Composite Effects of Polymorphisms near Multiple Regulatory Elements Create a Major-Effect QTL

Many agriculturally, evolutionarily, and medically important characters vary in a quantitative fashion. Unfortunately, the genes and sequence variants accounting for this variation remain largely unknown due to a variety of biological and technical challenges. Drosophila melanogaster contains high levels of sequence variation and low linkage disequilibrium, allowing us to dissect the effects of many causative variants within a single locus. Here, we take advantage of these features to identify and characterize the sequence polymorphisms that comprise major effect QTL alleles segregating at the bric-a-brac locus. We show that natural bric-a-brac alleles with large effects on cuticular pigmentation reflect a cumulative impact of polymorphisms that affect three functional regions: a promoter, a tissue-specific enhancer, and a Polycomb response element. Analysis of allele-specific expression at the bric-a-brac locus confirms that these polymorphisms modulate transcription at the cis-regulatory level. Our results establish that a single QTL can act through a confluence of multiple molecular mechanisms and that sequence variation in regions flanking experimentally validated functional elements can have significant quantitative effects on transcriptional activity and phenotype. These findings have important design and conceptual implications for basic and medical genomics

A new measure of irregularity of distribution

AbstractWe introduce a new measure of irregularity of distribution-the range, Ψ, that is similar to the nonuniformity ϕ∞ but much easier to compute. It is shown that for Pτ-nets and for initial segments of LPτ-sequences Ψ ≤ 2τ and as the number of points increases this is the lowest possible order of magnitude Ψ = O(1)

Similar patterns of linkage disequilibrium and nucleotide diversity in native and introduced populations of the pea aphid, Acyrthosiphon pisum

<p>Abstract</p> <p>Background</p> <p>The pea aphid, <it>Acyrthosiphon pisum</it>, is an emerging genomic model system for studies of polyphenisms, bacterial symbioses, host-plant specialization, and the vectoring of plant viruses. Here we provide estimates of nucleotide diversity and linkage disequilibrium (LD) in native (European) and introduced (United States) populations of the pea aphid. Because introductions can cause population bottlenecks, we hypothesized that U.S. populations harbor lower levels of nucleotide diversity and higher levels of LD than native populations.</p> <p>Results</p> <p>We sampled four non-coding loci from 24 unique aphid clones from the U. S. (12 from New York and 12 from California) and 24 clones from Europe (12 alfalfa and 12 clover specialists). For each locus, we sequenced approximately 1 kb from two amplicons spaced ~10 kb apart to estimate both short range and longer range LD. We sequenced over 250 kb in total. Nucleotide diversity averaged 0.6% across all loci and all populations. LD decayed slowly within ~1 kb but reached much lower levels over ~10 kb. Contrary to our expectations, neither LD nor nucleotide diversity were significantly different between native and introduced populations.</p> <p>Conclusion</p> <p>Both introduced and native populations of pea aphids exhibit low levels of nucleotide diversity and moderate levels of LD. The introduction of pea aphids to North America has not led to a detectable reduction of nucleotide diversity or increase in LD relative to native populations.</p

Genome-Enabled Hitchhiking Mapping Identifies QTLs for Stress Resistance in Natural \u3ci\u3eDrosophila\u3c/i\u3e

Identification of genes underlying complex traits is an important problem. Quantitative trait loci (QTL) are mapped using marker-trait co-segregation in large panels of recombinant genotypes. Most frequently, recombinant inbred lines derived from two isogenic parents are used. Segregation pat-terns are also studied in pedigrees from multiple families. Great advances have been made through creative use of these techniques, but narrow sampling and inadequate power represent strong limi-tations. Here, we propose an approach combining the strengths of both techniques. We established a mapping population from a sample of natural genotypes and applied artificial selection for a com-plex character. Selection changed the frequencies of alleles in QTLs contributing to the selection re-sponse. We infer QTLs with dense genotyping microarrays by identifying blocks of linked markers undergoing selective changes in allele frequency. We demonstrated this approach with an experi-mental population composed from 20 isogenic strains. Selection for starvation survival was executed in three replicated populations with three control non-selected populations. Three individuals per population were genotyped using Affymetrix GeneChips. Two regions of the genome, one each on the left arms of the second and third chromosomes, showed significant divergence between control and selected populations. For the former region, we inferred allele frequencies in selected and control populations by pyrosequencing. We conclude that the allele frequency difference, averaging approx-imately 40% between selected and control lines, contributed to selection response. Our approach can contribute to the fine scale decomposition of the genetics of direct and indirect selection responses and genotype by environment interactions

Genome-Enabled Hitchhiking Mapping Identifies QTLs for Stress Resistance in Natural \u3ci\u3eDrosophila\u3c/i\u3e

Identification of genes underlying complex traits is an important problem. Quantitative trait loci (QTL) are mapped using marker-trait co-segregation in large panels of recombinant genotypes. Most frequently, recombinant inbred lines derived from two isogenic parents are used. Segregation pat-terns are also studied in pedigrees from multiple families. Great advances have been made through creative use of these techniques, but narrow sampling and inadequate power represent strong limi-tations. Here, we propose an approach combining the strengths of both techniques. We established a mapping population from a sample of natural genotypes and applied artificial selection for a com-plex character. Selection changed the frequencies of alleles in QTLs contributing to the selection re-sponse. We infer QTLs with dense genotyping microarrays by identifying blocks of linked markers undergoing selective changes in allele frequency. We demonstrated this approach with an experi-mental population composed from 20 isogenic strains. Selection for starvation survival was executed in three replicated populations with three control non-selected populations. Three individuals per population were genotyped using Affymetrix GeneChips. Two regions of the genome, one each on the left arms of the second and third chromosomes, showed significant divergence between control and selected populations. For the former region, we inferred allele frequencies in selected and control populations by pyrosequencing. We conclude that the allele frequency difference, averaging approx-imately 40% between selected and control lines, contributed to selection response. Our approach can contribute to the fine scale decomposition of the genetics of direct and indirect selection responses and genotype by environment interactions

Genomic Analysis of Differentiation between Soil Types Reveals Candidate Genes for Local Adaptation in Arabidopsis lyrata

Serpentine soil, which is naturally high in heavy metal content and has low calcium to magnesium ratios, comprises a difficult environment for most plants. An impressive number of species are endemic to serpentine, and a wide range of non-endemic plant taxa have been shown to be locally adapted to these soils. Locating genomic polymorphisms which are differentiated between serpentine and non-serpentine populations would provide candidate loci for serpentine adaptation. We have used the Arabidopsis thaliana tiling array, which has 2.85 million probes throughout the genome, to measure genetic differentiation between populations of Arabidopsis lyrata growing on granitic soils and those growing on serpentinic soils. The significant overrepresentation of genes involved in ion transport and other functions provides a starting point for investigating the molecular basis of adaptation to soil ion content, water retention, and other ecologically and economically important variables. One gene in particular, calcium-exchanger 7, appears to be an excellent candidate gene for adaptation to low Ca∶Mg ratio in A. lyrata

Threshold Response to Stochasticity in Morphogenesis

During development of biological organisms, multiple complex structures are formed. In many instances, these structures need to exhibit a high degree of order to be functional, although many of their constituents are intrinsically stochastic. Hence, it has been suggested that biological robustness ultimately must rely on complex gene regulatory networks and clean-up mechanisms. Here we explore developmental processes that have evolved inherent robustness against stochasticity. In the context of the Drosophila eye disc, multiple optical units, ommatidia, develop into crystal-like patterns. During the larva-to-pupa stage of metamorphosis, the centers of the ommatidia are specified initially through the diffusion of morphogens, followed by the specification of R8 cells. Establishing the R8 cell is crucial in setting up the geometric, and functional, relationships of cells within an ommatidium and among neighboring ommatidia. Here we study a mathematical model of these spatio-temporal processes in the presence of stochasticity, defining and applying measures that quantify order within the resulting spatial patterns. We observe a universal sigmoidal response to increasing transcriptional noise. Ordered patterns persist up to a threshold noise level in the model parameters. As the noise is further increased past a threshold point of no return, these ordered patterns rapidly become disordered. Such robustness in development allows for the accumulation of genetic variation without any observable changes in phenotype. We argue that the observed sigmoidal dependence introduces robustness allowing for sizable amounts of genetic variation and transcriptional noise to be tolerated in natural populations without resulting in phenotype variation

Coordinated evolution of co-expressed gene clusters in the Drosophila transcriptome

Abstract Background Co-expression of genes that physically cluster together is a common characteristic of eukaryotic transcriptomes. This organization of transcriptomes suggests that coordinated evolution of gene expression for clustered genes may also be common. Clusters where expression evolution of each gene is not independent of their neighbors are important units for understanding transcriptome evolution. Results We used a common microarray platform to measure gene expression in seven closely related species in the Drosophila melanogaster subgroup, accounting for confounding effects of sequence divergence. To summarize the correlation structure among genes in a chromosomal region, we analyzed the fraction of variation along the first principal component of the correlation matrix. We analyzed the correlation for blocks of consecutive genes to assess patterns of correlation that may be manifest at different scales of coordinated expression. We find that expression of physically clustered genes does evolve in a coordinated manner in many locations throughout the genome. Our analysis shows that relatively few of these clusters are near heterochromatin regions and that these clusters tend to be over-dispersed relative to the rest of the genome. This suggests that these clusters are not the byproduct of local gene clustering. We also analyzed the pattern of co-expression among neighboring genes within a single Drosophila species: D. simulans. For the co-expression clusters identified within this species, we find an under-representation of genes displaying a signature of recurrent adaptive amino acid evolution consistent with previous findings. However, clusters displaying co-evolution of expression among species are enriched for adaptively evolving genes. This finding points to a tie between adaptive sequence evolution and evolution of the transcriptome. Conclusion Our results demonstrate that co-evolution of expression in gene clusters is relatively common among species in the D. melanogaster subgroup. We consider the possibility that local regulation of expression in gene clusters may drive the connection between adaptive sequence and coordinated gene expression evolution

Laser annealing of metal nanoparticles implanted in dielectrics

The interaction of excimer laser pulses with silica consisting ion-synthesized copper nanoparticles is studied. Using optical reflectance of composite layers it is established that at the initial stage laser annealing leads to the fragmentation of the nanoparticles to smaller ones. After continuous irradiation by several pulses, the nanoparticles become larger due to the heating of the surrounding glass. The laser treatment for a longer time (more than several tens pulses) results in the dissociation of nanoparticles into small clusters and individual atoms. The mechanisms responsible for the modification of the composite material under high power laser radiation are discussed. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)

A Pipeline for Classifying Deleterious Coding Mutations in Agricultural Plants

The impact of deleterious variation on both plant fitness and crop productivity is not completely understood and is a hot topic of debates. The deleterious mutations in plants have been solely predicted using sequence conservation methods rather than function-based classifiers due to lack of well-annotated mutational datasets in these organisms. Here, we developed a machine learning classifier based on a dataset of deleterious and neutral mutations in Arabidopsis thaliana by extracting 18 informative features that discriminate deleterious mutations from neutral, including 9 novel features not used in previous studies. We examined linear SVM, Gaussian SVM, and Random Forest classifiers, with the latter performing best. Random Forest classifiers exhibited a markedly higher accuracy than the popular PolyPhen-2 tool in the Arabidopsis dataset. Additionally, we tested whether the Random Forest, trained on the Arabidopsis dataset, accurately predicts deleterious mutations in Orýza sativa and Pisum sativum and observed satisfactory levels of performance accuracy (87% and 93%, respectively) higher than obtained by the PolyPhen-2. Application of Transfer learning in classifiers did not improve their performance. To additionally test the performance of the Random Forest classifier across different angiosperm species, we applied it to annotate deleterious mutations in Cicer arietinum and validated them using population frequency data. Overall, we devised a classifier with the potential to improve the annotation of putative functional mutations in QTL and GWAS hit regions, as well as for the evolutionary analysis of proliferation of deleterious mutations during plant domestication; thus optimizing breeding improvement and development of new cultivars