High-throughput sequencing for biology and medicine.
Ontology highlight
ABSTRACT:
SUBMITTER: Soon WW
PROVIDER: S-EPMC3564260 | biostudies-other | 2013
REPOSITORIES: biostudies-other
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Project description:BackgroundThe metabolic syndrome (MetS) is a collection of co-occurring complex disorders including obesity, hypertension, dyslipidemia, and insulin resistance. The Lyon hypertensive and Lyon normotensive rats are models of MetS sensitivity and resistance, respectively. To identify genetic determinants and mechanisms underlying MetS, an F2 intercross between Lyon hypertensive and Lyon normotensive was comprehensively studied.Methods and resultsMultidimensional data were obtained including genotypes of 1536 single-nucleotide polymorphisms, 23 physiological traits, and >150 billion nucleotides of RNA-seq reads from the livers of F2 intercross offspring and parental rats. Phenotypic and expression quantitative trait loci (eQTL) were mapped. Application of systems biology methods identified 17 candidate MetS genes. Several putative causal cis-eQTL were identified corresponding with phenotypic QTL loci. We found an eQTL hotspot on rat chromosome 17 that is causally associated with multiple MetS-related traits and found RGD1562963, a gene regulated in cis by this eQTL hotspot, as the most likely eQTL driver gene directly affected by genetic variation between Lyon hypertensive and Lyon normotensive rats.ConclusionsOur study sheds light on the intricate pathogenesis of MetS and demonstrates that systems biology with high-throughput sequencing is a powerful method to study the pathogenesis of complex genetic diseases.
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Project description:Human leukocyte antigen (HLA) genes are the most polymorphic in the human genome. They play a pivotal role in the immune response and have been implicated in numerous human pathologies, especially autoimmunity and infectious diseases. Despite their importance, however, they are rarely characterized comprehensively because of the prohibitive cost of standard technologies and the technical challenges of accurately discriminating between these highly related genes and their many allelles. Here we demonstrate a high-resolution, and cost-effective methodology to type HLA genes by sequencing, which combines the advantage of long-range amplification, the power of high-throughput sequencing platforms, and a unique genotyping algorithm. We calibrated our method for HLA-A, -B, -C, and -DRB1 genes with both reference cell lines and clinical samples and identified several previously undescribed alleles with mismatches, insertions, and deletions. We have further demonstrated the utility of this method in a clinical setting by typing five clinical samples in an Illumina MiSeq instrument with a 5-d turnaround. Overall, this technology has the capacity to deliver low-cost, high-throughput, and accurate HLA typing by multiplexing thousands of samples in a single sequencing run, which will enable comprehensive disease-association studies with large cohorts. Furthermore, this approach can also be extended to include other polymorphic genes.
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Project description:High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.
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