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      Genetic adaptations in the population history of Arabidopsis thaliana

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          Abstract

          A population encounters a variety of environmental stresses, so the full source of its resilience can only be captured by collecting all the signatures of adaptation to the selection of the local environment in its population history. Based on the multiomic data of Arabidopsis thaliana, we constructed a database of phenotypic adaptations (p-adaptations) and gene expression (e-adaptations) adaptations in the population. Through the enrichment analysis of the identified adaptations, we inferred a likely scenario of adaptation that is consistent with the biological evidence from experimental work. We analyzed the dynamics of the allele frequencies at the 23,880 QTLs of 174 traits and 8,618 eQTLs of 1,829 genes with respect to the total SNPs in the genomes and identified 650 p-adaptations and 3,925 e-adaptations [false discovery rate (FDR) = 0.05]. The population underwent large-scale p-adaptations and e-adaptations along 4 lineages. Extremely cold winters and short summers prolonged seed dormancy and expanded the root system architecture. Low temperatures prolonged the growing season, and low light intensity required the increased chloroplast activity. The subtropical and humid environment enhanced phytohormone signaling pathways in response to the biotic and abiotic stresses. Exposure to heavy metals selected alleles for lower heavy metal uptake from soil, lower growth rate, lower resistance to bacteria, and higher expression of photosynthetic genes were selected. The p-adaptations are directly interpretable, while the coadapted gene expressions reflect the physiological requirements for the adaptation. The integration of this information characterizes when and where the population has experienced environmental stress and how the population responded at the molecular level.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

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              Detecting the number of clusters of individuals using the software structure: a simulation study

              The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                December 2023
                25 September 2023
                25 September 2023
                : 13
                : 12
                : jkad218
                Affiliations
                Graduate School of Agricultural and Life Sciences, The University of Tokyo , 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
                Research and Development Initiative, Chuo University , 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
                Fisheries Resources Institute, Japan Fisheries Research and Education Agency , 2-12-4 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-8648, Japan
                Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology , 4-5-7 Konan, Minato-ku, Tokyo 108-8477, Japan
                Author notes
                Corresponding author: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. Email: kishino@ 123456g.ecc.u-tokyo.ac.jp

                Conflict of interest The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-3244-359X
                https://orcid.org/0000-0001-5789-7689
                https://orcid.org/0000-0001-5838-0374
                Article
                jkad218
                10.1093/g3journal/jkad218
                10700115
                37748020
                717db7a8-a03a-4681-81c0-91d06c950bc6
                © The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 May 2023
                : 14 September 2023
                : 17 October 2023
                Page count
                Pages: 15
                Funding
                Funded by: Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research;
                Award ID: 22K11950
                Categories
                Plant Genetics and Genomics
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140
                Genetics/136

                Genetics
                arabidopsis thaliana,coadaptation,gene expression adaptation,phenotypic adaptation,population history

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