BackgroundAlthough genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis.ResultsWe combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells.ConclusionsWe highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0948-6) contains supplementary material, which is available to authorized users.

In patients with metastatic castrate resistant prostate cancer (mCRPC), circulating tumor DNA (ctDNA) analysis offers novel opportunities for the development of non-invasive biomarkers informative of treatment response with novel agents targeting the androgen-receptor (AR) pathway, such as abiraterone or enzalutamide. However, the relationship between ctDNA abundance, detectable somatic genomic alterations and clinical progression of mCRPC remains unexplored. Our study aimed to investigate changes in plasma DNA during disease progression and their associations with clinical variables in mCRPC patients. We analyzed ctDNA in two cohorts including 94 plasma samples from 25 treatment courses (23 patients) and 334 plasma samples from 125 patients, respectively. We conducted whole-genome sequencing (plasma-Seq) for genome-wide profiling of somatic copy number alterations and targeted sequencing of 31 prostate cancer-associated genes. The combination of plasma-Seq with targeted AR analyses identified prostate cancer-related genomic alterations in 16 of 25 (64%) treatment courses in the first cohort, in which we demonstrated that AR amplification does not always correlate with poor abiraterone and enzalutamide therapy outcome. As we observed a wide variability of ctDNA levels, we evaluated ctDNA levels and their association with clinical parameters and included the second, larger cohort for these analyses. Employing altogether 428 longitudinal plasma samples from 148 patients, we identified the presence of bone metastases, increased lactate dehydrogenase and prostate-specific antigen (PSA) as having the strongest association with high ctDNA levels. In summary, ctDNA alterations are observable in the majority of patients with mCRPC and may eventually be useful to guide clinical decision-making in this setting.

Prolonged exposure to abnormally high calcium concentrations is thought to be a core mechanism underlying hippocampal damage in epileptic patients; however, no prior study has characterized calcium activity during seizures in the live, intact hippocampus. We have directly investigated this possibility by combining whole-brain electroencephalographic (EEG) measurements with microendoscopic calcium imaging of pyramidal cells in the CA1 hippocampal region of freely behaving mice treated with the pro-convulsant kainic acid (KA). We observed that KA administration led to systematic patterns of epileptiform calcium activity: a series of large-scale, intensifying flashes of increased calcium fluorescence concurrent with a cluster of low-amplitude EEG waveforms. This was accompanied by a steady increase in cellular calcium levels (>5 fold increase relative to the baseline), followed by an intense spreading calcium wave characterized by a 218% increase in global mean intensity of calcium fluorescence (n = 8, range [114–349%], p < 10−4; t-test). The wave had no consistent EEG phenotype and occurred before the onset of motor convulsions. Similar changes in calcium activity were also observed in animals treated with 2 different proconvulsant agents, N-methyl-D-aspartate (NMDA) and pentylenetetrazol (PTZ), suggesting the measured changes in calcium dynamics are a signature of seizure activity rather than a KA-specific pathology. Additionally, despite reducing the behavioral severity of KA-induced seizures, the anticonvulsant drug valproate (VA, 300 mg/kg) did not modify the observed abnormalities in calcium dynamics. These results confirm the presence of pathological calcium activity preceding convulsive motor seizures and support calcium as a candidate signaling molecule in a pathway connecting seizures to subsequent cellular damage. Integrating in vivo calcium imaging with traditional assessment of seizures could potentially increase translatability of pharmacological intervention, leading to novel drug screening paradigms and therapeutics designed to target and abolish abnormal patterns of both electrical and calcium excitation.

MotivationNext-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost.ResultsWe describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study.ConclusionsWe ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging ‘big data’ problems in biomedical research brought on by the expansion of NGS technologies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0736-4) contains supplementary material, which is available to authorized users.