Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by meta-analysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.YIELD is the most important and complex trait in crops. It reflects the interaction of the environment with all growth and development processes that occur throughout the life cycle (Quarrie et al. 2006). Crop yield is directly and multiply determined by yield-component traits (such as seed weight and seed number). Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that “fine-mapped” quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al. 2005; Tian et al. 2006b; Burstin et al. 2007; Xie et al. 2008; Xing et al. 2008; Xue et al. 2008), seed weight (Ishimaru 2003; Song et al. 2005; Shomura et al. 2008; Wang et al. 2008; Xie et al. 2006, 2008; Xing et al. 2008; Xue et al. 2008), flowering time (Cockram et al. 2007; Song et al. 2007; Xie et al. 2008; Xue et al. 2008), plant height (Salamini 2003; Ashikari et al. 2005; Xie et al. 2008; Xue et al. 2008), branching (Clark et al. 2006; Burstin et al. 2007; Xing et al. 2008), biomass yield (Quarrie et al. 2006; Burstin et al. 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001; Brown 2002; Yuan et al. 2002; Waller et al. 2005; Zhang 2007; Warrington et al. 2008), and root architecture (Hochholdinger et al. 2008).Many experiments have explored the genetic basis of yield and yield-associated traits (yield components and yield-related traits) in crops. Summaries of identified QTL have been published for wheat (MacCaferri et al. 2008), barley (Von Korff et al. 2008), rice, and maize (http://www.gramene.org/). The results show several common patterns. First, QTL for yield and yield-associated traits tend to be clustered in the genome, which suggests that the QTL of the yield-associated traits have pleiotropic effects on yield. Second, this kind of pleiotropy has not been well analyzed genetically. The QTL for yield (complicated factor), therefore, have not been associated with any yield-associated traits (relatively simple factors, such as plant height). Therefore, they are unlikely to predict accurately potential candidate genes for yield. Third, only a few loci (rarely >10) have been found for each of these traits. Thus, the genetic architecture of yield has remained ambiguous. Fourth, trials were carried out in a few environments and how the mode of expression of QTL for these complex traits might respond in different environments is unclear.In this study, the genetic architecture of crop yield was analyzed through the QTL mapping of seed yield and eight yield-associated traits in two related populations of rapeseed (Brassica napus) that were grown in 10 natural environments. The complexity of the genetic architecture of seed yield was demonstrated by QTL meta-analysis. The idea of estimating indicator QTL (QTL of yield-associated traits, which are defined as the potential genetic determinants of the colocalized QTL for yield) for yield QTL in conjunction with the identification of candidate genes is described.     

Genetic Control of Photoperiod Sensitivity in Maize Revealed by Joint Multiple Population Analysis

Variation in maize for response to photoperiod is related to geographical adaptation in the species. Maize possesses homologs of many genes identified as regulators of flowering time in other species, but their relation to the natural variation for photoperiod response in maize is unknown. Candidate gene sequences were mapped in four populations created by crossing two temperate inbred lines to two photoperiod-sensitive tropical inbreds. Whole-genome scans were conducted by high-density genotyping of the populations, which were phenotyped over 3 years in both short- and long-day environments. Joint multiple population analysis identified genomic regions controlling photoperiod responses in flowering time, plant height, and total leaf number. Four key genome regions controlling photoperiod response across populations were identified, referred to as ZmPR1–4. Functional allelic differences within these regions among phenotypically similar founders suggest distinct evolutionary trajectories for photoperiod adaptation in maize. These regions encompass candidate genes CCA/LHY, CONZ1, CRY2, ELF4, GHD7, VGT1, HY1/SE5, TOC1/PRR7/PPD-1, PIF3, ZCN8, and ZCN19.MAIZE (Zea mays L. subsp. mays) was domesticated in southern Mexico and its center of diversity is in tropical Latin America (Goodman 1999; Matsuoka et al. 2002), where precipitation rates and day lengths cycle annually. The presumed ancestor of maize, teosinte (Zea mays L. subsp. parviglumis), likely evolved photoperiod sensitivity to synchronize its reproductive phases to the wetter, short-day growing season (Ribaut et al. 1996; Campos et al. 2006). A critical event in the postdomestication evolution of maize was its spread from tropical to temperate regions of the Americas (Goodman 1988), requiring adaptation to longer day lengths. The result of this adaptation process is manifested today as a major genetic differentiation between temperate and tropical maize (Liu et al. 2003) and substantially reduced photoperiod sensitivity of temperate maize (Gouesnard et al. 2002). Tropical maize exhibits delayed flowering time, increased plant height, and a greater total leaf number when grown in temperate latitudes with daily dark periods <11 hr (Allison and Daynard 1979; Warrington and Kanemasu 1983a,b). Identifying the genes underlying maize photoperiod sensitivity will provide insight into the postdomestication evolution of maize and may reduce barriers to the use of diverse tropical germplasm resources for improving temperate maize production (Holland and Goodman 1995; Liu et al. 2003; Ducrocq et al. 2009).Natural variation at key genes in flowering time pathways is related to adaptation and evolution of diverse plant species (Caicedo et al. 2004; Shindo et al. 2005; Turner et al. 2005; Cockram et al. 2007; Izawa 2007; Slotte et al. 2007). Identification of some of the genes controlling adaptation in numerous plant species relied on regulatory pathways elucidated in Arabidopsis (Simpson and Dean 2002). Many key genes in the Arabidopsis flowering time regulatory pathways are conserved across diverse plant species (Kojima et al. 2002; Hecht et al. 2007; Kwak et al. 2008), but their functions have diverged, resulting in unique regulatory pathways in some phylogenetic groups (Colasanti and Coneva 2009). For example, FRI and FLC control most natural variation for vernalization response in Arabidopsis (Caicedo et al. 2004; Shindo et al. 2005), but wheat and barley appear to lack homologs of these genes and regulate vernalization response with different genes (Yan et al. 2004).Maize exhibits tremendous natural variation for flowering time (Gouesnard et al. 2002; Camus-Kulandaivelu et al. 2006), for which numerous QTL have been identified (Chardon et al. 2004). In contrast, only a few flowering time mutants are known and only a handful of flowering time genes, including DWARF8 (D8), DELAYED FLOWERING1 (DLF1), VEGETATIVE TO GENERATIVE TRANSITION1 (VGT1), and INDETERMINATE GROWTH1 (ID1), have been cloned in maize (Thornsberry et al. 2001; Colasanti et al. 2006; Muszynski et al. 2006; Salvi et al. 2007; Colasanti and Coneva 2009). Variation at or near D8 and VGT1 is related to latitudinal adaptation, but these genes do not appear to regulate photoperiod responses and account for only a limited proportion of the standing flowering time variation in maize (Camus-Kulandaivelu et al. 2006, 2008; Ducrocq et al. 2008; Buckler et al. 2009).Quantitative trait loci (QTL) mapping was a key first step to identifying the genes underlying natural variation for flowering time in Arabidopsis (Koornneef et al. 2004). Photoperiodic QTL have been mapped previously in individual biparental maize mapping populations (Koester et al. 1993; Moutiq et al. 2002; Wang et al. 2008; Ducrocq et al. 2009). Such studies are informative with respect to the parents from which the populations were derived, but often do not reflect the genetic heterogeneity of broader genetic reference populations (Holland 2007).Association mapping (Thornsberry et al. 2001; Ersoz et al. 2007) and combined analysis of multiple biparental crosses (Rebaï et al. 1997; Rebaï and Goffinet 2000; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008) represent alternative approaches to understanding the variation in genetic control for complex traits among diverse germplasm. Association mapping has limited power to identify genes that affect traits closely associated with population structure, such as flowering time in maize (Camus-Kulandaivelu et al. 2006; Ersoz et al. 2007). In contrast, joint QTL analysis of multiple populations is not hindered by the associations between causal genes and population structure. Combined QTL analysis of multiple mapping populations provides improved power to detect QTL, more precise estimation of their effects and positions, and better understanding of their functional allelic variation and distribution across more diverse germplasm compared to single-population mapping (Rebaï et al. 1997; Wu and Jannink 2004; Jourjon et al. 2005; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008; Buckler et al. 2009). Joint analysis also provides a direct test of the importance of higher-order epistatic interactions between founder alleles at individual loci with genetic backgrounds (Jannink and Jansen 2001; Blanc et al. 2006). In this study, joint analysis of multiple populations was used to test directly the hypothesis that diverse tropical maize lines carry functionally similar alleles at key photoperiod loci, which would imply genetic homogeneity for a common set of mutations and a shared evolutionary pathway for photoperiod insensitivity.The objective of this study was to integrate candidate gene analyses with photoperiod QTL mapping across multiple maize populations. We tested candidate floral regulators known from other species for associations with natural variation for photoperiod response in maize. We analyzed flowering time in four interrelated recombinant inbred line (RIL) populations, each derived from crosses between temperate and tropical maize parents (Figure 1), in both long- and short-day environments to characterize their responses to distinct photoperiods. Joint population analysis provided high resolution of many QTL positions, permitting robust testing of underlying candidate genes. We directly and indirectly mapped homologs of flowering time candidates genes from Arabidopsis, rice, and barley on a dense consensus genetic map of these four populations, permitting identification of homologs that colocalize with genome regions associated with variation for photoperiod response. These mapping families are being integrated into the maize nested association mapping (NAM) population (Buckler et al. 2009; McMullen et al. 2009) because they were genotyped with the maize NAM map SNP markers, they involve the common parent B73, and their seed and genotypic information (File S1 cont.) are publicly available. Their availability further expands the genetic diversity represented by the maize NAM population and enhances this valuable public community resource.Open in a separate windowFigure 1.—Factorial mating of two temperate (B73 and B97) and two tropical (CML254 and Ki14) inbred maize lines to create four related recombinant inbred line mapping populations.     

Polymorphic Genes of Major Effect: Consequences for Variation,Selection and Evolution in Arabidopsis thaliana

The importance of genes of major effect for evolutionary trajectories within and among natural populations has long been the subject of intense debate. For example, if allelic variation at a major-effect locus fundamentally alters the structure of quantitative trait variation, then fixation of a single locus can have rapid and profound effects on the rate or direction of subsequent evolutionary change. Using an Arabidopsis thaliana RIL mapping population, we compare G-matrix structure between lines possessing different alleles at ERECTA, a locus known to affect ecologically relevant variation in plant architecture. We find that the allele present at ERECTA significantly alters G-matrix structure—in particular the genetic correlations between branch number and flowering time traits—and may also modulate the strength of natural selection on these traits. Despite these differences, however, when we extend our analysis to determine how evolution might differ depending on the ERECTA allele, we find that predicted responses to selection are similar. To compare responses to selection between allele classes, we developed a resampling strategy that incorporates uncertainty in estimates of selection that can also be used for statistical comparisons of G matrices.THE structure of the genetic variation that underlies phenotypic traits has important consequences for understanding the evolution of quantitative traits (Fisher 1930; Lande 1979; Bulmer 1980; Kimura 1983; Orr 1998; Agrawal et al. 2001). Despite the infinitesimal model''s allure and theoretical tractability (see Orr and Coyne 1992; Orr 1998, 2005a,b for reviews of its influence), evidence has accumulated from several sources (artificial selection experiments, experimental evolution, and QTL mapping) to suggest that genes of major effect often contribute to quantitative traits. Thus, the frequency and role of genes of major effect in evolutionary quantitative genetics have been a subject of intense debate and investigation for close to 80 years (Fisher 1930; Kimura 1983; Orr 1998, 2005a,b). Beyond the conceptual implications, the prevalence of major-effect loci also affects our ability to determine the genetic basis of adaptations and species differences (e.g., Bradshaw et al. 1995, 1998).Although the existence of genes of major effect is no longer in doubt, we still lack basic empirical data on how segregating variation at such genes affects key components of evolutionary process (but see Carrière and Roff 1995). In other words, How does polymorphism at genes of major effect alter patterns of genetic variation and covariation, natural selection, and the likely response to selection? The lack of data stems, in part, from the methods used to detect genes of major effect: experimental evolution (e.g., Bull et al. 1997; Zeyl 2005) and QTL analysis (see Erickson et al. 2004 for a review) often detect such genes retrospectively after they have become fixed in experimental populations or the species pairs used to generate the mapping population. The consequences of polymorphism at these genes on patterns of variation, covariation, selection, and the response to selection—which can be transient (Agrawal et al. 2001)—are thus often unobserved.A partial exception to the absence of data on the effects of major genes comes from artificial selection experiments, in which a substantial evolutionary response to selection in the phenotype after a plateau is often interpreted as evidence for the fixation of a major-effect locus (Frankham et al. 1968; Yoo 1980a,b; Frankham 1980; Shrimpton and Robertson 1988a,b; Caballero et al. 1991; Keightley 1998; see Mackay 1990 and Hill and Caballero 1992 for reviews). However, many of these experiments report only data on the selected phenotype (e.g., bristle number) or, alternatively, the selected phenotype and some measure of fitness (e.g., Frankham et al. 1968, Yoo 1980b; Caballero et al. 1991; Mackay et al. 1994; Fry et al. 1995; Nuzhdin et al. 1995; Zur Lage et al. 1997), making it difficult to infer how a mutation will affect variation, covariation, selection, and evolutionary responses for a suite of traits that might affect fitness themselves. One approach is to document how variation at individual genes of major effect affects the genetic variance–covariance matrix (“G matrix”; Lande 1979), which represents the additive genetic variance and covariance between traits.Although direct evidence for variation at major-effect genes altering patterns of genetic variation, covariation, and selection is rare, there is abundant evidence for the genetic mechanisms that could produce these dynamics. A gene of major effect could have these consequences due to any of at least three genetic mechanisms: (1) pleiotropy, where a gene of major effect influences several traits, including potentially fitness, simultaneously, (2) physical linkage or linkage disequilibrium (LD), in which a gene of major effect is either physically linked or in LD with other genes that influence other traits under selection, and (3) epistasis, in which the allele present at a major-effect gene alters the phenotypic effect of other loci and potentially phenotypes under selection. Evidence for these three evolutionary genetic mechanisms leading to changes in suites of traits comes from a variety of sources, including mutation accumulation experiments (Clark et al. 1995; Fernandez and Lopez-Fanjul 1996), mutation induction experiments (Keightley and Ohnishi 1998), artificial selection experiments (Long et al. 1995), and transposable element insertions (Rollmann et al. 2006). For pleiotropy in particular, major-effect genes that have consequences on several phenotypic traits are well known from the domestication and livestock breeding literature [e.g., myostatin mutations in Belgian blue cattle and whippets (Arthur 1995; Grobet et al. 1997; Mosher et al. 2007), halothane genes in pigs (Christian and Rothschild 1991; Fujii et al. 1991), and Booroola and Inverdale genes in sheep (Amer et al. 1999; Visscher et al. 2000)]. While these data suggest that variation at major-effect genes could—and probably does—influence variation, covariation, and selection on quantitative traits, data on the magnitude of these consequences remain lacking.Recombinant inbred line (RIL) populations are a promising tool for investigating the influence of major-effect loci. During advancement of the lines from F₂''s to RILs, alternate alleles at major-effect genes (and most of the rest of the genome) will be made homozygous, simplifying comparisons among genotypic classes. Because of the high homozygosity, individuals within RILs are nearly genetically identical, facilitating phenotyping of many genotypes under a range of environments. In addition, because of recombination, alternative alleles are randomized across genetic backgrounds—facilitating robust comparisons between sets of lines differing at a major-effect locus.Here we investigate how polymorphism at an artificially induced mutation, the erecta locus in Arabidopsis thaliana, affects the magnitude of these important evolutionary genetic parameters under ecologically realistic field conditions. We use the Landsberg erecta (Ler) × Columbia (Col) RIL population of A. thaliana to examine how variation at a gene of major effect influences genetic variation, covariation, and selection on quantitative traits in a field setting. The Ler × Col RIL population is particularly suitable, because it segregates for an artificially induced mutation at the erecta locus, which has been shown to influence a wide variety of plant traits. The Ler × Col population thus allows a powerful test of the effects of segregating variation at a gene—chosen a priori—with numerous pleiotropic effects. The ERECTA gene is a leucine-rich receptor-like kinase (LRR-RLK) (Torii et al. 1996) and has been shown to affect plant growth rates (El-Lithy et al. 2004), stomatal patterning and transpiration efficiency (Masle et al. 2005; Shpak et al. 2005), bacterial pathogen resistance (Godiard et al. 2003), inflorescence and floral organ size and shape (Douglas et al. 2002; Shpak et al. 2003, 2004), and leaf polarity (Xu et al. 2003; Qi et al. 2004).Specifically, we sought to answer the following questions: (1) Is variation at erecta significantly associated with changes to the G matrix? (2) Is variation at erecta associated with changes in natural selection on genetically variable traits? And (3) is variation at erecta associated with significantly different projected evolutionary responses to selection?     

Nested Association Mapping for Identification of Functional Markers

Identification of functional markers (FMs) provides information about the genetic architecture underlying complex traits. An approach that combines the strengths of linkage and association mapping, referred to as nested association mapping (NAM), has been proposed to identify FMs in many plant species. The ability to identify and resolve FMs for complex traits depends upon a number of factors including frequency of FM alleles, magnitudes of their genetic effects, disequilibrium among functional and nonfunctional markers, statistical analysis methods, and mating design. The statistical characteristics of power, accuracy, and precision to identify FMs with a NAM population were investigated using three simulation studies. The simulated data sets utilized publicly available genetic sequences and simulated FMs were identified using least-squares variable selection methods. Results indicate that FMs with simple additive genetic effects that contribute at least 5% to the phenotypic variability in at least five segregating families of a NAM population consisting of recombinant inbred progeny derived from 28 matings with a single reference inbred will have adequate power to accurately and precisely identify FMs. This resolution and power are possible even for genetic architectures consisting of disequilibrium among multiple functional and nonfunctional markers in the same genomic region, although the resolution of FMs will deteriorate rapidly if more than two FMs are tightly linked within the same amplicon. Finally, nested mating designs involving several reference parents will have a greater likelihood of resolving FMs than single reference designs.THE primary purpose for identifying functional markers (FMs) associated with complex traits in plant species is to provide molecular genetic information underlying variability upon which both artificial and natural selection are based. FMs are defined as polymorphic sites within genomes that causally affect phenotypic trait variability (Andersen and Lubberstedt 2003). This definition is a pragmatic recognition that phenotypic variability can be due to genomic variability located outside of open reading frames. Forward genetics approaches to associate naturally occurring structural genomic variants with phenotypic variability can be broadly categorized as (1) linkage mapping, also referred to as quantitative trait locus (QTL) mapping, (2) association genetic mapping, also known as linkage disequilibrium (LD) mapping, and (3) designs that combine linkage and LD mapping.The third approach based on the concept of combining LD with QTL mapping is a natural extension of the multifamily QTL approach and has been referred as joint linkage and linkage disequilibrium mapping (JLLDM) (Xiong and Jin 2000; Farnir et al. 2002; Wu et al. 2002; Perez-Enciso 2003; Jung et al. 2005) in samples from natural populations. The combined approach also has been applied to designed mapping families sampled from plant breeding populations (Xu 1998a; Jannink and Jansen 2000; Jannink and Wu 2003; Jansen et al. 2003). A special case of designed mapping families that are interconnected, known as nested association mapping (NAM), was proposed by Yu et al. (2008). As originally proposed, a NAM population consists of multiple families of recombinant inbred lines (RILs) derived from multiple inbred lines crossed to a single reference inbred line. Implicitly, genomic information is composed of high-density genotypes of parental inbred lines and low-density genotypes from segregating progeny. If the segregating progeny are RILs or doubled haploid lines (DHLs), then the genomic information can be “immortalized” for associations with phenotypes obtained through long-term longitudinal studies (Nordborg and Weigel 2008).A NAM population consisting of 25 families with 200 RILs for each family has been developed and released as a genetic resource for identification of FMs in maize (Yu et al. 2008). Other publicly available NAM populations are being developed for several species including Arabidopsis thaliana (Buckler and Gore 2007), barley (R. Wise, personal communication), sorghum (J. Yu, personal communication), and soybean (B. Diers, personal communication).The power, accuracy, and precision of identifying FMs in experimental NAM populations have not been investigated for complex genetic architectures. These statistical properties depend upon a number of factors including the following:

1. Data analysis method: Some methods are more powerful than others; however, experimental biologists prefer methods implemented in existing software packages. Are least-squares methods sufficiently powerful to identify FMs in established and developing NAM populations?
2. Frequency of functional markers and magnitudes of genetic effects: Development of a NAM population will change the allele frequencies of the FM relative to the reference population from which the lines are sampled. How will allele frequency and magnitude of genetic effects in a typical NAM population affect the ability to identify FMs?
3. Disequilibrium among functional and nonfunctional markers: Disequilibrium may exist among alleles within subpopulations even when there is no physical basis for genetic linkage. To what extent can the NAM design address consequences of gametic disequilibrium (population structure) in the reference population?
4. Multiple FMs in the same genomic region: If multiple FMs are physically located in the same genomic region, will equilibrium among the parental lines enable resolution of multiple FMs?
5. Mating design: An appropriate mating design can maximize the number of families that are informative for FMs. Will multiple-reference mating designs improve the probability of identifying FMs?

These five questions were addressed.     

Identification of Selection Signatures in Cattle Breeds Selected for Dairy Production

Genetic correlations are expected to be high among functionally related traits and lower between groups of traits with distinct functions (e.g., reproductive vs. resource-acquisition traits). Here, we explore the quantitative-genetic and QTL architecture of floral organ sizes, vegetative traits, and life history in a set of Brassica rapa recombinant inbred lines within and across field and greenhouse environments. Floral organ lengths were strongly positively correlated within both environments, and analysis of standardized G-matrices indicates that the structure of genetic correlations is ∼80% conserved across environments. Consistent with these correlations, we detected a total of 19 and 21 additive-effect floral QTL in the field and the greenhouse, respectively, and individual QTL typically affected multiple organ types. Interestingly, QTL × QTL epistasis also appeared to contribute to observed genetic correlations; i.e., interactions between two QTL had similar effects on filament length and two estimates of petal size. Although floral and nonfloral traits are hypothesized to be genetically decoupled, correlations between floral organ size and both vegetative and life-history traits were highly significant in the greenhouse; G-matrices of floral and vegetative traits as well as floral and life-history traits differed across environments. Correspondingly, many QTL (45% of those mapped in the greenhouse) showed environmental interactions, including approximately even numbers of floral and nonfloral QTL. Most instances of QTL × QTL epistasis for floral traits were environment dependent.EVOLUTIONARY responses to selection are dependent on genetic architecture. The proportion of phenotypic variation with a heritable genetic basis affects the response to selection, as does the structure of genetic correlations among selected traits. For example, an evolutionary response will be constrained if selection favors an increase in the value of two traits that are negatively correlated; i.e., a negative correlation is antagonistic to the joint vector of selection. Alternatively, if the vector of selection is parallel to the genetic correlation, then trait covariation is reinforcing and the population mean may more rapidly approach favored trait values (Etterson and Shaw 2001; Merilä and Björklund 2004). One measure of genetic architecture is the G-matrix (Lynch and Walsh 1998), which is composed of genetic variances (diagonal matrix elements) and genetic covariances among traits (off-diagonal matrix elements). G-matrices have been shown to vary across environments (Donohue et al. 2000; Conner et al. 2003; Brock and Weinig 2007), indicating that the molecular-genetic underpinnings of matrix elements (e.g., identity and/or relative effect of additive and epistatic loci, degree of pleiotropy, etc.) and the traits'' evolutionary potential vary across environments. Few studies, however, have related matrix and QTL architectures; and, therefore, the molecular-genetic underpinnings of quantitative-genetic estimates remain unclear (but see Gardner and Latta 2007; Kelly 2009).In angiosperms, covariances between floral whorls (e.g., petal and stamen length) are frequently positive among functionally related traits. These positive correlations can arise from pollinator-mediated (or pollination-mediated) selection for specific allometric relationships among floral traits and ensuing linkage disequilibrium (LD) among causal loci (Berg 1959, 1960; also referred to as phenotypic integration, see Pigliucci 2003; Klingenberg 2008). For example, in outcrossing species, male fitness may be more dependent on the frequency and efficiency of pollinator visitation than female fitness (Bell 1985; but see Hodgins and Barrett 2008). Anther placement relative to the corolla opening can affect the efficiency of pollen dissemination (Conner and Via 1993; Morgan and Conner 2001); in addition, comparative work indicates that petal–stamen length correlations are stronger than stamen–pistil length correlations in outcrossers, whereas species that reproduce via autogamous selfing show the opposite pattern (Ushimaru and Nakata 2002). Alternatively, strong floral integration could be attributed to the developmental hypothesis that genetic correlations arise due to pleiotropic genes coregulating floral whorls (Herrera 2001; Herrera et al. 2002). Strong correlations resulting from linkage disequilibrium or from developmentally based pleiotropy may constrain the evolution of novel reproductive morphologies when biotic or abiotic factors (and selection) change (Cheverud 1984; Clark 1987; Smith and Rausher 2008; Agrawal and Stinchcombe 2009).Similar to genetic covariances among floral traits, covariances between floral and nonfloral traits could also alter the evolutionary response of reproductive traits. In contrast to hypotheses regarding the adaptive significance of floral-trait integration, genetic correlations between floral and nonfloral traits (e.g., vegetative or phenological traits) are hypothesized to be disadvantageous (Berg 1960). More specifically, floral allometry may be shaped by selection for reproductive success, as described above, whereas vegetative morphology is shaped primarily by selection to optimize other functions, such as light capture. If floral and nonfloral traits have a common genetic basis, then selection on phenological or morphological traits may result in maladaptive expression of floral organ size. As a result, functionally integrated floral traits are predicted to be genetically decoupled from vegetative and phenological traits (Berg 1960).QTL mapping provides a powerful tool to explore the genetic architecture of evolutionarily important traits. The QTL architecture of interspecific floral traits has been explored in diverse systems (Bradshaw et al. 1995; Fishman et al. 2002; Goodwillie et al. 2006; Bouck et al. 2007; Moyle 2007); however, insight into the molecular genetic basis of intraspecific floral variation comes almost exclusively from Arabidopsis thaliana (Juenger et al. 2000, 2005) and Mimulus guttatus (Hall et al. 2006). Floral traits in these intraspecific crosses are polygenic with a majority of detected QTL being of small to moderate effect size. Consistent with other quantitative-genetic studies (reviewed in Ashman and Majetic 2006), floral traits in A. thaliana and M. guttatus mapping populations exhibited moderate to high genetic correlations. In both systems, mapped QTL often affected multiple floral traits. In the few cases where QTL underlying intraspecific floral morphology have been evaluated, only a single growth environment was used; estimation of floral quantitative genetics across environments and subsequent comparison with the QTL architecture underlying observed across-environment patterns are lacking.Using a segregating progeny of Brassica rapa (recombinant inbred lines, RILs) and a small sample of crop and wild accessions, we examine the quantitative-genetic and QTL architecture of floral traits under field and greenhouse environments. Specifically, we address the following questions: (1) Does this RIL population express significant genetic (co)variation for floral traits when growing in the field or greenhouse? (2) Is there significant genetic variation for vegetative traits and days to flowering in field and greenhouse environments, and is there evidence for genetic correlations between floral and nonfloral traits? (3) Does the genetic architecture of floral and nonfloral traits, as measured by the G-matrix, differ across environments? (4) What is the number and effect size of additive and epistatic QTL in field and greenhouse environments? (5) What is the relationship between mapped QTL and quantitative genetic estimates of trait (co)variation within and between floral and nonfloral traits? And (6) what is the relationship between the quantitative-genetic architecture of floral traits in the RILs vs. in the accessions?     

Cis-regulatory Changes at FLOWERING LOCUS T Mediate Natural Variation in Flowering Responses of Arabidopsis thaliana

Flowering time, a critical adaptive trait, is modulated by several environmental cues. These external signals converge on a small set of genes that in turn mediate the flowering response. Mutant analysis and subsequent molecular studies have revealed that one of these integrator genes, FLOWERING LOCUS T (FT), responds to photoperiod and temperature cues, two environmental parameters that greatly influence flowering time. As the central player in the transition to flowering, the protein coding sequence of FT and its function are highly conserved across species. Using QTL mapping with a new advanced intercross-recombinant inbred line (AI-RIL) population, we show that a QTL tightly linked to FT contributes to natural variation in the flowering response to the combined effects of photoperiod and ambient temperature. Using heterogeneous inbred families (HIF) and introgression lines, we fine map the QTL to a 6.7 kb fragment in the FT promoter. We confirm by quantitative complementation that FT has differential activity in the two parental strains. Further support for FT underlying the QTL comes from a new approach, quantitative knockdown with artificial microRNAs (amiRNAs). Consistent with the causal sequence polymorphism being in the promoter, we find that the QTL affects FT expression. Taken together, these results indicate that allelic variation at pathway integrator genes such as FT can underlie phenotypic variability and that this may be achieved through cis-regulatory changes.MOLECULAR analysis of the phenotypic variation in life history traits is key to understanding how plants evolve in diverse natural environments. Among such traits, flowering time is critical for the reproductive success of the plant and is highly variable among natural Arabidopsis thaliana strains, providing an attractive paradigm for studying adaptive evolution (Johanson et al. 2000; Hagenblad and Nordborg 2002; Stinchcombe et al. 2004; Lempe et al. 2005; Shindo et al. 2005; Werner et al. 2005a). Two major environmental parameters that modulate flowering time are light and temperature (Koornneef et al. 1998). Temperature and light conditions vary substantially within the geographical range of A. thaliana, and natural populations presumably need to adapt to the local environment to ensure reproductive success. Flowering in A. thaliana is generally accelerated by long photoperiods, vernalization (exposure to winter-like conditions), and elevated ambient temperatures (Bäurle and Dean 2006). All these cues favor flowering of A. thaliana during spring or early summer, although the contribution from each individual cue and the interactions among them vary depending on the local environmental conditions (Wilczek et al. 2009).Flowering time is controlled through several genetic cascades that converge on a set of integrator genes including FLOWERING LOCUS T (FT), which encodes a protein that is highly conserved in flowering plants (Kardailsky et al. 1999; Kobayashi et al. 1999; Ahn et al. 2006). FT and its homologs are very likely an integral part of the mobile signal (florigen) that is produced in leaves and travels to the shoot apex to induce flowering (Abe et al. 2005; Wigge et al. 2005; Lifschitz et al. 2006; Corbesier et al. 2007; Jaeger and Wigge 2007; Lin et al. 2007; Mathieu et al. 2007; Tamaki et al. 2007; Notaguchi et al. 2008). In A. thaliana, FT expression is controlled by photoperiod, vernalization, and ambient growth temperature. Photoperiod in conjunction with the circadian clock promotes daily oscillations in FT RNA levels, which are greatly elevated at the end of long days. The central role of FT in determining the timing of flowering appears to be conserved in many species, making FT an attractive target for altering flowering time in cereals and other plants of economic importance (recently reviewed by Kobayashi and Weigel 2007; Turck et al. 2008).Wild strains of A. thaliana show extensive variation in flowering time and much of this is due to variation in the activity of the floral repressor FLOWERING LOCUS C (FLC). While some of this variation maps to FLC itself, much of it is due to differential activity at the epistatically acting FRIGIDA (FRI) locus (Michaels and Amasino 1999; Sheldon et al. 1999; Johanson et al. 2000; Michaels et al. 2003; Lempe et al. 2005; Shindo et al. 2005, 2006). Flowering is typically substantially delayed when the FRI/FLC system is active, unless these plants are first vernalized. However, FRI and FLC do not explain all of the flowering time variation seen in wild strains, and functionally divergent alleles of several additional flowering regulators, including CRYPTOCHROME 2 (CRY2), HUA2, FLOWERING LOCUS M (FLM), PHYTOCHROME C (PHYC), and PHYTOCHROME D (PHYD), have been identified in different strains of A. thaliana (Aukerman et al. 1997; Alonso-Blanco et al. 1998; El-Assal et al. 2001; Werner et al. 2005b; Balasubramanian et al. 2006a; Wang et al. 2007). Finally, there are many genotype-by-environment interactions that dramatically affect the contribution of a specific locus to the overall phenotype.The study of natural variation in A. thaliana has been greatly facilitated through the use of recombinant inbred line (RIL) populations (Koornneef et al. 2004). We have recently established two advanced intercross (AI)-RIL sets, in which the genetic map is greatly expanded, allowing for high-resolution QTL mapping (Balasubramanian et al. 2009). Here we use one of the new AI-RIL populations along with an independent F₂ population to identify the molecular basis of a light and temperature-sensitive flowering time QTL that mapped to the promoter of the FT gene. We show that FT is likely the causal gene for variation in light and temperature-sensitive flowering. Our results, in combination with those from other species, suggest that cis-regulatory variation rather than structural variation at FT contributes to phenotypic variation in natural populations.     

Identification of the Major Sex-Determining Region of Turbot (Scophthalmus maximus)

Sex determination in fish is a labile character in evolutionary terms. The sex-determining (SD) master gene can differ even between closely related fish species. This group is an interesting model for studying the evolution of the SD region and the gonadal differentiation pathway. The turbot (Scophthalmus maximus) is a flatfish of great commercial value, where a strong sexual dimorphism exists for growth rate. Following a QTL and marker association approach in five families and a natural population, we identified the main SD region of turbot at the proximal end of linkage group (LG) 5, close to the SmaUSC-E30 marker. The refined map of this region suggested that this marker would be 2.6 cM and 1.4 Mb from the putative SD gene. This region appeared mostly undifferentiated between males and females, and no relevant recombination frequency differences were detected between sexes. Comparative genomics of LG5 marker sequences against five model species showed no similarity of this chromosome to the sex chromosomes of medaka, stickleback, and fugu, but suggested a similarity to a sex-associated QTL from Oreochromis spp. The segregation analysis of the closest markers to the SD region demonstrated a ZW/ZZ model of sex determination in turbot. A small proportion of families did not fit perfectly with this model, which suggests that other minor genetic and/or environmental factors are involved in sex determination in this species.SEX ratio is a central demographic parameter directly related to the reproductive potential of individuals and populations (Penman and Piferrer 2008). The phenotypic sex depends on the processes of both sex determination and sex differentiation. Exogenous factors, such as temperature, hormones, or social behavior, can modify the gonad development pathway in fish (Baroiller and D''Cotta 2001; Piferrer and Guiguen 2008). Both genetic (GSD) and environmental sex determination has been reported in this group (Devlin and Nagahama 2002; Penman and Piferrer 2008), although primary sex determination is genetic in most species (Valenzuela et al. 2003). Among GSD, single, multiple, or polygenic sex-determining (SD) gene systems have been documented (Kallman 1984; Matsuda et al. 2002; Lee et al. 2004; Vandeputte et al. 2007).Sex determination in fish can evolve very rapidly (Woram et al. 2003; Peichel et al. 2004; Ross et al. 2009). Different sex determination mechanisms have been reported between congeneric species and even between populations of the same species (Almeida-Toledo and Foresti 2001; Lee et al. 2004; Mank et al. 2006). The evolution of sex chromosomes involves the suppression of recombination between homologous chromosomes probably to maintain sex-related coadapted gene blocks (Charlesworth et al. 2005; Tripathi et al. 2009). The sex determination pathway appears to be less conserved than other developmental processes (Penman and Piferrer 2008). However, differences are more related to the top of the hierarchy in the developmental pathway, while downstream genes are more conserved (Wilkins 1995; Marín and Baker 1998). As a consequence, the SD master gene in fish can vary among related species (Kondo et al. 2003; Tanaka et al. 2007; Alfaqih et al. 2009). In this sense, fish represent an attractive model for studying the evolution of SD mechanisms and sex chromosomes (Peichel et al. 2004; Kikuchi et al. 2007).A low proportion of fish species have demonstrated sex-associated chromosome heteromorphisms (Almeida-Toledo and Foresti 2001; Devlin and Nagahama 2002; Penman and Piferrer 2008). This is congruent with the rapid evolution of the SD region in fish, and thus in most species the male and female version of this chromosome region appears largely undifferentiated. In spite of this, indirect clues related to progenies of sex/chromosome-manipulated individuals or to segregation of morphologic/molecular sex-associated markers indicate that mechanisms of sex determination in fish are similar to other vertebrates (Penman and Piferrer 2008). With the arrival of genomics, large amounts of different genetic markers and genomic information are available for scanning genomes to look for their association with sex determination. Quantitative trait loci (QTL) (Cnaani et al. 2004; Peichel et al. 2004) or marker association (Felip et al. 2005; Chen et al. 2007) approaches have been used to identify the SD regions in some fish species. Also, microarrays constructed from gonadal ESTs have been applied to detect differentially expressed genes in the process of gonadal differentiation (Baron et al. 2005). Further, the increased genomic resources in model and aquaculture species have allowed the development of both comparative genomics (Woram et al. 2003; Kikuchi et al. 2007; Tripathi et al. 2009) and candidate gene (Shirak et al. 2006; Alfaqih et al. 2009) strategies to identify and characterize the SD region in fish. This has permitted the identification of the SD region in eight fish, including both model and aquaculture species (reviewed in Penman and Piferrer 2008).The turbot is a highly appreciated European aquaculture species, whose harvest is expected to increase from the current 9000 tons to >15,000 tons in 2012 (S. Cabaleiro, personal communication). Females of this species reach commercial size 4–6 months before males do, explaining the interest of the industry in obtaining all-female populations. Although some differences between families can be observed in the production process at farms, sex ratio is usually balanced at ∼1:1. Neither mitotic nor meiotic chromosomes have shown sex-associated heteromorphisms in turbot (Bouza et al. 1994; Cuñado et al. 2001). The proportion of sexes observed in triploid and especially gynogenetic progenies moved Cal et al. (2006a,b) to suggest an XX/XY mechanism in turbot with some additional, either environmental or genetic, factor involved. However, Haffray et al. (2009) have recently claimed a ZZ/ZW mechanism on the basis of the analysis of a large number of progenies from steroid-treated parents. These authors also suggested some (albeit low) influence of temperature in distorting sex proportions after the larval period. Finally, hybridizations between brill (Scophthalmus rhombus) and turbot render monosex progenies, depending on the direction of the cross performed, which suggests different SD mechanisms in these congeneric species (Purdom and ThaCker 1980).In this study, we used the turbot genetic map (Bouza et al. 2007, 2008; Martínez et al. 2008) to look for sex-associated QTL in this species. The identification of a major QTL in a specific linkage group (LG) in the five families analyzed prompted us to refine the genetic map at this LG and to perform a comparative genomics approach against model fish species for a precise location and characterization of the putative SD region. Also, sex-associated QTL markers were screened in a large natural population to provide additional support to our findings and to obtain population parameters at sex-related markers that could aid in interpreting the evolution of this genomic region.     

The Genetic Basis of Transgressive Ovary Size in Honeybee Workers

Ovarioles are the functional unit of the female insect reproductive organs and the number of ovarioles per ovary strongly influences egg-laying rate and fecundity. Social evolution in the honeybee (Apis mellifera) has resulted in queens with 200–360 total ovarioles and workers with usually 20 or less. In addition, variation in ovariole number among workers relates to worker sensory tuning, foraging behavior, and the ability to lay unfertilized male-destined eggs. To study the genetic architecture of worker ovariole number, we performed a series of crosses between Africanized and European bees that differ in worker ovariole number. Unexpectedly, these crosses produced transgressive worker phenotypes with extreme ovariole numbers that were sensitive to the social environment. We used a new selective pooled DNA interval mapping approach with two Africanized backcrosses to identify quantitative trait loci (QTL) underlying the transgressive ovary phenotype. We identified one QTL on chromosome 11 and found some evidence for another QTL on chromosome 2. Both QTL regions contain plausible functional candidate genes. The ovariole number of foragers was correlated with the sugar concentration of collected nectar, supporting previous studies showing a link between worker physiology and foraging behavior. We discuss how the phenotype of extreme worker ovariole numbers and the underlying genetic factors we identified could be linked to the development of queen traits.THE number of ovariole filaments per ovary is an important female reproductive character that affects fecundity across insect taxa (Richard et al. 2005; Makert et al. 2006). Social insect lineages have evolved a strong dimorphism in ovariole number between reproductive and nonreproductive castes. For example, while most families of bees consistently have 6 total ovarioles, and most species in the family Apidae have 8, the highly social species in the genus Apis (the honeybees) have queens that can have >360 total ovarioles and workers that often have <10 (Winston 1987; Michener 2003). This queen–worker dimorphism is of primary importance because it translates into differential reproductive potential that defines the social roles of these female castes (Winston 1987) and classifies social species in general (Sherman et al. 1995). Furthermore, ovary size (i.e., ovariole number) is the most sensitive indicator of caste-specific development in honeybees (Dedej et al. 1998). The extreme increase in ovariole number for queen honeybees enables high egg-laying rates (>1500 per day) and is apparently a result of selection for increased colony reproduction (growth and fission by swarming) (Seeley 1997). Honeybee queens are thus highly specialized for egg laying, similar to queens of several other social insect taxa, such as army ants or higher termites (Hölldobler and Wilson 1990). Honeybee workers in contrast, do not normally reproduce but perform all other essential activities including foraging for nectar, pollen, and water; caring for brood; and building, maintaining, and defending the colony (Winston 1987; Seeley 1997).While worker honeybees have drastically reduced ovariole numbers relative to queens, they have retained functional ovaries and can produce unfertilized (haploid) male-destined eggs in the absence of queen pheromonal inhibition (Velthuis 1970; Page and Robinson 1994). In the absence of a queen, variation in worker ovariole number translates into differential reproductive success (Makert et al. 2006), but in the presence of a queen this variation is correlated with several other worker attributes. Variation in worker ovariole number may underlie the pollen hoarding syndrome of honeybees, a set of correlated behavioral and physiological traits associated with biases in pollen vs. nectar foraging within honeybee colonies (Amdam et al. 2004, 2006). Ovariole number is thus an important phenotype associated with queen–worker dimorphism but also worker reproduction and division of labor.In honeybees, adult ovariole number is determined during larval development by nutrition. Nurse workers feed queen-destined larvae an overabundance of food while the diet of worker-destined larvae is restricted in the fourth and fifth larval instar (Beetsma 1985). Nurse feeding behavior and thus indirect genetic effects of the colony environment can strongly influence larval developmental trajectory (Beekman et al. 2000; Allsopp et al. 2003; Linksvayer et al. 2009). The differential feeding affects larval gene networks sensitive to nutritional status (the target of rapamycin (TOR) pathway; Patel et al. 2007) to change DNA methylation (Kucharski et al. 2008) and juvenile hormone (JH) titers, with titers higher in queen- than in worker-destined larvae (Hartfelder and Engels 1998). Until the fourth instar, queen- and worker-destined larvae have the same number of ovariole primordia (Reginato and Cruz-Landim 2001). Lower JH titer in workers coincides with disintegration of parts of the cytoskeleton in the germ cells and apoptosis, which decreases ovariole number by the fifth instar (Capella and Hartfelder 1998, 2002). In accord, worker ovarioles can be rescued by JH application during the fourth and early fifth instar (Capella and Hartfelder 1998, 2002).Worker ovariole number and the extent of queen–worker dimorphism for ovariole number vary between species of Apis and between recognized races and strains of Apis mellifera. Both A. cerana and A. mellifera workers typically have <20 total ovarioles (Kapil 1962; Michener and Brothers 1974), but A. cerana queens have only ∼140 ovarioles (Velthuis et al. 1971) while A. mellifera have 200–360 (Michener 1974). In contrast, A. dorsata queens have 248–274 ovarioles and workers have 22–106 (Velthuis et al. 1971). Ruttner and Hesse (1981) studied seven races of A. mellifera and found mean total worker ovariole numbers ranging from 6.4 in A. mellifera mellifera to 18.8 in A. mellifera capensis. Several studies provide evidence that variation in worker ovariole number within populations and between strains has a strong genetic component (Diniz et al. 1993; Thuller et al. 1996, 1998; Jordan et al. 2008).Here, we compared the distribution of worker ovariole number in colonies from a population of feral Africanized bees in Arizona with commercial European bees. Africanized and European bees are derived from lineages separated for ∼1 million years (Whitfield et al. 2006a) and differ in a variety of traits including body size, development time, defensiveness, and behavioral traits associated with the pollen hoarding syndrome (Winston et al. 1983, 1987; Pankiw 2003). Genome scans have identified a number of loci that differ between Africanized and European lineages, and at least some of these genetic differences seem to be the result of divergent selection (Pankiw 2003; Whitfield et al. 2006a; Zayed and Whitfield 2008). In addition, QTL mapping studies for body size and defensive behavior (Hunt et al. 1998, 2007) have suggested few genes with major effect underlying some of these lineage differences.We first describe crosses between Africanized and European bees that revealed segregating variation for extreme ovariole number in workers that were sensitive to the social environment. Next, we describe the results of selective pooled DNA QTL mapping of worker ovariole number in two Africanized backcrosses with transgressive worker ovariole phenotypes, and we list potential candidate genes in the regions of the detected QTL. Finally, we demonstrate that variation in ovariole number, albeit unusual, correlates with differences in worker foraging behavior that have previously been shown to be linked to normal variation in ovariole number.     

Patterns and Processes of Genome-Wide Divergence Between North American and African Drosophila melanogaster

Genomic tools and analyses are now being widely used to understand genome-wide patterns and processes associated with speciation and adaptation. In this article, we apply a genomics approach to the model organism Drosophila melanogaster. This species originated in Africa and subsequently spread and adapted to temperate environments of Eurasia and the New World, leading some populations to evolve reproductive isolation, especially between cosmopolitan and Zimbabwean populations. We used tiling arrays to identify highly differentiated regions within and between North America (the United States and Caribbean) and Africa (Cameroon and Zimbabwe) across 63% of the D. melanogaster genome and then sequenced representative fragments to study their genetic divergence. Consistent with previous findings, our results showed that most differentiation was between populations living in Africa vs. outside of Africa (i.e., “out-of-Africa” divergence), with all other geographic differences being less substantial (e.g., between cosmopolitan and Zimbabwean races). The X chromosome was much more strongly differentiated than the autosomes between North American and African populations (i.e., greater X divergence). Overall differentiation was positively associated with recombination rates across chromosomes, with a sharp reduction in regions near centromeres. Fragments surrounding these high F_ST sites showed reduced haplotype diversity and increased frequency of rare and derived alleles in North American populations compared to African populations. Nevertheless, despite sharp deviation from neutrality in North American strains, a small set of bottleneck/expansion demographic models was consistent with patterns of variation at the majority of our high F_ST fragments. Although North American populations were more genetically variable compared to Europe, our simulation results were generally consistent with those previously based on European samples. These findings support the hypothesis that most differentiation between North America and Africa was likely driven by the sorting of African standing genetic variation into the New World via Europe. Finally, a few exceptional loci were identified, highlighting the need to use an appropriate demographic null model to identify possible cases of selective sweeps in species with complex demographic histories.THE study of genetic differentiation between populations and species has recently been empowered by the use of genomic techniques and analysis (e.g., Noor and Feder 2006; Stinchcombe and Hoekstra 2008). In the past decade, genetic studies of adaptation and speciation have taken advantage of emerging molecular techniques to scan the genomes of diverging populations for highly differentiated genetic regions (e.g., Wilding et al. 2001; Emelianov et al. 2003; Beaumont and Balding 2004; Campbell and Bernatchez 2004; Scotti-Saintagne et al. 2004; Achere et al. 2005; Turner et al. 2005; Vasemagi et al. 2005; Bonin et al. 2006, 2007; Murray and Hare 2006; Savolainen et al. 2006; Yatabe et al. 2007; Nosil et al. 2008, 2009; Turner et al. 2008a,b; Kulathinal et al. 2009). As a result, genome scans can identify candidate regions that may be associated with adaptive evolution between diverging populations and, more broadly, are able to describe genome-wide patterns and processes of population differentiation (Begun et al. 2007; Stinchcombe and Hoekstra 2008).Genome scans in well-studied genetic model species such as Drosophila melanogaster gain particular power because differentiated loci are mapped to a well-annotated genome. Moreover, the evolutionary history of D. melanogaster is rich with adaptive and demographic events with many parallels to human evolution. Most notable is the historical out-of-Africa migration and subsequent adaptation to temperate ecological environments of Europe, Asia, North America, and Australia. This has resulted in widespread genetic and phenotypic divergence between African and non-African populations (e.g., David and Capy 1988; Begun and Aquadro 1993; Capy et al. 1994; Colegrave et al. 2000; Rouault et al. 2001; Takahashi et al. 2001; Caracristi and Schlötterer 2003; Baudry et al. 2004; Pool and Aquadro 2006; Schmidt et al. 2008; Yukilevich and True 2008a,b). Further, certain populations in Africa and in the Caribbean vary in their degree of reproductive isolation from populations in more temperate regions (Wu et al. 1995; Hollocher et al. 1997; Yukilevich and True 2008a,b). In particular, the Zimbabwe and nearby populations of southern Africa are strongly sexually isolated from all other populations, designating them as a distinct behavioral race (Wu et al. 1995).D. melanogaster has received a great deal of attention from the population geneticists in studying patterns of sequence variation across African and non-African populations. Many snapshots have been taken of random microsatellite and SNP variants spread across X and autosomes, and these have generated several important conclusions. Polymorphism patterns in European populations are characterized by reduced levels of nucleotide and haplotype diversity, an excess of high frequency-derived polymorphisms, and elevated levels of linkage disequilibrium relative to African populations (e.g., Begun and Aquadro 1993; Andolfatto 2001; Glinka et al. 2003; Haddrill et al. 2005; Ometto et al. 2005; Thornton and Andolfatto 2006; Hutter et al. 2007; Singh et al. 2007). These results have been generally interpreted as compatible with population size reduction/bottlenecks followed by recent population expansions. On the other hand, African populations are generally assumed either to have been relatively constant in size over time or to have experienced population size expansions. They generally show higher levels of nucleotide and haplotype diversity, an excess of rare variants, and a deficit of high frequency-derived alleles (Glinka et al. 2003; Ometto et al. 2005; Pool and Aquadro 2006; Hutter et al. 2007; but see Haddrill et al. 2005 for evidence of bottlenecks in Africa).Previous work also shows that the ratio of X-linked to autosomal polymorphism deviates from neutral expectations in opposite directions in African and European populations with more variation on the X than expected in Africa and less variation on the X than expected in Europe (Andolfatto 2001; Kauer et al. 2002; Hutter et al. 2007; Singh et al. 2007). The deviation from neutrality in the ratio of X-autosome polymorphism may be explained by positive selection being more prevalent on the X in Europe and/or by a combination of bottlenecks and male-biased sex ratios in Europe and female-biased sex ratios in Africa (Charlesworth 2001; Hutter et al. 2007; Singh et al. 2007). The selective explanation stems from the argument that, under the hitchhiking selection model, X-linked loci are likely to be more affected by selective sweeps than autosomal loci (Maynard Smith and Haigh 1974; Charlesworth et al. 1987; Vicoso and Charlesworth 2006, 2009).The relative contribution of selective and demographic processes in shaping patterns of genomic variation and differentiation is highly debated (Wall et al. 2002; Glinka et al. 2003; Haddrill et al. 2005; Ometto et al. 2005; Schöfl and Schlötterer 2004; Thornton and Andolfatto 2006; Hutter et al. 2007; Singh et al. 2007; Shapiro et al. 2007; Stephan and Li 2007; Hahn 2008; Macpherson et al. 2008; Noor and Bennett 2009; Sella et al. 2009). This is especially the case in D. melanogaster because derived non-African populations have likely experienced a complex set of demographic events during their migration out of Africa (e.g., Thornton and Andolfatto 2006; Singh et al. 2007; Stephan and Li 2007), making population genetics signatures of demography and selection difficult to tease apart (e.g., Macpherson et al. 2008). Thus it is still unclear what role selection has played in shaping overall patterns of genomic variation and differentiation relative to demographic processes in this species.While there is a long tradition in studying arbitrarily or opportunistically chosen sequences in D. melanogaster, genomic scans that focus particularly on highly differentiated sites across the genome have received much less attention. Such sites are arguably the best candidates to resolve the debate on which processes have shaped genomic differentiation within species (e.g., Przeworski 2002). Recently, a genome-wide scan of cosmopolitan populations in the United States and in Australia was performed to investigate clinal genomic differentiation on the two continents (Turner et al. 2008a). Many single feature polymorphisms differentiating Northern and Southern Hemisphere populations were identified. Among the most differentiated loci in common between continents, 80% were differentiated in the same orientation relative to the Equator, implicating selection as the likely explanation (Turner et al. 2008a). Larger regions of genomic differentiation within and between African and non-African populations have also been discovered, some of them possibly being driven by divergent selection (e.g., Dopman and Hartl 2007; Emerson et al. 2008; Turner et al. 2008a, Aguade 2009). Despite this recent progress, we still know relatively little about large-scale patterns of genomic differentiation in this species, especially between African and non-African populations, and whether most of this differentiation is consistent with demographic processes alone or if it requires selective explanations.In this work, we explicitly focus on identifying differentiated sites across the genome between U.S., Caribbean, West African, and Zimbabwean populations. This allows us to address several fundamental questions related to genomic evolution in D. melanogaster, such as the following: (1) Do genome-wide patterns of differentiation reflect patterns of reproductive isolation? (2) Is genomic differentiation random across and within chromosomes or are some regions overrepresented? (3) What are the population genetics properties of differentiated sites and their surrounding sequences? (4) Can demographic historical processes alone explain most of the observed differentiation on a genome-wide level or is it necessary to involve selection in their explanation?In general, our findings revealed that most genomic differentiation within D. melanogaster shows an out-of-Africa genetic signature. These results are inconsistent with the notion that most genomic differentiation occurs between cosmopolitan and Zimbabwean reproductively isolated races. Further, we found that the X is more differentiated between North American and African populations and more strongly deviates from pure neutrality in North American populations relative to autosomes. Nevertheless, our article shows that much of this deviation from neutrality is broadly consistent with several demographic null models, with a few notable exceptions. Athough this does not exclude selection as a possible alternative mechanism for the observed patterns, it supports the idea that most differentiation in D. melanogaster was likely driven by the sorting of African standing genetic variation into the New World.     

Age Structure,Changing Demography and Effective Population Size in Atlantic Salmon (Salmo salar)

Effective population size (N_e) is a central evolutionary concept, but its genetic estimation can be significantly complicated by age structure. Here we investigate N_e in Atlantic salmon (Salmo salar) populations that have undergone changes in demography and population dynamics, applying four different genetic estimators. For this purpose we use genetic data (14 microsatellite markers) from archived scale samples collected between 1951 and 2004. Through life table simulations we assess the genetic consequences of life history variation on N_e. Although variation in reproductive contribution by mature parr affects age structure, we find that its effect on N_e estimation may be relatively minor. A comparison of estimator models suggests that even low iteroparity may upwardly bias N_e estimates when ignored (semelparity assumed) and should thus empirically be accounted for. Our results indicate that N_e may have changed over time in relatively small populations, but otherwise remained stable. Our ability to detect changes in N_e in larger populations was, however, likely hindered by sampling limitations. An evaluation of N_e estimates in a demographic context suggests that life history diversity, density-dependent factors, and metapopulation dynamics may all affect the genetic stability of these populations.THE effective size of a population (N_e) is an evolutionary parameter that can be informative on the strength of stochastic evolutionary processes, the relevance of which relative to deterministic forces has been debated for decades (e.g., Lande 1988). Stochastic forces include environmental, demographic, and genetic components, the latter two of which are thought to be more prominent at reduced population size, with potentially detrimental consequences for average individual fitness and population persistence (Newman and Pilson 1997; Saccheri et al. 1998; Frankham 2005). The quantification of N_e in conservation programs is thus frequently advocated (e.g., Luikart and Cornuet 1998; Schwartz et al. 2007), although gene flow deserves equal consideration given its countering effects on genetic stochasticity (Frankham et al. 2003; Palstra and Ruzzante 2008).Effective population size is determined mainly by the lifetime reproductive success of individuals in a population (Wright 1938; Felsenstein 1971). Variance in reproductive success, sex ratio, and population size fluctuations can reduce N_e below census population size (Frankham 1995). Given the difficulty in directly estimating N_e through quantification of these demographic factors (reviewed by Caballero 1994), efforts have been directed at inferring N_e indirectly through measurement of its genetic consequences (see Leberg 2005, Wang 2005, and Palstra and Ruzzante 2008 for reviews). Studies employing this approach have quantified historical levels of genetic diversity and genetic threats to population persistence (e.g., Nielsen et al. 1999b; Miller and Waits 2003; Johnson et al. 2004). N_e has been extensively studied in (commercially important) fish species, due to the common availability of collections of archived samples that facilitate genetic estimation using the temporal method (e.g., Hauser et al. 2002; Shrimpton and Heath 2003; Gomez-Uchida and Banks 2006; Saillant and Gold 2006).Most models relating N_e to a population''s genetic behavior make simplifying assumptions regarding population dynamics. Chiefly among these is the assumption of discrete generations, frequently violated in practice given that most natural populations are age structured with overlapping generations. Here, theoretical predictions still apply, provided that population size and age structure are constant (Felsenstein 1971; Hill 1972). Ignored age structure can introduce bias into temporal genetic methods for the estimation of N_e, especially for samples separated by time spans that are short relative to generation interval (Jorde and Ryman 1995; Waples and Yokota 2007; Palstra and Ruzzante 2008). Moreover, estimation methods that do account for age structure (e.g., Jorde and Ryman 1995) still assume this structure to be constant. Population dynamics will, however, likely be altered as population size changes, thus making precise quantifications of the genetic consequences of acute population declines difficult (Nunney 1993; Engen et al. 2005; Waples and Yokota 2007). This problem may be particularly relevant when declines are driven by anthropogenic impacts, such as selective harvesting regimes, that can affect age structure and N_e simultaneously (Ryman et al. 1981; Allendorf et al. 2008). Demographic changes thus have broad conservation implications, as they can affect a population''s sensitivity to environmental stochasticity and years of poor recruitment (Warner and Chesson 1985; Ellner and Hairston 1994; Gaggiotti and Vetter 1999). Consequently, although there is an urgent need to elucidate the genetic consequences of population declines, relatively little is understood about the behavior of N_e when population dynamics change (but see Engen et al. 2005, 2007).Here we focus on age structure and N_e in Atlantic salmon (Salmo salar) river populations in Newfoundland and Labrador. The freshwater habitat in this part of the species'' distribution range is relatively pristine (Parrish et al. 1998), yet Atlantic salmon in this area have experienced demographic declines, associated with a commercial marine fishery, characterized by high exploitation rates (40–80% of anadromous runs; Dempson et al. 2001). A fishery moratorium was declared in 1992, with rivers displaying differential recovery patterns since then (Dempson et al. 2004b), suggesting a geographically variable impact of deterministic and stochastic factors, possibly including genetics. An evaluation of those genetic consequences thus requires accounting for potential changes in population dynamics as well as in life history. Life history in Atlantic salmon can be highly versatile (Fleming 1996; Hutchings and Jones 1998; Fleming and Reynolds 2004), as exemplified by the high variation in age-at-maturity displayed among and within populations (Hutchings and Jones 1998), partly reflecting high phenotypic plasticity (Hutchings 2004). This diversity is particularly evident in the reproductive biology of males, which can mature as parr during juvenile freshwater stages (Jones and King 1952; Fleming and Reynolds 2004) and/or at various ages as anadromous individuals, when returning to spawn in freshwater from ocean migration. Variability in life history strategies is further augmented by iteroparity, which can be viewed as a bet-hedging strategy to deal with environmental uncertainty (e.g., Orzack and Tuljapurkar 1989; Fleming and Reynolds 2004). Life history diversity and plasticity may allow salmonid fish populations to alter and optimize their life history under changing demography and population dynamics, potentially acting to stabilize N_e. Reduced variance in individual reproductive success at low breeder abundance (genetic compensation) will achieve similar effects and might be a realistic aspect of salmonid breeding systems (Ardren and Kapuscinski 2003; Fraser et al. 2007b). Little is currently known about the relationships between life history plasticity, demographic change and N_e, partly due to scarcity of the multivariate data required for these analyses.Our objective in this article is twofold. First, we use demographic data for rivers in Newfoundland to quantify how life history variation influences age structure in Atlantic salmon and hence N_e and its empirical estimation from genetic data. We find that variation in reproductive contribution by mature parr has a much smaller effect on the estimation of N_e than is often assumed. Second, we use temporal genetic data to estimate N_e and quantify the genetic consequences of demographic changes. We attempt to account for potential sources of bias, associated with (changes in) age structure and life history, by using four different analytical models to estimate N_e: a single-sample estimator using the linkage disequilibrium method (Hill 1981), the temporal model assuming discrete generations (Nei and Tajima 1981; Waples 1989), and two temporal models for species with overlapping generations (Waples 1990a,b; Jorde and Ryman 1995) that differ principally in assumptions regarding iteroparity. A comparison of results from these different estimators suggests that iteroparity may often warrant analytical consideration, even when it is presumably low. Although sometimes limited by statistical power, a quantification and comparison of temporal changes in N_e among river populations suggests a more prominent impact of demographic changes on N_e in relatively small river populations.     

Map-Based Cloning of the Gene Associated With the Soybean Maturity Locus E3

Photosensitivity plays an essential role in the response of plants to their changing environments throughout their life cycle. In soybean [Glycine max (L.) Merrill], several associations between photosensitivity and maturity loci are known, but only limited information at the molecular level is available. The FT3 locus is one of the quantitative trait loci (QTL) for flowering time that corresponds to the maturity locus E3. To identify the gene responsible for this QTL, a map-based cloning strategy was undertaken. One phytochrome A gene (GmPhyA3) was considered a strong candidate for the FT3 locus. Allelism tests and gene sequence comparisons showed that alleles of Misuzudaizu (FT3/FT3; JP28856) and Harosoy (E3/E3; PI548573) were identical. The GmPhyA3 alleles of Moshidou Gong 503 (ft3/ft3; JP27603) and L62-667 (e3/e3; PI547716) showed weak or complete loss of function, respectively. High red/far-red (R/FR) long-day conditions enhanced the effects of the E3/FT3 alleles in various genetic backgrounds. Moreover, a mutant line harboring the nonfunctional GmPhyA3 flowered earlier than the original Bay (E3/E3; PI553043) under similar conditions. These results suggest that the variation in phytochrome A may contribute to the complex systems of soybean flowering response and geographic adaptation.FLOWERING represents the transition from the vegetative to the reproductive phase in plants. Various external cues, such as photoperiod and temperature, are known to initiate plant flowering under the appropriate seasonal conditions. Of these cues, light is the most important, being received by several photoreceptors, including the red light (R) and the far-red light (FR)-absorbing phytochromes and the blue/UV-A absorbing cryptochromes and phototorpins (Chen et al. 2004).Phytochrome is the best characterized of these photoreceptors. All higher plant phytochromes are thought to exist as specific dimer combinations (Sharrock and Clack 2004), with each monomer being attached to a light-absorbing linear tetrapyrrole, phytochromobilin. The phytochrome apoproteins are synthesized within the cytosol and assemble autocatalytically with a chromophore to form the phytochrome holoproteins. The R-absorbing form (Pr) is thought to be inactive but is then converted to the active FR-absorbing form (Pfr) by R absorption. The absorption of light triggers the transfer of the phytochrome to the nucleus, where it regulates gene expression. In most plant species, the phytochrome apoproteins are encoded by a small gene family. Type I phytochrome is degraded in the light and is abundant in dark-grown seedlings, whereas type II phytochrome is relatively stable in the light (reviewed by Bae and Choi 2008). In Arabidopsis, five phytochromes (PhyA–E) have been characterized (Clack et al. 1994; Quail et al. 1995). PhyA is type I and is responsible for the very low fluence response and high irradiance response, whereas the other phytochromes are type II and are responsible for red-far/red reversible low fluence response (reviewed by Whitelam et al. 1998).It is well known that mutations in the phytochrome A gene affect the photoperiodic control of flowering. In Arabidopsis, a phyA mutant flowered later in either long-day or short-day conditions with a night break (Johnson et al. 1994; Reed et al. 1994). In rice, combinations of mutant alleles of phytochrome genes conferred various effects on the flowering phenotype. For example, the phyA phyB and phyA phyC double mutants grown under natural-day-length conditions showed earlier flowering phenotypes than wild-type plants (Takano et al. 2005). In pea, a long-day plant, loss- or gain-of-function phyA mutants displayed late or early flowering phenotypes, respectively (Weller et al. 1997, 2001). It is likely that photoperiodic response via phyA signaling is important for crop adaptation to a wide range of growing conditions.In soybean [Glycine max (L.) Merrill], several maturity loci, designated as E loci (Cober et al. 1996a), have been characterized by classical methods. These are E1 and E2 (Bernard 1971), E3 (Buzzell 1971), E4 (Buzzell and Voldeng 1980), E5 (McBlain and Bernard 1987), E6 (Bonato and Vello 1999), and E7 (Cober and Voldeng 2001). Of these, the E1, E3, and E4 loci have been suggested to be related to photoperiod sensitivity under various light conditions (Saidon et al. 1989; Cober et al. 1996b; Abe et al. 2003). In previous studies, using the same populations as in this study, three flowering-time quantitative trait loci (QTL)—FT1, FT2, and FT3 loci—were identified and considered to be identical with the maturity loci E1, E2, and E3, respectively (Yamanaka et al. 2001; Watanabe et al. 2004). Although many loci related to soybean flowering and maturity have been identified, and some candidate genes were recognized using near isogenic lines (NILs) (Tasma and Shoemaker 2003), most of the genes responsible for these loci have not yet been isolated except for the E4 gene. Liu et al. (2008) reported an association between phytochrome A and photoperiod sensitivity. A retrotransposon sequence inserted into the exon of the e4 allele conferred an early flowering phenotype under long-day conditions extended by incandescent lighting.A relationship between the E3 gene and some photoreceptor genes was suggested from different photosensitivity responses of various soybean NILs (Cober et al. 1996a). Cober and Voldeng (1996) also reported a linkage relationship between the E3 and Dt1 loci, which is related to a determinate or indeterminate growth habit phenotype. Additionally, Molnar et al. (2003) reported that Satt229, on linkage group (LG) L, was a proximal simple sequence repeat (SSR) marker to the E3 loci. According to the Soybean Genome Database (Shultz et al. 2006a,b, 2007; http://soybeangenome.siu.edu/) and the Legume Information System (LIS; http://www.comparative-legumes.org/), there are numerous QTL and >60 loci associated with various agronomic traits in the region between Dt1 and Satt373 (∼30–40 cM). This extremely large number of QTL may be the result of linkage between the Dt1 and E3 loci because both loci can affect many aspects of plant morphology. Among these QTL, several associations with the E3 gene have been reported (Mansur et al. 1996; Orf et al. 1999; Funatsuki et al. 2005; Kahn et al. 2008).To identify the genes responsible for the target QTL, fine mapping and map-based cloning strategies are necessary (Salvi and Tuberosa 2005). QTL analysis using intercross-derived populations, such as F₂ and recombinant inbred lines (RILs), have some limitations in genome resolution (10–30 cM) because of the simultaneous segregation of several loci affecting the same trait (Kearsey and Farquhar 1998). Additional strategies are therefore required to locate QTL more precisely. The use of NILs that differ at a single QTL is an effective approach for fine mapping and characterization of an individual locus (Salvi and Tuberosa 2005). However, the development of NILs through repeated backcrossing is time-consuming and laborious (Tuinstra et al. 1997). The use of a residual heterozygous line (RHL), as proposed by Yamanaka et al. (2004), and which is derived from RIL, is a powerful tool for precisely evaluating QTL (Haley et al. 1994). An RHL harbors a heterozygous region where the target QTL is located and a homozygous background in most other regions of the genome. Tuinstra et al. (1997) used a similar term, heterogeneous inbred family, for a selfed RHL population to identify the QTL associated with seed weight in sorghum.This RHL strategy has already been used to identify loci underlying resistance to pathogens in soybean (Njiti et al. 1998; Meksem et al. 1999; Triwitayakorn et al. 2005). After identification of the target loci, novel DNA markers tightly linked to the loci were developed using the amplified fragment length polymorphism (AFLP) method (Meksem et al. 2001a,b). Physical contigs, screened by sequence-characterized amplified region (SCAR) markers converted from these AFLP fragments, are ideal sources for identifying candidate genes for the target traits (Ruben et al. 2006).The aim of this study is to characterize the FT3 locus using a map-based cloning strategy and to confirm the gene responsible for the E3/FT3 locus by allelism tests through comparisons of gene sequences and photosensitivity of several alleles.     

Phenotypic Consequences of Aneuploidy in Arabidopsis thaliana

Aneuploid cells are characterized by incomplete chromosome sets. The resulting imbalance in gene dosage has phenotypic consequences that are specific to each karyotype. Even in the case of Down syndrome, the most viable and studied form of human aneuploidy, the mechanisms underlying the connected phenotypes remain mostly unclear. Because of their tolerance to aneuploidy, plants provide a powerful system for a genome-wide investigation of aneuploid syndromes, an approach that is not feasible in animal systems. Indeed, in many plant species, populations of aneuploid individuals can be easily obtained from triploid individuals. We phenotyped a population of Arabidopsis thaliana aneuploid individuals containing 25 different karyotypes. Even in this highly heterogeneous population, we demonstrate that certain traits are strongly associated with the dosage of specific chromosome types and that chromosomal effects can be additive. Further, we identified subtle developmental phenotypes expressed in the diploid progeny of aneuploid parent(s) but not in euploid controls from diploid lineages. These results indicate long-term phenotypic consequences of aneuploidy that can persist after chromosomal balance has been restored. We verified the diploid nature of these individuals by whole-genome sequencing and discuss the possibility that trans-generational phenotypic effects stem from epigenetic modifications passed from aneuploid parents to their diploid progeny.THE genome of aneuploid individuals contains incomplete chromosome sets. The balance between chromosome types, and the genes they encode, is compromised, resulting in altered expression of many genes, including genes with dosage-sensitive effects on phenotypes. In humans, only a few types of aneuploid karyotypes are viable (Hassold and Hunt 2001), highlighting the deleterious effect of chromosome imbalance. The most commonly known viable form of aneuploidy in humans is Down syndrome, which results from a trisomy of chromosome 21 in an otherwise diploid background. Down syndrome patients exhibit many specific phenotypes, sometimes visible only in a subset of patients (Antonarakis et al. 2004). For phenotypes found in all Down syndrome patients, the penetrance of each phenotype varies between patients (Antonarakis et al. 2004). Despite the increasing amount of information available about the human genome and the availability of a mouse model for Down syndrome (O''Doherty et al. 2005), the genes responsible for most of the phenotypes associated with Down syndrome are still unknown (Patterson 2007; Korbel et al. 2009; Patterson 2009). Recently, detailed phenotypic analyses of as many as 30 aneuploid patients have allowed the identification of susceptibility regions for several specific phenotypes (Patterson 2007, 2009; Korbel et al. 2009; Lyle et al. 2009), but the specific genes remain to be identified. Understanding the physiology of aneuploidy is not only relevant to those individuals with aneuploid genomes but also to understanding cancer since most cancerous cells are aneuploid (Matzke et al. 2003; Pihan and Doxsey 2003; Storchova and Pellman 2004; Holland and Cleveland 2009; Williams and Amon 2009) or the consequences of copy number variation and dosage sensitivity (Dear 2009; Henrichsen et al. 2009).Plants are more tolerant of aneuploidy than animals (Matzke et al. 2003) for reasons that remain unclear. Since the discovery of the Datura trisomic “chromosome mutants” by Blakeslee (1921, 1922), viable trisomics of each chromosome type have been described in numerous species. Trisomics exhibit phenotypes specific to the identity of the triplicated chromosome (Blakeslee 1922; Khush 1973; Koornneef and Van der Veen 1983; Singh 2003). More complex aneuploids, i.e., individuals carrying more than one additional chromosome, can be viable as well and have been observed in many plants species, especially among the progeny of triploid individuals (McClintock 1929; Levan 1942; Johnsson 1945; Khush 1973). Some species appear to be more tolerant of complex aneuploidies than others, suggesting a genetic basis for aneuploidy tolerance (Satina and Blakeslee 1938; Khush 1973; Ramsey and Schemske 2002; Henry et al. 2009). Aneuploid individuals frequently appear spontaneously within polyploid plant populations, presumably due to a failure to equally partition the multiple chromosome sets at meiosis (Randolph 1935; Doyle 1986). These aneuploids exhibit few or subtle phenotypic abnormalities and can often compete with their euploid progenitors (Ramsey and Schemske 1998). Plants therefore provide an excellent opportunity for a genome-wide investigation of aneuploid syndromes: sample size is not limited, phenotypes can be described and assessed in detail, and plant aneuploid populations provide a complex mixture of viable karyotypes.In this article, we report our investigation of the relationship between phenotype and karyotype in populations of aneuploid Arabidopsis thaliana plants. All simple trisomics of A. thaliana have been previously isolated and phenotypically characterized (Steinitz-Sears 1962; Lee-Chen and Steinitz-Sears 1967; Steinitz-Sears and Lee-Chen 1970; Koornneef and Van der Veen 1983), demonstrating that they are tolerated in A. thaliana. We previously reported that aneuploid swarms—populations of aneuploid individuals of varying aneuploid karyotypes—could be obtained from the progeny of triploid A. thaliana individuals (Henry et al. 2005, 2009). Using a combination of a quantitative PCR-based method and flow cytometry, we were able to derive the full aneuploid karyotype of each of these individuals (Henry et al. 2006). We further crossed triploid A. thaliana to diploid or tetraploid individuals and demonstrated that at least 44 of the 60 possible aneuploid karyotypes that could result from these crosses (aneuploid individuals carrying between 11 and 19 chromosomes) were viable and successfully produced adult plants. Taken together, these populations and methods make it possible to explore the basis of aneuploid syndromes in A. thaliana. In this study, we were able to phenotypically characterize at least one individual from 25 different aneuploid karyotypes falling between diploidy and tetraploidy. We demonstrated that specific phenotypes are affected by the dosage of specific chromosome types. The effect of the dosage of specific chromosome types on traits was additive and could be used to predict the observed phenotype. The availability of multiple generations of aneuploid and euploid individuals allowed us to investigate potential long-term effects of aneuploidy as well as parent-of-origin effects on aneuploid phenotypes.     

Mating-System Variation,Demographic History and Patterns of Nucleotide Diversity in the Tristylous Plant Eichhornia paniculata

Inbreeding in highly selfing populations reduces effective size and, combined with demographic conditions associated with selfing, this can erode genetic diversity and increase population differentiation. Here we investigate the role that variation in mating patterns and demographic history play in shaping the distribution of nucleotide variation within and among populations of the annual neotropical colonizing plant Eichhornia paniculata, a species with wide variation in selfing rates. We sequenced 10 EST-derived nuclear loci in 225 individuals from 25 populations sampled from much of the geographic range and used coalescent simulations to investigate demographic history. Highly selfing populations exhibited moderate reductions in diversity but there was no significant difference in variation between outcrossing and mixed mating populations. Population size interacted strongly with mating system and explained more of the variation in diversity within populations. Bayesian structure analysis revealed strong regional clustering and selfing populations were highly differentiated on the basis of an analysis of F_st. There was no evidence for a significant loss of within-locus linkage disequilibrium within populations, but regional samples revealed greater breakdown in Brazil than in selfing populations from the Caribbean. Coalescent simulations indicate a moderate bottleneck associated with colonization of the Caribbean from Brazil ∼125,000 years before the present. Our results suggest that the recent multiple origins of selfing in E. paniculata from diverse outcrossing populations result in higher diversity than expected under long-term equilibrium.THE rate of self-fertilization in hermaphrodite organisms is expected to affect a number of important features of population genetic structure and diversity. Most directly, homozygosity increases as a function of the selfing rate and thus reduces the effective population size (N_e), up to twofold with complete selfing (Pollak 1987; Charlesworth et al. 1993; Nordborg 2000). Further, because of increased homozygosity, crossing over rarely occurs between heterozygous sites, thus increasing linkage disequilibrium (LD). Higher LD causes stronger hitchhiking effects such as selective sweeps, background selection, and Hill–Robertson interference, all of which are expected to further reduce the amount of neutral genetic variation within populations (reviewed in Charlesworth and Wright 2001).Population genetic processes resulting from inbreeding may be further augmented by demographic and life-history characteristics associated with the selfing habit. In particular, selfing populations can be founded by single individuals, resulting in striking reductions in diversity as a result of genetic bottlenecks and reproductive isolation. The capacity for uniparental reproduction gives many selfers prolific colonizing ability and the capacity to establish after long-distance dispersal, especially in comparison with obligate outcrossers (Baker 1955; Pannell and Barrett 1998). The colonization–extinction dynamics typical of many selfing species and limited pollen-mediated gene flow also increase differentiation among populations, resulting in considerable population subdivision (Hamrick and Godt 1990, 1996; Schoen and Brown 1991). Although the total amounts of among-population variation may be less affected by these processes (Pannell and Charlesworth 1999; Ingvarsson 2002), the demographic and life-history characteristics of many selfing species are likely to result in nonequilibrium conditions occurring in selfing populations.In many taxa where selfing has evolved it may be of relatively recent origin (Schoen et al. 1997; Takebayashi and Morrell 2001; Foxe et al. 2009; Guo et al. 2009). Where selfing has recently established, demographic forces associated with colonization may be as important as the mating system per se in structuring patterns of diversity. For example, if selfing originates through the establishment of a small number of founders, we would expect a sharp reduction in diversity relative to the outcrossing progenitor and a strong signature of a genetic bottleneck. In contrast, if selfing has evolved recently through the spread of genetic modifiers of small effect, newly established populations may retain significant amounts of ancestral polymorphism from their outcrossing progenitors. In this latter case populations may retain considerably more variation than expected under long-term equilibrium predictions.Molecular evidence for reduced nucleotide diversity and greater differentiation among populations of selfing taxa compared to populations of related outcrossing taxa has been reported from Leavenworthia (Liu et al. 1998, 1999), Arabidopsis (Savolainen et al. 2000; Wright et al. 2002), Solanum (Baudry et al. 2001), Mimulus (Sweigart and Willis 2003), Amsinckia (Perusse and Schoen 2004), and Caenorhabditis (Graustein et al. 2002; Cutter et al. 2006; Cutter 2008). In each case the reduction in diversity was more severe than the twofold reduction predicted for selfing populations at equilibrium. This indicates that factors in addition to the mating system are reducing diversity, but it has been difficult to uncouple the relative importance of genetic hitchhiking from the ecology and demographic history of selfing taxa. This challenge parallels similar difficulties in efforts to distinguish selective from demographic explanations in population genetic studies of Drosophila (Haddrill et al. 2005; Ometto et al. 2005; Thornton and Andolfatto 2006; Jensen et al. 2008). However, in many plant populations, especially those with annual life histories and small structured populations, demographic processes may play a more prominent role in causing reduced diversity than increased hitchhiking associated with selfing.Molecular population genetic studies of selfing in plants have generally focused on either small samples from a large number of populations (e.g., Sweigart and Willis 2003; Nordborg et al. 2005) or relatively large within-population samples from a small number of populations (e.g., Baudry et al. 2001). Ideally, a deeper sampling both within and among populations combined with independent ecological and historical information is required to improve understanding of the interplay of demographic and selective factors. Here we address these issues by examining patterns of nucleotide diversity within a large sample of populations of Eichhornia paniculata (Pontederiaceae), an annual species for which there is considerable ecological and demographic information (reviewed in Barrett and Husband 1997).E. paniculata occurs primarily in northeastern (N.E.) Brazil and the Caribbean islands of Cuba and Jamaica. Various lines of evidence suggest that Brazil is the original source region for Caribbean populations (reviewed in Barrett et al. 2009). Populations of E. paniculata exhibit striking mating-system diversity, ranging from predominantly outcrossing to those that are highly selfing (outcrossing rate, t = 0.002–0.96; n = 54 populations) (Barrett and Husband 1990; Barrett et al. 1992). Variation in mating system is associated with the evolutionary breakdown of the species'' tristylous genetic polymorphism and the spread and fixation of selfing variants capable of autonomous self-pollination (Barrett et al. 1989). Populations of E. paniculata are characterized by three morph structures: trimorphic with long-, mid-, and short-styled morphs (hereafter L-, M-, and S-morphs); dimorphic, with two floral morphs, most commonly the L- and M-morphs; and monomorphic, primarily composed of selfing variants of the M-morph. The morph structure and presence of selfing variants within populations explain ∼60% of the variation in outcrossing rates among populations (Barrett and Husband 1990). Trimorphic populations are largely outcrossing, dimorphic populations display mixed mating, and monomorphic populations are highly selfing. Patterns of allozyme variation indicate a reduction in diversity with increased selfing rates and greater among-population differentiation (Glover and Barrett 1987; Barrett and Husband 1990; Husband and Barrett 1993). Finally, studies of the inheritance of mating-system modifiers (Fenster and Barrett 1994; Vallejo-Marín and Barrett 2009) in combination with allozyme (Husband and Barrett 1993) and molecular evidence (Barrett et al. 2009) indicate that the transition from outcrossing to selfing in E. paniculata has occurred on multiple occasions.The goal of our study was to investigate the relation between mating-system variation and neutral molecular diversity for a large sample of E. paniculata populations encompassing most of the geographical range. This was accomplished by collecting multilocus nucleotide sequence data from 225 individuals sampled from 25 populations including trimorphic, dimorphic, and monomorphic populations. Because it has been previously demonstrated that this sequence of morph structures is strongly associated with increasing rates of self-fertilization (see Barrett and Husband 1990), we predicted a decrease in neutral diversity and increases in F_st and linkage disequilibrium from floral trimorphism to monomorphism. This extensive population-level sampling across a wide range of selfing rates allowed us to investigate the relative importance of mating system, geography, and current population size in structuring genetic variation. We also applied the approaches of Bayesian clustering (Pritchard et al. 2000; Falush et al. 2003; Gao et al. 2007) and divergence population genetics (Wakeley and Hey 1997; Hey and Nielsen 2004; Becquet and Przeworski 2007) to investigate the demographic history of E. paniculata and to provide a framework for understanding island colonization and the transition from outcrossing to selfing.     

Genomic Admixture Analysis in European Populus spp. Reveals Unexpected Patterns of Reproductive Isolation and Mating

Admixture between genetically divergent populations facilitates genomic studies of the mechanisms involved in adaptation, reproductive isolation, and speciation, including mapping of the loci involved in these phenomena. Little is known about how pre- and postzygotic barriers will affect the prospects of “admixture mapping” in wild species. We have studied 93 mapped genetic markers (microsatellites, indels, and sequence polymorphisms, ∼60,000 data points) to address this topic in hybrid zones of Populus alba and P. tremula, two widespread, ecologically important forest trees. Using genotype and linkage information and recently developed analytical tools we show that (1) reproductive isolation between these species is much stronger than previously assumed but this cannot prevent the introgression of neutral or advantageous alleles, (2) unexpected genotypic gaps exist between recombinant hybrids and their parental taxa, (3) these conspicuous genotypic patterns are due to assortative mating and strong postzygotic barriers, rather than recent population history. We discuss possible evolutionary trajectories of hybrid lineages between these species and outline strategies for admixture mapping in hybrid zones between highly divergent populations. Datasets such as this one are still rare in studies of natural hybrid zones but should soon become more common as high throughput genotyping and resequencing become feasible in nonmodel species.ADMIXTURE or hybrid zones between genetically divergent populations are increasingly being explored for their use in studies of adaptation, reproductive isolation, and speciation (Rieseberg et al. 1999; Martinsen et al. 2001; Wu 2001; Vines et al. 2003; Payseur et al. 2004; reviewed by Coyne and Orr 2004), especially for their potential in identifying recombinants for gene mapping (otherwise known as “admixture mapping”; Chakraborty and Weiss 1988; Briscoe et al. 1994; Rieseberg et al. 1999; Reich et al. 2005; Slate 2005; Zhu et al. 2005; Lexer et al. 2007; Nolte et al. 2009). In many taxa of animals and plants, recombinants are created by admixture between divergent populations or species in hybrid zones or ecotones (Buerkle and Lexer 2008; Gompert and Buerkle 2009). The growing interest of evolutionary geneticists in admixture has its roots in both basic evolutionary genetics and breeding.With respect to evolutionary genetics, admixed populations have been viewed as important resources for studying the genetics of adaptation and speciation, since the discovery that by fitting geographical clines of allele frequencies across hybrid zones, the strength of intrinsic and extrinsic (ecological) barriers to gene flow can be estimated (Barton and Hewitt 1985; Barton and Gale 1993). More recently, the genomics era has taken these concepts to a new level by providing genetic or physical genome maps for many species so that clines or introgression patterns of individual loci can be compared to their genomic background (see below; Falush et al. 2003; Gompert and Buerkle 2009). Thus, hybrid zones permit the identification and study of quantitative trait loci (QTL), genes, or other genetic elements involved in reproductive isolation and speciation in situ, directly in natural populations, if sufficient genetic recombination has occurred (Rieseberg and Buerkle 2002). In applied genetics, studies of hybrid zones yield information on the genomic architecture of barriers to introgression, which is of great interest to breeders concerned with the establishment of pedigrees for tree selection and domestication (Stettler et al. 1996).Most animal or plant hybrid zones studied to date involve hybridization between parental populations that are much more divergent than the admixed human populations that have been used successfully for gene mapping in human medical genetics (e.g., Reich et al. 2005; Zhu et al. 2005). Little experience exists with interpreting genomic patterns of ancestry and admixture in such highly divergent, nonhuman populations. Early genomic work on hybrid zones, based on dominant genetic markers, suggested the feasibility of mapping genome regions involved in reproductive isolation and speciation (Rieseberg et al. 1999; Rogers et al. 2001), but these studies did not allow tests for selection on genotypes at single loci in different genomic backgrounds. This became possible only recently due to the development of novel analytical tools suited to large numbers of codominant markers, especially linkage models of Bayesian admixture analysis (Falush et al. 2003, 2007) and methods to fit “genomic clines” of codominant marker genotypes across complete genomic admixture gradients (Lexer et al. 2007; Gompert and Buerkle 2009; Nolte et al. 2009; Teeter et al. 2010). Great advances also have been made in interpreting single-locus estimates of genetic divergence between populations and species (Beaumont 2005; Foll and Gaggiotti 2008; Excoffier et al. 2009a). Here, we bring these approaches together to yield novel insights into genomic patterns of reproductive isolation and mating in hybrid zones of two widespread and important members of the “model tree” genus Populus. Our goal was to infer patterns of reproductive isolation and the likely evolutionary trajectories of hybrid populations and to develop strategies for genetic mapping in admixed populations.Populus alba (white poplar) and P. tremula (European aspen) are ecologically divergent (floodplain vs. upland habitat) hybridizing tree species related to P. trichocarpa, the first completely sequenced forest tree (Tuskan et al. 2006). The two species are highly differentiated for neutral DNA-based markers (Lexer et al. 2007) and numerous phenotypic and ecological traits (Lexer et al. 2009). Mosaic hybrid zones between these species often form in riparian habitats (Lexer et al. 2005; hybrids sometimes referred to as P. × canescens) and have been proposed as potential “mapping populations” for identifying QTL and genes of interest in evolutionary biology (Lexer et al. 2007; Buerkle and Lexer 2008) and breeding (Fossati et al. 2004; Lexer et al. 2004). Previous studies of these hybrid zones were conducted with a relatively small number of genetic markers and without making use of linkage information; the genomic composition of hybrid zones between these species has never been studied with a genomewide panel of codominant markers with known linkage relationships. Specifically, we address the following questions in this contribution:(1) What does an analysis of admixture and differentiation based on a genome-wide panel of mapped markers tell us about patterns of reproductive isolation and mating in hybrid zones of European Populus species? (2) What are the likely roles of pre- and postzygotic barriers vs. recent, localized historical factors in generating the observed genomic patterns? (3) What are the practical implications for admixture mapping in hybrid zones between highly divergent populations? We showcase where the genetic peculiarities of hybrid zones will limit their use for gene mapping and where they suggest new approaches that were perhaps not foreseen by geneticists with a focus on human medical applications.     

The Effect of Recent Admixture on Inference of Ancient Human Population History

Despite the widespread study of genetic variation in admixed human populations, such as African-Americans, there has not been an evaluation of the effects of recent admixture on patterns of polymorphism or inferences about population demography. These issues are particularly relevant because estimates of the timing and magnitude of population growth in Africa have differed among previous studies, some of which examined African-American individuals. Here we use simulations and single-nucleotide polymorphism (SNP) data collected through direct resequencing and genotyping to investigate these issues. We find that when estimating the current population size and magnitude of recent growth in an ancestral population using the site frequency spectrum (SFS), it is possible to obtain reasonably accurate estimates of the parameters when using samples drawn from the admixed population under certain conditions. We also show that methods for demographic inference that use haplotype patterns are more sensitive to recent admixture than are methods based on the SFS. The analysis of human genetic variation data from the Yoruba people of Ibadan, Nigeria and African-Americans supports the predictions from the simulations. Our results have important implications for the evaluation of previous population genetic studies that have considered African-American individuals as a proxy for individuals from West Africa as well as for future population genetic studies of additional admixed populations.STUDIES of archeological and genetic data show that anatomically modern humans originated in Africa and more recently left Africa to populate the rest of the world (Tishkoff and Williams 2002; Barbujani and Goldstein 2004; Garrigan and Hammer 2006; Reed and Tishkoff 2006; Campbell and Tishkoff 2008; Jakobsson et al. 2008; Li et al. 2008). Given the central role Africa has played in the origin of diverse human populations, understanding patterns of genetic variation and the demographic history of populations within Africa is important for understanding the demographic history of global human populations. The availability of large-scale single-nucleotide polymorphism (SNP) data sets coupled with recent advances in statistical methodology for inferring parameters in population genetic models provides a powerful means of accomplishing these goals (Keinan et al. 2007; Boyko et al. 2008; Lohmueller et al. 2009; Nielsen et al. 2009).It is important to realize that studies of African demographic history using genetic data have come to qualitatively different conclusions regarding important parameters. Some recent studies have found evidence for ancient (>100,000 years ago) two- to fourfold growth in African populations (Adams and Hudson 2004; Marth et al. 2004; Keinan et al. 2007; Boyko et al. 2008). Other studies have found evidence of very recent growth (Pluzhnikov et al. 2002; Akey et al. 2004; Voight et al. 2005; Cox et al. 2009; Wall et al. 2009) or could not reject a model with a constant population size (Pluzhnikov et al. 2002; Voight et al. 2005). It is unclear why studies found such different parameter estimates. However, these studies all differ from each other in the amount of data considered, the types of data used (e.g., SNP genotypes vs. full resequencing), the genomic regions studied (e.g., noncoding vs. coding SNPs), and the types of demographic models considered (e.g., including migration vs. not including migration postseparation of African and non-African populations).Another important way in which studies of African demographic history differ from each other is in the populations sampled. Some studies have focused on genetic data from individuals sampled from within Africa (Pluzhnikov et al. 2002; Adams and Hudson 2004; Voight et al. 2005; Keinan et al. 2007; Cox et al. 2009; Wall et al. 2009), while other studies included American individuals with African ancestry (Adams and Hudson 2004; Akey et al. 2004; Marth et al. 2004; Boyko et al. 2008). While there is no clear correspondence between those studies which sampled native African individuals (as opposed to African-Americans) and particular growth scenarios, it is clear from previous studies that African-American populations do differ from African populations in their recent demographic history. In particular, genetic studies suggest that there is wide variation in the degree of European admixture in most African-American individuals in the United States and that they have, on average, ∼80% African ancestry and 20% European ancestry (Parra et al. 1998; Pfaff et al. 2001; Falush et al. 2003; Patterson et al. 2004; Tian et al. 2006; Lind et al. 2007; Reiner et al. 2007; Price et al. 2009; Bryc et al. 2010). Furthermore, both historical records and genetic evidence suggest that the admixture process began quite recently, within the last 20 generations (Pfaff et al. 2001; Patterson et al. 2004; Seldin et al. 2004; Tian et al. 2006). Recent population admixture can alter patterns of genetic variation in a discernible and predictable way. For example, recently admixed populations will exhibit correlation in allele frequencies (i.e., linkage disequilibrium) among markers that differ in frequency between the parental populations. This so-called admixture linkage disequilibrium (LD) (Chakraborty and Weiss 1988) can extend over long physical distances (Lautenberger et al. 2000) and decays exponentially with time the since the admixture process began (i.e., recently admixed populations typically exhibit LD over a longer physical distance than anciently admixed populations).While it is clear that African-American populations have a different recent demographic history than do African populations from within Africa and that admixture tracts can be identified in admixed individuals (Falush et al. 2003; Patterson et al. 2004; Tang et al. 2006; Sankararaman et al. 2008a,b; Price et al. 2009; Bryc et al. 2010), the effect that admixture has on other patterns of genetic variation remains unclear. For example, Xu et al. (2007) found similar LD decay patterns when comparing African-American and African populations. It is also unclear whether the recent admixture affects our ability to reconstruct ancient demographic events (such as expansions that predate the spread of humans out of Africa) from whole-genome SNP data. Most studies of demographic history have summarized the genome-wide SNP data by allele frequency or haplotype summary statistics. If these summary statistics are not sensitive to the recent European admixture, then the African-American samples may yield estimates of demographic parameters that are close to the true demographic parameters for the ancestral, unsampled, African populations. This would suggest that the differences in growth parameter estimates obtained from African populations cannot be explained by certain studies sampling African-American individuals and others sampling African individuals from within Africa. However, if these statistics are sensitive to recent admixture, then they may give biased estimates of growth parameters.Here, we examine the effect of recent admixture on the estimation of population demography. In particular, we estimate growth parameters from simulated data sets using SNP frequencies as well as a recently developed haplotype summary statistic (Lohmueller et al. 2009). We compare the demographic parameter estimates made from the admixed and nonadmixed populations and find that some parameter estimates are qualitatively similar between the two populations when inferred using allele frequencies. Inferences of growth using haplotype-based approaches appear to be more sensitive to recent admixture than inferences based on SNP frequencies. We discuss implications that our results have for interpreting studies of demography in admixed populations.