97 results found
Beltrami C, Besnier M, Shantikumar S, et al., 2017, Human Pericardial Fluid Contains Exosomes Enriched with Cardiovascular-Expressed MicroRNAs and Promotes Therapeutic Angiogenesis, MOLECULAR THERAPY, Vol: 25, Pages: 679-693, ISSN: 1525-0016
Chen T-D, Rotival M, Chiu L-Y, et al., 2017, Identification of Ceruloplasmin as a Gene that Affects Susceptibility to Glomerulonephritis Through Macrophage Function, GENETICS, Vol: 206, Pages: 1139-1151, ISSN: 0016-6731
Coan PM, Hummel O, Diaz AG, et al., 2017, Genetic, physiological and comparative genomic studies of hypertension and insulin resistance in the spontaneously hypertensive rat, DISEASE MODELS & MECHANISMS, Vol: 10, Pages: 297-306, ISSN: 1754-8403
Heinig M, Adriaens ME, Schafer S, et al., 2017, Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy, GENOME BIOLOGY, Vol: 18, ISSN: 1474-760X
Imprialou M, Petretto E, Bottolo L, 2017, Expression QTLs Mapping and Analysis: A Bayesian Perspective., Pages: 189-215
The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.
Krishnan ML, Van Steenwinckel J, Schang A-L, et al., 2017, Integrative genomics of microglia implicates DLG4 (PSD95) in the white matter development of preterm infants, NATURE COMMUNICATIONS, Vol: 8, ISSN: 2041-1723
McDermott-Roe C, Leleu M, Rowe GC, et al., 2017, Transcriptome-wide co-expression analysis identifies LRRC2 as a novel mediator of mitochondrial and cardiac function, PLOS ONE, Vol: 12, ISSN: 1932-6203
Moreno-Moral A, Pesce F, Behmoaras J, et al., 2017, Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease., Methods Mol Biol, Vol: 1488, Pages: 337-362
Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.
Petretto E, 2017, Genetics of Neurodevelopmental Disorders: Connecting The Dots in The Brain, 18th International Congress of Developmental Biology, Publisher: ELSEVIER SCIENCE BV, Pages: S4-S4, ISSN: 0925-4773
Rackham OJL, Langley SR, Oates T, et al., 2017, A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation, GENETICS, Vol: 205, Pages: 1443-1458, ISSN: 0016-6731
Rodriguez-Martinez A, Posma JM, Ayala R, et al., 2017, MWASTools: an R/Bioconductor package for metabolome-wide association studies., Bioinformatics
Summary: MWASTools is an R package designed to provide an integrated pipeline to analyze metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of MWAS results. Availability: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Contact: email@example.com. Supplementary information: Supplementary data are available at Bioinformatics online.
Srivastava PK, Bagnati M, Delahaye-Duriez A, et al., 2017, Genome-wide analysis of differential RNA editing in epilepsy, GENOME RESEARCH, Vol: 27, Pages: 440-450, ISSN: 1088-9051
Suresh J, Harmston N, Lim KK, et al., 2017, An embryonic system to assess direct and indirect Wnt transcriptional targets, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
Delahaye-Duriez A, Srivastava P, Shkura K, et al., 2016, Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery, GENOME BIOLOGY, Vol: 17, ISSN: 1474-760X
Diaz-Montana JJ, Rackham OJL, Diaz-Diaz N, et al., 2016, Web-based Gene Pathogenicity Analysis (WGPA): a web platform to interpret gene pathogenicity from personal genome data, BIOINFORMATICS, Vol: 32, Pages: 635-637, ISSN: 1367-4803
Johnson MR, Shkura K, Langley SR, et al., 2016, Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease, NATURE NEUROSCIENCE, Vol: 19, Pages: 223-+, ISSN: 1097-6256
Lewin A, Saadi H, Peters JE, et al., 2016, MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues, BIOINFORMATICS, Vol: 32, Pages: 523-532, ISSN: 1367-4803
Madan B, Ke Z, Harmston N, et al., 2016, Wnt addiction of genetically defined cancers reversed by PORCN inhibition, ONCOGENE, Vol: 35, Pages: 2197-2207, ISSN: 0950-9232
Martinez-Micaelo N, Gonzalez-Abuin N, Ardevol A, et al., 2016, Leptin signal transduction underlies the differential metabolic response of LEW and WKY rats to cafeteria diet, JOURNAL OF MOLECULAR ENDOCRINOLOGY, Vol: 56, Pages: 1-10, ISSN: 0952-5041
Martinez-Micaelo N, Gonzalez-Abuin N, Terra X, et al., 2016, Identification of a nutrient-sensing transcriptional network in monocytes by using inbred rat models on a cafeteria diet, DISEASE MODELS & MECHANISMS, Vol: 9, Pages: 1231-1239, ISSN: 1754-8403
Moreno-Moral A, Petretto E, 2016, From integrative genomics to systems genetics in the rat to link genotypes to phenotypes, DISEASE MODELS & MECHANISMS, Vol: 9, Pages: 1097-1110, ISSN: 1754-8403
Pedrigi RM, Mehta VV, Bovens SM, et al., 2016, Influence of shear stress magnitude and direction on atherosclerotic plaque composition, Royal Society Open Science, Vol: 3, ISSN: 2054-5703
Rackham OJL, Firas J, Fang H, et al., 2016, A predictive computational framework for direct reprogramming between human cell types, NATURE GENETICS, Vol: 48, Pages: 331-335, ISSN: 1061-4036
Behmoaras J, Diaz AG, Venda L, et al., 2015, Macrophage Epoxygenase Determines a Profibrotic Transcriptome Signature, JOURNAL OF IMMUNOLOGY, Vol: 194, Pages: 4705-4716, ISSN: 0022-1767
Buyandelger B, Mansfield C, Kostin S, et al., 2015, MIp Interacting Protein 1 (MIP1) Plays a Role for Cardiomyopathy, Scientific Sessions and Resuscitation Science Symposium of the American-Heart-Association (AHA), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
Buyandelger B, Mansfield C, Kostin S, et al., 2015, ZBTB17 (MIZ1) Is Important for the Cardiac Stress Response and a Novel Candidate Gene for Cardiomyopathy and Heart Failure, CIRCULATION-CARDIOVASCULAR GENETICS, Vol: 8, Pages: 643-652, ISSN: 1942-325X
Dominy KM, Roufosse C, de Kort H, et al., 2015, Use of Quantitative Real Time Polymerase Chain Reaction to Assess Gene Transcripts Associated With Antibody-Mediated Rejection of Kidney Transplants, TRANSPLANTATION, Vol: 99, Pages: 1981-1988, ISSN: 0041-1337
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