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22 results for “Background rates”
Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
<p>Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This can be thought of as a "linked selection signal" that reflects the impact of selection varying according to the placement of functional regions and recombination rates along the genome. Previous work has shown that negative selection against the steady influx of new deleterious mutations into conserved regions is the predominant mode of selection in humans. However, the theoretic model that underpins these results, classic Background Selection theory, is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of the linked selection, which models the effects of weak draft created according to how polygenic additive fitness variance is distributed along the genome. We use a recent model that jointly predicts the equilibrium fitness variance and substitution rates due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three human populations. While our model can accommodate weaker selection, we initially find evidence across three human populations of very strong selection against deleterious mutations consistent with previous work. However, the corollary predicted substitution rates for conserved regions are unreasonably low, and in disagreement with observed rates. We hypothesize this could be due to selected sites experiencing a further diminished population size due to selective interference. When we account for this in our method, we find evidence of weakly deleterious mutations in conserved regions which brings the predicted substitution rate into agreement with observations. However, these models lead to implausibly large mutation rate estimates. Overall, while our model of the genomic linked selection signal brings us a step towards uniting population and quantitative genetic selection models with the substitution process, our work suggests considerable uncertainty remains about the processes generating fitness variance in humans.</p>
Binary black hole merger rate constraints using GWTC-3 and full-O3 stochastic background constraints
<h1>README</h1> <p>This dataset contains posterior measurements of the redshift-dependent merger rate, mass distribution, and spin distribution of binary black holes following the O3b observing run of the LIGO-Virgo-KAGRA network, including both direct compact binary detections and constraints on the astrophysical gravitational-wave background.</p> <p>In particular, the goal of this work is to constrain a more complex model for the black hole merger rate, with the comoving rate density evolving as</p> <p>$$<br>R(z) \propto \frac{(1+z)^\alpha}{1 + \left(\frac{1+z}{1+z_p}\right)^{\alpha + \beta}}.<br>$$</p> <p>At redshifts \(z < z_p\), the merger rate grows approximately as \(R(z) \propto (1+z)^\alpha\), whereas at \(z>z_p\) it falls as \(R(z) \propto (1+z)^{-\beta}\).</p> <p>The analysis was performed as described in <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab9743">Callister <em>et al</em> (2020)</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021)</a>, now using binary black hole detections from the GWTC-3 catalog (<a href="https://link.aps.org/doi/10.1103/PhysRevX.13.041039">Abbott <em>et al</em> 2023a</a>, <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">2023b</a>).</p> <ul> <li>The parameter estimation samples used are those provided by the LIGO-Virgo-KAGRA collaboration at https://zenodo.org/records/8177023</li> <li>Selection effects are calculated and mitigated using the suite of pipeline injections available at https://zenodo.org/records/7890398</li> <li>Cross-correlation measurements of the stochastic gravitational-wave background are available at https://dcc.ligo.org/LIGO-G2001287, and correspond to results presented in <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021).</a></li> </ul> <p>As discussed in <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">Abbott <em>et al</em> (2023b)</a>, the results of this combined BBH + stochastic analysis are categorically unchanged relative to results previously obtained using GWTC-2 events (<a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> 2021</a>); sensitivities are not yet sufficient to resolve the redshift at which the black hole merger rate peaks and turns over.</p> <h1>Contents</h1> <ul> <li><code><strong>processed_emcee_samples_together_r00r01.npy</strong></code>: File containing posterior samples when jointly analyzing BBH detections and stochastic background upper limits.</li> <li><code><strong>processed_emcee_samples_noStochastic_r00r01.npy</strong></code>: File containing posterior samples analyzing only direct BBH detections.</li> <li><code><strong>run_emcee_plPeak.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_together_r00r01.npy</code></li> <li><code><strong>run_emcee_plPeak_noStochastic.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_noStochastic_r00r01.npy</code></li> </ul> <h1>Accessing posterior samples</h1> <p>Posterior samples are contained in the files <code>processed_emcee_samples_together_r00r01.npy</code> and <code>processed_emcee_samples_noStochastic_r00r01.npy</code>. This is loaded via python as, e.g.</p> <blockquote> <p>>>> import numpy as np</p> <p>>>> dataset = np.load('processed_emcee_samples_together_r00r01.npy')</p> </blockquote> <p>Contained in this file is a single <code>numpy</code> array of size <code>(# of posterior samples, # of hyperparameters)</code>:</p> <blockquote> <p>>>> dataset.shape</p> <p>(1152, 13)</p> </blockquote> <p> </p> <p>The 13 hyperparameters are defined as follows:</p> <table> <tbody> <tr> <td>Column</td> <td>Name</td> <td>Definition</td> </tr> <tr> <td><code>dataset[:, 0]</code></td> <td><code>xeff_mu</code></td> <td>Mean effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 1]</code></td> <td><code>xeff_sig</code></td> <td>Standard deviation of effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 2]</code></td> <td><code>R0</code></td> <td>Total BBH merger rate at redshift \(z=0\)</td> </tr> <tr> <td><code>dataset[:, 3]</code></td> <td><code>mMin</code></td> <td>Minimum black hole mass</td> </tr> <tr> <td><code>dataset[:, 4]</code></td> <td><code>mMax</code></td> <td>Maximum black hole mass</td> </tr> <tr> <td><code>dataset[:, 5]</code></td> <td><code>lmbda</code></td> <td>Power-law index on primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 6]</code></td> <td><code>mu_peak</code></td> <td>Mean of Gaussian peak in primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 7]</code></td> <td><code>sig_peak</code></td> <td>Standard deviation of Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 8]</code></td> <td><code>frac_peak</code></td> <td>Fraction of events occupying Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 9]</code></td> <td><code>bq</code></td> <td>Power-law index on mass ratio distribution \(p(q\|m_1)\)</td> </tr> <tr> <td><code>dataset[:, 10]</code></td> <td><code>alpha</code></td> <td>Slope of \(R(z) \propto (1+z)^\alpha \) at low redshifts</td> </tr> <tr> <td><code>dataset[:, 11]</code></td> <td><code>beta</code></td> <td>Slope of \(R(z) \propto (1+z)^{-\beta}\) at high redshifts</td> </tr> <tr> <td><code>dataset[:, 12]</code></td> <td><code>zpeak</code></td> <td>Redshift at which \(R(z)\) peaks and turns over</td> </tr> </tbody> </table> <p> </p> <p>The exact usage of the above parameters can be seen in the included scripts <code>run_emcee_plPeak.py</code> and <code>run_emcee_plPeak_noStochastic.py</code>, with which the inference was performed. </p>
Background rates literature review and visualization for Guillain Barré Syndrome (GBS)
<p>Background rates literature review spreadsheet and visualization in forest plots for Guillain Barré Syndrome (GBS)</p>
SPEAC Webinar: Use of background rates for observed / expected analysis
<p>The SPEAC team organized a webinar on April 24th, 2023, focused on the use of background rates for observed/expected analysis. The webinar featured three presentations, followed by a Q&A session. More details:</p> <ul> <li>Background rates of AESI for COVID-19, how have they been used? by Miriam Sturkenboom (SPEAC) - [00:00 - 10: 58]</li> <li>FDA COVID-19 vaccine safety surveillance: Use of background rates and observed/expected analyses by Brendan Day (US FDA-CBER) - [10:58 - 23:58]</li> <li>COVID-19 vaccines: Observed-to-expected analyses by Maria Gordillo Maranon and Catherine Cohet (EMA) - [23:58 - 23:58]</li> <li>Q&A - [39:57 - 58:59]</li> </ul> <p>The recording can be accessed below. </p>
Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
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Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation
Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae).
Generalized convulsion: Background rates literature review and visualization
<p>AESI Background rates literature review and visualization for Generalized convulsion</p>
Aseptic Meningitis: Background rates literature review and visualization
<p>Literature review of background rates for aseptic meningitis and visualization in forest plots.</p>
Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation
Open the record for dataset details and reuse information.
AESI Background Rates Literature Review & Visualization for Guillain-Barré Syndrome (GBS)
<p>AESI Background Rates Literature Review & Visualization for GBS</p>
AESI Background Rates Literature Review & Visualitzation for ASM
<p>AESI Background Rates Literature Review & Visualitzation for ASM</p>
AESI Background Rates Literature Review & Visualitzation for Facial Palsy
<p>AESI Background Rates Literature Review & Visualitzation for Facial Palsy</p>
AESI Background Rates Literature Review & Visualitzation for Anaphylaxis
<p>AESI Background Rates Literature Review & Visualitzation for Anaphylaxis</p>
AESI Background Rates Literature Review & Visualitzation for ADEM
<p>AESI Background Rates Literature Review & Visualitzation for ADEM</p>
AESI Background Rates Literature Review & Visualitzation for Myelitis
<p>SPEAC</p>
Immune Thrombocytopenia: Background rates literature review and visualization
<p>Immune Thrombocytopenia: Background rates literature review and forest plots for visualization </p>
Comparison of the Efficacy and Safety of Fixed-rate Basal Infusion Mode and Time-programmed Decremental Background Infusion Mode of Intravenous PCA Following Mixed Surgery
ClinicalTrials.gov study NCT07375121. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Intertidal gobies acclimate rate of luminance change for background matching with shifts in seasonal temperature
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Data from: Detecting recent selective sweeps while controlling for mutation rate and background selection
A composite likelihood ratio test implemented in the program SweepFinder is a commonly used method for scanning a genome for recent selective sweeps. SweepFinder uses information on the spatial pattern (along the chromosome) of the site frequency spectrum (SFS) around the selected locus. To avoid confounding effects of background selection and variation in the mutation process along the genome, the method is typically applied only to sites that are variable within species. However, the power to detect and localize selective sweeps can be greatly improved if invariable sites are also included in the analysis. In the spirit of a Hudson-Kreitman-Aguadé test, we suggest adding fixed differences relative to an outgroup to account for variation in mutation rate, thereby facilitating more robust and powerful analyses. We also develop a method for including background selection, modeled as a local reduction in the effective population size. Using simulations we show that these advances lead to a gain in power while maintaining robustness to mutation rate variation. Furthermore, the new method also provides more precise localization of the causative mutation than methods using the spatial pattern of segregating sites alone.
Data from: Detecting recent selective sweeps while controlling for mutation rate and background selection
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