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.     

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.     

Genetic Modifiers of dFMR1 Encode RNA Granule Components in Drosophila

Mechanisms of neuronal mRNA localization and translation are of considerable biological interest. Spatially regulated mRNA translation contributes to cell-fate decisions and axon guidance during development, as well as to long-term synaptic plasticity in adulthood. The Fragile-X Mental Retardation protein (FMRP/dFMR1) is one of the best-studied neuronal translational control molecules and here we describe the identification and early characterization of proteins likely to function in the dFMR1 pathway. Induction of the dFMR1 in sevenless-expressing cells of the Drosophila eye causes a disorganized (rough) eye through a mechanism that requires residues necessary for dFMR1/FMRP''s translational repressor function. Several mutations in dco, orb2, pAbp, rm62, and smD3 genes dominantly suppress the sev-dfmr1 rough-eye phenotype, suggesting that they are required for dFMR1-mediated processes. The encoded proteins localize to dFMR1-containing neuronal mRNPs in neurites of cultured neurons, and/or have an effect on dendritic branching predicted for bona fide neuronal translational repressors. Genetic mosaic analyses indicate that dco, orb2, rm62, smD3, and dfmr1 are dispensable for translational repression of hid, a microRNA target gene, known to be repressed in wing discs by the bantam miRNA. Thus, the encoded proteins may function as miRNA- and/or mRNA-specific translational regulators in vivo.THE subcellular localization and regulated translation of stored mRNAs contributes to cellular asymmetry and subcellular specialization (Lecuyer et al. 2007; Martin and Ephrussi 2009). In mature neurons, local protein synthesis at active synapses may contribute to synapse-specific plasticity that underlies persistent forms of memory (Casadio et al. 1999; Ashraf et al. 2006; Sutton and Schuman 2006; Richter and Klann 2009). During this process, synaptic activity causes local translation of mRNAs normally stored in translationally repressed synaptic mRNPs (Sutton and Schuman 2006; Richter and Klann 2009). While specific neuronal translational repressors and microRNAs have been implicated in this process, their involvement in local translation that underlies memory, as well as the underlying mechanisms, are generally not well understood (Schratt et al. 2006; Keleman et al. 2007; Kwak et al. 2008; Li et al. 2008; Richter and Klann 2009). Furthermore, it remains possible that there are neuron-specific, mRNA-specific, and stimulus-pattern specific pathways for neuronal translational control (Raab-Graham et al. 2006; Giorgi et al. 2007).The Fragile-X Mental Retardation protein (FMRP) is among the best studied of neuronal translational repressors, in part due to its association with human neurodevelopmental disease (Pieretti et al. 1991; Mazroui et al. 2002; Gao 2008). Consistent with function in synaptic translation required for memory formation, mutations in FMRP are associated with increased synaptic translation, enhanced LTD, increased synapse growth, and also with enhanced long-term memory (Zhang et al. 2001; Huber et al. 2002; Bolduc et al. 2008; Dictenberg et al. 2008).FMRP co-immunoprecipitates with components of the RNAi and miRNA machinery and appears to be required for aspects of miRNA function in neurons (Caudy et al. 2002; Ishizuka et al. 2002; Jin et al. 2004b; Gao 2008). In addition, FMRP associates with neuronal polyribosomes as well as with Staufen-containing ribonucleoprotein (mRNP) granules easily observed in neurites of cultured neurons (Feng et al. 1997; Krichevsky and Kosik 2001; Mazroui et al. 2002; Kanai et al. 2004; Barbee et al. 2006; Bramham and Wells 2007; Bassell and Warren 2008; Dictenberg et al. 2008). FMRP-containing neuronal mRNPs contain not only several ubiquitous translational control molecules, but also CaMKII and Arc mRNAs, whose translation is locally controlled at synapses (Rook et al. 2000; Krichevsky and Kosik 2001; Kanai et al. 2004; Barbee et al. 2006). Thus, FMRP-containing RNA particles are probably translationally repressed and transported along microtubules from the neuronal cell body to synaptic sites in dendrites where local synaptic activity can induce their translation (Kiebler and Bassell 2006; Dictenberg et al. 2008).The functions of FMRP/dFMR1 in mRNA localization as well as miRNA-dependent and independent forms of translational control is likely to require several other regulatory proteins. To identify such proteins, we used a previously designed and validated genetic screen (Wan et al. 2000; Jin et al. 2004a; Zarnescu et al. 2005). The overexpression of dFMR1 in the fly eye causes a “rough-eye” phenotype through a mechanism that requires (a) key residues in dFMR1 that mediate translational repression in vitro; (b) Ago1, a known components of the miRNA pathway; and (c) a DEAD-box helicase called Me31B, which is a highly conserved protein from yeast (Dhh1p) to humans (Rck54/DDX6) functioning in translational repression and present on neuritic mRNPs (Wan et al. 2000; Laggerbauer et al. 2001; Jin et al. 2004a; Coller and Parker 2005; Barbee et al. 2006; Chu and Rana 2006). To identify other Me31B-like translational repressors and neuronal granule components, we screened mutations in 43 candidate proteins for their ability to modify dFMR1 induced rough-eye phenotype. We describe the results of this genetic screen and follow up experiments to address the potential cellular functions of five genes identified as suppressors of sev-dfmr1.     

Mitochondrial Superoxide Radicals Differentially Affect Muscle Activity and Neural Function

Cellular superoxide radicals (O₂⁻) are mostly generated during mitochondrial oxygen metabolism. O₂⁻ serves as the raw material for many reactive oxygen species (ROS) members like H₂O₂ and OH^.− radicals following its catalysis by superoxide dismutase (SOD) enzymes and also by autocatalysis (autodismutation) reactions. Mitochondrial ROS generation could have serious implications on degenerative diseases. In model systems overproduction of mitochondrial O₂⁻ resulting from the loss of SOD2 function leads to movement disorders and drastic reduction in life span in vertebrates and invertebrates alike. With the help of a mitochondrial SOD2 loss-of-function mutant, Sod2ⁿ²⁸³, we measured the sensitivity of muscles and neurons to ROS attack. Neural outputs from flight motor neurons and sensory neurons were unchanged in Sod2ⁿ²⁸³ and the entire neural circuitry between the giant fiber (GF) and the dorsal longitudinal muscles (DLM) showed no overt defect due to elevated ROS. Such insensitivity of neurons to mitochondrial superoxides was further established through neuronal expression of SOD2, which failed to improve survival or locomotive ability of Sod2ⁿ²⁸³. On the other hand, ultrastructural analysis of Sod2ⁿ²⁸³ muscles revealed fewer mitochondria and reduced muscle ATP production. By targeting the SOD2 expression to the muscle we demonstrate that the early mortality phenotype of Sod2ⁿ²⁸³ can be ameliorated along with signs of improved mobility. In summary, muscles appear to be more sensitive to superoxide attack relative to the neurons and such overt phenotypes observed in SOD2-deficient animals can be directly attributed to the muscle.BETWEEN Drosophila, mouse, and human, the enzymatic antioxidant defense system shares similar organization both structurally (Landis and Tower 2005) and functionally. Besides having a good degree of homology (Duttaroy et al. 1994; Landis and Tower 2005), other significant similarities include the presence of a single copy of Sod1 and Sod2 genes in each with no degree of functional complementation between these enzymes (Copin et al. 2000). While vertebrates have developed additional antioxidant defense enzymes such as glutathione peroxidase (Gpx) and extracellular superoxide dismutase (EcSOD or Sod3), neither Gpx nor an active SOD3 has been demonstrated in Drosophila, although a Sod3-like sequence has been identified (Landis and Tower 2005). Complete loss of SOD2 function is fatally injurious for both mice and Drosophila (Li et al. 1995; Lebovitz et al. 1996; Kirby et al. 2002; Duttaroy et al. 2003). The severe phenotypic effects of SOD2 loss of function have been attributed to elevated DNA damage and protein carbonylation (Golden and Melov 2001). SOD2 loss of function has also been attributed to “free radical attack” or “oxidative insult” on mitochondria where obvious mitochondrial damage was apparent from the inactivation of mitochondrial Fe-S cluster enzymes aconitase and succinate dehydrogenase (Melov et al. 1999; Kirby et al. 2002; Paul et al. 2007). Furthermore, impairment of cellular signaling, specifically those induced by reactive oxygen species (ROS) (Klotz 2005), might also play a very significant role in the early mortality effects of SOD2-deficient flies as indicated recently (Wicks et al. 2009).Sod2 null mice with damaged mitochondria display a number of pathologies including cardiomyopathy (Li et al. 1995), neurodegeneration, and seizures (Melov et al. 1998). Drosophila mutants of mitochondrial dysfunction are also claimed to be associated with neurodegeneration (Kretzschmar et al. 1997; Min and Benzer 1997, 1999; Rogina et al. 1997; Palladino et al. 2002, 2003; Celotto et al. 2006). In addition to the neurons, muscles are important targets for oxidative modification (Choksi and Papaconstantinou 2008; Choksi et al. 2008). Aerobic muscles with high mitochondrial content and high myoglobin levels, for example, show a significant increase in oxidative modification of all electron transport chain proteins compared to muscles with fewer mitochondria and less myoglobin (anaerobic muscle) (Choksi and Papaconstantinou 2008; Choksi et al. 2008). Mice lacking the Cu-ZnSOD enzyme suffer from a rapid loss of skeletal muscle mass, resembling an accelerated sarcopenia (Jackson 2006; Muller et al. 2006). We therefore set out to measure the impact of heightened superoxide concentration on neurons and muscles of Sod2ⁿ²⁸³ flies that are devoid of SOD2, the principal scavenger of superoxide radicals in mitochondria (Duttaroy et al. 2003; Belton et al. 2006).     

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.     

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?     

Inferring Bacterial Genome Flux While Considering Truncated Genes

Bacterial gene content variation during the course of evolution has been widely acknowledged and its pattern has been actively modeled in recent years. Gene truncation or gene pseudogenization also plays an important role in shaping bacterial genome content. Truncated genes could also arise from small-scale lateral gene transfer events. Unfortunately, the information of truncated genes has not been considered in any existing mathematical models on gene content variation. In this study, we developed a model to incorporate truncated genes. Maximum-likelihood estimates (MLEs) of the new model reveal fast rates of gene insertions/deletions on recent branches, suggesting a fast turnover of many recently transferred genes. The estimates also suggest that many truncated genes are in the process of being eliminated from the genome. Furthermore, we demonstrate that the ignorance of truncated genes in the estimation does not lead to a systematic bias but rather has a more complicated effect. Analysis using the new model not only provides more accurate estimates on gene gains/losses (or insertions/deletions), but also reduces any concern of a systematic bias from applying simplified models to bacterial genome evolution. Although not a primary purpose, the model incorporating truncated genes could be potentially used for phylogeny reconstruction using gene family content.GENE content variation as a key feature of bacterial genome evolution has been well recognized (Garcia-Vallvé et al. 2000; Ochman and Jones 2000; Snel et al. 2002; Welch et al. 2002; Kunin and Ouzounis 2003; Fraser-Liggett 2005; Tettelin et al. 2005) and gained increasing attention in recent years. Various methods have been employed to study the variation of gene content in the form of gene insertions/deletions (or gene gains/losses); there are studies of population dynamics (Nielsen and Townsend 2004), birth-and-death evolutionary models (Berg and Kurland 2002; Novozhilov et al. 2005), phylogeny-dependent studies including parsimony methods (Mirkin et al. 2003; Daubin et al. 2003a,b; Hao and Golding 2004), and maximum-likelihood methods (Hao and Golding 2006, 2008b; Cohen et al. 2008; Cohen and Pupko 2010; Spencer and Sangaralingam 2009). The pattern of gene presence/absence also contains phylogenetic signals (Fitz-Gibbon and House 1999; Snel et al. 1999; Tekaia et al. 1999) and has been used for phylogenetic reconstruction (Dutilh et al. 2004; Gu and Zhang 2004; Huson and Steel 2004; Zhang and Gu 2004; Spencer et al. 2007a,b). All these studies make use of the binary information of gene presence or absence and neglect the existence of gene segments or truncated genes.Bacterial genomes are known to harbor pseudogenes. An intracellular species Mycobacterium leprae is an extreme case for both the proportion and the number of pseudogenes: estimated as 40% of the 3.2-Mb genome and 1116 genes (Cole et al. 2001). In free-living bacteria, pseudogenes can make up to 8% of the annotated genes in the genome (Lerat and Ochman 2004). Many pseudogenes result from the degradation of native functional genes (Cole et al. 2001; Mira et al. 2001). Pseudogenes could also result from the degradation of transferred genes and might even be acquired directly via lateral gene transfer. For instance, in plant mitochondrial genomes, which have an α-proteobacterial ancestry, most, if not all, of the laterally transferred genes are pseudogenes (Richardson and Palmer 2007). Furthermore, evidence has been documented that gene transfer could take place at the subgenic level in a wide range of organisms, e.g., among bacteria (Miller et al. 2005; Choi and Kim 2007; Chan et al. 2009), between ancient duplicates in archaea (Archibald and Roger 2002), between different organelles (Hao and Palmer 2009; Hao 2010), and between eukaryotes (Keeling and Palmer 2001). A large fraction of pseudogenes have been shown to arise from failed lateral transfer events (Liu et al. 2004) and most of them are transient in bacterial genomes (Lerat and Ochman 2005). Zhaxybayeva et al. (2007) reported that genomes with truncated homologs might erroneously lead to false inferences of “gene gain” rather than multiple instances of “gene loss.” This raises the question of how a false diagnosis of gene absence affects the estimation of insertion/deletion rates. Recently, we showed that the effect of a false diagnosis of gene absence on estimation of insertion/deletion rates is not systematic, but rather more complicated (Hao and Golding 2008a). To further address the problem, a study incorporating the information of truncated genes is highly desirable. This will not only yield more accurate estimates of the rates of gene insertions/deletions, but also provide a quantitative view of the effect of truncated genes on rate estimation, which has been understudied in bacterial genome evolution.In this study, we developed a model that considers the information of truncated genes and makes use of a parameter-rich time-reversible rate matrix. Rate variation among genes is allowed in the model by incorporating a discrete Γ-distribution. We also allow rates to vary on different parts of the phylogeny (external branches vs. internal branches). Consistent with previous studies, the rates of gene insertions/deletions are comparable to or larger than the rates of nucleotide substitution and the rates of gene insertions/deletions are further inflated in closely related groups and on external branches, suggesting high rates of gene turnover of recently transferred genes. The results from the new model also suggest that many recently truncated genes are in the process of being rapidly deleted from the genome. Some other interesting estimates in the model are also presented and discussed. One implication of the study, though not primary, is that the state of truncated genes could serve as an additional phylogenetic character for phylogenetic reconstruction using gene family content.     

Association Mapping of Quantitative Disease Resistance in a Natural Population of Loblolly Pine (Pinus taeda L.)

Genetic resistance to disease incited by necrotrophic pathogens is not well understood in plants. Whereas resistance is often quantitative, there is limited information on the genes that underpin quantitative variation in disease resistance. We used a population genomic approach to identify genes in loblolly pine (Pinus taeda) that are associated with resistance to pitch canker, a disease incited by the necrotrophic pathogen Fusarium circinatum. A set of 498 largely unrelated, clonally propagated genotypes were inoculated with F. circinatum microconidia and lesion length, a measure of disease resistance, data were collected 4, 8, and 12 weeks after inoculation. Best linear unbiased prediction was used to adjust for imbalance in number of observations and to identify highly susceptible and highly resistant genotypes (“tails”). The tails were reinoculated to validate the results of the full population screen. Significant associations were detected in 10 single nucleotide polymorphisms (SNPs) (out of 3938 tested). As hypothesized for genes involved in quantitative resistance, the 10 SNPs had small effects and proposed roles in basal resistance, direct defense, and signal transduction. We also discovered associated genes with unknown function, which would have remained undetected in a candidate gene approach constrained by annotation for disease resistance or stress response.GENETIC interactions between host and pathogen populations result in abundant natural variation in the genes involved in host disease resistance. Most of the studies leading to identification and cloning of disease resistance genes are focused on major gene disease resistance (Johal and Briggs 1992; Dangl and Jones 2001; Jones and Dangl 2006). In cases where resistance is associated with single genes, genetic effects are large in magnitude and detection is straightforward. In contrast, quantitative disease resistance is typically conditioned by many genes with relatively small effects. Quantitative resistance is generally considered to be more durable but also more difficult to investigate relative to major gene resistance, since the effects of individual genes are small and phenotyping experiments must be performed with high levels of precision. As a consequence, the genes and mechanisms of quantitative disease resistance are poorly understood, in part due to the smaller effect of individual genes on the resistance phenotype. Interactions between plants and necrotrophic pathogens often exhibit quantitative resistance (Balint-Kurti et al. 2008; Poland et al. 2009).Pitch canker disease of loblolly pine and other pine species is incited by the necrotrophic pathogen Fusarium circinatum and is manifest as resinous lesions in stems and branches (Dwinell et al. 1985; Enebak and Stanosz 2003; Carey et al. 2005; Sakamoto and Gordon 2006). There is evidence for heritable resistance to pitch canker in loblolly pine (Kayihan et al. 2005) as well as other pine species (Hodge and Dvorak 2000, 2007). In this article we report the first population-wide phenotypic screen of a clonally propagated population of loblolly pine for association testing (Eckert et al. 2010). Clonal propagation of this population enabled precise phenotyping, which was required to obtain the resolution needed to identify candidates for quantitative disease resistance loci.Pine species in general exhibit high levels of nucleotide variation and low linkage disequilibrium (LD) (Brown et al. 2004). An association genetic approach relies on the premise that historical, unrecorded recombination events over many generations have reduced LD between markers and quantitative trait loci such that only those marker-trait pairs that are tightly linked remain detectable; this may enable “fine mapping” to identify genes underlying quantitative variation (Flint-Garcia et al. 2003; Neale and Savolainen 2004). Association-based approaches have been used to identify candidate genes underlying traits in plants (Zhao et al. 2007; Stich et al. 2008; Wang et al. 2008; Yahiaoui et al. 2008; Inostroza et al. 2009; Stracke et al. 2009), based in part on applications in humans (D''alfonso et al. 2002; McGuffin et al. 2003; Easton et al. 2007; Lee et al. 2007), livestock (Martinez et al. 2006; Charlier et al. 2008; Goddard and Hayes 2009), and Drosophila (Kennington et al. 2007; Norry et al. 2007; Jiang et al. 2009). Recent association studies in tree species have evaluated single candidate genes or a modest number of candidate genes for association (Thumma et al. 2005; Gonzalez-Martinez et al. 2007, 2008; Ingvarsson et al. 2008; Eckert et al. 2009a). Association mapping has been used to identify disease resistance genes in several crop species including sugarcane, maize, barley, and potato (Flint-Garcia et al. 2005; Wei et al. 2006; Yu and Buckler 2006; Malosetti et al. 2007; Stich et al. 2008; Inostroza et al. 2009; Murray et al. 2009). The population analyzed in this study was genotyped at 3938 SNP loci that were selected without regard to the functional annotation of ESTs from which they were derived. Thus, we reasoned that the status of any particular marker as a candidate disease resistance gene would be determined by association testing, as opposed to previous studies in which markers were typically evaluated on the basis of their presumed roles in disease resistance in other species.Several different, but not mutually exclusive hypotheses have been proposed regarding the genetic origins of quantitative resistance (Poland et al. 2009), providing a useful framework for understanding evolution of resistance to necrotrophic pathogens. These six hypotheses proposed by Poland et al. (2009) predict that quantitative disease resistance is conditioned by: (1) genes regulating morphological and developmental phenotypes; (2) mutations in genes involved in basal defense causing small, incremental levels of resistance; (3) components of chemical warfare, through the action of genes producing antibiotic or antifungal compounds; (4) genes involved in defense signal transduction pathways; (5) weak forms of defeated R genes; and/or (6) genes not yet known to be involved in disease resistance.In this study, our main objective was to evaluate the genetic architecture of pitch canker disease resistance: to quantify the extent to which genes contribute to variation in the disease phenotype, to evaluate the hypothesis that disease resistance was quantitative, and to identify candidate genes for resistance as well as quantify their magnitude of effect. In the process of identifying candidate genes for resistance we were also able to evaluate support for hypotheses recently put forth by Poland et al. (2009) regarding the biological roles and origins of quantitative resistance genes.     

Sexual Isolation in Acinetobacter baylyi Is Locus-Specific and Varies 10,000-Fold Over the Genome

Naturally transformable bacteria acquire chromosomal DNA from related species at lower frequencies than from cognate DNA sources. To determine how genome location affects heterogamic transformation in bacteria, we inserted an nptI marker into random chromosome locations in 19 different strains of the Acinetobacter genus (>24% divergent at the mutS/trpE loci). DNA from a total of 95 nptI-tagged isolates was used to transform the recipient Acinetobacter baylyi strain ADP1. A total of >1300 transformation assays revealed that at least one nptI-tagged isolate for each of the strains/species tested resulted in detectable integration of the nptI marker into the ADP1 genome. Transformation frequencies varied up to ∼10,000-fold among independent nptI insertions within a strain. The location and local sequence divergence of the nptI flanking regions were determined in the transformants. Heterogamic transformation depended on RecA and was hampered by DNA mismatch repair. Our studies suggest that single-locus-based studies, and inference of transfer frequencies from general estimates of genomic sequence divergence, is insufficient to predict the recombination potential of chromosomal DNA fragments between more divergent genomes. Interspecies differences in overall gene content, and conflicts in local gene organization and synteny are likely important determinants of the genomewide variation in recombination rates between bacterial species.HORIZONTAL gene transfer (HGT) contributes to bacterial evolution by providing access to DNA evolved and retained in separate species or strains (Cohan 1994a,b; Bergstrom et al. 2000; Ochman et al. 2000; Feil et al. 2001; Koonin 2003; Lawrence and Hendrickson 2003; Fraser et al. 2007). Multilocus sequence typing (MLST) has provided strong evidence for frequent transfer and recombination of chromosomal DNA between related bacterial strains within the same species (Maiden et al. 1998; Enright et al. 2002). HGT occurring by natural transformation allows bacteria to exploit the presence of nucleic acids in their environment for the purposes of nutrition, DNA repair, reacquisition of lost genes, and/or acquisition of novel genetic diversity (Redfield 1993; Mehr and Seifert 1998; Dubnau 1999; Claverys et al. 2000; Szöllösi et al. 2006; Johnsen et al. 2009). It can be inferred from observations of the presence of extracellular DNA in most environments that bacteria are constantly exposed to DNA from a variety of sources, without such exposure necessarily producing observable changes in the genetic compositions of bacterial populations over evolutionary time (Thomas and Nielsen 2005; Nielsen et al. 2007a,b).The absence of sequence similarity between the donor DNA and the DNA of the recipient bacterium is the strongest barrier to the horizontal acquisition of chromosomal genes in bacteria (Matic et al. 1996; Vulic et al. 1997; Majewski 2001; Townsend et al. 2003) as illegitimate recombination occurs only at extremely low frequencies in bacteria (Hülter and Wackernagel 2008a). Single-locus transfer models have been extensively applied and have demonstrated a log-linear decrease in recombination frequencies with increasing sequence divergence for Bacillus subtilis (Roberts and Cohan, 1993; Zawadzki et al. 1995), Acinetobacter baylyi (Young and Ornston 2001), Escherichia coli (Shen and Huang 1986; Vulic et al. 1997), and Streptococcus pneumoniae (Majewski et al. 2000). For instance, heterogamic transformation between nonmutator isolates at the rpoB locus of B. mojavensis is undetectable at sequence divergences >16.7% (Zawadzki et al. 1995) and between S. pneumoniae isolates with sequence divergences >18% (Majewski et al. 2000). In A. baylyi, the nonmutator sequence divergence limit for detectable transformation at the pcaH locus of strain ADP1 was found to be 20% (Young and Ornston 2001), and up to 24% overall divergence yielded transformants at 16S rRNA loci in strain DSM587 (Strätz et al. 1996).Several recent studies also show that short stretches (<200 bp) of DNA sequence identity can facilitate additive or substitutive integration of longer stretches (>1000 bp) of heterologous DNA in bacteria (Prudhomme et al. 1991, 2002; de Vries and Wackernagel 2002; Hülter and Wackernagel 2008a). Thus, the uptake of DNA in bacteria can facilitate larger substitutions within gene sequences and the integration of additional DNA material on the basis of recombination initiated in flanking DNA stretches (either at one or both ends) with high sequence similarity (Nielsen et al. 2000). On the other hand, segments of heterologous DNA interrupting the synteny of homologous DNA have also been shown to be a barrier in intraspecies transformation in S. pneumoniae (Pasta and Sicard 1996, 1999).The various studies of the interspecies transfer potential of single genes demonstrate that the immediate local sequence divergence of the transferred locus is of high importance in determining recombination frequencies in hosts up to 20% divergent (at the housekeeping gene level). However, it can be hypothesized that the broader structural, organizational, and biochemical properties of the genome region surrounding a particular locus will determine its transfer potential to more divergent host species (Cohan 2001; Lawrence 2002). The interspecies transfer potential of various genome regions/loci between more diverged species (>20% at the housekeeping gene level) may therefore differ substantially from a log-linear model (determined experimentally for more closely related species) as local gene organization becomes less conserved with evolutionary time. The barriers to gene exchange between divergent bacterial species is likely a combination of inefficient recombination due to both mismatched base pairs (the main determinator in the log-linear model) and conflicting gene order and organization across the local recombining DNA regions. In addition, selective barriers due to negative effects on host fitness of the transferred DNA regions may become increasingly important for the removal of recombination events from the bacterial population. Recent bioinformatics-based genome analysis of E. coli and Salmonella genomes suggests various parts of the bacterial genome may have different suceptibilities to undergo evolutionarily successful recombination leading to temporal fragmentation of speciation (Lawrence 2002; Retchless and Lawrence 2007). Nevertheless, few studies have experimentally tested the effect of variable species and chromosome locations of genes on their transfer potential between bacteria (Ravin and Chen 1967; Ravin and Chakrabarti 1975; Siddiqui and Goldberg 1975; Cohan et al. 1991; Huang et al. 1991; Fall et al. 2007).Here, we determine to what extent genome location contributes to sexual isolation between the recipient A. baylyi strain ADP1 and 19 sequence divergent (24–27% divergent at the mutS/trpE loci) donor Acinetobacter strains and species (carrying a selectable nptI gene in a total of 95 random genome locations).