Guidelines for genetic data analysis

The IWC Scientific Committee recently adopted guidelines for quality control of DNA data. Once data have been collected, the next step is to analyse the data and make inferences that are useful for addressing practical problems in conservation and management of cetaceans. This is a complex exercise, as numerous analyses are possible and users have a wide range of choices of software programs for implementing the analyses. This paper reviews the underlying issues, illustrates application of different types of genetic data analysis to two complex management problems (involving common minke whales and humpback whales), and concludes with a number of recommendations for best practices in the analysis of population genetic data. An extensive Appendix provides a detailed review and critique of most types of analyses that are used with population genetic data for cetaceans.Publisher PDFPeer reviewe

GenePath: a System for Automated Construction of Genetic Networks from Mutant Data

Motivation: Genetic pathways are often used in the analysis of biological phenomena. In classical genetics, they are constructed manually from experimental data on mutants. The field lacks formalism to guide such analysis, and accounting for all the data becomes complicated when large amounts of data are considered. Results: We have developed GenePath, an intelligent assistant that mimics expert geneticists in the analysis of genetic data. GenePath employs expert-defined patterns to uncover gene relations from the data, and uses these relations as constraints that guide the search for a plausible genetic network. GenePath provides formalism to genetic data analysis, facilitates the consideration of all the available data in a consistent and systematic manner, and aids in the examination of the large number of possible consequences of a planned experiment. It also provides an explanation mechanism that traces back every finding to the pertinent data. GenePath was successfully tested on several genetic problems. Availability: GenePath can be accessed at http://genepath.org. Supplementary information: Supplementary material is available at http://genepath.org/bi-supp

Application of Bayesian Hierarchical Models in Genetic Data Analysis

Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets in medical research. This dissertation focuses on several important issues in genetic data analysis, graphical network modeling, feature selection, and covariance estimation. First, we develop a gene network modeling method for discrete gene expression data, produced by technologies such as serial analysis of gene expression and RNA sequencing experiment, which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We derive the gene network structures by selecting covariance matrices of the Gaussian distribution with a hyper-inverse Wishart prior. We incorporate prior network models based on Gene Ontology information, which avails existing biological information on the genes of interest. Next, we consider a variable selection problem, where the variables have natural grouping structures, with application to analysis of chromosomal copy number data. The chromosomal copy number data are produced by molecular inversion probes experiments which measure probe-specific copy number changes. We propose a novel Bayesian variable selection method, the hierarchical structured variable se- lection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We further provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Finally, we propose a Bayesian method of estimating high-dimensional covariance matrices that can be decomposed into a low rank and sparse component. This covariance structure has a wide range of applications including factor analytical model and random effects model. We model the covariance matrices with the decomposition structure by representing the covariance model in the form of a factor analytic model where the number of latent factors is unknown. We introduce binary indicators for estimating the rank of the low rank component combined with a Bayesian graphical lasso method for estimating the sparse component. We further extend our method to a graphical factor analytic model where the graphical model of the residuals is of interest. We achieve sparse estimation of the inverse covariance of the residuals in the graphical factor model by employing a hyper-inverse Wishart prior method for a decomposable graph and a Bayesian graphical lasso method for an unrestricted graph

Automated construction of genetic networks from mutant data

Geneticists use mutations to investigate biological phenomena. Mutations cause changes of organism’s phenotype and may reveal which genes participate in a certain biological process and how. To represent these functional interactions between genes, a gene regulatory network is an often used formalism. We have developed a system called GenePath (1) for automated construction of genetic networks from mutant data

Spatially and genetically distinct African trypanosome virulence variants defined by host interferon-g response

We describe 2 spatially distinct foci of human African trypansomiasis in eastern Uganda. The Tororo and Soroti foci of <i>Trypanosoma brucei rhodesiense</i> infection were genetically distinct as characterized by 6 microsatellite and 1 minisatellite polymorphic markers and were characterized by differences in disease progression and host-immune response. In particular, infections with the Tororo genotype exhibited an increased frequency of progression to and severity of the meningoencephalitic stage and higher plasma interferon (IFN)–γ concentration, compared with those with the Soroti genotype. We propose that the magnitude of the systemic IFN-γ response determines the time at which infected individuals develop central nervous system infection and that this is consistent with the recently described role of IFN-γ in facilitating blood-brain barrier transmigration of trypanosomes in an experimental model of infection. The identification of trypanosome isolates with differing disease progression phenotypes provides the first field-based genetic evidence for virulence variants in T. <i>brucei rhodesiense</i>

Evidence for variable selective pressures at MC1R

It is widely assumed that genes that influence variation in skin and hair pigmentation are under selection. To date,the melanocortin 1 receptor (MC1R) is the only gene identified that explains substantial phenotypic variance inhuman pigmentation. Here we investigate MC1R polymorphism in several populations, for evidence of selection.We conclude that MC1R is under strong functional constraint in Africa, where any diversion from eumelanin production (black pigmentation) appears to be evolutionarily deleterious. Although many of the MC1R amino acid variants observed in non-African populations do affect MC1R function and contribute to high levels of MC1R diversity in Europeans, we found no evidence, in either the magnitude or the patterns of diversity, for its enhancement by selection; rather, our analyses show that levels of MC1R polymorphism simply reflect neutral expectations underrelaxation of strong functional constraint outside Africa

estMOI: estimating multiplicity of infection using parasite deep sequencing data.

Individuals living in endemic areas generally harbour multiple parasite strains. Multiplicity of infection (MOI) can be an indicator of immune status and transmission intensity. It has a potentially confounding effect on a number of population genetic analyses, which often assume isolates are clonal. Polymerase chain reaction-based approaches to estimate MOI can lack sensitivity. For example, in the human malaria parasite Plasmodium falciparum, genotyping of the merozoite surface protein (MSP1/2) genes is a standard method for assessing MOI, despite the apparent problem of underestimation. The availability of deep coverage data from massively parallizable sequencing technologies means that MOI can be detected genome wide by considering the abundance of heterozygous genotypes. Here, we present a method to estimate MOI, which considers unique combinations of polymorphisms from sequence reads. The method is implemented within the estMOI software. When applied to clinical P.falciparum isolates from three continents, we find that multiple infections are common, especially in regions with high transmission