学术报告     首页 > 其他 > 通知公告 > 学术报告
进化与遗传前沿交叉系列论坛
0

  报告时间:2019年6月13日(星期四)上午9点至12点 

  报告地点:云南省人民政府西南生物多样性实验室报告厅1-24-26报告厅 

  报 告 人:英国伦敦大学学院(University College London)杨子恒院士及中国科学院数学与系统科学研究院朱天琪副研究员    

  Seminar 1 

  Title: Inference of cross-species introgression using genomic sequence data 

  Invited Speaker: Prof. Ziheng YANG  University College London 

  Abstract: 

  Recent studies have highlighted the importance of cross-species gene flow or introgression, and a number of efforts have been taken to use genomic sequence data to infer introgression events and to estimate the timing and intensity of introgression. 

  We have implemented a multispecies-coalescent-with-introgression (MSci)model, an extension of the multispecies-coalescent (MSC) model to incorporate introgression, in our Bayesian Markov chain Monte Carlo (MCMC) program bpp. The MSci model accommodates deep coalescence (or incomplete lineage sorting) as well as introgression/hybridization and provides a natural framework for such inference. Both computer simulation and real data analysis suggest that hundreds or thousands of loci are needed to estimate the introgression proportion reliably. We estimated the intensity of introgression using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, driven by differential selection against introgressed alleles.  

  Bio-Sketch 

  Ziheng Yang is the RA Fisher Professor of Statistical Genetics in University College London. He develops statistical methods and computer software in molecular phylogenetics and population genetics. Models and methods he developed are widely used in popular software packages in the field. He maintains software packages PAML and BPP. His papers have attracted 60K citations, with h=90. Recently he has been developing Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference under the multispecies coalescent model. He was elected a Fellow of the Royal Society in 2006. He has published two graduate-level textbooks (2006 and 2014).  

  Seminar 2 

  Title: Beyesian Molecular Dating with Genomic Data 

  Invited Speaker: Dr.Tianqi Zhu (Associate Professor) 

  Abstract: 

  With the advancement of the sequencing technology, the amount of genome data increases explosively. Genome data enable us to estimate the evolutionary distance accurately, which is the product of divergence time and evolutionary rate. However, molecular data do not provide the information about times and rates separately. By combining prior information about times and rates, fossil calibrations which provide information about absolute times, and information from molecular data, Bayesian MCMC methods make it possible to estimate the absolute divergence time as well as the evolutionary rate. With the development of phylogenetic models and computational methods in recent years, multiple loci data can be analyzed under complex models. Nowadays, Bayesian methods have become the prevailing dating methods. In this talk, I will introduce the framework of Bayesian dating, and the progress made in the related area. 

  Bio-Sketch 

  Dr.Tianqi Zhu graduated from Peking University in 2012, majored in probability theory and statistics. From 2012 to 2016, she worked in Beijing Institute of Genomics, CAS as an assistant professor, and she visited UCL and KTH as visiting professor during this period. In 2016 she started her lab in Academy of Mathematics and Systems Science. Her main research interests focus on Bayesian statistics and computational systematics, and her recent research projects include model selection, Bayesian estimation of species divergence times incorporating molecular & fossil information and maximum likelihood implementation of the Isolation-with-Migration models for multiple species. She published papers in top journals as MBE, Systematic Biology and PNAS.  

  Welcome to the seminar! 

  动物进化与遗传前沿交叉卓越创新中心 

  2019年5月29日 


Copyright © 2018-2019 中国科学院昆明动物研究所 .All Rights Reserved
地址:云南省昆明市五华区教场东路32号  邮编:650223
电子邮件:zhanggq@mail.kiz.ac.cn  滇ICP备05000723号