IntroUNET: Identifying introgressed alleles via semantic segmentation.

A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient-ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.

The utility of genomic prediction models in evolutionary genetics.

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.

Population-scale genomic data sets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information from genomic sequences to infer demographic events such as population size changes and gene flow among closely related populations/species, construct recombination maps, and uncover loci underlying recent adaptation. To date, most methods make use of only one or a few summaries of the input sequences and therefore ignore potentially useful information encoded in the data. The most sophisticated of these approaches involve likelihood calculations, which require theoretical advances for each new problem, and often focus on a single aspect of the data (e.g., only allele frequency information) in the interest of mathematical and computational tractability. Directly interrogating the entirety of the input sequence data in a likelihood-free manner would thus offer a fruitful alternative. Here, we accomplish this by representing DNA sequence alignments as images and using a class of deep learning methods called convolutional neural networks (CNNs) to make population genetic inferences from these images. We apply CNNs to a number of evolutionary questions and find that they frequently match or exceed the accuracy of current methods. Importantly, we show that CNNs perform accurate evolutionary model selection and parameter estimation, even on problems that have not received detailed theoretical treatments. Thus, when applied to population genetic alignments, CNNs are capable of outperforming expert-derived statistical methods and offer a new path forward in cases where no likelihood approach exists.

Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments.

A challenge to improve an integrative phenotype, like yield, is the interaction between the broad range of possible molecular and physiological traits that contribute to yield and the multitude of potential environmental conditions in which they are expressed. This study collected data on 31 phenotypic traits, 83 annotated metabolites, and nearly 22,000 transcripts from a set of 57 diverse, commercially relevant maize hybrids across three years in central U.S. Corn Belt environments. Although variability in characteristics created a complex picture of how traits interact produce yield, phenotypic traits and gene expression were more consistent across environments, while metabolite levels showed low repeatability. Phenology traits, such as green leaf number and grain moisture and whole plant nitrogen content showed the most consistent correlation with yield. A machine learning predictive analysis of phenotypic traits revealed that ear traits, phenology, and root traits were most important to predicting yield. Analysis suggested little correlation between biomass traits and yield, suggesting there is more of a sink limitation to yield under the conditions studied here. This work suggests that continued improvement of maize yields requires a strong understanding of baseline variation of plant characteristics across commercially-relevant germplasm to drive strategies for consistently improving yield.

GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity.

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data. The procedure, called Genome Order Optimization by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations. We demonstrate the method using both simulated data (calibrated from experiments on Drosophila melanogaster) and real data from five distinct crosses within the flowering plant genus Mimulus. Application of GOOGA to the Mimulus data corrects numerous errors (misplaced sequences) in the M. guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.

Rapid molecular evolution across amniotes of the IIS/TOR network.

The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades.

Speciation and introgression between Mimulus nasutus and Mimulus guttatus.

Mimulus guttatus and M. nasutus are an evolutionary and ecological model sister species pair differentiated by ecology, mating system, and partial reproductive isolation. Despite extensive research on this system, the history of divergence and differentiation in this sister pair is unclear. We present and analyze a population genomic data set which shows that M. nasutus budded from a central Californian M. guttatus population within the last 200 to 500 thousand years. In this time, the M. nasutus genome has accrued genomic signatures of the transition to predominant selfing, including an elevated proportion of nonsynonymous variants, an accumulation of premature stop codons, and extended levels of linkage disequilibrium. Despite clear biological differentiation, we document genomic signatures of ongoing, bidirectional introgression. We observe a negative relationship between the recombination rate and divergence between M. nasutus and sympatric M. guttatus samples, suggesting that selection acts against M. nasutus ancestry in M. guttatus.

Co-expression network analysis of duplicate genes in maize (Zea mays L.) reveals no subgenome bias.

BACKGROUND: Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. RESULTS: To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks - maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize. CONCLUSIONS: Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types, duplication ages and co-expression consequences.

Western corn rootworm (Diabrotica virgifera virgifera) transcriptome assembly and genomic analysis of population structure.

BACKGROUND: Western corn rootworm (WCR) is one of the most significant insect pests of maize in North America. WCR has dramatically increased its range in the last century, invading key maize production areas in the US and abroad. In addition, this species has a history of evolving traits that allow it to escape various control options. Improved genetic and genomic resources are crucial tools for understanding population history and the genetic basis of trait evolution. Here we produce and analyze a transcriptome assembly for WCR. We also perform whole genome population resequencing, and combine these resources to better understand the evolutionary history of WCR. RESULTS: The WCR transcriptome assembly presented here contains approximately 16,000 unigenes, many of which have high similarity to other insect species. Among these unigenes we found several gene families that have been implicated in insecticide resistance in other species. We also identified over 500,000 unigene based SNPs among 26 WCR populations. We used these SNPs to scan for outliers among the candidate genes, and to understand how population processes have shaped genetic variation in this species. CONCLUSIONS: This study highlights the utility of transcriptomic and genomic resources as foundational tools for dealing with highly adaptive pest species. Using these tools we identified candidate gene families for insecticide resistance and reveal aspects of WCR population history in light of the species' recent range expansion.

Parallel up-regulation of the profilin gene family following independent domestication of diploid and allopolyploid cotton (Gossypium).

Cotton is remarkable among our major crops in that four species were independently domesticated, two allopolyploids and two diploids. In each case thousands of years of human selection transformed sparsely flowering, perennial shrubs into highly productive crops with seeds bearing the vastly elongated and abundant single-celled hairs that comprise modern cotton fiber. The genetic underpinnings of these transformations are largely unknown, but comparative gene expression profiling experiments have demonstrated up-regulation of profilin accompanying domestication in all three species for which wild forms are known. Profilins are actin monomer binding proteins that are important in cytoskeletal dynamics and in cotton fiber elongation. We show that Gossypium diploids contain six profilin genes (GPRF1-GPRF6), located on four different chromosomes (eight chromosomes in the allopolyploid). All but one profilin (GPRF6) are expressed during cotton fiber development, and both homeologs of GPRF1-GPRF5 are expressed in fibers of the allopolyploids. Remarkably, quantitative RT-PCR and RNAseq data demonstrate that GPRF1-GPRF5 are all up-regulated, in parallel, in the three independently domesticated cottons in comparison with their wild counterparts. This result was additionally supported by iTRAQ proteomic data. In the allopolyploids, there This usage of novel should be fine, since it refers to a novel evolutionary process, not a novel discovery has been novel recruitment of the sixth profilin gene (GPRF6) as a result of domestication. This parallel up-regulation of an entire gene family in multiple species in response to strong directional selection is without precedent and suggests unwitting selection on one or more upstream transcription factors or other proteins that coordinately exercise control over profilin expression.

IntroUNET: identifying introgressed alleles via semantic segmentation.

A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient-ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.

Chronosequence of invasion reveals minimal losses of population genomic diversity, niche expansion, and trait divergence in the polyploid, leafy spurge.

Rapid evolution may play an important role in the range expansion of invasive species and modify forecasts of invasion, which are the backbone of land management strategies. However, losses of genetic variation associated with colonization bottlenecks may constrain trait and niche divergence at leading range edges, thereby impacting management decisions that anticipate future range expansion. The spatial and temporal scales over which adaptation contributes to invasion dynamics remain unresolved. We leveraged detailed records of the ~130-year invasion history of the invasive polyploid plant, leafy spurge (Euphorbia virgata), across ~500 km in Minnesota, U.S.A. We examined the consequences of range expansion for population genomic diversity, niche breadth, and the evolution of germination behavior. Using genotyping-by-sequencing, we found some population structure in the range core, where introduction occurred, but panmixia among all other populations. Range expansion was accompanied by only modest losses in sequence diversity, with small, isolated populations at the leading edge harboring similar levels of diversity to those in the range core. The climatic niche expanded during most of the range expansion, and the niche of the range core was largely non-overlapping with the invasion front. Ecological niche models indicated that mean temperature of the warmest quarter was the strongest determinant of habitat suitability and that populations at the leading edge had the lowest habitat suitability. Guided by these findings, we tested for rapid evolution in germination behavior over the time course of range expansion using a common garden experiment and temperature manipulations. Germination behavior diverged from the early to late phases of the invasion, with populations from later phases having higher dormancy at lower temperatures. Our results suggest that trait evolution may have contributed to niche expansion during invasion and that distribution models, which inform future management planning, may underestimate invasion potential without accounting for evolution.

Variance component estimates, phenotypic characterization, and genetic evaluation of bovine congestive heart failure in commercial feeder cattle.

The increasing incidence of bovine congestive heart failure (BCHF) in feedlot cattle poses a significant challenge to the beef industry from economic loss, reduced performance, and reduced animal welfare attributed to cardiac insufficiency. Changes to cardiac morphology as well as abnormal pulmonary arterial pressure (PAP) in cattle of mostly Angus ancestry have been recently characterized. However, congestive heart failure affecting cattle late in the feeding period has been an increasing problem and tools are needed for the industry to address the rate of mortality in the feedlot for multiple breeds. At harvest, a population of 32,763 commercial fed cattle were phenotyped for cardiac morphology with associated production data collected from feedlot processing to harvest at a single feedlot and packing plant in the Pacific Northwest. A sub-population of 5,001 individuals were selected for low-pass genotyping to estimate variance components and genetic correlations between heart score and the production traits observed during the feeding period. At harvest, the incidence of a heart score of 4 or 5 in this population was approximately 4.14%, indicating a significant proportion of feeder cattle are at risk of cardiac mortality before harvest. Heart scores were also significantly and positively correlated with the percentage Angus ancestry observed by genomic breed percentage analysis. The heritability of heart score measured as a binary (scores 1 and 2 = 0, scores 4 and 5 = 1) trait was 0.356 in this population, which indicates development of a selection tool to reduce the risk of congestive heart failure as an EPD (expected progeny difference) is feasible. Genetic correlations of heart score with growth traits and feed intake were moderate and positive (0.289-0.460). Genetic correlations between heart score and backfat and marbling score were -0.120 and -0.108, respectively. Significant genetic correlation to traits of high economic importance in existing selection indexes explain the increased rate of congestive heart failure observed over time. These results indicate potential to implement heart score observed at harvest as a phenotype under selection in genetic evaluation in order to reduce feedlot mortality due to cardiac insufficiency and improve overall cardiopulmonary health in feeder cattle.

Duplicate gene evolution, homoeologous recombination, and transcriptome characterization in allopolyploid cotton.

BACKGROUND: Modern allotetraploid cotton contains an "A" and "D" genome from an ancestral polyploidy event that occurred approximately 1-2 million years ago. Diploid A- and D-genome species can be compared to the A- and D-genomes found within these allotetraploids to make evolutionary inferences about polyploidy. In this paper we present a comprehensive EST assembly derived from diploid and model allotetraploid cottons and demonstrate several evolutionary inferences regarding genic evolution that can be drawn from these data. RESULTS: We generated a set of cotton expressed sequence tags (ESTs), comprising approximately 4.4 million Sanger and next-generation (454) transcripts supplemented by approximately 152 million Illumina reads from diploid and allotetraploid cottons. From the EST alignments we inferred 259,192 genome-specific single nucleotide polymorphisms (SNPs). Molecular evolutionary analyses of protein-coding regions demonstrate that the rate of nucleotide substitution has increased among both allotetraploid genomes relative to the diploids, and that the ratio of nonsynonymous to synonymous substitutions has increased in one of the two polyploid lineages we sampled. We also use these SNPs to show that a surprisingly high percentage of duplicate genes (~7 %) show a signature of non-independent evolution in the allotetraploid nucleus, having experienced one or more episodes of nonreciprocal homoeologous recombination (NRHR). CONCLUSIONS: In this study we characterize the functional and mutational properties of the cotton transcriptome, produce a large genome-specific SNP database, and detect illegitimate genetic exchanges between duplicate genomes sharing a common allotetraploid nucleus. Our findings have important implications for our understanding of the consequences of polyploidy and duplicate gene evolution. We demonstrate that cotton genes have experienced an increased rate of molecular evolution following duplication by polyploidy, and that polyploidy has enabled considerable levels of nonreciprocal exchange between homoeologous genes.

Partitioned expression of duplicated genes during development and evolution of a single cell in a polyploid plant.

Polyploidy is an important driver of eukaryotic evolution, evident in many animals, fungi, and plants. One consequence of polyploidy is subfunctionalization, in which the ancestral expression profile becomes partitioned among duplicated genes (termed homoeologs). Subfunctionalization appears to be a common phenomenon insofar as it has been studied, at the scale of organs. Here, we use a high-resolution methodology to investigate the expression of thousands of pairs of homoeologs during the development of a single plant cell, using as a model the seed trichomes ("cotton fiber") of allopolyploid (containing "A" and "D" genomes) cotton (Gossypium). We demonstrate that approximately 30% of the homoeologs are significantly A- or D-biased at each of three time points studied during fiber development. Genes differentially biased toward the A or D genome belong to different biological processes, illustrating the functional partitioning of genomic contributions during cellular development. Interestingly, expression of the biased genes was shifted strongly toward the agronomically inferior D genome. Analyses of homoeologous gene expression during development of this cell showed that one-fifth of the genes exhibit changes in A/D ratios, indicating that significant alteration in duplicated gene expression is fairly frequent even at the level of development and maturation of a single cell. Comparing changes in homoeolog expression in cultivated versus wild cotton showed that most homoeolog expression bias reflects polyploidy rather than domestication. Evidence suggests, however, that domestication may increase expression bias in fibers toward the D genome, potentially implicating D-genome recruitment under human selection during domestication.

The evolution of spinnable cotton fiber entailed prolonged development and a novel metabolism.

A central question in evolutionary biology concerns the developmental processes by which new phenotypes arise. An exceptional example of evolutionary innovation is the single-celled seed trichome in Gossypium ("cotton fiber"). We have used fiber development in Gossypium as a system to understand how morphology can rapidly evolve. Fiber has undergone considerable morphological changes between the short, tightly adherent fibers of G. longicalyx and the derived long, spinnable fibers of its closest relative, G. herbaceum, which facilitated cotton domestication. We conducted comparative gene expression profiling across a developmental time-course of fibers from G. longicalyx and G. herbaceum using microarrays with approximately 22,000 genes. Expression changes between stages were temporally protracted in G. herbaceum relative to G. longicalyx, reflecting a prolongation of the ancestral developmental program. Gene expression and GO analyses showed that many genes involved with stress responses were upregulated early in G. longicalyx fiber development. Several candidate genes upregulated in G. herbaceum have been implicated in regulating redox levels and cell elongation processes. Three genes previously shown to modulate hydrogen peroxide levels were consistently expressed in domesticated and wild cotton species with long fibers, but expression was not detected by quantitative real time-PCR in wild species with short fibers. Hydrogen peroxide is important for cell elongation, but at high concentrations it becomes toxic, activating stress processes that may lead to early onset of secondary cell wall synthesis and the end of cell elongation. These observations suggest that the evolution of long spinnable fibers in cotton was accompanied by novel expression of genes assisting in the regulation of reactive oxygen species levels. Our data suggest a model for the evolutionary origin of a novel morphology through differential gene regulation causing prolongation of an ancestral developmental program.

Gene expression in developing fibres of Upland cotton (Gossypium hirsutum L.) was massively altered by domestication.

BACKGROUND: Understanding the evolutionary genetics of modern crop phenotypes has a dual relevance to evolutionary biology and crop improvement. Modern upland cotton (Gossypium hirsutum L.) was developed following thousands of years of artificial selection from a wild form, G. hirsutum var. yucatanense, which bears a shorter, sparser, layer of single-celled, ovular trichomes ('fibre'). In order to gain an insight into the nature of the developmental genetic transformations that accompanied domestication and crop improvement, we studied the transcriptomes of cotton fibres from wild and domesticated accessions over a developmental time course. RESULTS: Fibre cells were harvested between 2 and 25 days post-anthesis and encompassed the primary and secondary wall synthesis stages. Using amplified messenger RNA and a custom microarray platform designed to interrogate expression for 40,430 genes, we determined global patterns of expression during fibre development. The fibre transcriptome of domesticated cotton is far more dynamic than that of wild cotton, with over twice as many genes being differentially expressed during development (12,626 versus 5273). Remarkably, a total of 9465 genes were diagnosed as differentially expressed between wild and domesticated fibres when summed across five key developmental time points. Human selection during the initial domestication and subsequent crop improvement has resulted in a biased upregulation of components of the transcriptional network that are important for agronomically advanced fibre, especially in the early stages of development. About 15% of the differentially expressed genes in wild versus domesticated cotton fibre have no homology to the genes in databases. CONCLUSIONS: We show that artificial selection during crop domestication can radically alter the transcriptional developmental network of even a single-celled structure, affecting nearly a quarter of the genes in the genome. Gene expression during fibre development within accessions and expression alteration arising from evolutionary change appears to be 'modular' - complex genic networks have been simultaneously and similarly transformed, in a coordinated fashion, as a consequence of human-mediated selection. These results highlight the complex alteration of the global gene expression machinery that resulted from human selection for a longer, stronger and finer fibre, as well as other aspects of fibre physiology that were not consciously selected. We illustrate how the data can be mined for genes that were unwittingly targeted by aboriginal and/or modern domesticators during crop improvement and/or which potentially control the improved qualities of domesticated cotton fibre.See Commentary: http://www.biomedcentral.com/1741-7007/8/137.

Estimation of Cry3Bb1 resistance allele frequency in field populations of western corn rootworm using a genetic marker.

Following the discovery of western corn rootworm (WCR; Diabrotica virgifera virgifera) populations resistant to the Bacillus thuringiensis (Bt) protein Cry3Bb1, resistance was genetically mapped to a single locus on WCR chromosome 8 and linked SNP markers were shown to correlate with the frequency of resistance among field-collected populations from the US Corn Belt. The purpose of this paper is to further investigate the relationship between one of these resistance-linked markers and the causal resistance locus. Using data from laboratory bioassays and field experiments, we show that one allele of the resistance-linked marker increased in frequency in response to selection, but was not perfectly linked to the causal resistance allele. By coupling the response to selection data with a genetic model of the linkage between the marker and the causal allele, we developed a model that allowed marker allele frequencies to be mapped to causal allele frequencies. We then used this model to estimate the resistance allele frequency distribution in the US Corn Belt based on collections from 40 populations. These estimates suggest that chromosome 8 Cry3Bb1 resistance allele frequency was generally low (<10%) for 65% of the landscape, though an estimated 13% of landscape has relatively high (>25%) resistance allele frequency.

RNase T2 genes from rice and the evolution of secretory ribonucleases in plants.

The plant RNase T2 family is divided into two different subfamilies. S-RNases are involved in rejection of self-pollen during the establishment of self-incompatibility in three plant families. S-like RNases, on the other hand, are not involved in self-incompatibility, and although gene expression studies point to a role in plant defense and phosphate recycling, their biological roles are less well understood. Although S-RNases have been subjects of many phylogenetic studies, few have included an extensive analysis of S-like RNases, and genome-wide analyses to determine the number of S-like RNases in fully sequenced plant genomes are missing. We characterized the eight RNase T2 genes present in the Oryza sativa genome; and we also identified the full complement of RNase T2 genes present in other fully sequenced plant genomes. Phylogenetics and gene expression analyses identified two classes among the S-like RNase subfamily. Class I genes show tissue specificity and stress regulation. Inactivation of RNase activity has occurred repeatedly throughout evolution. On the other hand, Class II seems to have conserved more ancestral characteristics; and, unlike other S-like RNases, genes in this class are conserved in all plant species analyzed and most are constitutively expressed. Our results suggest that gene duplication resulted in high diversification of Class I genes. Many of these genes are differentially expressed in response to stress, and we propose that protein characteristics, such as the increase in basic residues can have a defense role independent of RNase activity. On the other hand, constitutive expression and phylogenetic conservation suggest that Class II S-like RNases may have a housekeeping role.

Reciprocal silencing, transcriptional bias and functional divergence of homeologs in polyploid cotton (gossypium).

Polyploidy is an important force in the evolution of flowering plants. Genomic merger and doubling induce an extensive array of genomic effects, including immediate and long-term alterations in the expression of duplicate genes ("homeologs"). Here we employed a novel high-resolution, genome-specific, mass-spectrometry technology and a well-established phylogenetic framework to investigate relative expression levels of each homeolog for 63 gene pairs in 24 tissues in naturally occurring allopolyploid cotton (Gossypium L.), a synthetic allopolyploid of the same genomic composition, and models of the diploid progenitor species. Results from a total of 2177 successful expression assays permitted us to determine the extent of expression evolution accompanying genomic merger of divergent diploid parents, genome doubling, and genomic coevolution in a common nucleus subsequent to polyploid formation. We demonstrate that 40% of homeologs are transcriptionally biased in at least one stage of cotton development, that genome merger per se has a large effect on relative expression of homeologs, and that the majority of these alterations are caused by cis-regulatory divergence between the diploid progenitors. We describe the scope of transcriptional subfunctionalization and 15 cases of probable neofunctionalization among 8 tissues. To our knowledge, this study represents the first characterization of transcriptional neofunctionalization in an allopolyploid. These results provide a novel temporal perspective on expression evolution of duplicate genomes and add to our understanding of the importance of polyploidy in plants.