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Methods in Statistical Genetics and BioinformaticsPosted by: National Institutes of Health (NIH)
Posted date: 2019-Nov-21
Two postdoctoral positions are available in the Biostatistics and Computational Biology Branch (BCBB) at the National Institute of Environmental Health Sciences, NIH, in Research Triangle Park, North Carolina, under Dr. Alison Motsinger-Reif (https://www.niehs.nih.gov/research/atniehs/labs/bb/staff/motsinger-reif/index.cfm).
The successful candidate will develop and apply methods to address problems in genetic association mapping. We are interested in methods development for genome-wide association studies, for detecting gene-gene and gene-environment interactions, and for integrating data across “-omes”. There are a number of opportunities for collaboration and application, in studies of common, complex diseases and drug and chemical response traits. However, research topics are not limited to those, and candidates with different topics of interest are encouraged to apply.
Example ongoing projects that could be the focus of these positions include the following:
1) Genetic epidemiology in the Environmental Polymorphisms Registry (EPR):
The EPR provides a unique resource for a variety of studies dedicated to clarifying the interaction of genetic and environmental determinants of human health. Nearly 20,000 participants have contributed biological samples (blood, DNA, and other materials) as well as disease and exposure data that support a wide range of research questions. Continuing enrollment and participation of these subjects, coupled with the ability to link their data with additional information (external data plus continuing surveys on exposures, follow-up clinic and lab-based sub-studies, electronic medical records), is of increasing value in this era of personalized medicine. Currently ~6000 participants are being sequenced by whole genome sequencing. This data will offer an unprecedented opportunity to dissect the genetic and environmental etiology of a broad range of important health outcomes. The unique structure of the data, and the many levels of data to be integrated require continued development in machine learning, statistical genetics, data mining, etc. Methods will focus on methods to identify gene-gene and gene-environment interactions.
2) Integrative Omics in myalgic encephalomyelitis (ME/CFS):
ME/CFS patients suffer from a range of debilitating conditions with significant pain and fatigue. Given the lack of diagnostic molecular markers for ME/CFS and a very limited understanding of its etiology, there is critical need to understand the etiology and mechanisms of ME/CFS predisposition and severity, as well as to define set of ME/CFS clinical ontology for stratification of the disease. While early studies showed promise in identifying different metabolic, immunologic, or microbial biomarkers of ME/CFS, these studies were limited in scope, sample size, or, importantly, integration across datatypes. A prospective cohort has been designed to address our overarching hypothesis that the immune system’s etiological role in ME/CFS is predicated on two major factors: first, that immune cells themselves are programmed to respond aberrantly to environmental stimuli, and second, that ME/CFS patients harbor microbes that aberrantly stimulate immune cells. This prospective cohort includes ME/CFS patients from which clinical metadata and immune, metabolome and gut microbiome markers analyzed longitudinally. Acquiring these datatypes simultaneously allows us to test our hypothesis in an integrated fashion: first, we can evaluate whether alterations in metabolism can instigate aberrant inflammation or reflect altered immune activity, and second, whether microbial dysbiosis is associated with specific ME/CFS inflammatory markers or markers of immune dysfunction. We will develop and apply advanced bioinformatic analyses to identify homogeneous, therapeutically actionable subgroups of ME/CFS patients. We hypothesize that clustering of patients across more than one type of molecular or genomic investigation will provide a particularly strong indication. We will: 1) computationally stratify clinical aspects of ME/CFS; 2) use biclustering techniques to identify candidate ME/CFS subgroups in each of the genomic modalities; and 3) use integrative multimodality clustering to identify significant subgroups defined by more than one genomic investigation.
3) The Human Imprintome Project:
Genomic imprinting is an epigenetic phenomenon that causes genes to be expressed in a parent-of-origin-specific manner. Improving technology now allows the interrogation of imprinted regions from whole genome bi-sulfite sequencing. In this project we will comprehensively identify regulatory DNA methylation for imprinted genes, creating the first draft of the human “imprintome”. Epigenetically regulated imprinted genes are estimated to comprise 1-2% (200-400 genes) of the human genome, and are critical in the development of the early embryo; however, only ~30 imprint control regions (ICRs), regulating 70-80 genes, are known. Monoallelic expression of imprinted genes is regulated by parent-of-origin specific DNA methylation at ICRs that is established prior to germ-layer specification and maintained in somatic tissues throughout life. Therefore, methylation marks regulating the expression of these genes are functionally relevant, and are conserved across cell types, among individuals, and throughout aging. These unique features of ICRs provide a means to a comprehensive tool for multiplexed measurement of early acquired epigenetic modifications, and assess their link between exposures and disease. We will develop and apply bioinformatics approached to identify putative ICR that will be confirmed with further sequencing efforts. We will refine an existing algorithm to identify CpG dinucleotide content and spacing — as ICRs are known to regulate gene clusters and DNA functional elements. We will use ENCODE to prioritize regions in close proximity to DNase hypersensitive sites, transcription factor binding sites, CpG islands, genes and gene promoters. Methylation databases of sperm and oocyte sequences, from mouse models, and others will be used to identify likely parental origins of methylation for candidate ICRs. Dissemination of these results will be done with database development, and web-based tools to disseminate the results to the broad community.Qualifications:
Candidates should have or be very close to obtaining a Ph.D. in biostatistics, statistics, bioinformatics, genetics, computational biology or closely related areas. Programming skill is required. Excellent communication skills and fluency in both spoken and written English are essential.To Apply:
Interested candidates should submit their curriculum vitae, a detailed statement of their research interests, and the names and contact information for three references to Dr. Alison Motsinger-Reif at motsingerreifaaniehs.nih.gov. Recruitment is ongoing. Application Deadline Date: December 31, 2019.
The NIH is dedicated to building a diverse community in its training and employment programs. The NIH is an equal opportunity employer.