A Powerful New Quantitative Genetics Platform, Combining Caenorhabditis elegans High-Throughput Fitness Assays with a Large Collection of Recombinant Strains.

The genetic variants underlying complex traits are often elusive even in powerful model organisms such as Caenorhabditis elegans with controlled genetic backgrounds and environmental conditions. Two major contributing factors are: (1) the lack of statistical power from measuring the phenotypes of small numbers of individuals, and (2) the use of phenotyping platforms that do not scale to hundreds of individuals and are prone to noisy measurements. Here, we generated a new resource of 359 recombinant inbred strains that augments the existing C. elegans N2xCB4856 recombinant inbred advanced intercross line population. This new strain collection removes variation in the neuropeptide receptor gene npr-1, known to have large physiological and behavioral effects on C. elegans and mitigates the hybrid strain incompatibility caused by zeel-1 and peel-1, allowing for identification of quantitative trait loci that otherwise would have been masked by those effects. Additionally, we optimized highly scalable and accurate high-throughput assays of fecundity and body size using the COPAS BIOSORT large particle nematode sorter. Using these assays, we identified quantitative trait loci involved in fecundity and growth under normal growth conditions and after exposure to the herbicide paraquat, including independent genetic loci that regulate different stages of larval growth. Our results offer a powerful platform for the discovery of the genetic variants that control differences in responses to drugs, other aqueous compounds, bacterial foods, and pathogenic stresses

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

COPASutils: an R package for reading, processing, and visualizing data from COPAS large-particle flow cytometers.

The R package COPASutils provides a logical workflow for the reading, processing, and visualization of data obtained from the Union Biometrica Complex Object Parametric Analyzer and Sorter (COPAS) or the BioSorter large-particle flow cytometers. Data obtained from these powerful experimental platforms can be unwieldy, leading to difficulties in the ability to process and visualize the data using existing tools. Researchers studying small organisms, such as Caenorhabditis elegans, Anopheles gambiae, and Danio rerio, and using these devices will benefit from this streamlined and extensible R package. COPASutils offers a powerful suite of functions for the rapid processing and analysis of large high-throughput screening data sets

Example dose response plot.

<p>A dose response curve is plotted for four different strains in five different conditions using the plotDR function. Each point represents a single observation. Lines represent overall change in population size between doses. Dose response curves can be plotted for any trait present in the summarized data frame.</p

Example trait plots.

<p>Three possible plots made using the plotTrait function. (A) Well-wise scatter plots of the time-of-flight (TOF, a measure of length) values plotted against the extinction (EXT, a measure of optical density) values from raw data. (B) Histograms of the TOF values from raw data. (C) Heatmap of the population size from the summarized data (variable n) of each well. All data from this figure are included with the COPASutils package and deposited at <a href="https://github.com/AndersenLab/COPASutils-data" target="_blank">https://github.com/AndersenLab/COPASutils-data</a> and <a href="http://andersenlab.org/Research/Software/" target="_blank">http://andersenlab.org/Research/Software/</a>.</p

Example correlation matrix plot.

<p>A correlation matrix is plotted for all traits within a single plate using the plotCorMatrix function. Correlation matrix plots can be generated within a single plate (as shown) or between two plates. Positively correlated traits are shown in red, uncorrelated traits are shown in green, and negatively correlated traits are shown in blue. In this plot, all traits related to the mean and median values of optical properties of the objects (EXT, red, yellow, green) appear highly correlated, indicating that it may not be useful to include all such traits in further analysis.</p

COPASutils workflow map.

<p>Suggested workflow for COPAS data using the COPASutils package. Reading steps are shown in blue, processing steps are shown in yellow, and plotting steps are shown in green.</p