Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Despite the value of Recombinant Inbred Lines (RILs) for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to RILs for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here we describe such an extreme quantitative trait locus, or X-QTL, mapping strategy that builds on an existing multiparental population, the DSPR (Drosophila Synthetic Population Resource), and involves phenotyping and genotyping a population derived by mixing hundreds of DSPR RILs. Simulations demonstrate that challenging, yet experimentally tractable X-QTL designs ( > =4 replicates, > =5000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional RIL-based QTL mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated X-QTL experiment that identifies 7 QTL for caffeine resistance. Two mapped X-QTL factors replicate loci previously identified in RILs, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping X-QTL design has considerable advantages.

Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Despite the value of Recombinant Inbred Lines (RILs) for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to RILs for many traits leverages selecting phenotypically-extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here we describe such an extreme quantitative trait locus, or X-QTL mapping strategy that builds on an existing multiparental population, the DSPR (Drosophila Synthetic Population Resource), and involves phenotyping and genotyping a population derived by mixing hundreds of DSPR RILs. Simulations demonstrate that challenging, yet experimentally tractable X-QTL designs (>=4 replicates, >=5000 individuals/replicate, and a selection intensity of 5-10%) yield at least the same power as traditional RIL-based QTL mapping, and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated X-QTL experiment that identifies 7 QTL for caffeine resistance. Two mapped X-QTL factors replicate loci previously identified in RILs, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists a bulked phenotyping/genotyping X-QTL design has considerable advantages.

Harnessing Matrix Completion to Unify and Extend Viral Serology Studies

SummaryCharacterizing the antibody response against large panels of viral variants provides unique insight into key processes that shape viral evolution and host antibody repertoires, and has become critical to the development of new vaccine strategies. Given the enormous diversity of circulating virus strains and antibody responses, exhaustive testing of all antibody-virus interactions is unfeasible. However, prior studies have demonstrated that, despite the complexity of these interactions, their functional phenotypes can be characterized in a vastly simpler and lower-dimensional space, suggesting that matrix completion of relatively few measurements could accurately predict unmeasured antibody-virus interactions. Here, we combine available data from several of the largest-scale studies for both influenza and HIV-1 and demonstrate how matrix completion can substantially expedite experiments. We explore how prediction accuracy evolves as the number of available measurements changes and approximate the number of additional measurements necessary in several highly incomplete datasets (suggesting ∼250,000 measurements could be saved). In addition, we show how the method can be used to combine disparate datasets, even when the number of available measurements is below the theoretical limit for successful prediction. Our results suggest new approaches to improve ongoing experimental design, and could be readily generalized to other viruses or more broadly to other low-dimensional biological datasets.

A hybrid MIDAS approach for forecasting hotel demand using large panels of search data

The large amounts of hospitality and tourism-related search data sampled at different frequencies have long presented a challenge for hospitality and tourism demand forecasting. This study aims to evaluate the applicability of large panels of search series sampled at daily frequencies to improve the forecast precision of monthly hotel demand. In particular, a hybrid mixed-data sampling regression approach integrating a dynamic factor model and forecast combinations is the first reported method to incorporate mixed-frequency data while remaining parsimonious and flexible. A case study is undertaken by investigating Sanya, the southernmost city in Hainan province, as a tourist destination using 9 years of the experimental data set. Dynamic factor analysis is used to extract the information from large panels of web search series, and forecast combinations are attempted to obtain the final prediction results of the individual forecasts to enhance the prediction accuracy further. The empirical analysis results suggest that the developed hybrid forecast approach leads to improvements in monthly forecasts of hotel occupancy over its competitors.

Low-Rank Approximations of Nonseparable Panel Models

We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-differences approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.

Integrated cross-study datasets of genetic dependencies in cancer

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Endogenous complement human serum bactericidal assay (enc-hSBA) for vaccine effectiveness assessments against meningococcal serogroup B

Immunogenicity of vaccines against meningococcal serogroup B (MenB) has been assessed pre-licensure with a human serum bactericidal activity assay (hSBA), tested against small numbers of strains. We report the qualification/validation of an alternative qualitative hSBA which uses endogenous complement (enc-hSBA) present in the vaccinee’s serum. Serum samples were collected from adults pre-vaccination and post-vaccination with the 4-component MenB vaccine (4CMenB). A representative panel of invasive isolates and 4 antigen-specific indicator strains were used in qualification experiments. Each strain was tested in ≥3 experiments with pre/post-vaccination sera to evaluate intermediate precision. A 110-strain panel and the 4 indicator strains met qualification criteria, demonstrating assay precision. Assay robustness, specificity and sensitivity were demonstrated using the 4 indicator strains. Enc-hSBA is highly standardized, allows testing across large panels of epidemiologically-relevant MenB strains, and accounts for complement activity differences between vaccinees. Therefore, enc-hSBA enables a more accurate estimation of effectiveness for vaccines against MenB.

Time and Emotion During Lockdown and the Covid-19 Epidemic: Determinants of Our Experience of Time?

To fight against the spread of the coronavirus disease, more than 3 billion people in the world have been confined indoors. Although lockdown is an efficient solution, it has had various psychological consequences that have not yet been fully measured. During the lockdown period in France (April 2020), we conducted two surveys on two large panels of participants to examine how the lockdown disrupted their relationship with time and what this change in their experiences of time means. Numerous questions were asked about the experience of time but also the nature of life during the lockdown: the emotions felt, boredom, the activities performed, sleep quality, and the daily rhythm. The participants also completed a series of self-reported scales used to assess depression, anxiety, and impulsivity. The results showed that time seemed to pass more slowly during the lockdown compared to before. This feeling of a slowing down of time has little to do with living conditions during the lockdown and individual psychological characteristics. The main predictor of this time experience was boredom and partly mediated by the lack of activity. The feeling of being less happy and the presence of sleep disturbance also explained this specific experience of time albeit to a lesser extent.