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Landscape genomic approach to detect selection signatures in locally adapted Brazilian swine genetic groups.


ABSTRACT: Samples of 191 animals from 18 different Brazilian locally adapted swine genetic groups were genotyped using Illumina Porcine SNP60 BeadChip in order to identify selection signatures related to the monthly variation of Brazilian environmental variables. Using BayeScan software, 71 SNP markers were identified as FST outliers and 60 genotypes (58 markers) were found by Sam?ada software in 371 logistic models correlated with 112 environmental variables. Five markers were identified in both methods, with a Kappa value of 0.073 (95% CI: 0.011-0.134). The frequency of these markers indicated a clear north-south country division that reflects Brazilian environmental differences in temperature, solar radiation, and precipitation. Global spatial territory correlation for environmental variables corroborates this finding (average Moran's I = 0.89, range from 0.55 to 0.97). The distribution of alleles over the territory was not strongly correlated with the breed/genetic groups. These results are congruent with previous mtDNA studies and should be used to direct germplasm collection for the National gene bank.

SUBMITTER: Cesconeto RJ 

PROVIDER: S-EPMC5696410 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Landscape genomic approach to detect selection signatures in locally adapted Brazilian swine genetic groups.

Cesconeto Robson Jose RJ   Joost Stéphane S   McManus Concepta Margaret CM   Paiva Samuel Rezende SR   Cobuci Jaime Araujo JA   Braccini Jose J  

Ecology and evolution 20171012 22


Samples of 191 animals from 18 different Brazilian locally adapted swine genetic groups were genotyped using Illumina Porcine SNP60 BeadChip in order to identify selection signatures related to the monthly variation of Brazilian environmental variables. Using BayeScan software, 71 SNP markers were identified as F<sub>ST</sub> outliers and 60 genotypes (58 markers) were found by Samβada software in 371 logistic models correlated with 112 environmental variables. Five markers were identified in bo  ...[more]

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