The genomic history of East Asian Middle Neolithic millet- and rice-agricultural populations.

IF 11.1 Q1 CELL BIOLOGY
Jianxue Xiong, Yu Xu, Guoke Chen, Liping Yang, Yawei Zhou, Yiling Pan, Zhiyou Wang, Jiujiang Bai, Baoshuai Zhang, Guanghui Dong, Jingrong Pei, Xiaomin Yang, Liang Chen, Ningwu Kang, Yangyang Wu, Bangyan Wang, Kongyang Zhu, Panxin Du, Xiaolong Li, Hetong Wen, Xiaolin Ma, Tianyou Bai, Wanfa Gu, Yu Ye, Qian Wu, Xin Chang, Jingze Tan, Lei Gao, Dong Ge, Bicheng Li, Yishi Yang, Weiwei Feng, Yini Yang, Pengfei Sheng, Hailiang Meng, Rui Wang, Jiajing Zheng, Xin Jia, Li Jin, Chuan-Chao Wang, Shaoqing Wen
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Abstract

The Yellow and Yangtze river basins in China are among the world's oldest independent agricultural centers, known for the domestication of millet and rice, respectively, yet their genetic history is poorly understood. Here, we present genome-wide data from 74 Middle Neolithic genetic samples from these regions, showing marked genetic differentiation but bidirectional gene flow, supporting a demic diffusion model of mixed farming. Yellow River populations exhibit distinct genetic substructures resulting from interactions with surrounding groups during the mid-Neolithic expansion of millet agriculture. Upper Yellow River populations are genetically linked to Tibetan Plateau populations and possess the earliest adaptive EPAS1 haplotype (∼5,800 BP) among modern humans. Meanwhile, Yangtze River rice farmers show genetic affinity with Neolithic to present-day southeast coastal China and Austronesian populations, tracing the origins of proto-Austronesians farther north to the Yangtze River. These findings offer new insights into the impact of mid-Neolithic agricultural expansion on human genetic history.

东亚新石器时代中期谷子和水稻农业人口的基因组史。
中国的黄河流域和长江流域是世界上最古老的独立农业中心之一,分别以小米和水稻的驯化而闻名,但人们对它们的遗传历史知之甚少。在这里,我们展示了来自这些地区的74个新石器时代中期遗传样本的全基因组数据,显示出明显的遗传分化,但双向基因流动,支持混合农业的学术扩散模型。黄河种群表现出独特的遗传亚结构,这是新石器时代中期谷子农业扩张期间与周围群体相互作用的结果。与此同时,长江流域稻农与新石器时代到今天的中国东南沿海地区和南岛人群体表现出遗传亲缘关系,将原南岛人的起源追溯到长江以北更远的地方。这些发现为新石器时代中期农业扩张对人类遗传历史的影响提供了新的见解。
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CiteScore
7.10
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