Genetic Diversity and Population Structural Analysis Reveal the Unique Genetic Composition of Populus tomentosa Elite Trees

Author:

Kong Bo123ORCID,Ma Lexun123ORCID,Du Jiahua123,Zhang Pingdong123ORCID

Affiliation:

1. State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China

2. Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, Beijing Forestry University, Beijing 100083, China

3. College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China

Abstract

Genetic diversity analysis provides the scientific basis for the preservation, evaluation, and utilization of the germplasm resources of tree species. We explored the genetic diversity and structure of Populus tomentosa elite trees in North China using 13 nuclear microsatellite markers. We compared nine groups of accessions including 20 originating from Beijing (BJ), 122 from Hebei (HB), 20 from Shandong (SD), 113 from Henan (HN), 270 from Shanxi (SX), 54 from Shaanxi (SAX), 8 from Gansu (GS), 10 from Anhui (AH), and 6 from Jiangsu (JS). All of the studied primer pairs were polymorphic and generated 125 alleles. Analyses of molecular variance revealed that 79%, 14%, and 8% of the total variation was due to variations within the individual, among individuals, and among populations, respectively. Based on principal coordinate and STRUCTURE cluster analyses, individuals distributed in the southern region (HN, SAX, AH, and JS) were roughly classified into one group, while those distributed in the northeastern region (BJ, HB, and SD) and northwestern regions (SX) were separately divided into one group each. Moreover, the northwestern region included two-thirds of the SX trees, and the remainder were in the northeast region. By analyzing genetic diversity and structure within populations, individuals with different genetic backgrounds were screened for constituent training populations (TRS), including broad allelic variation for related traits. This ensures that the genomic prediction model can accurately capture genetic effects and provide reliable predictions across a broad spectrum of genetic backgrounds. Therefore, our results will benefit genome breeding technology.

Funder

National Key Research and Development Program of China

Publisher

MDPI AG

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