Dataset Information

Genome-level diversification of eight ancient tea populations in the Guizhou and Yunnan regions identifies candidate genes for core agronomic traits.

ABSTRACT: The ancient tea plant, as a precious natural resource and source of tea plant genetic diversity, is of great value for studying the evolutionary mechanism, diversification, and domestication of plants. The overall genetic diversity among ancient tea plants and the genetic changes that occurred during natural selection remain poorly understood. Here, we report the genome resequencing of eight different groups consisting of 120 ancient tea plants: six groups from Guizhou Province and two groups from Yunnan Province. Based on the 8,082,370 identified high-quality SNPs, we constructed phylogenetic relationships, assessed population structure, and performed genome-wide association studies (GWAS). Our phylogenetic analysis showed that the 120 ancient tea plants were mainly clustered into three groups and five single branches, which is consistent with the results of principal component analysis (PCA). Ancient tea plants were further divided into seven subpopulations based on genetic structure analysis. Moreover, it was found that the variation in ancient tea plants was not reduced by pressure from the external natural environment or artificial breeding (nonsynonymous/synonymous = 1.05). By integrating GWAS, selection signals, and gene function prediction, four candidate genes were significantly associated with three leaf traits, and two candidate genes were significantly associated with plant type. These candidate genes can be used for further functional characterization and genetic improvement of tea plants.

SUBMITTER: Lu L

PROVIDER: S-EPMC8355299 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Lung cancer has inherited susceptibility and show familial aggregation, the characteristics of familial lung cancer exhibit population heterogeneity. Despite previous studies, familial lung cancer in China's Yunnan-Guizhou plateau remains understudied. Methods: Between 2015 and 2017, 1,023 lung cancer patients (residents of Yunnan-Guizhou plateau) were enrolled with no limitation on other parameters, 152 subjects had familial lung cancer. Clinicopathologic parameters were analyzed and compared, 4,754 lung cancer patients from NCI-GDC were used to represent a general population. Results: Familial lung cancer (FLC) subjects showed unique characters: early-onset; increased rate of female, adenocarcinoma, stage IV and other cancer history; unbalance in anatomic sites; all ruling out significant difference in smoking status. Unbalanced distribution of co-existing diseases or symptoms was also discovered. FLC patients were more likely to develop benign lesions (polyps, nodules, cysts) early in life, especially early-growth of multiple pulmonary nodules at higher frequency. Typical diseases with family history like diabetes and hypertension were also increased in FLC population. Compared to GDC data, our subject population was younger: the age peak of our FLC group was in 50-59; our sporadic group had an age peak around 60; while GDC patients' age peak was in 60-69. Importantly, the biggest difference happened in age 40-49: our FLC group and sporadic group had 3 times and 2 times higher ratio than GDC population, respectively. Moreover, the age peaks of our FLC males and FLC females were both in 50-59; while our sporadic females had the age peak in 50-59, much earlier than sporadic males (around 60-69); reflecting gender-specific or age-specific characters in our subject population. Conclusions: Familial lung cancer in China's Yunnan-Guizhou plateau showed unique clinicopathologic characters, differences were found in gender, age, histologic type, TNM stage and co-existing diseases or symptoms. Identification of hereditary factors which lead to increased lung cancer risk will be a challenge of both scientific and clinical significance.

Dataset Information

Genome-level diversification of eight ancient tea populations in the Guizhou and Yunnan regions identifies candidate genes for core agronomic traits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets