Post-Doc @ Univ. of Maryland School of Medicine

A postdoctoral position is available in the laboratory of Dr. David Serre at the Institute for Genome Sciences at the University of Maryland School of Medicine. Our laboratory is interested in developing and applying novel genomic approaches to better understand the biology of malaria parasites and the vectors that transmit them.

We are seeking a highly motivated individual to take the lead on analyses of single-cell RNA-seq data generated from Plasmodiumparasites at different life stages of their development and under various conditions. This postdoctoral position is supported by NIH awards and involves collaborations with researchers at NIAID, Johns Hopkins School of Public Health and in malaria-endemic countries. There are also opportunities for the successful candidate to develop her/his own independent projects within the framework of this research.

A PhD in biology, bioinformatics, genomics or a related field is required. The work involves custom bioinformatic analyses of large amount of data and proficiency in one programming language and knowledge of statistics would be an advantage. Previous knowledge of parasitology is not required.

Interested candidates should send a cover letter and CV to David Serre (dserre@som.umaryland.edu)

QCB Colloquium Series | Dr. Hongyu Zhao

Dr. Hongyu Zhao
Department Chair and Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science, Yale University, School of Public Health
Lab Website

Dissecting Genetic Architecture of Complex Diseases From Genome Wide Association Studies

Wednesday, May 8, 2019
2 PM
RRI 301

Abstract: Genome-wide association study (GWAS) has been a great success in the past decade, with thousands of regions in the human genome implicated for hundreds of complex diseases. However, significant challenges remain in both identifying new risk loci and interpreting results, even for samples with tens of thousands of subjects. In this presentation, we describe our recent efforts to infer the genetic architecture of complex disease through random effects models, the development of functional annotations of the human genome, and the integrated analysis of these annotations with GWAS results. The effectiveness of our methods will be demonstrated through their applications to a large number of GWASs to identify tissues/cell types that are relevant to a specific disease, to infer shared genetic contributions to several diseases, and to improve genetic disease risk predictions. This is joint work with Qiongshi Lu, Yiming Hu, Jiming Jiang, Can Yang, Ryan Powels, Yixuan Ye, and others.