can have extraordinarily vari- able fruit sizes, ranging from a weight of a few grams to as much as half a kilogram (FIG. 4a). Examples of these varieties are Burpee?s ?Big Boy?, ?Big Girl? and ?Watermelon Beefsteak? at one extreme, and the many ?grape?, ?currant? and ?cherry? varieties at the other extreme. Steven Tanksley and his colleagues used seven wild species of tomato and seven different crossing designs to locate QTL for fruit size and weight 51 .This work, starting in the 1980s has identified at least 28 different QTL for fruit weight. Some of these QTL have a significant effect on the phenotype, explaining over 20% of the phenotypic variance 51 . Over the past ten years, finer-scale mapping tech- niques were used to localize one of these important QTL, fw2.2, to a narrow chromosomal region 52,53 . Recently, the statistical fog was pierced by the cloning of the fw2.2 QTL 54 . The fw2.2 QTL corresponds to a single open read- ing frame in a transformed cosmid (called ORFX) that is expressed in the floral organs. When the wild-type allele of the gene was introduced into a cultivated tomato, the transformed plants produced small fruit of roughly the expected weight (FIG. 4b). This transformation experiment marks the movement of QTL mapping beyond a purely statistical association between a gene and the phenotype. Beyond the obvious importance of QTL mapping for agriculture, some of the most striking examples of the use of QTL analysis to answer fundamental evolu- tionary questions come from the study of agricultural species. An evolutionary approach to QTL mapping has allowed new and powerful insights into the under- standing of plant domestication. The work of John Doebley and colleagues on the evolution of maize (Zea mays ssp. mays) from its probable wild ancestor, teosinte (Zea mays ssp. parviglumis), is a landmark in the field of evolutionary QTL analysis 55?58 . For the past decade, QTL that are responsible for key steps in the tors. QTL for the floral traits that distinguish the mon- keyflower species (and, presumably, pollinator prefer- ence) are therefore candidates for ?speciation? genes 38 . For each of eight floral traits studied, Bradshaw and col- leagues found at least one QTL accounting for more than 25% of the phenotypic VARIANCE in floral morphol- ogy, and concluded that the evolution of reproductive isolation might involve genes of major effect 38 . It is still too early to conclude that ?speciation? genes will be com- monly found in studies of reproductive isolation. Several similar studies are being conducted, including work on another pair of SYMPATRIC Mimulus species, M. guttatus and M. nasutus 41 , on the natural hybrids of Louisiana irises (FIG. 3) 42,43 and on the floral nectar spurs of sympatric columbines 44 . A final example of the use of QTL analysis in study- ing MACROEVOLUTION is found in continuing work on sun- flowers 45?47 and the Louisiana irises (FIG. 3) 42,43,48 . In both systems, natural hybrid populations exist in which the parents are adapted to different habitats. It has been suggested that hybrids can serve as a genetic bridge between isolated species, shuttling adaptive genes across the hybrid zone. Loren Rieseberg and his colleagues are VARIANCE A statistic that quantifies the dispersion of data about the mean. In quantitative genetics, the phenotypic variance (Vp) is the observed variation of a trait in a population. Vp can be partitioned into components, owing to genetic variance (Vg), environmental variance (Ve) and gene-by-environment correlations and interactions. SYMPATRIC Occurring in the same area without loss of identity from interbreeding. MACROEVOLUTION Evolution at or above the level of species. INTROGRESSIVE HYBRIDIZATION Incorporation of genes from one species into the gene pool of another species. DEHISCENCE The splitting open of a fruit. a b Figure 4 | Genetic basis of phenotypic variation in fruit size in the tomato. a | Fruits from the wild tomato species Lycopersicon pimpinellifolium (right) and the cultivated tomato ?giant red? (Lycopersicon esculentum). Reproduced with permission from Science 289, 85?88 � (2000) The American association for the Advancement of Science. b | Phenotypic effect of a cosmid that contains the small-fruit allele of the fw2.2 transgene in the Mogeor cultivar of L. esculentum. The fruit on the left is a tomato from the Mogeor line. When the same line carries a partially recessive large- fruit allele of fw2.2 on a transgene, the weight of the fruit is reduced (right) on average by 15.5 g (REF. 54). (Courtesy of Steven Tanksley, Department of Plant Pathology, Cornell University, USA.) � 2001 Macmillan Magazines Ltd 376 | MAY 2001 | VOLUME 2 www.nature.com/reviews/genetics REVIEWS Finally, QTL analysis has been brought to bear on a biological problem that has stymied geneticists and evolutionary biologists for many years. Many flower- ing plant genomes are thought to be the product of one or more polyploidization events 64,65 .As such, many of the world?s crop plants are polyploids, result- ing either from complete duplication of their own genomes or from interbreeding with another species with a failure of complete disjunction at meiosis 66 . One of the most interesting applications of QTL mapping to evolutionary biology is the exploration of the join- ing of these genomes 67 . Andrew Paterson and his colleagues have been exploring such a polyploid event in species of domes- ticated cotton (Gossypium) 68,69 . Present-day varieties of cultivated cotton are tetraploid, but are derived from two distinct diploid parental species 68 . The most important trait to a cotton farmer is the length and quality of the seed fibre. Interestingly, the QTL that contribute to fibre quality in domesticated cotton derive from the diploid parent species that possesses no spinnable fibre on its seeds. This indicates a possi- ble non-additive interaction between the two parental genomes with respect to seed fibre quality (BOX 3). This application of QTL analysis shows that the merger of genomes of divergent evolutionary histories can pro- duce ?unique avenues? for selection 68,69 . A step off the bandwagon The use of QTL mapping represents a rebirth for quantitative genetics. Quantitative geneticists have been successful at both developing robust evolution- ary theory and providing practical benefits to agricul- ture, despite treating genes as a black box. QTL map- ping represents a promising link between this statistical approach and an explicit understanding of the molecular basis of variation in complex traits. Although I have highlighted examples of the power and promise of QTL mapping for evolutionary biolo- gy, there are numerous and important caveats to keep in mind. Maps and markers. Genetic maps are time consum- ing and expensive to construct. Despite this, the number of markers is rarely the limiting factor in QTL mapping experiments; the number of progeny examined is, however, crucial 70,71 . Each progeny rep- resents an opportunity to identify a unique recombi- nation event between markers. Any QTL that exists between two completely linked markers will segregate with both markers and will be indistinguishable from either. Because recombination rates vary across the genome 72 , some QTL will be harder or easier to detect depending on their genetic location. For example, the centromeric regions, which are known to be sup- pressed for recombination 72 and to be poor for genet- ic variation 73 , are a veritable black hole for QTL map- ping: even two markers that are physically far apart will seem to be genetically close together near the centromere. Localization of a gene in these regions can be difficult. evolution of maize have been identified 59 . These include QTL for the distinct change in branching from the bushy, multi-stemmed teosinte to the single- stemmed maize varieties used in agriculture today (FIG. 5), and QTL for the change in fruit architecture between teosinte, with its encased kernels, to corn with its kernels exposed on the ear 58,60 . The actual gene that underlies the distinct morphological change in branching pattern, called teosinte branched 1, has been identified (FIG. 5) 61 . Furthermore, teosinte branched 1 has been subjected to a molecular population genetic analysis to understand the evolutionary dynamics of the locus 62 . An evolutionary approach to QTL mapping can extend beyond single species, yielding more insight into the evolution of plant domestication. Diverse taxa in common taxonomic groups often share gene order over large chromosomal segments.?Comparative QTL map- ping? is possible because chromosomes of these different taxa can be aligned on the basis of common reference loci. The grass species sorghum, rice and maize, were each independently domesticated ~10,000 years ago. Each species has been selected to have large seeds, daylength-insensitive flowering and reduced fruit shat- tering. A small number of QTL were located for these traits 63 . Interestingly, the approximate location of the QTL for each trait mapped to roughly corresponding locations in each of the three species, despite 65 million years of reproductive isolation between these species 63 . This conservation of QTL location indicates that, 10,000 years ago, ancient farmers across three continents might have been independently selecting many of the same genes to obtain convergent phenotypes in each of the three species 63 . abc Figure 5 | The evolution of apical dominance in maize (Zea mays). a | The maize crops that are cultivated today are probably a domesticated form of the wild Mexican grass, teosinte. Note the bushy form of the teosinte, Zea mays ssp. mexicana, shown here. b | The single-stalk branching pattern of wild-type maize (Inbred A158). c | A maize plant that is mutant for the teosinte branched 1 (tb1) gene. The tb1 locus is likely to have had an important role in the evolution of maize plant architecture. (Reprinted with permission from REF. 61 � (1997) Macmillan Magazines Ltd.) � 2001 Macmillan Magazines Ltd NATURE REVIEWS | GENETICS VOLUME 2 | MAY 2001 | 377 REVIEWS Box 3 | Proposed formation of cultivated polyploid cotton (Gossypium) Most of the world?s cotton crop is made up of two tetraploid (2n = 4x = 52) species, Gossypium hirsutum (?Upland? cotton) and Gossypium barbadense (?Pima?, ?Sea Island? or ?Egyptian? cotton) 68 . These species have been cultivated to produce long, spinnable fibres on their seeds: in each panel, the seed, with fibre removed, is shown on the left and the fibre from that one seed, if any, is shown on the right. In the figure, a | both tetraploid species are thought to have arisen by the hybridization of two diploid ancestors: a maternal Old World diploid species (denoted the ?A? genome) and a paternal New World diploid species (called the ?D? genome) 68 .So, the resulting tetraploid has an ?AD? genome. The possible ?D? genome parents of the tetraploid are two, extant, neotropical species, Gossypium raimondii or a sister species of Gossypium gossypioides (2n = 26). The possible ?A? genome ancestors are two extant Old World species, Gossypium arboreum or Gossypium herbaceum (2n = 26). Interestingly, when one looks at the most distinctive and important trait to a cotton farmer, fibre, in both wild and domesticated cotton species, only the ?A? genome diploid and the ?AD? tetraploid taxa produce seeds that are covered in long, spinnable fibres (a) 68 . In Asia, a domesticated ?A? genome diploid is still bred and cultivated for its fibre 68 . However, although wild ?D? genome diploid species produce hairy seeds, none produce spinnable fibre and none has ever been successfully domesticated for fibre production 68 . Of course, humans have successfully domesticated and selected ?AD? tetraploids for high yield and quality of fibre. Therefore, the interaction of the ?A? and ?D? genomes in the tetraploid domesticated species produces higher quality and higher quantity fibre than is found in either of the diploid ancestors, even the currently domesticated ?A? genome diploids. In b, an F 2 mapping population was created that was derived from a cross of the two species of cultivated ?AD? tetraploids for which they had developed detailed genetic maps 116 . These two species have very different seed fibres, and quantitative trait loci (QTL) that distinguished the parental types for various fibre characteristics were sought 68 . Remarkably, most of the QTL that influenced fibre quality and yield were located on the portion of the genome contributed by the ?D? genome (hypothetical data illustrated) 68 . Remember that the ?D? portion of the genome is derived from an ancestor that has no spinnable fibre. So, most of the genetic variation available for improvement of cultivated cotton fibres apparently comes from the parent without fibre. Perhaps thousands of years of selection on ?A? genome diploids exhausted genetic variation for fibre quality, but that selection was absent from the ?D? genome diploids because their seed fibres were not desirable to ancient farmers. (Images courtesy of Andrew Paterson, Applied Genetic Technology Center, Departments of Crop and Soil Sciences, Botany, and Genetics, University of Georgia, USA, and Thea Wilkins, Department of Agronomy and Range Science, University of California, Davis, USA.) Gossypium herbaceum Gossypium hirsutum Gossypium raimondii 2n = 26 'A' diploid genome 'D' diploid genome 2n = 26 2n = 4x = 52 'AD' tetraploid X a Gossypium hirsutum Gossypium barbadense A AD tetraploid Parents F 1 D X A AD tetraploid D F 2 X QTL for fibre length QTL for fibre thickness QTL for cotton mass QTL for number of fruits b � 2001 Macmillan Magazines Ltd 378 | MAY 2001 | VOLUME 2 www.nature.com/reviews/genetics REVIEWS Experimental and statistical concerns. QTL mapping is a statistical approach and evolutionary biologists must be aware of the inherent limitations and biases of the statistical procedures themselves 11,77 . For example, QTL analyses assume that the distribution of trait values are normally distributed. Important experimental consider- ations that are involved in implementing these statistical tests include the heritability of the trait being mapped, the precision with which the trait can be measured and the size of the mapping population 11 . Shortcomings in any of these areas can undermine the accuracy and power of the QTL analysis. The limits of QTL detection are determined by sev- eral factors, including recombination, the number of progeny in the mapping population and the number of markers 70,71 . QTL mapping always underestimates the number of genes that are involved in controlling a trait because only genes of sufficiently large phenotypic effect will be detectable as QTL 70,71,78,79 . Imagine staring out at the African savanna with very poor binoculars ? you will see the elephants (QTL of large effect), but perhaps not the gazelles (QTL of small effect). With a progeny population of fewer than 500 individuals, regardless of marker density, there is little statistical power to identify QTL of small effect 71 . William Beavis summarized the results of several QTL mapping experiments on yield and height of maize, including replicate studies of the same crosses 70,71 . Although the same QTL were detected across studies, some of those detected were unique to each cross. Even the replicate studies did not detect the same QTL. Another form of sampling bias, the use of only a few divergent parental lines in a minimum number of environmental conditions, can lead to the underesti- mation of the number of genes and their effects. For example, Paterson and colleagues did a mapping experiment for tomato in three environments. Of the 29 QTL detected, 4 were detected in all three environ- ments, 10 in two environments and 15 in only one environment 80 . Clearly, the environment will be shown to have an important role in QTL mapping. At one level, the environment might complicate our efforts to map QTL, but understanding the interaction of QTL with the environment will be crucial to our under- standing of gene function and evolution. It is also possible that the QTL of large phenotypic effect that we see are an artefact of the strong directional selection often used to create the phenotypically diver- gent parental lines that are used for mapping 19 .Strong selection can fix alleles that normally segregate in the base population. In addition, artificial selection might create repeated bottlenecks through which only a sam- ple of segregating alleles can pass. Only segregating alle- les can be detected. So, fewer QTL will be detected and those that are eventually detected might explain an inflated portion of the phenotypic variance. In addition to the estimation of the number of QTL, the magnitude of QTL effects might also be biased by small sample sizes 70,71 . In his study on QTL experiments in maize, Beavis showed that in all studies, one or a few Moving from QTL to genes. A QTL is almost never an actual genetic locus. A QTL is a chromosomal segment, potentially encompassing many hundreds of individual loci, most of which have nothing to do with the pheno- typic trait of interest. An actual locus that contributes to a phenotype is a veritable needle in a haystack of QTL. Conversely, a QTL might contain many genes that con- tribute to the phenotype of interest. Although there have been examples of QTL map- ping that yield an actual locus, these examples are rare 54,61 . Remember that it took more than ten years to winnow a single fruit size QTL to the actual gene. There are 28 known fruit size QTL in tomato 51 . Although it is doubtful that it will take 270 more years to find all the genes that influence fruit size in tomato, finding those genes will be time consuming and expensive. In the near future, only in those organisms for which genetic information is abundant will we be able to find the actual genes that underlie the phenotypes of interest. Even in model organisms, the ability to move from QTL to gene will not be easy. In A. thaliana, the estimat- ed genetic map is 586 centiMorgans (cM) and the phys- ical size is ~125 megabases. On average, there are 213 kilobases of DNA and ~50 genes per cM in A. thaliana 1,72 . Even in the best QTL studies, many QTL are defined by markers that are more than 10 cM apart. Sorting through 500 genes requires time and money. Even if a genome project has identified each of the genes in that interval, proving that any particular gene is responsible for variation in a trait of interest is not a trivial exercise. Association studies hold some promise in assess- ing the correlations between specific genetic variants (usually SNPs) and trait differences on a population level 74,75 . The most commonly used approach search- es for differences in allele frequency between individ- uals with a particular phenotype and unrelated con- trol individuals. However, many statistical caveats accompany such studies and they have, to date, been plagued by numerous spurious (false-positive) correlations 74,76 . Along similar lines, a ?candidate gene? approach might help in linking QTL with particular genes. In this approach, a gene known to be in a particular pathway, or have a predicted function, can be related to genes already known to have specific phenotypic effects, and will be considered to be a gene correlated to a QTL. As many genes will probably be included within the chro- mosomal boundaries of the QTL, it will still be difficult to provide convincing molecular evidence, such as com- plementation, that a candidate gene is the locus that contributes to the trait under study. Those biologists working with less genetically endowed organisms might be able to lever the genetic information from model organisms by taking advan- tage of homology. In this way, a ?reverse quantitative genetics? approach could be fruitful in that one could ask how much phenotypic variation in a non-model organism is explained by the homologue of a gene with a similar phenotype in a model organism. � 2001 Macmillan Magazines Ltd NATURE REVIEWS | GENETICS VOLUME 2 | MAY 2001 | 379 REVIEWS LOD SCORE (Base 10 ?logarithm of the odds?, or ?log-odds?.) A method of hypothesis testing. The logarithm of the ratio between likelihoods under the null and alternative hypotheses. MONTE CARLO SIMULATION The use of randomly generated or sampled data and computer simulations to obtain approximate solutions to complex mathematical and statistical problems. PERMUTATION TEST A method of hypothesis testing. In these tests, an empirical distribution of a test statistic is obtained by permuting the original sample many times. Each permuted sample is considered to be a sample of the population under the null hypothesis. BAYESIAN APPROACH An alternative statistical method that allows the use of prior information to evaluate the posterior probabilities of different hypotheses. SIMPLEX SEGREGATION Segregation in polyploids. Segregation with no crossovers of the simplex genotype Aaaa would result in a gametic ratio of 1/2 Aa and 1/2 aa. understanding of quantitative traits. Genomics will greatly assist in providing numerous markers and more complete maps. Genomic techniques might also make it possible to create larger progeny arrays as the cost of genotyping will probably decrease markedly. Theoretically, this QTL mapping approach is as applicable to animals, including humans, as it is to plants. However, there are some significant advan- tages to studying complex traits in plants. Plants are easy to replicate and one can generate several parental lines and large progeny arrays. The variable that is often limiting in QTL studies is the cost of generating and maintaining large numbers of proge- ny. For humans, the number of progeny is necessarily small. Generating inbred lines in plants is generally possible. Because animals tend to be outcrossing, inbreeding can be a problem. QTL mapping in populations or species of out- breeding organisms faces additional statistical and biological challenges. In these cases, the parents used in the mapping cross, either from controlled crosses or natural populations, are not necessarily fixed for alternate alleles, as is the case with inbred parental lines. The parents might be polymorphic for segre- gating alleles. Several experimental and statistical solutions to these challenges have been suggested for outbred species 89?92 . Statistical approaches using pedigrees are being developed 93,94 that should be applicable to humans. In highly heterozygous organ- isms, QTL mapping can be done in the F 1 generation itself, on the basis of SIMPLEX SEGREGATION of polymor- phic markers 95,96 . In addition, new approaches have recently been presented for QTL mapping in poly- ploids and even in hybrid zones 97,98 . Finally, in a plant system, one can easily assess QTL in several realistic ecological conditions. Parents can be taken out of the field and offspring can be grown back in the field. If our ultimate goal is to understand how genes form complex phenotypes, we must come to realize that the environment has a cru- cial role. Replicated microarray studies that can simultaneously assess genome-wide gene expression and can be used on field experiments might eventual- ly be a tool that geneticists and ecologists find invalu- able. Understanding how the environment interacts with genes to yield phenotypes might be the most significant challenge to all geneticists. QTL of large effect were identified, along with several QTL of small effect. This distribution was more skewed in experiments that used small numbers of progeny. The fewer the progeny, the higher were the estimated effects of the largest QTL identified. Finally, the issue of significance testing is still incom- pletely resolved. The statistical tests for assessing if a QTL actually exists are many and not independent. So, QTL mapping will yield a significant number of false QTL (ghost peaks). One commonly used solution to this problem is to use a conservative threshold value to reduce the probability of false positives 81,82 . For human data, it has been estimated that the threshold value should be a LOD SCORE of 3.3 (REF. 82). This value indicates that the probability that a QTL occurs in a particular interval is over 1,000 times more likely than the null hypothesis that no QTL exists in the interval. MONTE CARLO SIMULATIONS 83?85 and PERMUTATION TESTS 86,87 are two other approaches that are used to explicitly determine if a QTL is significant. BAYESIAN APPROACHES to QTL mapping are being introduced 32,88 . Future challenges Given the caveats described above, knocking down the straw man of quantitative genetics (many genes of small, additive effect) might be more difficult than initial efforts have led us to believe. After all, how much phenotypic variation does a QTL have to explain before we call it a QTL of ?large? effect? Does the quantitative genetic model really predict that we will find no single QTL that explains any significant amount of the total phenotypic variation? Given the numerous caveats that apply to QTL mapping studies, the number of genes estimated by QTL mapping should be viewed as a hypothesis of genetic architec- ture. Furthermore, it is crucial that evolutionary biol- ogists define their questions with the caveat that they might never actually find genes. In agriculture, having a QTL might be enough to serve in a marker-assisted selection programme. In how many evolutionary studies will knowing only a relatively large chromoso- mal region be informative? The challenge for evolu- tionary biologists will be to think carefully about how understanding the genetic basis of complex traits will inform their studies, especially if those conclusions rest on knowing the actual genes underlying the trait of interest. When is identifying a large chromosomal segment interesting enough to justify an expensive and time-consuming hunt for QTL? Children of the corn At some level, all geneticists, from molecular geneticists to population geneticists, are interested in finding the connection between the gene and the phenotype. Understanding this connection is most difficult for the cases of complex traits, such as most human diseases and many examples of adaptive evolution. QTL map- ping holds some promise in helping us to make this connection. With the advent of genomics, geneticists see a path through the fog and there is growing awareness that an understanding of human disease will require an Links FURTHER INFORMATION Arabidopsis thaliana | rice | Mendelism | R. A. Fisher | Francis Galton | Karl Pearson | Arabidopsis Biological Resource Centre (ABRC) | Cereon Genomics Arabidopsis SNP collection | Nottingham Arabidopsis Stock Centre: Columbia � Landsberg RI lines | Rockefeller University?s collection of genetic analysis software | The Arabidopsis Information Resource (TAIR) | The Institute for Genomic Research | Rodney Mauricio?s lab ENCYCLOPEDIA OF LIFE SCIENCES Adaptation genetics | Quantitative genetics � 2001 Macmillan Magazines Ltd 380 | MAY 2001 | VOLUME 2 www.nature.com/reviews/genetics REVIEWS 1. Kaul, S. et al. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796?815 (2000). 2. Theologis, A. et al. Sequence and analysis of chromosome 1 of the plant Arabidopsis thaliana. 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