{"data":{"id":"10.6084/m9.figshare.7010495.v3","type":"dois","attributes":{"doi":"10.6084/m9.figshare.7010495.v3","prefix":"10.6084","suffix":"m9.figshare.7010495.v3","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Lin, Ya-Ping","nameType":"Personal","givenName":"Ya-Ping","familyName":"Lin","affiliation":[],"nameIdentifiers":[]},{"name":"Chu-Yin Liu","affiliation":[],"nameIdentifiers":[]},{"name":"Kai-Yi Chen","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Supplemental figures and files for \"Assessment of genetic differentiation and linkage disequilibrium in Solanum pimpinellifolium using genome-wide high-density SNP markers.\""}],"publisher":"figshare","container":{},"publicationYear":2019,"subjects":[{"subject":"70602 Horticultural Crop Improvement (Selection and Breeding)","subjectScheme":"FOR"},{"subject":"FOS: Agriculture, forestry and fisheries","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Agriculture, forestry and fisheries","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2019-02-17","dateType":"Created"},{"date":"2019-02-17","dateType":"Updated"},{"date":"2019","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.7010495","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["9405686 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\u003cb\u003eFigure S1\u003c/b\u003e\u003cb\u003e. The cross-validation error of K value in ADMIXTURE. \u003c/b\u003eThis figure shows the cross-validation of ADMIXTURE for the 98 \u003ci\u003eS. pimpinellifolium\u003c/i\u003e accessions. The lowest cross-validation error is best K = 3. \u003cb\u003eFigure S2.\u003c/b\u003e\u003cb\u003e LD decay of the whole genome. \u003c/b\u003eThe pairwise LD through the whole genome was plotted. The red curve indicates non-linear regression. The dotted line indicates the fixed \u003ci\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/i\u003e on 0.1.\u003cbr\u003e \u003cb\u003eFigure S3. 50 kb interval LD decay of each chromosome. \u003c/b\u003eThe pairwise LD of each chromosome was plotted individually and zoomed in to 50 kb interval to observe the fast LD decay. The red curves indicate non-linear regression. Black dotted lines indicate the fixed \u003ci\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/i\u003e on 0.1. \u003cb\u003eFigure S4. The local LD of each chromosome. \u003c/b\u003eThe LD of paired flanking sequencing units were plotted along each chromosome. The red dotted line indicates the baseline of \u003ci\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/i\u003e, and the orange line indicates the heterochromatic region. \u003cb\u003eFigure S5.\u003c/b\u003e\u003cb\u003e Pairwise isolation by distance of 98 accessions. \u003c/b\u003eThis plot presents the correlation between pairwise genetic distance and geographic distance. \u003cb\u003eFigure S6.\u003c/b\u003e\u003cb\u003e Pairwise isolation by distance of SolCAP meta-analysis. \u003c/b\u003eThis plot presents the correlation between pairwise genetic distance and geographic distance for the collection of 214 samples from the previous studies (Blanca \u003ci\u003eet al.\u003c/i\u003e 2012, 2015; Sim \u003ci\u003eet al.\u003c/i\u003e 2012a). \u003cb\u003eFigure S7.\u003c/b\u003e\u003cb\u003e The cross-validation error of SolCAP meta-analysis. \u003c/b\u003eThis figure shows the cross-validation of ADMIXTURE analysis for the SolCAP genotypes of the 214 \u003ci\u003eS. pimpinellifolium\u003c/i\u003e accessions in the previous studies (Blanca \u003ci\u003eet al.\u003c/i\u003e 2012, 2015; Sim \u003ci\u003eet al.\u003c/i\u003e 2012a). The lowest cross-validation error shows best K = 15. \u003cb\u003eFigure S8. The genome patterns of 214 samples in SolCAP meta-analysis. \u003c/b\u003eThe labels on the top indicate accessions; the labels on the bottom indicate sample ID in this meta-analysis. \u003cb\u003eFigure S9.\u003c/b\u003e\u003cb\u003e The PCA of SolCAP meta-analysis. \u003c/b\u003eA) The PCA plot of bi-allelic SNPs. B) The PCA plot after removing those SNPs of reverse-complement allele designation.\u003cb\u003eFile S1. The analysis of isolation by distance. \u003c/b\u003eThe isolation by distance was performed for the 98 \u003ci\u003eS. pimpinellifolium\u003c/i\u003e accessions with the RAD SNP dataset.\u003cb\u003eFile S2. \u003c/b\u003e\u003cb\u003eThe meta-analysis of \u003ci\u003eS. pimpinellifolium\u003c/i\u003e SolCAP genotyping data. \u003c/b\u003eWe performed a meta-analysis of 214 \u003ci\u003eS. pimpinellifolium\u003c/i\u003e accessions using SolCAP genotyping data downloaded from previous studies Sim \u003ci\u003eet al.\u003c/i\u003e 2012a, Blanca \u003ci\u003eet al.\u003c/i\u003e 2012, and Blanca \u003ci\u003eet al.\u003c/i\u003e 2015.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHJlc291cmNlIHhtbG5zPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCIgeG1sbnM6eHNpPSJodHRwOi8vd3d3LnczLm9yZy8yMDAxL1hNTFNjaGVtYS1pbnN0YW5jZSIgeHNpOnNjaGVtYUxvY2F0aW9uPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCBodHRwOi8vc2NoZW1hLmRhdGFjaXRlLm9yZy9tZXRhL2tlcm5lbC00LjEvbWV0YWRhdGEueHNkIj4KICA8aWRlbnRpZmllciBpZGVudGlmaWVyVHlwZT0iRE9JIj4xMC42MDg0L005LkZJR1NIQVJFLjcwMTA0OTUuVjM8L2lkZW50aWZpZXI+CiAgPGNyZWF0b3JzPgogICAgPGNyZWF0b3I+CiAgICAgIDxjcmVhdG9yTmFtZT5ZYS1QaW5nIExpbjwvY3JlYXRvck5hbWU+CiAgICA8L2NyZWF0b3I+CiAgICA8Y3JlYXRvcj4KICAgICAgPGNyZWF0b3JOYW1lPkNodS1ZaW4gTGl1PC9jcmVhdG9yTmFtZT4KICAgIDwvY3JlYXRvcj4KICAgIDxjcmVhdG9yPgogICAgICA8Y3JlYXRvck5hbWU+S2FpLVlpIENoZW48L2NyZWF0b3JOYW1lPgogICAgPC9jcmVhdG9yPgogIDwvY3JlYXRvcnM+CiAgPHRpdGxlcz4KICAgIDx0aXRsZT5TdXBwbGVtZW50YWwgZmlndXJlcyBhbmQgZmlsZXMgZm9yICJBc3Nlc3NtZW50IG9mIGdlbmV0aWMgZGlmZmVyZW50aWF0aW9uIGFuZCBsaW5rYWdlIGRpc2VxdWlsaWJyaXVtIGluIFNvbGFudW0gcGltcGluZWxsaWZvbGl1bSB1c2luZyBnZW5vbWUtd2lkZSBoaWdoLWRlbnNpdHkgU05QIG1hcmtlcnMuIjwvdGl0bGU+CiAgPC90aXRsZXM+CiAgPGRlc2NyaXB0aW9ucz4KICAgIDxkZXNjcmlwdGlvbiBkZXNjcmlwdGlvblR5cGU9IkFic3RyYWN0Ij4mbHQ7cCZndDsmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7Jmx0O2ImZ3Q7RmlndXJlIFMxJmx0Oy9iJmd0OyZsdDtiJmd0Oy4gVGhlIGNyb3NzLXZhbGlkYXRpb24gZXJyb3IKb2YgSyB2YWx1ZSBpbiBBRE1JWFRVUkUuICZsdDsvYiZndDtUaGlzIGZpZ3VyZSBzaG93cyB0aGUKY3Jvc3MtdmFsaWRhdGlvbiBvZiBBRE1JWFRVUkUgZm9yIHRoZSA5OCAmbHQ7aSZndDtTLgpwaW1waW5lbGxpZm9saXVtJmx0Oy9pJmd0OyBhY2Nlc3Npb25zLiBUaGUgbG93ZXN0IGNyb3NzLXZhbGlkYXRpb24gZXJyb3IgaXMgYmVzdCBLID0KMy4mbHQ7L3AmZ3Q7CgombHQ7cCZndDsmbHQ7YiZndDtGaWd1cmUgUzIuJmx0Oy9iJmd0OyZsdDtiJmd0OyBMRCBkZWNheSBvZiB0aGUgd2hvbGUKZ2Vub21lLiAmbHQ7L2ImZ3Q7VGhlIHBhaXJ3aXNlIExEIHRocm91Z2ggdGhlCndob2xlIGdlbm9tZSB3YXMgcGxvdHRlZC4gVGhlIHJlZCBjdXJ2ZSBpbmRpY2F0ZXMgbm9uLWxpbmVhciByZWdyZXNzaW9uLiBUaGUKZG90dGVkIGxpbmUgaW5kaWNhdGVzIHRoZSBmaXhlZCAmbHQ7aSZndDtyJmx0O3N1cCZndDsyJmx0Oy9zdXAmZ3Q7Jmx0Oy9pJmd0OyBvbiAwLjEuJmx0O2JyJmd0OyZsdDsvcCZndDsKCiZsdDtwJmd0OyZsdDtiJmd0O0ZpZ3VyZSBTMy4gNTAga2IgaW50ZXJ2YWwgTEQgZGVjYXkgb2YKZWFjaCBjaHJvbW9zb21lLiAmbHQ7L2ImZ3Q7VGhlIHBhaXJ3aXNlIExEIG9mIGVhY2ggY2hyb21vc29tZQp3YXMgcGxvdHRlZCBpbmRpdmlkdWFsbHkgYW5kIHpvb21lZCBpbiB0byA1MCBrYiBpbnRlcnZhbCB0byBvYnNlcnZlIHRoZSBmYXN0IExECmRlY2F5LiBUaGUgcmVkIGN1cnZlcyBpbmRpY2F0ZSBub24tbGluZWFyIHJlZ3Jlc3Npb24uIEJsYWNrIGRvdHRlZCBsaW5lcwppbmRpY2F0ZSB0aGUgZml4ZWQgJmx0O2kmZ3Q7ciZsdDtzdXAmZ3Q7MiZsdDsvc3VwJmd0OyZsdDsvaSZndDsgb24gMC4xLiZsdDsvcCZndDsKCiZsdDtwJmd0OyZsdDtiJmd0O0ZpZ3VyZSBTNC4gVGhlIGxvY2FsIExEIG9mIGVhY2gKY2hyb21vc29tZS4gJmx0Oy9iJmd0O1RoZSBMRCBvZiBwYWlyZWQgZmxhbmtpbmcKc2VxdWVuY2luZyB1bml0cyB3ZXJlIHBsb3R0ZWQgYWxvbmcgZWFjaCBjaHJvbW9zb21lLiBUaGUgcmVkIGRvdHRlZCBsaW5lCmluZGljYXRlcyB0aGUgYmFzZWxpbmUgb2YgJmx0O2kmZ3Q7ciZsdDtzdXAmZ3Q7MiZsdDsvc3VwJmd0OyZsdDsvaSZndDssCmFuZCB0aGUgb3JhbmdlIGxpbmUgaW5kaWNhdGVzIHRoZSBoZXRlcm9jaHJvbWF0aWMgcmVnaW9uLiZsdDsvcCZndDsKCiZsdDtwJmd0OyZsdDtiJmd0O0ZpZ3VyZSBTNS4mbHQ7L2ImZ3Q7Jmx0O2ImZ3Q7IFBhaXJ3aXNlIGlzb2xhdGlvbiBieQpkaXN0YW5jZSBvZiA5OCBhY2Nlc3Npb25zLiAmbHQ7L2ImZ3Q7VGhpcyBwbG90IHByZXNlbnRzIHRoZQpjb3JyZWxhdGlvbiBiZXR3ZWVuIHBhaXJ3aXNlIGdlbmV0aWMgZGlzdGFuY2UgYW5kIGdlb2dyYXBoaWMgZGlzdGFuY2UuJmx0Oy9wJmd0OwoKJmx0O3AmZ3Q7Jmx0O2ImZ3Q7RmlndXJlIFM2LiZsdDsvYiZndDsmbHQ7YiZndDsgUGFpcndpc2UgaXNvbGF0aW9uIGJ5CmRpc3RhbmNlIG9mIFNvbENBUCBtZXRhLWFuYWx5c2lzLiAmbHQ7L2ImZ3Q7VGhpcyBwbG90IHByZXNlbnRzIHRoZQpjb3JyZWxhdGlvbiBiZXR3ZWVuIHBhaXJ3aXNlIGdlbmV0aWMgZGlzdGFuY2UgYW5kIGdlb2dyYXBoaWMgZGlzdGFuY2UgZm9yIHRoZQpjb2xsZWN0aW9uIG9mIDIxNCBzYW1wbGVzIGZyb20gdGhlIHByZXZpb3VzIHN0dWRpZXMgKEJsYW5jYSAmbHQ7aSZndDtldCBhbC4mbHQ7L2kmZ3Q7CjIwMTIsIDIwMTU7IFNpbSAmbHQ7aSZndDtldCBhbC4mbHQ7L2kmZ3Q7IDIwMTJhKS4mbHQ7L3AmZ3Q7CgombHQ7cCZndDsmbHQ7YiZndDtGaWd1cmUgUzcuJmx0Oy9iJmd0OyZsdDtiJmd0OyBUaGUgY3Jvc3MtdmFsaWRhdGlvbiBlcnJvcgpvZiBTb2xDQVAgbWV0YS1hbmFseXNpcy4gJmx0Oy9iJmd0O1RoaXMgZmlndXJlIHNob3dzIHRoZQpjcm9zcy12YWxpZGF0aW9uIG9mIEFETUlYVFVSRSBhbmFseXNpcyBmb3IgdGhlIFNvbENBUCBnZW5vdHlwZXMgb2YgdGhlIDIxNCAmbHQ7aSZndDtTLiBwaW1waW5lbGxpZm9saXVtJmx0Oy9pJmd0OyBhY2Nlc3Npb25zIGluIHRoZQpwcmV2aW91cyBzdHVkaWVzIChCbGFuY2EgJmx0O2kmZ3Q7ZXQgYWwuJmx0Oy9pJmd0OyAyMDEyLCAyMDE1OyBTaW0gJmx0O2kmZ3Q7ZXQgYWwuJmx0Oy9pJmd0OyAyMDEyYSkuIFRoZSBsb3dlc3QgY3Jvc3MtdmFsaWRhdGlvbiBlcnJvcgpzaG93cyBiZXN0IEsgPSAxNS4mbHQ7L3AmZ3Q7CgombHQ7cCZndDsmbHQ7YiZndDtGaWd1cmUgUzguIFRoZSBnZW5vbWUgcGF0dGVybnMgb2YgMjE0CnNhbXBsZXMgaW4gU29sQ0FQIG1ldGEtYW5hbHlzaXMuICZsdDsvYiZndDtUaGUgbGFiZWxzIG9uIHRoZSB0b3AKaW5kaWNhdGUgYWNjZXNzaW9uczsgdGhlIGxhYmVscyBvbiB0aGUgYm90dG9tIGluZGljYXRlIHNhbXBsZSBJRCBpbiB0aGlzCm1ldGEtYW5hbHlzaXMuJmx0Oy9wJmd0OwoKJmx0O3AmZ3Q7Jmx0O2ImZ3Q7RmlndXJlIFM5LiZsdDsvYiZndDsmbHQ7YiZndDsgVGhlIFBDQSBvZiBTb2xDQVAKbWV0YS1hbmFseXNpcy4gJmx0Oy9iJmd0O0EpIFRoZSBQQ0EgcGxvdCBvZiBiaS1hbGxlbGljIFNOUHMuIEIpIFRoZSBQQ0EgcGxvdCBhZnRlciByZW1vdmluZyB0aG9zZQpTTlBzIG9mIHJldmVyc2UtY29tcGxlbWVudCBhbGxlbGUgZGVzaWduYXRpb24uJmx0Oy9wJmd0OyZsdDtwJmd0OyZsdDtiJmd0O0ZpbGUgUzEuIFRoZSBhbmFseXNpcyBvZgppc29sYXRpb24gYnkgZGlzdGFuY2UuICZsdDsvYiZndDtUaGUgaXNvbGF0aW9uCmJ5IGRpc3Rh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