美国默克公司人类基因研究博士后职位

The Genetics team within the newly formed Genetics and Pharmacogenomics (GpGx) department at Merck Research Laboratories (MRL) is currently seeking a postdoctoral fellow for an exciting collaboration with Harvard Medical School to better understand the impact of the non protein-coding genome on human health and disease. The ideal candidate should have a deep understanding of human genetics, preferably with a background in statistical and population genetics, bioinformatics, and/or computational genomics. 



GpGx is tasked with identifying novel drug targets with strong genetic support (see, for example PMID 23868113). To this end, the department is generating whole genome sequence (WGS) data from highly prioritized families segregating rare, apparently Mendelian forms of common, complex disease, as well as from populations. While a large body of work exists on the functional consequences of protein coding variants (see PMID: 11337480; 20354512), relatively little is known about the interpretation of non protein-coding variants. To further understand the impact of non-coding variation, the postdoctoral fellow will generate, optimize, and apply computational tools to assess whether variants identified through WES, WGS, and SNP typing are likely to be disease-relevant or not. The goal of this work is the discovery and validation of promising genetic targets that will enter the drug discovery pipeline within Early Development and Discovery Sciences at Merck & Co. 



Patients and families for WGS will be identified through the Personal Genomics Counseling Service (PGCS) at Brigham and Women’s Hospital. PGCS treats a large number of patients with extremely rare diseases, including patients from consanguineous families. As a postdoc in GpGx, you will collaborate closely with PGCS and Dr. Shamil Sunyaev at Harvard Medical School (HMS), which is across the street from MRL. You will be an active member of both the Sunyaev lab and GpGx, attending lab meetings at both places and receiving mentorship from Dr. Sunyaev, as well as GpGx scientists. You will be uniquely positioned to take advantage of the research, educational, and social opportunities at both HMS and MRL. This position represents a true collaboration between academia and industry.

Responsibilities include but are not limited to:
• Working with clinicians at BWH Personal Genomics Counseling Service to select patients and families for WGS
• Analyzing WGS data from selected patients and family members
• Collaborating with HMS faculty and MRL scientists to optimize existing tools for prioritization of coding and non-coding variants from WGS data
• Developing new computational tools, as needed
• Identifying and prioritizing coding and non-coding sequence variants that have the highest probability of being pathogenic
• Working with MRL scientists to design wet lab experiments to sort out and validate genetic findings

Qualifications:
Education Minimum Requirement:
• PhD or MD/PhD in Human Genetics, Population Genetics, Computational Genomics, Bioinformatics, Biostatistics, or a related field

Required Experience and Skills:
• PhD, or met all the requirements for a PhD by June 2015, with 3 years or less postdoctoral experience
• Understanding of human genetic sequence variation and/or population genetics;
• Strong bioinformatics and statistical skills, including programming skills (using e.g. R, Matlab, or Perl/Python) and the ability to query databases;
• Experience in the design and bioinformatics analysis of large-scale genetic datasets, preferably next-generation sequencing data;
• Strong communication, collaboration, and leadership skills, including the ability to communicate complex data analysis results in a clear, concise manner to biologists, clinicians, and senior management.

Preferred Experience and Skills:
• Experience in interpreting whole genome data (GWAS, WES, or WGS), including concepts related to population genetics (e.g., linkage disequilibrium, genetic architecture of human disease) and functional annotation of human genetic variation (e.g., coding and non-coding variants);
• History of publication in high impact, peer-reviewed journals …