Locations and patterns of meiotic recombination in two-generation pedigrees

<p>Abstract</p> <p>Background</p> <p>Meiotic crossovers are the major mechanism by which haplotypes are shuffled to generate genetic diversity. Previously available methods for the genome-wide, high-resolution identification of meiotic crossover sites are limited by the laborious nature of the assay (as in sperm typing).</p> <p>Methods</p> <p>Several methods have been introduced to identify crossovers using high density single nucleotide polymorphism (SNP) array technologies, although programs are not widely available to implement such analyses.</p> <p>Results</p> <p>Here we present a two-generation "reverse pedigree analysis" method (analyzing the genotypes of two children relative to each parent) and a web-accessible tool to determine and visualize inheritance differences among siblings and crossover locations on each parental gamete. This approach is complementary to existing methods and uses informative markers which provide high resolution for locating meiotic crossover sites. We introduce a segmentation algorithm to identify crossover sites, and used a synthetic data set to determine that the segmentation algorithm specificity was 92% and sensitivity was 89%. The use of reverse pedigrees allows the inference of crossover locations on the X chromosome in a maternal gamete through analysis of two sons and their father. We further analyzed genotypes from eight multiplex autism families, observing a 1.462 maternal to paternal recombination ratio and no significant differences between affected and unaffected children. Meiotic recombination results from pediSNP can also be used to identify haplotypes that are shared by probands within a pedigree, as we demonstrated with a multiplex autism family.</p> <p>Conclusion</p> <p>Using "reverse pedigrees" and defining unique sets of genotype markers within pedigree data, we introduce a method that identifies inherited allelic differences and meiotic crossovers. We implemented the method in the pediSNP software program, and we applied it to several data sets. This approach uses data from two generations to identify crossover sites, facilitating studies of recombination in disease. pediSNP is available online at <url>http://pevsnerlab.kennedykrieger.org/pediSNP</url>.</p

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis.

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used

10 most phenotypically active misclassified compounds.

<p>10 most phenotypically active misclassified compounds.</p

Classification results for all eight test compounds.

<p>Classification results for all eight test compounds.</p

Maximum Sequential Weighted Overlap.

<p><b>(A)</b> HDAC inhibitors follow a consistent, stereotypical phenotypic track, shown here in the “Cell Damage” and “Structural Breakdown” (tubulin intensity outside cell boundary, Hoescht stain outside nucleus, increased tetraploidy signal) dimensions. <b>(B)</b> Different compounds move through “Structural Breakdown” with differing potencies and sensitivities. <b>(C)</b> The optimal overlap between abexinostat and panobinostat uses the highest seven concentrations of abexinostat and a non-consecutive subset of doses of panobinostat.</p

p-H2A.X Stain Normalization.

<p><b>(A)</b> Violin plot depicting the distributions of cytoplasmic p-H2A.X stain intensity across all cells in 32 wells containing 32 different compounds at the highest concentrations used in analysis. The distributions show high levels of inter-compound variation, suggesting the dimension could be used to distinguish compounds. <b>(B)</b> Violin plot depicting the distributions of the same measurements as described in (A) normalized to the background level of 488nm intensity in each well. While some small differences remain, almost all inter-well variation has been eliminated.</p

Phenotypic Analysis of Test Compounds.

<p><b>(A)</b> Plot of phenotypic trajectory means for all eight test compounds in the “Cell Damage” and “Tubulin Intensity” dimensions. The extent of phenotypic trajectory means in the 154 test compounds has been included in grey for reference. Proteasome inhibitors are indicated by solid lines; unclassified compounds are indicated by dotted lines. <b>(B)</b> Plot of phenotypic trajectory means for all eight test compounds in the “Cell Damage” and “Blebbishness” dimensions. <b>(C)</b> The complete hierarchical clustering resulting from including the eight test compounds in the set. The eight test compounds have been indicated with black triangles. For more detail, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149439#pone.0149439.s002" target="_blank">S2 Fig</a>.</p

Clustering in Alternate Models.

<p><b>(A)</b> Hierarchical clustering resulting from our complete model. <b>(B)</b> Clustering resulting from using the 16 best PCA dimensions, rather than the 16 best LDA dimensions. <b>(C)</b> Clustering resulting from 11 best dimensions extracted through mechanism class-based LDA. <b>(D)</b> Clustering resulting from using only 8 LDA dimensions. <b>(E)</b> Clustering resulting from using only 8 PCA dimensions. <b>(F)</b> Clustering resulting from using only the highest concentration of each compound (no dose response). <b>(G)</b> Clustering resulting from using simple overlap in the MSWO calculation rather than weighted overlap.</p

Example Phenotypic Tracks.

<p><b>(A)</b> Plot of the means of the phenotypic trajectories of all 154 compounds in two highly informative dimensions, “Cell Damage” and “DNA Damage”. Topoisomerase inhibitors (red) and antimetabolites (magenta) have been highlighted. <b>(B)</b> Plot of phenotypic trajectory means in the “Cell Damage” and “Tubulin Intensity” dimensions. Microtubule stabilizers (yellow), microtubule inhibitors (purple), and HSP90 inhibitors (green) have been highlighted. <b>(C)</b> Plot of phenotypic trajectory means in the “Cell Damage” and “Blebbishness” dimensions. Proteasome inhibitors (blue) and CDK inhibitors (teal) have been highlighted. <b>(D)</b> Plot of phenotypic trajectory means in the “Cell Damage” and “Structural Damage” dimensions. HDAC inhibitors (dark orange) and CDK inhibitors (teal) have been highlighted. <b>(E)</b> Example microscopy image of untreated cells. The red channel depicts α-tubulin intensity, the green channel Hoescht stain intensity, and the blue channel p-H2A.X stain intensity. <b>(F–T)</b> Example microscopy images of cells treated by 15 different compounds from the mechanism classes highlighted in Fig 4A–4D. The points in the phenotypic trajectories to which the images correspond have been indicated where appropriate.</p

Hierarchical Compound Clustering.

<p><b>(A)</b> Dendrogram of similarity-based hierarchical clustering. For more detail, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149439#pone.0149439.s001" target="_blank">S1 Fig</a>. <b>(B)</b> Heatmap depicting phenotypic activity of all compounds in the dendrogram in Fig 5A. <b>(C)</b> Similarity matrix for all 154 training compounds (white indicates the minimum similarity of 0, black indicates the maximum similarity of 1).</p