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      Landscape genomics reveals genetic signals of environmental adaptation of African wild eggplants

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          Abstract

          Crop wild relatives (CWR) provide a valuable resource for improving crops. They possess desirable traits that confer resilience to various environmental stresses. To fully utilize crop wild relatives in breeding and conservation programs, it is important to understand the genetic basis of their adaptation. Landscape genomics associates environments with genomic variation and allows for examining the genetic basis of adaptation. Our study examined the differences in allele frequency of 15,416 single nucleotide polymorphisms (SNPs) generated through genotyping by sequencing approach among 153 accessions of 15 wild eggplant relatives and two cultivated species from Africa, the principal hotspot of these wild relatives. We also explored the correlation between these variations and the bioclimatic and soil conditions at their collection sites, providing a comprehensive understanding of the genetic signals of environmental adaptation in African wild eggplant. Redundancy analysis (RDA) results showed that the environmental variation explained 6% while the geographical distances among the collection sites explained 15% of the genomic variation in the eggplant wild relative populations when controlling for population structure. Our findings indicate that even though environmental factors are not the main driver of selection in eggplant wild relatives, it is influential in shaping the genomic variation over time. The selected environmental variables and candidate SNPs effectively revealed grouping patterns according to the environmental characteristics of sampling sites. Using four genotype–environment association methods, we detected 396 candidate SNPs (2.5% of the initial SNPs) associated with eight environmental factors. Some of these SNPs signal genes involved in pathways that help adapt to environmental stresses such as drought, heat, cold, salinity, pests, and diseases. These candidate SNPs will be useful for marker‐assisted improvement and characterizing the germplasm of this crop for developing climate‐resilient eggplant varieties. The study provides a model for applying landscape genomics to other crops' wild relatives.

          Abstract

          Environmental selection plays a key role in the genetic variation within African wild relative populations that are both widespread and diverse for climate and soil conditions.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

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                Author and article information

                Contributors
                emmanuel.omondi@worldveg.org
                maarten.vanzonneveld@worldveg.org
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                09 July 2024
                July 2024
                : 14
                : 7 ( doiID: 10.1002/ece3.v14.7 )
                : e11662
                Affiliations
                [ 1 ] Genetic Resources and Seed Unit World Vegetable Center Tainan Taiwan
                [ 2 ] Biotechnology, World Vegetable Center Tainan Taiwan
                [ 3 ] Department of Horticulture National Taiwan University Taipei Taiwan
                [ 4 ] Plant Pathology World Vegetable Center Tainan Taiwan
                Author notes
                [*] [* ] Correspondence

                Emmanuel O. Omondi and Maarten Van Zonneveld, Genetic Resources and Seed Unit, World Vegetable Center, Headquarters, 60 Yi‐Min Liao, Shanhua, Tainan 74151, Taiwan.

                Email: emmanuel.omondi@ 123456worldveg.org and maarten.vanzonneveld@ 123456worldveg.org

                Author information
                https://orcid.org/0000-0001-5212-7685
                Article
                ECE311662 ECE-2023-09-01573.R2
                10.1002/ece3.11662
                11232056
                38983700
                f6718f05-f8ef-420c-bc9e-a66d6b0a24f7
                © 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 June 2024
                : 09 September 2023
                : 18 June 2024
                Page count
                Figures: 7, Tables: 3, Pages: 20, Words: 14200
                Funding
                Funded by: Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung , doi 10.13039/501100006456;
                Award ID: 81275070
                Categories
                Ecological Genetics
                Population Genetics
                Research Article
                Research Article
                Custom metadata
                2.0
                July 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.5 mode:remove_FC converted:09.07.2024

                Evolutionary Biology
                climate‐resilient,crop wild relatives,environmental stress,genotype–environment association,marker‐assisted improvement

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