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Selection of Optimal Ancestry Informative Markers for Classification and Ancestry Proportion Estimation in Pigs.
Liang, Zuoxiang; Bu, Lina; Qin, Yidi; Peng, Yebo; Yang, Ruifei; Zhao, Yiqiang.
Afiliação
  • Liang Z; Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, China.
  • Bu L; State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
  • Qin Y; Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, China.
  • Peng Y; State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
  • Yang R; State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
  • Zhao Y; Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, China.
Front Genet ; 10: 183, 2019.
Article em En | MEDLINE | ID: mdl-30915106
ABSTRACT
Using small sets of ancestry informative markers (AIMs) constitutes a cost-effective method to accurately estimate the ancestry proportions of individuals. This study aimed to generate a small and effective number of AIMs from ∼60 K single nucleotide polymorphism (SNP) data of porcine and estimate three ancestry proportions [East China pig (ECHP), South China pig (SCHP), and European commercial pig (EUCP)] from Asian breeds and European domestic breeds. A total of 186 samples of 10 pure breeds were divided into three groups ECHP, SCHP, and EUCP. Using these samples and a one-vs.-rest SVM classifier, we found that using only seven AIMs could completely separate the three groups. Subsequently, we utilized supervised ADMIXTURE to calculate ancestry proportions and found that the 129 AIMs performed well on ancestry estimates when pseudo admixed individuals were used. Furthermore, another 969 samples of 61 populations were applied to evaluate the performance of the 129 AIMs. We also observed that the 129 AIMs were highly correlated with estimates using ∼60 K SNP data for three ancestry components ECHP (Pearson correlation coefficient (r) = 0.94), SCHP (r = 0.94), and EUCP (r = 0.99). Our results provided an example of using a small number of pig AIMs for classifications and estimating ancestry proportions with high accuracy and in a cost-effective manner.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article