Although the completion of the Human Genome Project helps develop efficient treatment/prevention programs, it will raise new and non-trivial public health issues. Many of these issues fall under the professional purview of health educators. Yet, no studies have evaluated if health educators (HEs) are ready to adopt genomic competencies into health promotion. This dissertation addresses this issue by examining three research questions in three separate studies: 1) Why must HEs develop genomic competencies? 2) What are HEs’ knowledge of, and attitudes toward genomic competencies? And 3) what is HEs’ likelihood of adopting genomic competencies into health promotion? The first theoretical study proposed five arguments supporting the need for HEs to develop their genomic competencies and integrate public health genomics into health promotion. These arguments touched on various dimensions of HEs’ professional goals and ranged from professional responsibilities and competencies, to the availability of funding for genomic-related research or interventions and opportunities for future employment. For the second study, a web-based survey was developed and distributed to all members of four major health education organizations. A total of 1,925 HEs’ completed the survey and 1,607 responses were utilized in the final analysis. This study indicated that participants had deficient knowledge and unfavorable attitudes toward the CDCproposed genomic competencies. In the third study, a theoretical model was developed to predict HEs’ likelihood to incorporate genomic competencies into their practice. Using techniques from Structural Equation Modeling (SEM), the model was tested with the same data of the second study. Findings supported the proposed theoretical model. While genomic knowledge, attitudes, and self-efficacy were significantly associated with HEs’ likelihood to incorporate genomic competencies into their practice, attitudes was the strongest predictor of likelihood. In summary, these studies indicated that participating HEs had deficient genomic knowledge, unfavorable attitudes toward a set of CDC-proposed genomic competencies, and low likelihood to adopt genomic competencies into health promotion. Relevant training should be developed and advocated. As the SEM analysis results indicated the survey findings supported the proposed theoretical model, which can be utilized to steer future training for HEs.

The major histocompatibility complex (MHC) is a genomic region containing genes of immunomodulatory importance. MHC class I genes encode cell-surface glycoproteins that present peptides to circulating T cells, playing a key role in recognition of self and non-self. Studies of MHC loci in vertebrates have examined levels of polymorphism and molecular evolutionary processes generating diversity. The bovine MHC (BoLA) has been associated with disease susceptibility, resistance and progression. To delineate mechanisms by which MHC class I genes evolved to function optimally in a species like cattle, it is necessary to study genomic organization of BoLA to define gene content, and investigate characteristics of expressed class I molecules. This study describes development of a physical map of BoLA class I region derived from screening two BAC libraries, isolating positive clones and confirming gene content, order and chromosomal location through PCR, novel BAC end sequencing techniques, and selected BAC shotgun cloning and/or sequencing and FISH analysis. To date, this is the most complete ordered BAC array encompassing the BoLA class I region from the class III boundary to the extended class I region. Characterization of a frameshift allele exhibiting trans-species polymorphism in Bos and Bison by flow cytometry, real-time RT-PCR, 1D and 2D gel analysis is also described. This frameshift allele encodes an early termination signal within the antigen recognition site (ARS) of exon 3 of the BoLA BSA-Adel class I gene predicting a truncated class I protein that is soluble. An ability to assess MHC diversity in populations and provision of animals with defined MHC haplotypes and genetic content for experimental research is necessary in developing a basis upon which to build functional studies to elucidate associations between haplotype and disease in bovids. The BoLA class I region is immunologically important for disease association studies in an economically important species. This study provides knowledge of gene content and organization within the class I MHC region in cattle, providing a template for more detailed analysis and elucidation of complex disease associations through functional genomics and comparative analysis, as well as evolution of the MHC in bovids to optimize a population’s immune response.

Chemokines triggered by Toll-like receptors (TLRs) are small chemoattractant proteins, which mainly regulate leukocyte trafficking in inflammatory reactions via interaction with G protein-coupled receptors. Forty-two chemokines and 19 cognate receptors have been found in the human genome. Prior to this study, only 11 chicken chemokines and 7 receptors had been reported. The objectives of this study were to identify systematically chicken chemokines and their cognate receptor genes in the chicken genome and to annotate these genes and ligand-receptor binding by a comparative genomics approach. Twenty-three chemokine and 14 chemokine receptor genes were identified in the chicken genome. The number of coding exons in these genes and the syntenies are highly conserved between human, mouse, and chicken although the amino acid sequence homologies are generally low between mammalian and chicken chemokines. Chicken genes were named with the systematic nomenclature used in humans and mice based on phylogeny, synteny, and sequence homology. The independent nomenclature of chicken chemokines and chemokine receptors suggests that the chicken may have ligand-receptor pairings similar to mammals. The TLR family represents evolutionarily conserved components of the patternrecognizing receptors (PRRs) of the innate immune system that recognize specific pathogen-associated molecular patterns (PAMPs) through their ectodomains (ECDs). TLR's ECDs contain 19 to 25 tandem copies of leucine-rich repeat (LRR) motifs. TLRs play important roles in the activation of pro-inflammatory cytokines, chemokines and modulation of antigen-specific adaptive immune responses. To date, nine TLRs have been reported in chicken, along with a non-functional TLR8. Two non-mammalian TLRs, TLR21 and TLR22, have been identified in pufferfish and zebrafish. The objectives of this study were to determine if there is the existence of chicken genes homologous to fish-specific TLRs, and if possible ligands of these receptors exist. After searching the chicken genome sequence and EST database, a novel chicken TLR homologous to fish TLR21 was identified. Phylogenetic analysis indicated that the identified chicken TLR is the orthologue of TLR21 in fish. Bioinformatic analysis of potential PAMP binding sites within LRR insertions showed that CpG DNA is the putative ligand of this receptor.

DNA methylation serves as a major epigenetic modification crucial to the normal organismal development and the onset and progression of complex diseases such as cancer. Computational predictions for DNA methylation profiling serve multiple purposes. First, accurate predictions can contribute valuable information for speeding up genome-wide DNA methylation profiling so that experimental resources can be focused on a few selected while computational procedures are applied to the bulk of the genome. Second, computational predictions can extract functional features and construct useful models of DNA methylation based on existing data, and can therefore be used as an initial step toward quantitative identification of critical factors or pathways controlling DNA methylation patterns. Third, computational prediction of DNA methylation can provide benchmark data to calibrate DNA methylation profiling equipment and to consolidate profiling results from different equipments or techniques. This thesis is written based on our study on the computational analysis of the DNA methylation patterns of the human genome. Particularly, we have established computational models (1) to predict the methylation patterns of the CpG islands in normal conditions, and (2) to detect the CpG islands that are unmethylated in normal conditions but aberrantly methylated in cancer conditions. When evaluated using the CD4 lymphocyte data of Human Epigenome Project (HEP) data set based on bisulfite sequencing, our computational models for predicting the methylation status of CpG islands in the normal conditions can achieve a high accuracy of 93-94%, specificity of 94%, and sensitivity of 92-93%. And, when evaluated using the aberrant methylation data from the MethCancerDB database for aberrantly methylated genes in cancer, our models for detecting the CpG islands that are unmethylated in normal conditions but aberrantly methylated in colon or prostate cancer can achieve an accuracy of 92-93%, specificity of 98-99%, and sensitivity of 92-93%.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 179-191).

Title from title page of PDF (University of Missouri--St. Louis, viewed March 3, 2010). Includes bibliographical references (p. 26).

Genomic data facilitate opportunities to track complex population histories of divergence and gene flow. We used 47,506 single-nucleotide polymorphisms (SNPs) to investigate cattle population history. Cattle are descendants of two independently domesticated lineages, taurine and indicine, that diverged 200,000 or more years ago. We found that New World cattle breeds, as well as many related breeds of cattle in southern Europe, exhibit ancestry from both the taurine and indicine lineages. Although European cattle are largely descended from the taurine lineage, gene flow from African cattle (partially of indicine origin) contributed substantial genomic components to both southern European cattle breeds and their New World descendants. We extended these analyses to compare timing of admixture in several breeds of taurine-indicine hybrid origin. We developed a metric, scaled block size (SBS), that uses the unrecombined block size of introgressed regions of chromosomes to differentiate between recent and ancient admixture. By comparing test individuals to standards with known recent hybrid ancestry, we were able to differentiate individuals of recent hybrid origin from other admixed individuals using the SBS metric. We genotyped SNP loci using the bovine 50K SNP panel. The selection of sites to include in SNP analyses can influence inferences from the data, especially when particular populations are used to select the array of polymorphic sites. To test the impact of this bias on the inference of population genetic parameters, we used empirical and simulated data representing the three major continental groups of cattle: European, African, and Indian. We compared the inference of population histories for simulated data sets across different ascertainment conditions using F[subscript ST] and principal components analysis (PCA). Ascertainment bias that results in an over-representation of within-group polymorphism decreases estimates of F[subscript ST] between groups. Geographically biased selection of polymorphic SNPs changes the weighting of principal component axes and can bias inferences about proportions of admixture and population histories using PCA. By combining empirical and simulated data, we were able to both test methods for inferring population histories from genomic SNP data and apply these methods to practical problems. / text

Species evolution can often be adequately described with a phylogenetic tree. Interestingly, this is the case also for the evolution of homologous genes; a gene in an ancestral species may – through gene duplication, gene loss, lateral gene transfer (LGT), and speciation events – give rise to a gene family distributed across contemporaneous species. However, molecular sequence evolution and genetic recombination make the history – the gene tree – non-trivial to reconstruct from present-day sequences. This history is of biological interest, e.g., for inferring potential functional equivalences of extant gene pairs. In this thesis, we present biologically sound probabilistic models for gene family evolution guided by species evolution – effectively yielding a gene-species tree reconciliation. Using Bayesian Markov-chain Monte Carlo (MCMC) inference techniques, we show that by taking advantage of the information provided by the species tree, our methods achieve more reliable gene tree estimates than traditional species tree-uninformed approaches. Specifically, we describe a comprehensive model that accounts for gene duplication, gene loss, a relaxed molecular clock, and sequence evolution, and we show that the method performs admirably on synthetic and biological data. Further-more, we present two expansions of the inference procedure, enabling it to pro-vide (i) refined gene tree estimates with timed duplications, and (ii) probabilistic orthology estimates – i.e., that the origin of a pair of extant genes is a speciation. Finally, we present a substantial development of the model to account also for LGT. A sophisticated algorithmic framework of dynamic programming and numerical methods for differential equations is used to resolve the computational hurdles that LGT brings about. We apply the method on two bacterial datasets where LGT is believed to be prominent, in order to estimate genome-wide LGT and duplication rates. We further show that traditional methods – in which gene trees are reconstructed and reconciled with the species tree in separate stages – are prone to yield inferior gene tree estimates that will overestimate the number of LGT events. / Arters evolution kan i många fall beskrivas med ett träd, vilket redan Darwins anteckningsböcker från HMS Beagle vittnar om. Detta gäller också homologa gener; en gen i en ancestral art kan – genom genduplikationer, genförluster, lateral gentransfer (LGT) och artbildningar – ge upphov till en genfamilj spridd över samtida arter. Att från sekvenser från nu levande arter rekonstruera genfamiljens framväxt – genträdet – är icke-trivialt på grund av genetisk rekombination och sekvensevolution. Genträdet är emellertid av biologiskt intresse, i synnerhet för att det möjliggör antaganden om funktionellt släktskap mellan nutida genpar. Denna avhandling behandlar biologiskt välgrundade sannolikhetsmodeller för genfamiljsevolution. Dessa modeller tar hjälp av artevolutionens starka inverkan på genfamiljens historia, och ger väsentligen upphov till en förlikning av genträd och artträd. Genom Bayesiansk inferens baserad på Markov-chain Monte Carlo (MCMC) visar vi att våra metoder presterar bättre genträdsskattningar än traditionella ansatser som inte tar artträdet i beaktning. Mer specifikt beskriver vi en modell som omfattar genduplikationer, genförluster, en relaxerad molekylär klocka, samt sekvensevolution, och visar att metoden ger högkvalitativa skattningar på både syntetiska och biologiska data. Vidare presenterar vi två utvidgningar av detta ramverk som möjliggör (i) genträdsskattningar med tidpunkter för duplikationer, samt (ii) probabilistiska ortologiskattningar – d.v.s. att två nutida gener härstammar från en artbildning. Slutligen presenterar vi en modell som inkluderar LGT utöver ovan nämnda mekanismer. De beräkningsmässiga svårigheter som LGT ger upphov till löses med ett intrikat ramverk av dynamisk programmering och numeriska metoder för differentialekvationer. Vi tillämpar metoden för att skatta LGT- och duplikationsraten hos två bakteriella dataset där LGT förmodas ha spelat en central roll. Vi visar också att traditionella metoder – där genträd skattas och förlikas med artträdet i separata steg – tenderar att ge sämre genträdsskattningar, och därmed överskatta antalet LGT-händelser. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 5: Manuscript.</p>

Prostate cancer is the second leading cause of cancer deaths among men. Targeted analyses of DNA from prostate cancers have identified recurrent somatic alterations that promote tumor growth and survival. Only recently, however, has the comprehensive analysis of cancer genomes become possible due to rapid advances in DNA sequencing technology.

The development of cancer from a population of precancerous cells is a rapid evolutionary process. During progression, cells evolve several new traits for survive and proliferation via a few key `driver' mutations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional `passenger' mutations. Passengers are widely believed to have no role in cancer, yet many passengers fall within functional genomic elements that may have potentially deleterious effects on the cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter neoplastic progression. Evolutionary simulations suggest that moderately-deleterious passengers accumulate during progression and largely evade natural selection. Accumulation is possible because of cancer's unique evolutionary constraints: an initially small population size, an elevated mutation rate, and a need to acquire several driver mutations within a short evolutionary timeframe. Cancer dynamics can be theoretically understood as a tug-of-war between rare, strongly-beneficial drives and frequent mildly-deleterious passengers. In this formalism, passengers present a barrier to cancer progression describable by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers collapse. In essence, cancer progression can be subverted by its own unique evolutionary constraints. The collective burden of passengers explain several oncological phenomena that are difficult to explain otherwise. Genomics data confirms that many passengers are likely damaging and have largely evaded negative selection, while age-incidence curves and the distribution of mutation totals suggests that drivers and passengers exhibit competing effects. These data also provide estimates of the strength of drivers and passengers. Finally, we use our model to explore cancer treatments. We identify two broad regimes of adaptive evolutionary dynamics and use these regimes to understand outcomes from various treatment strategies. Our theory explains previously paradoxical treatment outcomes and suggest that passengers could serve as a biomarker of response to mutagenic therapies. Deleterious passengers are targetable by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Our results suggest a unique framework for understanding cancer progression as a balance between driver and passenger mutations.