CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett.7, 1225–1241 (2004).


    Google Scholar
     

  • 2.

    Cushman, S. A. et al. Editorial: the least cost path from landscape genetics to landscape genomics: challenges and opportunities to explore NGS data in a spatially explicit context. Front. Genet.9, 215 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 3.

    Pereira, A. Plant abiotic stress challenges from the changing environment. Front. Plant Sci.7, 1123 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 4.

    Rellstab, C. et al. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol.24, 4348–4370 (2015).

    PubMed 

    Google Scholar
     

  • 5.

    Zhu, J. K. Abiotic stress signaling and responses in plants. Cell167, 313–324 (2016).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 6.

    Allendorf, F. W., Hohenlohe, P. A. & Luikart, G. Genomics and the future of conservation genetics. Nat. Rev. Genet.11, 697–709 (2010).

    PubMed 
    CAS 

    Google Scholar
     

  • 7.

    Radwan, J. & Babik, W. The genomics of adaptation. Proc. Biol. Sci.279, 5024–5028 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 8.

    Li, Y. et al. Ten years of landscape genomics: challenges and opportunities. Front. Plant Sci.8, 2136 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 9.

    Cushman, S. A. Grand challenges in evolutionary and population genetics: the importance of integrating epigenetics, genomics, modeling, and experimentation. Front. Genet.5, 197 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 10.

    Guggisberg, A. et al. The genomic basis of adaptation to calcareous and siliceous soils in Arabidopsis lyrata. Mol. Ecol.27, 5088–5103 (2018).

    PubMed 

    Google Scholar
     

  • 11.

    Brennan, R. S. et al. Integrative population and physiological genomics reveals mechanisms of adaptation in killifish. Mol. Biol. Evol.35, 2639–2653 (2018).

    PubMed 
    CAS 

    Google Scholar
     

  • 12.

    Chen, C. et al. Population genomics provide insights into the evolution and adaptation of the eastern honey bee (Apis cerana). Mol. Biol. Evol.35, 2260–2271 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 13.

    Dittberner, H. et al. Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana. Mol. Ecol.27, 4052–4065 (2018).

    PubMed 
    CAS 

    Google Scholar
     

  • 14.

    Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol.35, 792–806 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 15.

    Ahrens, C. W., Byrne, M. & Rymer, P. D. Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species. Mol. Ecol.28, 2502–2516 (2019).

    PubMed 
    CAS 

    Google Scholar
     

  • 16.

    Wright, S. Evolution in Mendelian populations. Genetics16, 97–159 (1931).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 17.

    Miao, C. Y. et al. Landscape genomics reveal that ecological character determines adaptation: a case study in smoke tree (Cotinus coggygria Scop.). BMC Evol. Biol.17, 202 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 18.

    Li, J. X. et al. Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics. BMC Plant Biol.18, 306 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 19.

    Arciero, E. et al. Demographic history and genetic adaptation in the Himalayan region inferred from genome-wide SNP genotypes of 49 populations. Mol. Biol. Evol.35, 1916–1933 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 20.

    Friis, G. et al. Genome-wide signals of drift and local adaptation during rapid lineage divergence in a songbird. Mol. Ecol.27, 746–760 (2018).


    Google Scholar
     

  • 21.

    Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett.18, 1–16 (2015).

    PubMed 

    Google Scholar
     

  • 22.

    Guerrero, J. et al. Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics. N. Phytol.219, 378–390 (2018).

    CAS 

    Google Scholar
     

  • 23.

    Keller, S. R. et al. Local adaptation in the flowering-time gene network of balsam poplar, Populus balsamifera L. Mol. Biol. Evol.29, 3143–3152 (2012).

    PubMed 
    CAS 

    Google Scholar
     

  • 24.

    Manel, S. et al. Genome assemblies, genomic resources and their influence on the detection of the signal of positive selection in genome scans. Mol. Ecol.25, 170–184 (2016).

    PubMed 
    CAS 

    Google Scholar
     

  • 25.

    Fu, Z. Z. et al. Molecular data and ecological niche modeling reveal population dynamics of widespread shrub Forsythia suspensa (Oleaceae) in China’s warm-temperate zone in response to climate change during the Pleistocene. BMC Evol. Biol.14, 114 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 26.

    Hamrick, J. L. & Godt, M. J. Plant Population Genetics, Breeding, and Genetic Resources (Sinauer, Sunderland, 1990).

  • 27.

    Hewitt, G. M. Genetic consequences of climatic oscillations in the quaternary. Philos. Trans. R. Soc. Lond. B Biol. Sci.359, 183–195 (2004).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 28.

    Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol.28, 614–621 (2013).

    PubMed 

    Google Scholar
     

  • 29.

    Balkenhol, N. et al. Current status, future opportunities, and remaining challenges in landscape genetics. In (eds Balkenhol, N. C., et al.). Landscape Genetics: Concepts, Methods, Applications (Wiley, Hoboken, 2015).

  • 30.

    Yang, J. et al. Landscape population genomics of forsythia (Forsythia suspensa) reveal that ecological habitats determine the adaptive evolution of species. Front. Plant Sci.8, 481 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 31.

    Sun, X. et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE8, e58700 (2013).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 32.

    Sollars, E. S. A. et al. Genome sequence and genetic diversity of European ash trees. Nature541, 212–216 (2017).

    PubMed 
    CAS 

    Google Scholar
     

  • 33.

    Unver, T. et al. Genome of wild olive and the evolution of oil biosynthesis. Proc. Natl Acad. Sci. USA114, E9413–E9422 (2017).

    PubMed 
    CAS 

    Google Scholar
     

  • 34.

    Yang, X. et al. The chromosome-level quality genome provides insights into the evolution of the biosynthesis genes for aroma compounds of Osmanthus fragrans. Hortic. Res.5, 72 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 35.

    Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res.19, 1655–1664 (2009).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 36.

    Wallander, E. & Albert, V. A. Phylogeny and classification of Oleaceae based on rps16 and trnL-F sequence data. Am. J. Bot.87, 1827–1841 (2000).

    PubMed 
    CAS 

    Google Scholar
     

  • 37.

    Wang, T. Q. et al. TCM treatment of anemopyretic cold rule analysis. J. Tianjin Univ. Tradit. Chin. Med.37, 113–117 (2018).


    Google Scholar
     

  • 38.

    Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet.48, 134–143 (2016).

    PubMed 
    CAS 

    Google Scholar
     

  • 39.

    Najafi, S., Sorkheh, K. & Nasernakhaei, F. Characterization of the APETALA2/Ethylene-responsive factor (AP2/ERF) transcription factor family in sunflower. Sci. Rep.8, 11576 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Xie, Z. et al. AP2/ERF transcription factor regulatory networks in hormone and abiotic stress responses in Arabidopsis. Front. Plant Sci.10, 228 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 41.

    Chakraborty, U. & Pradhan, B. Drought stress-induced oxidative stress and antioxidative responses in four wheat (Triticum aestivum L.) varieties. Arch. Agron. Soil Sci.58, 617–630 (2012).

    CAS 

    Google Scholar
     

  • 42.

    Noureddine, Y. Changes of peroxidase activities under cold stress in annuals populations of medicago. Mol. Plant Breed.6, 5 (2015).


    Google Scholar
     

  • 43.

    Gong, L. et al. Transcriptome profiling of the potato (Solanum tuberosum L.) plant under drought stress and water-stimulus conditions. PLoS ONE10, e0128041 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 44.

    Wang, M. et al. Comparative transcriptome analysis to elucidate the enhanced thermotolerance of tea plants (Camellia sinensis) treated with exogenous calcium. Planta249, 775–786 (2019).

    PubMed 
    CAS 

    Google Scholar
     

  • 45.

    Schöttler, M. A. et al. Photosynthetic complex stoichiometry dynamics in higher plants: biogenesis, function, and turnover of ATP synthase and the cytochrome b6f complex. J. Exp. Bot.66, 2373–2400 (2015).

    PubMed 

    Google Scholar
     

  • 46.

    Collakova, E. & DellaPenna, D. The role of homogentisate phytyltransferase and other tocopherol pathway enzymes in the regulation of tocopherol synthesis during abiotic stress. Plant Physiol.133, 930–940 (2003).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 47.

    Gavalas, N. A. & Clark, H. E. On the role of manganese in photosynthesis: kinetics of photoinhibition in manganese-deficent and 3-(4-chlorophenyl)-1, 1-dimethylurea-inhibited Euglena gracilis. Plant Physiol.47, 139–143 (1971).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 48.

    Chen, C. Y. et al. Structural basis of jasmonate-amido synthetase FIN219 in complex with glutathione S-transferase FIP1 during the JA signal regulation. Proc. Natl Acad. Sci. USA114, E1815–E1824 (2017).

    PubMed 
    CAS 

    Google Scholar
     

  • 49.

    Nisar, N. et al. Carotenoid metabolism in plant. Mol. Plant8, 68–82 (2015).

    PubMed 
    CAS 

    Google Scholar
     

  • 50.

    Landguth, E. L. et al. Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework. Mol. Ecol. Resour.20, 605–615 (2020).

    PubMed 

    Google Scholar
     

  • 51.

    Ram, S. Role of alcohol dehydrogenase, malate dehydrogenase and malic enzyme in flooding tolerance in Brachiaria Species. J. Plant Biochem. Biot.9, 45–47 (2000).

    CAS 

    Google Scholar
     

  • 52.

    Butsayawarapat, P. et al. Comparative transcriptome analysis of waterlogging-sensitive and tolerant zombi pea (Vigna vexillata) reveals energy conservation and root plasticity controlling waterlogging tolerance. Plants8, 264 (2019).

    PubMed Central 
    CAS 

    Google Scholar
     

  • 53.

    Ohsawa, T. & Ide, Y. Global patterns of genetic variation in plant species along vertical and horizontal gradients on mountains. Glob. Ecol. Biogeogr.17, 152–163 (2008).


    Google Scholar
     

  • 54.

    Yang, J. et al. Landscape genomics analysis of Achyranthes bidentata reveal adaptive genetic variations are driven by environmental variations relating to ecological habit. Popul. Ecol.59, 355–362 (2017).


    Google Scholar
     

  • 55.

    Fu, Z. Z. et al. Population genetics of the widespread shrub Forsythia suspensa (Oleaceae) in warm-temperate China using microsatellite loci: implication for conservation. Plant Syst. Evol.302, 1–9 (2016).


    Google Scholar
     

  • 56.

    Marcais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics27, 764–770 (2011).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 57.

    Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol.36, 338–345 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 58.

    Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive κ-mer weighting and repeat separation. Genome Res.27, 722–736 (2017).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 59.

    Chakraborty, M. et al. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Res.44, e147 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 60.

    Vaser, R. et al. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res.27, 737–746 (2017).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 61.

    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE9, e112963 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 62.

    Simão, F. A. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics31, 3210 (2015).

    PubMed 

    Google Scholar
     

  • 63.

    Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics23, 1061–1067 (2007).

    PubMed 
    CAS 

    Google Scholar
     

  • 64.

    Zhang, J. et al. High-density genetic map construction and identification of a locus controlling weeping trait in an ornamental woody plant (Prunus mume Sieb. et Zucc). DNA Res.22, 1–9 (2015).


    Google Scholar
     

  • 65.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics25, 1754–1760 (2009).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 66.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.20, 1297–1303 (2010).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 67.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics25, 2078–2079 (2009).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 68.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet.38, 904–909 (2006).

    PubMed 
    CAS 

    Google Scholar
     

  • 69.

    Kumar, S. et al. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol. Biol. Evol.35, 1547–1549 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 70.

    Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour.10, 564–567 (2010).

    PubMed 

    Google Scholar
     

  • 71.

    Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet.8, e1002967 (2012).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 72.

    Foll, M. & Gaggiotti, O. E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics180, 977–993 (2008).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 73.

    Hijmans, R. J. et al. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl.127, 15–19 (2001).


    Google Scholar
     

  • 74.

    Frichot, E. et al. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol.30, 1687–1699 (2013).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 75.

    Joost, S. et al. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol. Ecol.16, 3955–3969 (2007).

    PubMed 
    CAS 

    Google Scholar
     

  • 76.

    Stucki, S. et al. High performance computation of landscape genomic models including local indicators of spatial association. Mol. Ecol. Resour.17, 1072–1089 (2017).

    PubMed 
    CAS 

    Google Scholar
     

  • 77.

    Oksanen, J. et al. Vegan: Community Ecology Package. R. Package Version 2.4-5 (2017).

  • 78.

    Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol.215, 403–410 (1990).

    CAS 

    Google Scholar
     



  • Source link

    2 thoughts on “Genome sequencing and population genomics modeling provide insights into the local adaptation of weeping forsythia”
    1. Lower in caffeine than black tea, green tea has a fresh, light flavor and is verdant green in color. We’ve rounded up the most highly rated, sustainably sourced, and best-tasting green tea varieties to help you reach your happy place.

    2. The primary tasting notes of green tea leaves are savory, spicy, fruity, floral, and vegetal. Specific strains of tea can range from sweet, floral, and fruity, to vegetal and smoky, earthy or nutty.

    Leave a Reply

    Your email address will not be published. Required fields are marked *