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学术讲座:Genetics-Based Statistical Approaches in Personalized Medicine Development
 

时 间:2009年5月22日(周五)下午2:00
地 点:北京大学医学部逸夫教学楼203室
题  目:Genetics-Based Statistical Approaches in Personalized Medicine Development
报告人:美国杜克大学医学院  林敏博士
 
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北京大学临床研究所
2009年5月18日                                   

报告人简介:
Dr Lin is the assistant professor of Department of Biostatistics and Bioinformatics in Duke University School of Medicine. She graduated from Capital University of Medical Sciences, she studied Anticancer Pharmacology & Microbial Pharmacy for her Master Degree at Peking Union Medical College, and She got her PhD degree in Statistics from University of Florida. She is the member of American Statistical Association, Eastern North American Region of the International Biometric Society, and Institute of Mathematical Statistics. And she had involved in a lot of professional activities such as the Guest Co-editor of Special Issue on "Statistical Genetics in Clinical Research" for Journal of  Biopharmaceutical Statistics.

报告摘要:
The materialization of an emerging idea for personalized medicine – purported to apply the right drug in the right dose for the right person at the right time – relies critically on our ability to identify all relevant genetic variants in each patient and interpret this information in a clinically meaningful manner. With the release of the haplotype map, or HapMap, constructed for the entire human genome based on high-throughput single nucleotide polymorphisms (SNPs), there is a pressing need for sophisticated statistical models and methods that can detect specific DNA sequence variants responsible for drug response. Two types of approaches are currently widely used: candidate gene approach and genome-wide scanning. This talk will emphasize on a series of new statistical models within the concept of candidate gene approach derived to compute genes and genomes for a dynamic trait and study the interplay of genetic actions and the pattern of biological processes. Statistical methods in genome-wide association studies (GWAS) and the challenges in both approaches will also be discussed.

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