The analysis of molecular variance(AMOVA)is a widely used statistical method in population genetics and molec-ular ecology.The classic framework of AMOVA only supports haploid and diploid data,in which the number of h...The analysis of molecular variance(AMOVA)is a widely used statistical method in population genetics and molec-ular ecology.The classic framework of AMOVA only supports haploid and diploid data,in which the number of hierarchies ranges from two to four.In practice,natural populations can be classified into more hierarchies,and polyploidy is frequently observed in extant species.The ploidy level may even vary within the same species,and/or within the same individual.We generalized the framework of AMOVA such that it can be used for any number of hierarchies and any level of ploidy.Based on this framework,we present four methods to account for data that are multilocus genotypic and allelic phenotypic(with unknown allele dosage).We use simulated datasets and an empirical dataset to evaluate the performance of our framework.We make freely available our methods in a new software package,polygene,which is freely available at https://github.com/huangkang1987/polygene.展开更多
基金funded by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB31020302)the National Natural Science Foundation of China(31770411,31730104,31572278,31770425)+2 种基金the Young Elite Scientists Sponsorship Program by CAST(2017QNRC001)the National Key Programme of Research and Development,Ministry of Science and Technology(2016YFC0503200)the Shaanxi Science and Technology Innovation Team(2019TD-012).
文摘The analysis of molecular variance(AMOVA)is a widely used statistical method in population genetics and molec-ular ecology.The classic framework of AMOVA only supports haploid and diploid data,in which the number of hierarchies ranges from two to four.In practice,natural populations can be classified into more hierarchies,and polyploidy is frequently observed in extant species.The ploidy level may even vary within the same species,and/or within the same individual.We generalized the framework of AMOVA such that it can be used for any number of hierarchies and any level of ploidy.Based on this framework,we present four methods to account for data that are multilocus genotypic and allelic phenotypic(with unknown allele dosage).We use simulated datasets and an empirical dataset to evaluate the performance of our framework.We make freely available our methods in a new software package,polygene,which is freely available at https://github.com/huangkang1987/polygene.
