Announcement Next Gen Sequencing Lecture
Date: 8 November 2011
Time: 15:45 – 16:45
Location: LUMC Main Building Lecture room 5
Speakers: Quan Long - Gregor Mendel Institute of Molecular Plant Biology (Vienna, Austria)
Qingrun Zhang - Beijing Institute of Genomics, Chinese Academy of Sciences
Host: Kai Ye – Dept. of Molecular Epidemiology
Understanding genetic architecture of complex traits by population scale resequencing
Understanding the relationship between genotype and phenotype is a long-standing important question. Population scale re-sequencing provides new opportunity and challenge to this field. The first problem is that current main tools of NGS usually guarantee low false positives therefore have high false negative rates which biases the allele frequency; the second problem is that more variants discovered also provide more candidates for false discovery therefore researchers run the risk of overfitting. In this talk, we will present our efforts of re-sequencing and mapping complex traits for Arabidopsis thaliana, as part of 1001 genomes project.
Pattern Mining of Epistatic Interactions in Genome Wide Case-Control Association Studies
Being a frequently mentioned problem in biology, gene-gene interaction is important for gene mapping. However, there are still no satisfactory ways to handle it and most publications in the field of Genome Wide Association Studies (GWAS) are reporting single marker tests. At the same time, there is a long-standing effort in the field of computer science, called Frequent Itemset Mining (FIM), to identify patterns associated with some observations. In this work, taking advantage of existing efforts in FIM, we propose a method based on extending Apriori, a successful approach in FIM, to do GWAS. Our method, named AprioriGWAS, combines the advantages of Apriori and integrates the insights of GWAS. Simulations show that our method can identify genotype patterns effectively. Also, by analyzing real data, we found interesting genes/pathways that are involved in Age-related Macular Degeneration (AMD) and reveals the potential relationship between AMD and Diabetes mellitus.