濒危物种
群体基因组学
适应(眼睛)
基因组学
人口历史
进化生物学
人口
局部适应
生物
个人基因组学
基因组
遗传学
遗传变异
人口学
基因
社会学
神经科学
作者
Nan Lin,Yakun Wang,Jacob B. Landis,Xiankun Wang,Yuxuan He,Hengchang Wang,Xianhan Huang,Qun Liu,Jun Yang,Fude Shang,Tao Deng,Yanpei Liu
摘要
Endangered Tertiary relict trees represent an exceptional evolutionary heritage with small and isolated populations, yet little is known about how demographic history, local adaptation, and genetic load have affected their long-term survival and extinction risk. We performed whole-genome sequencing and population genomic analyses on Ulmus elongata L. K. Fu & C. S. Ding, an endangered Tertiary relict tree endemic to East Asia. By integrating genomes from U. elongata and seven other endangered trees from public databases, we identified rate-decelerated genes across endangered trees and genes under positive selection of U. elongata associated with tissue development, detoxification, and immune response, and signal transduction and regulation mechanisms potentially leading to endangered status. Demographic analyses revealed continuous population decline from the late Miocene to present, especially during the last glacial maximum (LGM) and last 10 000 years. Spearman correlation indicated a strong negative relationship between effective population size and human population density (rpopulation density = -0.90, P < 0.001) as well as cropland use (rcropland use = -0.89, P < 0.001). Genotype-environment association (GEA) analyses identified a set of candidate genes associated with temperature and precipitation, supporting a polygenic adaptation model in U. elongata. Overall, our findings underscore the severe population bottlenecks that have led to the fixation of strongly deleterious mutations and inbreeding, further compromising the adaptive potential and long-term viability of U. elongata. Furthermore, assessments of genomic vulnerability under future climate scenarios revealed higher genetic offsets in northern region of Fujian and Jiangxi populations, suggesting these regions require prioritized conservation efforts due to reduced adaptive capacity.
科研通智能强力驱动
Strongly Powered by AbleSci AI