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Barnard, R.

Publications and source records attributed to Barnard, R..

5 recordsLinked to original sources

RAREsim2: Flexible simulation of rare variant genetic data using real haplotypes

SummaryRealistic simulated data is critical for advancing methodological development and optimizing study design in genetics research. However, many genetic simulation tools are unable to replicate the distribution of rare variants or incorporate key genetic information, such as functional annotations and linkage disequilibrium. RAREsim, an accurate rare-variant simulation algorithm that uses real genetic haplotypes, was developed to address these limitations. Here, we introduce RAREsim2, an update that provides both streamlined software and new functionalities for simulating individual-level differences (e.g., cases vs controls, technological or batch effects) and variant-level differences to represent a variety of causal models. We demonstrate RAREsim2s utility with three rare variant association methods (Burden, SKAT, and SKAT-O) across several simulation scenarios: causal variants only in cases, causal variants in both cases and controls, and no causal variants. Type I Error was maintained and the optimal test matched previously known patterns: Burden performed best given a large amount of causal variants with the same direction of effect; SKAT performed best given causal variants with opposite directions of effect. SKAT-O was powerful across all simulation scenarios. We highlight RAREsim2s capabilities to simulate various genetic ancestries (African, East Asian, Non-Finnish European, and South Asian), gene sizes (~20-80 functional rare variants per gene), strengths of association (20%, 40%, 60% functional variants), and proportions of risk variants (1, 0.75, 0.5). Importantly, real genetic regions can be simulated to include known variant functions and disease associations. Ultimately, RAREsim2 offers additional flexibility and ease in simulating a multitude of realistic genetic scenarios. Availability and implementationThe RAREsim2 python package is available at https://github.com/Hendricks-Research-Team/RAREsim2 and the code for the example demonstration is available at https://github.com/JessMurphy/RAREsim2_demo. Contactjessica.murphy@cuanschutz.edu

bioinformatics↗

PD-1 blockade enhances HIV-1 vaccine-induced CD8+ T-cell responses in PWH early ART-treated

Current HIV-1 therapeutic vaccines do not generate T-cell immune responses in people with HIV-1 (PWH) able to eradicate the virus or to broadly control viral replication. The expression of inhibitory receptors (IRs) may limit vaccine efficacy by reducing specific effector functions in vaccine-induced T-cells. Recently, the clinical use of antibodies targeting IRs or immune checkpoint blockade (ICB) has demonstrated remarkable success in cancer remission. Therefore, there is a potential benefit of using ICB in PWH to improve the effectiveness of vaccine-induced T-cell responses. Here, we evaluated functional changes in vaccine-induced HIV-specific CD8 T-cell responses by in-vitro PD-1 and TIM-3 blockade during immune stimulation in PWH early ART-treated and vaccinated (Etvac). We compared Etvac to PWH early ART-treated (Et) and chronic ART-treated PWH (Chro). Our results demonstrate a significant increase in the functional responses of vaccine-induced HIV-1-specific CD8 T-cells upon PD-1 but not TIM-3 blockade in cellular proliferation, IFN{gamma} production, and activation. Although no effect was found in Et, similar findings were obtained in HIV-1-specific CD8 T-cells in Chro for combined PD-1/TIM-3 blockade. Moreover, we identify in Etvac a direct association between PD-1 expression in CD8+ T cells and in vitro response to anti-PD-1 treatment. Our findings support the potential adjuvant effect of PD-1 blockade to improve HIV-1 therapeutic vaccine efficacy in early ART-treated PWH for future human studies.

immunology↗

Rapid, Reliable, and Interpretable CNV Curation Visualizations for Diagnostic Settings with SeeNV

Copy number variants (CNVs), structural alterations in the genome involving duplication or deletion of DNA segments, are implicated in various health conditions. Despite their clinical significance, accurate identification and interpretation of CNVs remain challenging, especially in the context of whole exome sequencing (WES), which is commonly used in clinical diagnostic laboratories. While WES offers economic advantages over whole genome sequencing (WGS), it struggles with CNV detection due to technical noise introduced by laboratory and analytic processes. Manual curation of CNV calls generated by these tools is labor-intensive and error-prone. To address this, we introduce SeeNV, a command-line tool designed to aid manual curation of CNVs at scale. SeeNV is one solution to these issues developed in collaboration with and used by the Precision Diagnostics Laboratory at Childrens Hospital Colorado. SeeNV generates static infographics for each CNV, incorporating sample and cohort sequencing coverage statistics, CNV population frequency, and more, facilitating rapid and precise assessment. Using CNVs calls identified in publicly available WES and WGS samples, we show users can rapidly and reliably curate CNV calls, needing only 4.3 seconds to curate a call, achieving 0.93 precision and 0.72 recall. SeeNV is freely available for download on GitHub: https://github.com/MSBradshaw/SeeNV.

genomics↗

Plant traits are poor predictors of long-term ecosystem functioning

Earth is home to over 350,000 vascular plant species1 that differ in their traits in innumerable ways. Yet, a handful of functional traits can help explaining major differences among species in photosynthetic rate, growth rate, reproductive output and other aspects of plant performance2-6. A key challenge, coined "the Holy Grail" in ecology, is to upscale this understanding in order to predict how natural or anthropogenically driven changes in the identity and diversity of co-occurring plant species drive the functioning of ecosystems7, 8. Here, we analyze the extent to which 42 different ecosystem functions can be predicted by 41 plant traits in 78 experimentally manipulated grassland plots over 10 years. Despite the unprecedented number of traits analyzed, the average percentage of variation in ecosystem functioning that they jointly explained was only moderate (32.6%) within individual years, and even much lower (12.7%) across years. Most other studies linking ecosystem functioning to plant traits analyzed no more than six traits, and when including either only six random or the six most frequently studied traits in our analysis, the average percentage of explained variation in across-year ecosystem functioning dropped to 4.8%. Furthermore, different ecosystem functions were driven by different traits, with on average only 12.2% overlap in significant predictors. Thus, we did not find evidence for the existence of a small set of key traits able to explain variation in multiple ecosystem functions across years. Our results therefore suggest that there are strong limits in the extent to which we can predict the long-term functional consequences of the ongoing, rapid changes in the composition and diversity of plant communities that humanity is currently facing.

ecology↗

Exome sequencing of 457 autism families recruited online provides evidence for novel ASD genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. Additionally, we identified variants that are possibly associated with autism in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.2 provides statistical support for 34 ASD risk genes with at least one damaging variant identified in SPARK. Nine of these genes (BRSK2, DPP6, EGR3, FEZF2, ITSN1, KDM1B, NR4A2, PAX5 and RALGAPB) are newly emerging genes in autism, of which BRSK2 has the strongest statistical support as a risk gene for autism (TADA q-value = 0.0015). Future studies leveraging the thousands of individuals with ASD that have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate autism research that incorporates genetic etiology.

genetics↗