Postdoctoral Opportunities in Statistical Genetics with Liming Liang
The Liang lab of statistical genetics at Harvard School of Public Health invites applicants to a 2-3 year Postdoctoral Fellow position emphasizing the statistical designs and analysis of genome-wide studies for human complex diseases and traits. Our group focuses on
(1) Genetics of gene expression and methylation data, particular eQTL/meQTL mapping using both microarray and sequencing data from multiple tissues. Current studies include >2000 expression/methylation samples from multiple tissues and platforms, and samples from the NIH GTEx program.
(2) Biological network estimation using high throughput omic experiment (gene expression/methylation/metabolite) and its association with disease and trait of interest. We are particular interested in developing useful statistical model to maximize the utility of multiple related phenotypes and elucidate how genetic and epigenetic variants and their interaction with environmental factors involve in pathogenesis of complex diseases and traits. Current studies focus on Asthma, Lung Cancer, Diabetes and CVD with study subjects from both European and Asian populations.
(3) Statistical model to integrate functional data (eQTL/meQTL/metabolite QTL) with genome-wide association studies to improve power for disease variant discovery and risk prediction.
(4) New statistical approaches for the analysis of rare variants from next-generation sequencing data and exome SNP array. Ongoing projects include high depth sequencing on ~4000 breast cancer case-control subjects from multiple populations, ~6000 T2D case-control samples and >8000 CHD case-control samples with exome SNP data. The goals include fine mapping causal loci responsible for the disease and developing optimal design for next-generation sequencing studies.
The fellows will work closely with Dr. Liang, with other quantitative Ph.D’s in his group and the Program in Genetic Epidemiology and Statistical Genetics, and with collaborators at HSPH, HMS and the Broad Institute. Fellows will be mentored to facilitate transition to independent research careers by emphasizing acquisition of analytic, writing, and other research skills. Review of applications begins immediately.
Applicants should have a doctoral degree in Statistics/Biostatistics, Epidemiology, Bioinformatics, Computer Science or other relevant discipline with strong quantitative research background; practical experience working with large scale genetic data sets, developing new methods, and producing high-quality published work, are desirable.
Please submit a brief statement of interest, CV, contact information for at least 3 references, and one sample publication by email to Liming Liang, firstname.lastname@example.org. Address: Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Building 2, Room 207, 655 Huntington Ave, Boston, Massachusetts 02115. Phone: 617.432.5896. HSPH is an Equal Opportunity/Affirmative Action Employer. Women and minorities are encouraged to apply.
Postdoctoral Opportunities in Statistical Genetics with Alkes Price
September 28, 2015: A post-doctoral position is available in the statistical genetics research group of Dr. Alkes Price, a faculty member at the Harvard School of Public Health. The fellow will work closely with Dr. Price, with other quantitative Ph.D’s in his group, and with collaborators at HSPH, HMS and The Broad Institute. Questions that we aim to answer include: (1) What is the contribution of different functional classes of genetic variation to the heritability of quantitative and case-control traits, and how does this inform disease association and polygenic prediction methods, (2) Which association scoring statistics provide maximum power to identify disease genes within and across structured populations, accounting for the advantages of mixed model methods, and (3) How do common and rare variants contribute to the architecture of complex traits, and how does this inform strategies for disease mapping using whole-genome sequencing data.
Exceedingly strong quantitative research background; practical experience working with large real-world genetic data sets, developing new methods, and producing high-quality published work. Preference will be given to candidates with degrees in computer science or other applied quantitative fields.
Please submit a brief statement of interest, CV, contact information for at least 3 references, and two sample publications by email to Alkes Price, email@example.com.