TOPOLOGICAL METHODS FOR THE QUANTIFICATION AND ANALYSIS OF COMPLEX PHENOTYPES

Quantitative Trait Locus (QTL) mapping of complex traits, such as leaf venation or root structures, require the phenotyping and genotyping of large populations. Sufficient genotyping is accomplished with cost effective high-throughput assays, however labor costs often makes sufficient phenotyping prohibitively limited. In order to develop efficient high-throughput phenotyping platforms for complex traits algorithms and methods for quantifying these traits are needed. It is often desirable to study the spatial organization of these phenotypes from the images generated by high-throughput platforms. With the goal of quantifying the traits, many approaches try to identify several core traits useful in describing the phenotypic morphology. This simplification may lose important information about the phenotype. Rather than reducing the structural information, we introduce a novel method, the Persistence Intensity Array, for studying complex traits using tools from the emergent field of Topological Data Analysis. This approach uses the complete geometry of the phenotype and represents it as a simpler summary of the key topological shape features contained in the data. We demonstrate this method\u27s efficacy by through a simulated QTL analysis

Identification of Novel High-Frequency DNA Methylation Changes in Breast Cancer

Recent data have revealed that epigenetic alterations, including DNA methylation and chromatin structure changes, are among the earliest molecular abnormalities to occur during tumorigenesis. The inherent thermodynamic stability of cytosine methylation and the apparent high specificity of the alterations for disease may accelerate the development of powerful molecular diagnostics for cancer. We report a genome-wide analysis of DNA methylation alterations in breast cancer. The approach efficiently identified a large collection of novel differentially DNA methylated loci (∼200), a subset of which was independently validated across a panel of over 230 clinical samples. The differential cytosine methylation events were independent of patient age, tumor stage, estrogen receptor status or family history of breast cancer. The power of the global approach for discovery is underscored by the identification of a single differentially methylated locus, associated with the GHSR gene, capable of distinguishing infiltrating ductal breast carcinoma from normal and benign breast tissues with a sensitivity and specificity of 90% and 96%, respectively. Notably, the frequency of these molecular abnormalities in breast tumors substantially exceeds the frequency of any other single genetic or epigenetic change reported to date. The discovery of over 50 novel DNA methylation-based biomarkers of breast cancer may provide new routes for development of DNA methylation-based diagnostics and prognostics, as well as reveal epigenetically regulated mechanism involved in breast tumorigenesis

The Cycad Genotoxin MAM Modulates Brain Cellular Pathways Involved in Neurodegenerative Disease and Cancer in a DNA Damage-Linked Manner

Methylazoxymethanol (MAM), the genotoxic metabolite of the cycad azoxyglucoside cycasin, induces genetic alterations in bacteria, yeast, plants, insects and mammalian cells, but adult nerve cells are thought to be unaffected. We show that the brains of adult C57BL6 wild-type mice treated with a single systemic dose of MAM acetate display DNA damage (O6-methyldeoxyguanosine lesions, O6-mG) that remains constant up to 7 days post-treatment. By contrast, MAM-treated mice lacking a functional gene encoding the DNA repair enzyme O6-mG DNA methyltransferase (MGMT) showed elevated O6-mG DNA damage starting at 48 hours post-treatment. The DNA damage was linked to changes in the expression of genes in cell-signaling pathways associated with cancer, human neurodegenerative disease, and neurodevelopmental disorders. These data are consistent with the established developmental neurotoxic and carcinogenic properties of MAM in rodents. They also support the hypothesis that early-life exposure to MAM-glucoside (cycasin) has an etiological association with a declining, prototypical neurodegenerative disease seen in Guam, Japan, and New Guinea populations that formerly used the neurotoxic cycad plant for food or medicine, or both. These findings suggest environmental genotoxins, specifically MAM, target common pathways involved in neurodegeneration and cancer, the outcome depending on whether the cell can divide (cancer) or not (neurodegeneration). Exposure to MAM-related environmental genotoxins may have relevance to the etiology of related tauopathies, notably, Alzheimer's disease

A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments

For situations where the number of tested hypotheses is increasingly large, the power to detect statistically significant multiple treatment effects decreases. As is the case with microarray technology, often researchers are interested in identifying differentially expressed genes for more than two types of cells or treatments. A two-step procedure is proposed for the purpose of increasing power to detect significant effects (i.e., to identify differentially expressed genes). Specifically, in the first step, the null hypothesis of equality across the mean expression levels for all treatments is tested for each gene. In the second step, only pairwise comparisons corresponding to the genes for which the treatment means are statistically different in the first step are tested. We propose an approach to estimate the overall FDR for both fixed rejection regions and fixed FDR significance levels. Also proposed is a procedure to find the FDR significance levels used in the first step and the second step such that the overall FDR can be controlled below a pre-specified FDR significance level. When compared via simulation the two-step approach has increased power over a one-step procedure, and controls the FDR at a desire significance level.

Correct Estimation of Preferential Chromosome Pairing in Autotetraploids

In recent work, a statistical model was proposed for the purpose of estimating parameters associated with quantitative trait locus (QTL) mapping and preferential pairing within a polyploidy framework. The statistical model contained several parameters that, when estimated from experimental data, supplied information about QTL, including a preferential pairing factor. Among the results reported were estimates of preferential pairing, many of which indicated high levels of preferential pairing (p = 0.60) that were inconsistent with biological expectations. By using the biological inconsistencies as our motivation, we present a reformulated statistical method for estimating preferential pairing, and use this method to reanalyze the same autotetraploid alfalfa data and to conduct a simulation study. Our results directly contradict the current findings of significant preferential pairing and affirm the traditional view of random chromosome segregation in alfalfa

Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data

DNA methylation is an epigenetic modification that plays an important role in many biological processes. to better understand this epigenetic mechanism, genome-wide investigations of DNA methylation can be conducted via tiling array technology. in such DNA methylation profiling studies, statistical methods are employed to estimate the DNA methylation status of each probe represented on the tiling array. Although many of the current methods account for the inherent dependency between neighboring probes, they do not effectively utilize the wealth of information available in genomic annotation databases. Since previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, intergenic region), such genomic annotation information may be useful in predicting DNA methylation status. in this work, a novel statistical model is proposed within the hidden Markov model framework to incorporate genomic annotation information in the prediction of DNA methylation status. the advantages of incorporating genomic annotation via the proposed method are investigated through a simulation study and real data applications