{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:20:53Z","timestamp":1790421653997,"version":"4.1.0"},"reference-count":57,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Molecular Ecology Resources"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In recent years, genome\u2010scan methods have been extensively used to detect local signatures of selection and introgression. Most of these methods are either designed for one or the other case, which may impair the study of combined cases. Here, we introduce a series of versatile genome\u2010scan methods applicable for both cases, the detection of selection and introgression. The proposed approaches are based on nonparametric\n                    <jats:italic>k<\/jats:italic>\n                    \u2010nearest neighbour (kNN) techniques, while incorporating pairwise Fixation Index (\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>ST<\/jats:sub>\n                    ) and pairwise nucleotide differences (\n                    <jats:italic>\n                      d\n                      <jats:sub>xy<\/jats:sub>\n                    <\/jats:italic>\n                    ) as features. We benchmark our methods using a wide range of simulation scenarios, with varying parameters, such as recombination rates, population background histories, selection strengths, the proportion of introgression and the time of gene flow. We find that kNN\u2010based methods perform remarkably well compared with the\n                    <jats:italic>state\u2010of\u2010the\u2010art<\/jats:italic>\n                    . Finally, we demonstrate how to perform kNN\u2010based genome scans on real\u2010world genomic data using the population genomics R\u2010package\n                    <jats:sc>popgenome<\/jats:sc>\n                    .\n                  <\/jats:p>","DOI":"10.1111\/1755-0998.13221","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T11:15:15Z","timestamp":1594206915000},"page":"1597-1609","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Genome scans for selection and introgression based on\n                    <i>k<\/i>\n                    \u2010nearest neighbour techniques"],"prefix":"10.1111","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7035-9535","authenticated-orcid":false,"given":"Bastian","family":"Pfeifer","sequence":"first","affiliation":[{"name":"Research Unit of Statistical Bioinformatics Institute for Medical Informatics, Statistics and Documentation Medical University of Graz Graz Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Alachiotis","sequence":"additional","affiliation":[{"name":"Faculty of EEMCS University of Twente Enschede The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavlos","family":"Pavlidis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science Foundation for Research and Technology\u2010Hellas Crete Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael G.","family":"Schimek","sequence":"additional","affiliation":[{"name":"Research Unit of Statistical Bioinformatics Institute for Medical Informatics, Statistics and Documentation Medical University of Graz Graz Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature15393"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45681-3_2"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.31"},{"key":"e_1_2_8_5_1","unstructured":"Bache K. &Lichman M.(2013).UCI machine learning repository.Irvine CA:University of California. 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