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Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

The X Chromosome plays an important role in human development and disease. However, functional genomic and disease association studies of X genes greatly lag behind autosomal gene studies, in part owing to the unique biology of X-Chromosome inactivation (XCI). Because of XCI, most genes are only expressed from one allele. Yet, ∼30% of X genes “escape” XCI and are transcribed from both alleles, many only in a proportion of the population. Such interindividual differences are likely to be disease relevant, particularly for sex-biased disorders. To understand the functional biology for X-linked genes, we developed X-Chromosome inactivation for RNA-seq (XCIR), a novel approach to identify escape genes using bulk RNA-seq data. Our method, available as an R package, is more powerful than alternative approaches and is computationally efficient to handle large population-scale data sets. Using annotated XCI states, we examined the contribution of X-linked genes to the disease heritability in the United Kingdom Biobank data set. We show that escape and variable escape genes explain the largest proportion of X heritability, which is in large part attributable to X genes with Y homology. Finally, we investigated the role of each XCI state in sex-biased diseases and found that although XY homologous gene pairs have a larger overall effect size, enrichment for variable escape genes is significantly increased in female-biased diseases. Our results, for the first time, quantitate the importance of variable escape genes for the etiology of sex-biased disease, and our pipeline allows analysis of larger data sets for a broad range of phenotypes.

The results of studies evaluating spinal cord stimulation (SCS) for postlaminectomy syndrome (PLS) have yielded mixed results. This has led to an increased emphasis on objective outcome measures such as opioid prescribing.To determine the association between SCS and long-term opioid therapy (LOT) for PLS.In this cohort study, adults with PLS were identified using the TriNetx Diamond Network and separated based on whether they underwent SCS. Patients were stratified according to baseline opioid use (opioid-naive or receiving LOT) and subsequent opioid therapy over the 12-month period ranging from 3 to 15 months post-SCS implantation or post-PLS index date. Statistical analysis was performed from June to December 2021.SCS.The main outcome was cessation of opioid use among patients receiving LOT or abstinence from opioids among opioid-naive patients. Opioid-naive patients were defined as those receiving at most 2 opioid prescriptions per year, and patients on LOT were those receiving at least 6 opioid prescriptions per year.Among 552 937 eligible patients treated between December 2015 and May 2021, 26 179 with PLS received an SCS implant. The median (IQR) patient age was 60 (51-69) years; 305 802 patients (55.3%) were female. Among those reporting racial identify (37.0% [204 758 patients]), 9.3% (18 971 patients) were African American, 0.3% (648 patients) were Asian, and 90.4% (185 139 patients) were White. Compared with those who did not receive an SCS, individuals who received an SCS were more likely to be using opioids preimplantation (mean [SD] prescriptions: 4.3 [8.5] vs 4.1 [9.3]; P .001) but less likely to be using opioids after SCS implantation (mean [SD] prescriptions: 3.8 [8.2] vs 4.0 [9.4]; P = .006). In the 12-month study period, similar proportions in the SCS and no-SCS groups receiving baseline LOT remained on LOT (70.3% [n = 74 585] vs 69.2% [n = 3882], respectively; P = .10). In opioid-naive patients, SCS was associated with a small decreased likelihood of patients subsequently receiving LOT (7.6% vs 7.0%; difference, -0.6% [95% CI, -1.0% to -0.2%]; P = .003). In multivariable analysis, SCS was associated with an increased likelihood of not being on opioids in both opioid-naive (adjusted odds ratio [OR], 0.90 [95% CI, 0.85-0.96]; P .001) and LOT patients (adjusted OR, 0.93 [95% CI, 0.88-0.99]; P = .02). White patients were significantly more likely to be diagnosed with PLS (ie, underwent surgery) (90.4% vs 85.2%; difference, 5.2% [95% CI, 5.1%-5.4%]; P .001) and receive an SCS (93.7% vs 90.3%; difference, 3.4% [95% CI, 2.9% to 4.0%]; P .001) than patients of other racial identities.These findings suggest that under real-life conditions, SCS was associated with small, clinically questionable associations with opioid discontinuation and not starting opioids in the context of PLS.

Solar flare prediction is an increasingly important concern in spaceweather prediction. Major solar flares have potentially catastrophic consequences for human life and infrastructure, both in space and on earth. The current lack of highly predictive models for these events saw the heliophysics community turn to data driven approaches. In this paper, we describe a novel two-step regularised gradient boosted classification tree model approach to the analysis of large multivariate time series. Applied to the prediction of major flaring events, we demonstrate that along with high performance, the critical feature selection steps increase interpretability of otherwise complex models to offer insights that could help identify physical mechanisms giving rise to solar flares. This method was developed for the IEEE BigData Cup 2019 “Solar Flare Prediction from Time Series of Solar Magnetic Field Parameters”

Annual influenza vaccinations are currently recommended for all individuals 6 months and older. Antibodies induced by vaccination are an important mechanism of protection against infection. Despite the overall public health success of influenza vaccination, many individuals fail to induce a substantial antibody response. Systems-level immune profiling studies have discerned associations between transcriptional and cell subset signatures with the success of antibody responses. However, existing signatures have relied on small cohorts and have not been validated in large independent studies. We leveraged multiple influenza vaccination cohorts spanning distinct geographical locations and seasons from the Human Immunology Project Consortium (HIPC) and the Center for Human Immunology (CHI) to identify baseline (i.e., before vaccination) predictive transcriptional signatures of influenza vaccination responses. Our multicohort analysis of HIPC data identified nine genes (RAB24, GRB2, DPP3, ACTB, MVP, DPP7, ARPC4, PLEKHB2, and ARRB1) and three gene modules that were significantly associated with the magnitude of the antibody response, and these associations were validated in the independent CHI cohort. These signatures were specific to young individuals, suggesting that distinct mechanisms underlie the lower vaccine response in older individuals. We found an inverse correlation between the effect size of signatures in young and older individuals. Although the presence of an inflammatory gene signature, for example, was associated with better antibody responses in young individuals, it was associated with worse responses in older individuals. These results point to the prospect of predicting antibody responses before vaccination and provide insights into the biological mechanisms underlying successful vaccination responses.

Semen contains relatively ill-defined regulatory components that likely aid fertilization, but which could also interfere with defense against infection. Each ejaculate contains trillions of exosomes, membrane-enclosed subcellular microvesicles, which have immunosuppressive effects on cells important in the genital mucosa. Exosomes in general are believed to mediate inter-cellular communication, possibly by transferring small RNA molecules. We found that seminal exosome (SE) preparations contain a substantial amount of RNA from 20 to 100 nucleotides (nts) in length. We sequenced 20–40 and 40–100 nt fractions of SE RNA separately from six semen donors. We found various classes of small non-coding RNA, including microRNA (21.7% of the RNA in the 20–40 nt fraction) as well as abundant Y RNAs and tRNAs present in both fractions. Specific RNAs were consistently present in all donors. For example, 10 (of ∼2600 known) microRNAs constituted over 40% of mature microRNA in SE. Additionally, tRNA fragments were strongly enriched for 5’-ends of 18–19 or 30–34 nts in length; such tRNA fragments repress translation. Thus, SE could potentially deliver regulatory signals to the recipient mucosa via transfer of small RNA molecules.

We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework.

In this chapter we demonstrate the use of R Bioconductor packages pepStat and Pviz on a set of paired peptide microarrays generated from vaccine trial data. Data import, background correction, normalization, and summarization techniques are presented. We introduce a sliding mean method for amplifying signal and reducing noise in the data, and show the value of gathering paired samples from subjects. Useful visual summaries are presented, and we introduce a simple method for setting a decision rule for subject/peptide responses that can be used with a set of control peptides or placebo subjects.

Recent technical advances have transformed the field of immunology. We are now capable of measuring features of immune responses, including B- and T-cell specificity and repertoire, serum and intracellular cytokines, and more, on a scale never imagined before. As a consequence, the generation of big data sets has become routine and there is an urgent need for an analysis platform to facilitate data exploration and integration across assays and studies. Here we present ImmuneSpace, the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). The HIPC program, funded by the NIH, is a multi-center collaborative effort to characterize the status of the immune system in different populations under diverse stimulations and disease states. This ongoing effort has generated large amounts of varied high-throughput, high-dimensional biological data (flow cytometry, CyTOF, RNA-Seq, Luminex, among others). All data generated to date by HIPC, along with other selected datasets generated by other NIAID funded projects, have been made publicly available through ImmuneSpace and are ready to be explored using visualization and analysis tools built in ImmuneSpace. To this end, we hope that ImmuneSpace will act as a central immunological hub, allowing experimentalists, statisticians, and bioinformaticians to freely retrieve, explore and compare data across assays and across studies generated within and outside of HIPC.

Summary: MNase-Seq and ChIP-Seq have evolved as popular techniques to study chromatin and histone modification. Although many tools have been developed to identify enriched regions, software tools for nucleosome positioning are still limited. We introduce a flexible and powerful open-source R package, PING 2.0, for nucleosome positioning using MNase-Seq data or MNase– or sonicated– ChIP-Seq data combined with either single-end or paired-end sequencing. PING uses a model-based approach, which enables nucleosome predictions even in the presence of low read counts. We illustrate PING using two paired-end datasets from Saccharomyces cerevisiae and compare its performance with nucleR and ChIPseqR. Availability: PING 2.0 is available from the Bioconductor website at http://bioconductor.org. It can run on Linux, Mac and Windows. Contact: rgottard@fhcrc.org Supplementary Information: Supplementary material is available at Bioinformatics online.