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1.
Genome-wide detection of selective signatures in a Jinhua pig population
XU Zhong, SUN Hao, ZHANG Zhe, Zhao Qing-bo, Babatunde Shittu Olasege, Li Qiu-meng, Yue Yang, Ma Pei-pei, Zhang Xiang-zhe, Wang Qi-shan, Pan Yu-chun
Journal of Integrative Agriculture    2020, 19 (5): 1314-1322.   DOI: 10.1016/S2095-3119(19)62833-9
摘要119)      PDF    收藏
The aim of this study was to detect evidence for signatures of recent selection in the Jinhua pig genome.  These results can be useful to better understand the regions under selection in Jinhua pigs and might shed some lights on groups of genes that control production traits.  In the present study, we performed extended haplotype homozygosity (EHH) tests to identify significant core regions in 202 Jinhua pigs.  A total of 26 161 core regions spanning 636.42 Mb were identified, which occupied approximately 28% of the genome across all autosomes, and 1 158 significant (P<0.01) core haplotypes were selected.  Genes in these regions were related to several economically important traits, including meat quality, reproduction, immune responses and exterior traits.  A panel of genes including ssc-mir-365-2, KDM8, RABEP2, GSG1L, RHEB, RPH3AL and a signal pathway of PI3K-Akt were detected with the most extreme P-values.  The findings in our study could draw a comparatively genome-wide map of selection signature in the pig genome, and also help to detect functional candidate genes under positive selection for further genetic and breeding research in Jinhua and other pigs.
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2. Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter
LI Rui, LI Cun-jun, DONG Ying-ying, LIU Feng, WANG Ji-hua, YANG Xiao-dong , PAN Yu-chun
Journal of Integrative Agriculture    2011, 10 (10): 1595-1602.   DOI: 10.1016/S1671-2927(11)60156-9
摘要1915)      PDF    收藏
Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm (EnKF) assimilation by combing the crop growth model (CERES-Wheat) with remote sensing data, but also optimized and updated the key parameters (LAI) of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
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