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.     

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.     

The Genetic Signature of Conditional Expression

Conditionally expressed genes have the property that every individual in a population carries and transmits the gene, but only a fraction, φ, expresses the gene and exposes it to natural selection. We show that a consequence of this pattern of inheritance and expression is a weakening of the strength of natural selection, allowing deleterious mutations to accumulate within and between species and inhibiting the spread of beneficial mutations. We extend previous theory to show that conditional expression in space and time have approximately equivalent effects on relaxing the strength of selection and that the effect holds in a spatially heterogeneous environment even with low migration rates among patches. We support our analytical approximations with computer simulations and delineate the parameter range under which the approximations fail. We model the effects of conditional expression on sequence polymorphism at mutation–selection–drift equilibrium, allowing for neutral sites, and show that sequence variation within and between species is inflated by conditional expression, with the effect being strongest in populations with large effective size. As φ decreases, more sites are recruited into neutrality, leading to pseudogenization and increased drift load. Mutation accumulation diminishes the degree of adaptation of conditionally expressed genes to rare environments, and the mutational cost of phenotypic plasticity, which we quantify as the plasticity load, is greater for more rarely expressed genes. Our theory connects gene-level relative polymorphism and divergence with the spatial and temporal frequency of environments inducing gene expression. Our theory suggests that null hypotheses for levels of standing genetic variation and sequence divergence must be corrected to account for the frequency of expression of the genes under study.IN genetically and ecologically subdivided populations, some individuals will experience a local environment very different from others, making it difficult to evolve a single adaptation adequate for all local conditions. Phenotypic plasticity allows organisms to respond adaptively to spatially and temporally varying environments by developing alternative phenotypes that enhance fitness under local conditions (Scheiner 1993; Via et al. 1995). Examples of alternative phenotypes, i.e., polyphenisms, include the defensive morphologies in Daphnia and algae induced by the presence of predators (e.g., Lively 1986; DeWitt 1998; Harvell 1998; Hazel et al. 2004); the winged and wingless morphs of bean beetles responding to resource variation (e.g., Abouheif and Wray 2002; Roff and Gelinas 2003; Lommen et al. 2005); and bacterial genes involved in traits such as quorum sensing, antibiotic production, biofilm formation, and virulence (Fuqua et al. 1996). The developmental basis of such alternative phenotypes often lies in the inducible expression of some genes in some individuals by environmental variables. That is, all individuals carry and transmit the conditionally expressed genes but only a fraction of individuals, φ, express them when environmental conditions are appropriate.The genes underlying plastic traits should experience relaxed selection due to conditional expression. Wade and co-workers have shown that genes hidden from natural selection in a fraction of individuals in the population by X-linked (Whitlock and Wade 1995; Linksvayer and Wade 2009) or sex-limited expression (Wade 1998; Demuth and Wade 2007) experience relaxed selective constraint. In Drosophila spp., sequence data for genes with maternally limited expression quantitatively support the theoretical predictions both for within-species polymorphism (Barker et al. 2005; Cruickshank and Wade 2008) and for between-species divergence (Barker Et Al 2005; Demuth and Wade 2007; Cruickshank and Wade 2008). Furthermore, male-specific genes in the facultatively sexual pea aphid have been shown to have elevated levels of sequence variation due to relaxed selection (Brisson and Nuzhdin 2008). Genes with spatially restricted expression in a heterogeneous environment should likewise experience relaxed selection. Adaptation to the most common environment in an ecologically subdivided population (Rosenzweig 1987; Holt and Gaines 1992; Holt 1996) allows deleterious mutations to accumulate in traits expressed in rare environments (Kawecki 1994; Whitlock 1996).Here we extend these results by quantifying the consequences of relaxed selection on conditionally expressed genes. Specifically, we show that, with weak selection, spatial and temporal fluctuations in selection intensity generate approximately equivalent effects on mean trait fitness, even with low rates of migration between habitats, resulting in a great simplification of analytical results. Our analytical approximations are supported with deterministic and stochastic simulations, and we note the conditions under which the approximations fail. We then derive general expressions for (1) the expected level of sequence polymorphism within populations under mutation, migration, drift, and purifying selection with conditional gene expression; (2) the rate of sequence divergence among populations, for dominant and recessive mutations; and (3) the reduction in mean population fitness due to accumulation of deleterious mutations at conditionally expressed loci. We find that the rate of accumulation of deleterious mutations for conditionally expressed genes is accelerated and the probability of fixation of beneficial mutations is reduced, causing a reduction in the fitness of conditional traits and an inflation in sequence variation within and between species. Our results suggest that evolutionary null hypotheses must be adjusted to account for the frequency of expression of genes under study, such that signatures of elevated within- or between-species sequence variation are not necessarily evidence of the action of diversifying natural selection. Furthermore, if conditional expression is due to spatial heterogeneity, we show that the level of genetic variation in a sample will often depend on whether or not genotypes were sampled from the selective habitat, the neutral habitat, or both. In the discussion we address the scope and limitations of our theory, as well as its implications for the maintenance of genetic variation, adaptive divergence between species, constraints on phenotypic plasticity, and evolutionary inference from sequence data.     

Odorant Receptor Polymorphisms and Natural Variation in Olfactory Behavior in Drosophila melanogaster

Animals perceive and discriminate among a vast array of sensory cues in their environment. Both genetic and environmental factors contribute to individual variation in behavioral responses to these cues. Here, we asked to what extent sequence variants in six Drosophila melanogaster odorant receptor (Or) genes are associated with variation in behavioral responses to benzaldehyde by sequencing alleles from a natural population. Sequence analyses showed signatures of deviations from neutrality for Or42b and Or85f, and linkage disequilibrium analyses showed a history of extensive recombination between polymorphic markers for all six Or genes. We identified polymorphisms in Or10a, Or43a, and Or67b that were significantly associated with variation in response to benzaldehyde. To verify these associations, we repeated the analyses with an independent set of behavioral measurements of responses to a structurally similar odorant, acetophenone. Association profiles for both odorants were similar with many polymorphisms and haplotypes associated with variation in responsiveness to both odorants. Some polymorphisms, however, were associated with one, but not the other odorant. We also observed a correspondence between behavioral response to benzaldehyde and differences in Or10a and Or43a expression. These results illustrate that sequence variants that arise during the evolution of odorant receptor genes can contribute to individual variation in olfactory behavior and give rise to subtle shifts in olfactory perception.RESEARCHERS in many scientific fields have long appreciated that different animal species perceive the world differently. In fact, these differences are so striking that new disciplines have arisen to study the adaptations of sense organs to the environment (e.g., Ali 1978; Lythgoe 1979; Dusenbery 1992). Differences in sensory perception exist not only between species, but also between populations of a single species and between individuals within a population. What is the underlying genetic architecture for individual variation in sensory perception?Olfaction provides an excellent model for examining the underlying genetic mechanisms that result in variation in behavior. In both vertebrates and invertebrates, odorants are detected by families of odorant receptors expressed in populations of olfactory receptor neurons (ORNs), whose activation elicits a distinct spatial pattern of glomerular activity in the brain (Buck and Axel 1991; Vassar et al. 1994; Mombaerts et al. 1996; Laissue et al. 1999; Gao et al. 2000; Vosshall et al. 2000; Bhalerao et al. 2003; Wang et al. 2003). This combinatorial code allows for discrimination of a diverse repertoire of odorants.Drosophila melanogaster has a relatively simple olfactory system with only 60 odorant receptor (Or) genes (Vosshall and Stocker 2007) compared to ∼1000 in the mouse (Zhang and Firestein 2002; Zhang et al. 2004). The 60 genes are located throughout the genome, and 2 of these genes are alternatively spliced for a total of 62 identified proteins (Clyne et al. 1999; Gao and Chess 1999; Vosshall et al. 1999; Robertson et al. 2003). Furthermore, clusters of Ors throughout the genome suggest several recent gene duplication events (Robertson et al. 2003).The response spectra of individual ORNs have been extensively characterized using extracellular electrophysiological recordings from single sensilla on the antennae and maxillary palps. Recordings from basiconic sensilla on the antenna identified classes of neurons with distinct olfactory response profiles organized as two to four neurons in each sensillum with specific neuronal combinations occurring in distinct spatial regions of the antenna (de Bruyne et al. 1999, 2001).The majority of ORNs express a unique odorant receptor in addition to the highly conserved coreceptor, Or83b (Jones et al. 2005). Studies of a null mutant of Or83b implicated this receptor in positioning odorant receptor proteins in the sensory dendrites (Larsson et al. 2004; Benton et al. 2006). Odorant receptors in Drosophila have an atypical membrane topology with a cytoplasmic N terminus and an extracellular C terminus (Benton et al. 2006). Specific domains in the third cytoplasmic loops of two odorant receptors, Or22a and Or43a, have been implicated to interact with the third loop of Or83b (Benton et al. 2006). Drosophila odorant receptors act as ligand-gated nonselective cation channels formed by a dimeric complex between a unique Or and the Or83b coreceptor (Sato et al. 2008; Wicher et al. 2008).Several studies have examined ligand specificities of individual odorant receptor proteins and demonstrated that they respond to diverse and overlapping suites of ligands. Response profiles for many receptors have been characterized using the Gal4/UAS system to drive expression of individual odorant receptors in a mutant ORN lacking expression of its endogeneous receptor, followed by electrophysiological recording (Dobritsa et al. 2003; Hallem et al. 2004; Hallem and Carlson 2006). In addition, misexpression studies of Or43a resulted in a reduction of behavioral avoidance responses to benzaldehyde (Stortkuhl et al. 2005). This result combined with electrophysiological recordings from ORNs and heterologous expression in Xenopus oocytes further functionally characterized the odorant response profiles of this receptor (Wetzel et al. 2001) and identified several Or43a ligands, such as fruit- derived odorants benzaldehyde, cyclohexanone, cyclohexanol, and benzyl alcohol (Stortkuhl and Kettler 2001; Hallem et al. 2004).Despite advances in our understanding of odor coding, the molecular mechanisms responsible for variation in olfactory perception remain poorly understood. D. melanogaster is especially amenable to conducting such studies given its quantitatively simple olfactory system and since large numbers of genetically identical individuals can be reared in a common environment and these individuals can be subjected to simple, rapid, and highly reproducible quantitative behavioral assays Anholt and Mackay 2004). Here, we examine how molecular variation in odorant receptors contributes to variation in olfactory behavior in inbred lines derived from a natural population of D. melanogaster. We focused our analyses on six odorant receptors, Or7a, Or10a, Or42b, Or43a, Or67b, and Or85f, which have been shown by electrophysiology (Stortkuhl and Kettler 2001; Hallem et al. 2004; Stortkuhl et al. 2005; Hallem and Carlson 2006), through heterologous expression systems (Wetzel et al. 2001), or by calcium imaging studies (Wang et al. 2003) to respond to benzaldehyde. Significant variation in behavioral responses to benzaldehyde has been observed previously in this population and was normally distributed as is typical for a quantitative trait influenced by multiple genes (Wang et al. 2007). Here, we report associations between olfactory behavior and sequence variants in three Or genes. To validate the reliability of these associations we measured responses to a structurally similar odorant, acetophenone, in the same population, and showed that the associations with variation in responses to both odorants are largely similar with occasional molecular polymorphisms associated with variation in response to only one, but not the other odorant. These observations illustrate how sequence variants that arise during the evolution of Or genes can contribute to individual variation in olfactory behavior, how polymorphisms can give rise to subtle shifts in olfactory perception, and how naturally arising mutations within a population can combine to generate broad individual variation in sensory perception.     

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.     

Identity-by-Descent Matrix Decomposition Using Latent Ancestral Allele Models

Genetic linkage and association studies are empowered by proper modeling of relatedness among individuals. Such relatedness can be inferred from marker and/or pedigree information. In this study, the genetic relatedness among n inbred individuals at a particular locus is expressed as an n × n square matrix Q. The elements of Q are identity-by-descent probabilities, that is, probabilities that two individuals share an allele descended from a common ancestor. In this representation the definition of the ancestral alleles and their number remains implicit. For human inspection and further analysis, an explicit representation in terms of the ancestral allele origin and the number of alleles is desirable. To this purpose, we decompose the matrix Q by a latent class model with K classes (latent ancestral alleles). Let P be an n × K matrix with assignment probabilities of n individuals to K classes constrained such that every element is nonnegative and each row sums to 1. The problem then amounts to approximating Q by PP^T, while disregarding the diagonal elements. This is not an eigenvalue problem because of the constraints on P. An efficient algorithm for calculating P is provided. We indicate the potential utility of the latent ancestral allele model. For representative locus-specific Q matrices constructed for a set of maize inbreds, the proposed model recovered the known ancestry.HIGH-THROUGHPUT techniques allow extensive genotyping of individuals for thousands of SNP markers (Gibbs et al. 2003) and thereby provide accurate information about the genetic diversity within a population at many chromosomal loci. If two individuals within this population carry the same DNA sequence at a locus, and this sequence can be traced to the same common ancestor, the individuals are said to be identical by descent (IBD) for this segment (Chapman and Thompson 2003). Quite often, however, the ancestral source of a chromosomal segment is ambiguous and thus IBD relationships between haplotypes are given as probabilities. Various methods have been described to estimate the IBD probability of pairs of chromosomal segments (Meuwissen and Goddard 2001; Leutenegger et al. 2003). When pedigree relationships are known, these can be included to estimate IBD probabilities (Wang et al. 1995; Heath 1997; George et al. 2000; Meuwissen and Goddard 2000; Besnier and Carlborg 2007).In quantitative genetic analysis we seek to find and characterize associations between the large number of SNPs that are now available for many organisms and phenotypic variation for traits of interest (e.g., grain yield and time to flowering). Many current methods developed for this purpose make use of IBD information. For example, a locus-specific matrix of IBD probabilities can be incorporated into restricted maximum-likelihood (REML) procedures for fine mapping quantitative trait loci (Bink and Meuwissen 2004) as well as for marker-based genetic evaluation (Fernando and Grossman 1989) using mixed models. The IBD matrix takes the role of a covariance matrix in the REML procedure.Other approaches, however, require that chromosome segments (also referred to here as haplotypes or alleles) are assigned to independent ancestors. These approaches include regression approaches with genetic predictors (Malosetti et al. 2006) and Bayesian oligo-allelic approaches that sample the ancestral origin of each chromosomal segment (Heath 1997; Uimari and Sillanpaa 2001; Bink et al. 2008a). In the IBD matrix representation the ancestral alleles and their number remain implicit. For these approaches, the locus-specific matrix of IBD probabilities must therefore be decomposed into a matrix that links the chromosomal segments to independent ancestral alleles. This decomposition is addressed in this article.The individuals that we consider in this article are inbred. For n inbred individuals the IBD matrix at a given chromosomal position is thus n × n, because there is no need to distinguish between identical chromosomes. In diploid, outbred populations, each individual would be represented by two haplotypes (alleles) and the matrix would be 2n × 2n (Fernando and Grossman 1989). This is feasible if any phase ambiguity can be resolved. From now on, the term “individual” thus means chromosomal segment or haplotype. Analogously, ancestor will be shorthand for ancestral allele (ancestral haplotype).We propose two models of IBD matrix decomposition, a simple threshold model (TIBD) and a more sophisticated latent ancestral allele model (LAAM), that provide (1) an estimate of the number of independent ancestral alleles, (2) a concise, easy-to-interpret, summary of the relatedness, (3) an explicit (probabilistic) representation of the descent of alleles, and (4) the ability to sample alleles for each individual from a set of ancestral alleles in such a way that the probability that a pair of individuals shares the same allele corresponds to their IBD probability.The last two features of the model are essential for its use in Bayesian oligo-allelic approaches to quantitative trait locus (QTL) analysis (Uimari and Sillanpaa 2001; Bink et al. 2008a).     

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.     

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?     

The Conserved miR-51 microRNA Family Is Redundantly Required for Embryonic Development and Pharynx Attachment in Caenorhabditis elegans

A major question about cytokinesis concerns the role of the septin proteins, which localize to the division site in all animal and fungal cells but are essential for cytokinesis only in some cell types. For example, in Schizosaccharomyces pombe, four septins localize to the division site, but deletion of the four genes produces only a modest delay in cell separation. To ask if the S. pombe septins function redundantly in cytokinesis, we conducted a synthetic-lethal screen in a septin-deficient strain and identified seven mutations. One mutation affects Cdc4, a myosin light chain that is an essential component of the cytokinetic actomyosin ring. Five others cause frequent cell lysis during cell separation and map to two loci. These mutations and their dosage suppressors define a signaling pathway (including Rho1 and a novel arrestin) for repairing cell-wall damage. The seventh mutation affects the poorly understood RNA-binding protein Scw1 and severely delays cell separation when combined either with a septin mutation or with a mutation affecting the septin-interacting, anillin-like protein Mid2, suggesting that Scw1 functions in a pathway parallel to that of the septins. Taken together, our results suggest that the S. pombe septins participate redundantly in one or more pathways that cooperate with the actomyosin ring during cytokinesis and that a septin defect causes septum defects that can be repaired effectively only when the cell-integrity pathway is intact.THE fission yeast Schizosaccharomyces pombe provides an outstanding model system for studies of cytokinesis (McCollum and Gould 2001; Balasubramanian et al. 2004; Pollard and Wu 2010). As in most animal cells, successful cytokinesis in S. pombe requires an actomyosin ring (AMR). The AMR begins to assemble at the G₂/M transition and involves the type II myosin heavy chains Myo2 and Myp2 and the light chains Cdc4 and Rlc1 (Wu et al. 2003). Myo2 and Cdc4 are essential for cytokinesis under all known conditions, Rlc1 is important at all temperatures but essential only at low temperatures, and Myp2 is essential only under stress conditions. As the AMR constricts, a septum of cell wall is formed between the daughter cells. The primary septum is sandwiched by secondary septa and subsequently digested to allow cell separation (Humbel et al. 2001; Sipiczki 2007). Because of the internal turgor pressure of the cells, the proper assembly and structural integrity of the septal layers are essential for cell survival.Septum formation involves the β-glucan synthases Bgs1/Cps1/Drc1, Bgs3, and Bgs4 (Ishiguro et al. 1997; Le Goff et al. 1999; Liu et al. 1999, 2002; Martín et al. 2003; Cortés et al. 2005) and the α-glucan synthase Ags1/Mok1 (Hochstenbach et al. 1998; Katayama et al. 1999). These synthases are regulated by the Rho GTPases Rho1 and Rho2 and the protein kinase C isoforms Pck1 and Pck2 (Arellano et al. 1996, 1997, 1999; Nakano et al. 1997; Hirata et al. 1998; Calonge et al. 2000; Sayers et al. 2000; Ma et al. 2006; Barba et al. 2008; García et al. 2009b). The Rho GTPases themselves appear to be regulated by both GTPase-activating proteins (GAPs) and guanine-nucleotide-exchange factors (GEFs) (Nakano et al. 2001; Calonge et al. 2003; Iwaki et al. 2003; Tajadura et al. 2004; Morrell-Falvey et al. 2005; Mutoh et al. 2005; García et al. 2006, 2009a,b). In addition, septum formation and AMR function appear to be interdependent. In the absence of a normal AMR, cells form aberrant septa and/or deposit septal materials at random locations, whereas a mutant defective in septum formation (bgs1) is also defective in AMR constriction (Gould and Simanis 1997; Le Goff et al. 1999; Liu et al. 1999, 2000). Both AMR constriction and septum formation also depend on the septation initiation network involving the small GTPase Spg1 (McCollum and Gould 2001; Krapp and Simanis 2008). Despite this considerable progress, many questions remain about the mechanisms and regulation of septum formation and its relationships to the function of the AMR.One major question concerns the role(s) of the septins. Proteins of this family are ubiquitous in fungal and animal cells and typically localize to the cell cortex, where they appear to serve as scaffolds and diffusion barriers for other proteins that participate in a wide variety of cellular processes (Longtine et al. 1996; Gladfelter et al. 2001; Hall et al. 2008; Caudron and Barral 2009). Despite the recent progress in elucidating the mechanisms of septin assembly (John et al. 2007; Sirajuddin et al. 2007; Bertin et al. 2008; McMurray and Thorner 2008), the details of septin function remain obscure. However, one prominent role of the septins and associated proteins is in cytokinesis. Septins concentrate at the division site in every cell type that has been examined, and in Saccharomyces cerevisiae (Hartwell 1971; Longtine et al. 1996; Lippincott et al. 2001; Dobbelaere and Barral 2004) and at least some Drosophila (Neufeld and Rubin 1994; Adam et al. 2000) and mammalian (Kinoshita et al. 1997; Surka et al. 2002) cell types, the septins are essential for cytokinesis. In S. cerevisiae, the septins are required for formation of the AMR (Bi et al. 1998; Lippincott and Li 1998). However, this cannot be their only role, because the AMR itself is not essential for cytokinesis in this organism (Bi et al. 1998; Korinek et al. 2000; Schmidt et al. 2002). Moreover, there is no evidence that the septins are necessary for AMR formation or function in any other organism. A further complication is that in some cell types, including most Caenorhabditis elegans cells (Nguyen et al. 2000; Maddox et al. 2007) and some Drosophila cells (Adam et al. 2000; Field et al. 2008), the septins do not appear to be essential for cytokinesis even though they localize to the division site.S. pombe has seven septins, four of which (Spn1, Spn2, Spn3, and Spn4) are expressed in vegetative cells and localize to the division site shortly before AMR constriction and septum formation (Longtine et al. 1996; Berlin et al. 2003; Tasto et al. 2003; Wu et al. 2003; An et al. 2004; Petit et al. 2005; Pan et al. 2007; Onishi et al. 2010). Spn1 and Spn4 appear to be the core members of the septin complex (An et al. 2004; McMurray and Thorner 2008), and mutants lacking either of these proteins do not assemble the others at the division site. Assembly of a normal septin ring also depends on the anillin-like protein Mid2, which colocalizes with the septins (Berlin et al. 2003; Tasto et al. 2003). Surprisingly, mutants lacking the septins are viable and form seemingly complete septa with approximately normal timing. These mutants do, however, display a variable delay in separation of the daughter cells, suggesting that the septins play some role(s) in the proper completion of the septum or in subsequent processes necessary for cell separation (Longtine et al. 1996; An et al. 2004; Martín-Cuadrado et al. 2005).It is possible that the septins localize to the division site and yet are nonessential for division in some cell types because their role is redundant with that of some other protein(s) or pathway(s). To explore this possibility in S. pombe, we screened for mutations that were lethal in combination with a lack of septins. The results suggest that the septins cooperate with the AMR during cytokinesis and that, in the absence of septin function, the septum is not formed properly, so that an intact system for recognizing and repairing cell-wall damage becomes critical for cell survival.     

Molecular Isolation of the M Gene Suggests That a Conserved-Residue Conversion Induces the Formation of Bisexual Flowers in Cucumber Plants

Sex determination in plants involves a variety of mechanisms. Here, we report the map-based cloning and characterization of the unisexual-flower-controlling gene M. M was identified as a previously characterized putative 1-aminocyclopropane-1-carboxylic acid synthase gene, while the m allele that mutated at a conserved site (Gly33Cys) lost activity in the original enzymatically active allele.SEX determination in angiosperms, including crop plants, evolves a variety of mechanisms that involve a number of different genetic and epigenetic factors (Tanurdzic and Banks 2004). Due to its diversity in sex types and to the extensive physiological and genetic studies conducted on it, cucumber (Cucumis sativus L.; 2n = 2x = 14) is becoming a model plant for sex-determination research (Atsmon 1968; Tsao 1988; Perl-Treves 1999; Tanurdzic and Banks 2004). In cucumber plants, male and female flowers are generally produced separately in the same individual; however, certain lines also produce bisexual flowers. Preliminary genetic studies have indicated that three major genes are responsible for sex expression and segregation in the cucumber plantF/f, M/m, and A/a. The F gene may promote femaleness, while the m gene regulates the appearance of hermaphroditic flowers on the plant. Furthermore, in combination with the homozygous recessive f gene, the recessive a gene can intensify the androecious nature (Galun 1961; Robinson et al. 1976).Sex expression in cucumber plants can also be modified by various environmental factors and plant hormones such as ethylene (Atsmon 1968; Takahashi et al. 1983; Takahashi and Jaffe 1984; Perl-Treves 1999; Yamasaki et al. 2005). A series of studies (Kamachi et al. 1997, 2000; Trebitsh et al. 1997; Yamasaki et al. 2003a; Mibus and Tatlioglu 2004; Knopf and Trebitsh 2006) have been conducted to investigate the F/f gene. These studies have shown that CsACS1G, which encodes a key enzyme of the ethylene-synthesis pathway, is the candidate gene for the F/f locus. However, the M/m gene has not been studied in as much detail as the F/f gene. Here, we report the map-based cloning and characterization of the unisexual-flower-controlling M gene.     

Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.MUCH effort in genetics has been devoted to revealing the underlying genetic architecture of quantitative or complex traits. Traditionally, the polygenic model has been used extensively to estimate genetic variances and breeding values of natural and breeding populations, where an infinite number of genes is assumed to code for the trait of interest (Bulmer 1971; Falconer and Mackay 1996). The genetic variance of a quantitative trait can be decomposed into an additive part that corresponds to the effects of individual alleles and a part that is nonadditive because of interactions between alleles. Attention has generally been focused on the estimation of additive genetic variance (and heritability), since additive variation is directly proportional to the response of selection via the breeder''s equation (Falconer and Mackay 1996, Chap. 11). However, to estimate additive genetic variation and heritability accurately, it can be important to identify potential nonadditive sources in genetic evaluations (Misztal 1997; Ovaskainen et al. 2008; Waldmann et al. 2008), especially if the pedigree being analyzed contains a large proportion of full-sibs and clones, as these in particular give rise to nonadditive genetic relationships (Lynch and Walsh 1998, pp. 145). The polygenic model using pedigree and phenotypic information, i.e., the animal model (Henderson 1984), has been the model of choice for estimating genetic parameters in breeding and natural populations (Abney et al. 2000; Sorensen and Gianola 2002; O′Hara et al. 2008).Recent breakthroughs in molecular techniques have made it possible to create genome-wide, single nucleotide polymorphism (SNP) maps. These maps have helped to uncover a vast amount of new loci responsible for trait expression and have provided general insights into the genetic architecture of quantitative traits (e.g., Valdar et al. 2006; Visscher 2008; Flint and Mackay 2009). These insights can help when calculating disease risks in humans, when attempting to increase the yield from breeding programs, and when estimating relatedness in conservation programs. High-density SNPs of many species of importance to science and agriculture can now be scored quickly and relatively cheaply, for example, in mice (Valdar et al. 2006), chickens (Muir et al. 2008), and dairy cattle (VanRaden et al. 2009).In the analysis of populations of breeding stock, the inclusion of dense marker data has improved the predictive ability (i.e., reliability) of genetic evaluations compared to the traditional phenotype model, both in simulations (Meuwissen et al. 2001; Calus et al. 2008; Hayes et al. 2009) and when using real data (Legarra et al. 2008; VanRaden et al. 2009; González-Recio et al. 2009). Meuwissen et al. (2001) suggested that the effect of all markers should first be estimated, and then summed, to obtain genomic estimated breeding values (GEBVs). An alternative procedure, where all markers are used to compute the genomic relationship matrix (in place of the additive polygenic relationship matrix) has also been suggested (e.g., Villanueva et al. 2005; VanRaden 2008; Hayes et al. 2009); this matrix is then incorporated into the statistical analysis to estimate GEBVs. A comparison of both procedures (VanRaden 2008) yielded similar estimates of GEBVs in cases where the effect of an individual allele was small. In addition, if not all pedigree members have marker information, a combined relationship matrix derived from both genotyped and ungenotyped individuals could be computed; this has been shown to increase the accuracy of GEBVs (Legarra et al. 2009; Misztal et al. 2009). Another plausible option to incorporate marker information is to use low-density SNP panels within families and to trace the effect of SNPs from high-density genotyped ancestors, as suggested by Habier et al. (2009) and Weigel et al. (2009). However, fast and powerful computer algorithms, which can use the marker information as efficiently as possible in the analysis of quantitative traits, are needed to obtain accurate GEBVs from genome-wide marker data.This study describes the development of an efficient Bayesian method for incorporating general relationships into the genetic evaluation procedure. The method is based on expressing the multivariate normal prior distribution as a product of one-dimensional normal distributions, each conditioned on the descending variables. When evaluating the genetic parameters of natural and breeding populations, high-dimensional distributions are often used as prior distributions of various genetic effects, such as the additive polygenic effect (Wang et al. 1993), multivariate additive polygenic effects (Van Tassell and Van Vleck 1996), and quantitative trait loci (QTL) effects via the identical-by-decent matrix (Yi and Xu 2000). A Bayesian framework is adopted to obtain posterior distributions of all unknown parameters, estimated by using Markov chain Monte Carlo (MCMC) sampling algorithms in the software package WinBUGS (Lunn et al. 2000, 2009). By performing prior calculations in the form of the factorized product of simple univariate conditional distributions, the computational time of the MCMC estimation procedure is reduced considerably. This feature permits rapid inference for both the polygenic model and the genomic relationship model. Moreover, the decomposition allows for inbreeding of varying degree, since the correct genetic covariance structure can be inferred into the analysis. In this article, we test the method on two previously published pedigree data sets: phenotype data from a large pedigree of Scots pine, incorporation of information on both additive and dominance genetic relationships (Waldmann et al. 2008); and genomic information obtained from a genome-wide scan of a simulated animal population (Lund et al. 2009).     

Alternative Splicing Modulates Ubx Protein Function in Drosophila melanogaster

Polyploidy is an important aspect of the evolution of flowering plants. The potential of gene copies to diverge and evolve new functions is influenced by meiotic behavior of chromosomes leading to segregation as a single locus or duplicated loci. Switchgrass (Panicum virgatum) linkage maps were constructed using a full-sib population of 238 plants and SSR and STS markers to access the degree of preferential pairing and the structure of the tetraploid genome and as a step toward identification of loci underlying biomass feedstock quality and yield. The male and female framework map lengths were 1645 and 1376 cM with 97% of the genome estimated to be within 10 cM of a mapped marker in both maps. Each map coalesced into 18 linkage groups arranged into nine homeologous pairs. Comparative analysis of each homology group to the diploid sorghum genome identified clear syntenic relationships and collinear tracts. The number of markers with PCR amplicons that mapped across subgenomes was significantly fewer than expected, suggesting substantial subgenome divergence, while both the ratio of coupling to repulsion phase linkages and pattern of marker segregation indicated complete or near complete disomic inheritance. The proportion of transmission ratio distorted markers was relatively low, but the male map was more extensively affected by distorted transmission ratios and multilocus interactions, associated with spurious linkages.POLYPLOIDY is common among plants (Masterson 1994; Levin 2002) and is an important aspect of plant evolution. Widespread paleopolyploidy in flowering plant lineages suggests that ancient polyploidization events have contributed to the radiation of angiosperms (Soltis et al. 2009; Van de Peer et al. 2009a). Whole genome duplications are thought to be the sources of evolutionary novelty (Osborn et al. 2003; Freeling and Thomas 2006; Chen 2007; Hegarty and Hiscock 2008; Flagel and Wendel 2009; Leitch and Leitch 2008). Other attributes of polyploids considered to promote evolutionary success include increased vigor, masking of recessive alleles, and reproductive barriers arising from loss of one of the duplicate genes (Soltis and Soltis 2000; Comai 2005; Otto 2007; Van de Peer et al. 2009b). Among crop species, polyploidy likely contributed to trait improvement under artificial selection (Paterson 2005; Udall and Wendell 2006; Dubcovsky and Dvorak 2007; Hovav et al. 2008).Disomic inheritance in polyploids, in contrast to polysomic inheritance, presents opportunities for duplicated genes to diverge and evolve new functions. The relative age of whole genome duplications and the extent of homology between subgenomes greatly influence chromosomal pairing at meiosis (Soltis and Soltis 1995; Wolfe 2001; Ramsey and Schemske 2002). Polysomic inheritance resulting from random chromosome pairing is associated with doubling of a single set of chromosomes. Disomic inheritance resulting from preferential pairing is often associated with polyploidy arising from combinations of divergent genomes. The evolutionary process of diploidization leads to a shift from random to preferential pairing that is not well understood but is genetically defined in systems such as Ph1 of wheat (Triticum aestivum) and PrBn of Brassica napus (Riley and Chapman 1958; Vega and Feldman 1998; Jenczewski et al. 2003). The degree of preferential pairing also affects allelic diversity and the ability to detect linkage. Accurate information about chromosome pairing and whole or partial genome duplications is thus important for both evolutionary studies and in linkage analysis.Such information is extremely limited in the C4 panicoid species Panicum virgatum (switchgrass), which is now viewed as a promising energy crop in the United States and Europe (Lewandowski et al. 2003; McLaughlin and Kszos 2005) and is planted extensively for forage and soil conservation (Vogel and Jung 2001). Little is known about either its genome structure or inheritance. Much current bioenergy feedstock development is focused on tetraploid cytotypes (2n = 4x = 36) due to their higher yield potentials, and an initial segregation study indicated a high degree of preferential pairing in a single F1 mapping population (Missaoui et al. 2005). A once-dominant component of the tallgrass prairie in North America, switchgrass is largely self-incompatible (Martinez-Reyna and Vogel 2002) with predominantly tetraploid or octoploid cytotypes (Hultquist et al. 1997; Lu et al. 1998). Limited gene flow appears possible between different cytotypes suggested by DNA content variation within collection sites and seed lots (Nielsen 1944; Hultquist et al. 1997; Narasimhamoorthy et al. 2008). True diploids appear to be rare (Nielsen 1944; Young et al. 2010). Multivalents in meiosis have not been observed in tetraploids or F1 hybrids between upland and lowland tetraploids, although rare univalents occurred (Barnett and Carver 1967; Martinez-Reyna et al. 2001). However, polysomic inheritance may occur with random bivalent pairing (Howard and Swaminathan 1953).Sustainable production of switchgrass for bioenergy to meet the goal of reducing greenhouse gas emissions will require advances in feedstock production that include improvements in yield (Carroll and Somerville 2009). Switchgrass has extensive genetic diversity and potential for genetic improvements, but each cycle of phenotypic selection can take several years (McLaughlin and Kszos 2005; Parrish and Fike 2005; Bouton 2007). Detailed understanding of genome structure to enable efficient marker-assisted selection (MAS) can speed this process considerably. Complete linkage maps are therefore required to both understand chromosome pairing and allow MAS.We report the construction of the first complete linkage maps of two switchgrass genotypes. The linkage maps provide genetic evidence for disomic inheritance in lowland, tetraploid switchgrass. Gene-derived markers enabled a comparative analysis to sorghum, revealing syntenic relationships between the diploid sorghum genome and the tetraploid switchgrass subgenomes. Transmission ratio distortion and multilocus interactions were analyzed in detail to document their potential influence on map accuracy and map-based studies in switchgrass.