Joint eQTL assessment of whole blood and dura mater tissue from individuals with Chiari type I malformation.


BACKGROUND: Expression quantitative trait loci (eQTL) play an important role in the regulation of gene expression. Gene expression levels and eQTLs are expected to vary from tissue to tissue, and therefore multi-tissue analyses are necessary to fully understand complex genetic conditions in humans. Dura mater tissue likely interacts with cranial bone growth and thus may play a role in the etiology of Chiari Type I Malformation (CMI) and related conditions, but it is often inaccessible and its gene expression has not been well studied. A genetic basis to CMI has been established; however, the specific genetic risk factors are not well characterized. RESULTS: We present an assessment of eQTLs for whole blood and dura mater tissue from individuals with CMI. A joint-tissue analysis identified 239 eQTLs in either dura or blood, with 79% of these eQTLs shared by both tissues. Several identified eQTLs were novel and these implicate genes involved in bone development (IPO8, XYLT1, and PRKAR1A), and ribosomal pathways related to marrow and bone dysfunction, as potential candidates in the development of CMI. CONCLUSIONS: Despite strong overall heterogeneity in expression levels between blood and dura, the majority of cis-eQTLs are shared by both tissues. The power to detect shared eQTLs was improved by using an integrative statistical approach. The identified tissue-specific and shared eQTLs provide new insight into the genetic basis for CMI and related conditions.





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Publication Info

Lock, Eric F, Karen L Soldano, Melanie E Garrett, Heidi Cope, Christina A Markunas, Herbert Fuchs, Gerald Grant, David B Dunson, et al. (2015). Joint eQTL assessment of whole blood and dura mater tissue from individuals with Chiari type I malformation. BMC Genomics, 16. p. 11. 10.1186/s12864-014-1211-8 Retrieved from

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Herbert Edgar Fuchs

Professor of Neurosurgery

Clinical neuro-oncology research including collaborations studying molecular genetics of childhood brain tumors.
Potential role of the free electron laser in surgery of pediatric brain tumors. Current work includes animal models with human brain tumor xenografts in preclinical studies.
Collaboration with the neurooncology laboratory of Dr. Darell Bigner in preclinical studies of new therapeutic agents.


Gerald Arthur Grant

Allan H. Friedman Distinguished Professor of Neurosurgery

David B. Dunson

Arts and Sciences Distinguished Professor of Statistical Science

My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more.  We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers.  We are also interested in new inference framework and in studying theoretical properties of methods we develop.  

Some highlight application areas: 
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc.  Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.

(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.

(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease.  This includes an emphasis on infectious disease modeling, such as COVID-19.

Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).


Simon Gray Gregory

Professor in Neurosurgery

Dr. Gregory is a tenured Professor and Director of the Brain Tumor Omics Program (BTOP) in the Duke Department of Neurosurgery, the Vice Chair of Research in the Department of Neurology, and Director of the Molecular Genomics Core at the Duke Molecular Physiology Institute. 

As a neurogenomicist, Dr. Gregory applies the experience gained from leading the sequencing of chromosome 1 for the Human Genome Project to elucidating the mechanisms underlying multi-factorial diseases using genetic, genomic, and epigenetic approaches. Dr. Gregory’s primary areas of research involve understanding the molecular processes associated with disease development and progression in brain tumors and Alzheimer’s disease, novel drug induced white matter injury repair in multiple sclerosis, and social and behavioral response to oxytocin treatment animal models of autism. 

He is broadly regarded across Duke as a leader in the development of novel single cell and spatial molecular technologies towards understanding the pathogenic mechanisms of disease development. Dr. Gregory is also the Section Chair of Genomics and Epigenetics at the DMPI and Director of the Duke Center of Autoimmunity and MS in the Department of Neurology.

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