Genomic prediction (GP) is a novel tool used for prediction of EBVs by using molecular markers. Within the last decade, GP has been widely introduced into routine evaluations of cattle, pig and sheep populations, however, its application in poultry has been somewhat delayed, and studies published to date have been limited in terms of population size and marker densities. This study shows a thorough evaluation of the benefits that GP could bring into routine evaluations of broiler chickens, with particular attention given to the accuracy and bias of Genomic BLUP (GBLUP) predictions. The data used for these evaluations exceeds the numbers of both individuals and marker genotypes of previously published reports, with the studied population consisting of up to 23,500 individuals, genotyped for up to 600K SNPs. The evaluation of GBLUP is preceded by evaluation of the variance components using traditional restricted maximum likelihood (REML) approach sourcing information from phenotypic records and pedigree, which provide an up to date reference for the estimates of variance components. Chapter 2 tested several models exploring potential sources of genetic variation and revealed the presence of significant maternal genetic and environmental effects affecting several commercial traits. In Chapter 3, a vast dataset containing 1.3M birds spread over 24 generations was used to evaluate changes in genetic variance of juvenile body weight and hen housed production over time. The results showed a slow but steady decline of the variance. Chapter 4 provided initial estimates of the accuracy and bias of genomic predictions for several sex-limited and fitness traits, obtained for a moderately sized population of over 5K birds, genotyped with 600K Affymetrix Axiom panel from which several chips of varying marker densities were extracted. The accuracy of those predictions showed a great potential for most traits, with GBLUP performance exceeding that of traditional BLUP. Chapter 5 investigated the effect of marker choice, with two chips used: one created from GWAS hits and second from evenly spaced markers, both with constant density of 27K SNPs. The two chips were used to calculate genomic relationship matrices using Linkage Analysis and Linkage Disequilibrium approaches. Markers selected through GWAS performed better in Linkage Analysis than in Linkage Disequilibrium approach. The optimum results however were found for relationship matrices which regressed the genomic relationships back to expected pedigree-based relationships, with the best regression coefficient dependent on the chip used. Chapter 6 formed a comprehensive evaluation of the utility of GBLUP in a large broiler population, exceeding 23,500 birds genotyped using 600K Affymetrix Axiom panel. By splitting the data into variable scenarios of training and testing populations, with several lower density chips extracted from the full range of genotypes available, the effect of population size and marker density was evaluated. While the latter proved to have little effect once 20K SNPs threshold was exceeded, the effect of the population size was found to be the major limiting factor for the accuracy of EBV predictions. The discrepancy between empirical results found and theoretical expectations of accuracy based on the similar genomic and population parameters showed an underestimation of the previously proposed requirements.

This study analyzes the economic impact of a recent gene marker innovation for seedstock selection in beef cattle. Gene markers are being developed for many beef cattle attributes; this study focused on the tenderness quality of beef using two categories: tender and tough. The study begins by describing conventional procedures for seedstock selection, the science which underlies selection by gene markers and other non-genetic procedures currently being used to improve beef tenderness. After describing the commercialization of the gene marker innovation, a stylized model of a beef supply chain is constructed. The supply chain consists of a representative consumer, a producer/processor group and a monopolist supplier of the patented technology. Welfare changes resulting from the adoption of the innovation were simulated using four sets of demand elasticity data from literatures. An important focus of this research is determining how the economic surplus from the innovation will be shared by consumers, producers and the gene marker monopolist. The consumer and gene marker monopolist benefit from the technology unless the marginal and fixed cost variables (not estimated in this study) of the monopolist, are excessively high. Producer surplus was simulated as positive with three of the four elasticity data sets. The share of surplus capture by producers is generally low relative to the gains captured by consumers and the gene marker monopolist. Comparative static analysis reveal that the benefit from the innovation varies across breeds, being higher for breeds in which the favorable form of the marker gene is more likely to be present. Despite the apparent benefits of the innovation for beef supply chain participants, reported interviews with industry scientists reveal that markers should not be viewed as a replacement for conventional selection techniques. Indeed, selecting seedstock on the basis of a small number of available markers is not likely to produce the benefits that are currently being promised by life science companies. Consequently, this study recommends that the innovation be incorporated into existing seedstock selection practices. Much more analysis is needed to understand the full economic impact of gene markers for beef tenderness and for other beef quality attributes. / Land and Food Systems, Faculty of / Graduate

Candidate virulence genes were sought through the genetic analyses of two strains of Mycobacterium tuberculosis, one virulent, H37Rv, one attenuated, H37Ra. Derived from the same parent, H37, genomic differences between strains were first examined via two-dimensional DNA technologies: two-dimensional bacterial genome display, and bacterial comparative genomic hybridisation. The two-dimensional technologies were optimised for mycobacterial use, but failed to yield reproducible genomic differences between the two strains. Expression differences between strains during their infection of murine bone-marrow-derived macrophages were then assessed using Bacterial Artificial Chromosome Fingerprint Arrays. This technique successfully identified expression differences between intracellular M. tuberculosis H37Ra and H37Rv, and six candidate genes were confirmed via quantitative real-time PCR for their differential expression at 168 hours post-infection. Genes identified to be upregulated in the attenuated H37Ra were frdB, frdC, and frdD. Genes upregulated in the virulent H37Rv were pks2, aceE, and Rv1571. Further qPCR analysis of these genes at 4 and 96h post-infection revealed that the frd operon (encoding for the fumarate reductase enzyme complex or FRD) was expressed at higher levels in the virulent H37Rv at earlier time points while the expression of aceE and pks2 was higher in the virulent strain throughout the course of infection. Assessment of frd transcripts in oxygen-limited cultures of M. tuberculosis H37Ra and H37Rv showed that the attenuated strain displayed a lag in frdA and frdB expression at the onset of culture when compared to microaerophilic cultures of H37Rv and aerated cultures of H37Ra. Furthermore, inhibition of the fumarate reductase complex in intracellular bacteria resulted in a significant reduction of intracellular growth. Microarray technology was also applied in the expression analysis of intracellular bacteria at 168h post-infection. Forty-eight genes were revealed to be differentially expressed between the H37Ra and H37Rv strains, and a subset were further analysed via qPCR to confirm and validate the microarray data. phoP was expressed at a lower level in the attenuated M. tuberculosis H37Ra, whereas members of the phoPR regulon were up-regulated in the virulent H37Rv. Additionally, a group of genes (Rv3616c-Rv3613c) that may associate with the region of difference 1 were also up-regulated in the virulent H37Rv. / Medicine, Faculty of / Pathology and Laboratory Medicine, Department of / Graduate

Genomic conflict is pervasive in nature and affects a number of fundamental evolutionary processes. Genomic conflict occurs when different genetic entities within a species have different interests in terms of the optimal transmission strategy to future generations, resulting in antagonistic interactions between these elements. When this conflict is over the reproduction strategy within an individual, it can result in sex ratio biases in an individual’s offspring. For instance, genomic conflict occurs between maternally transmitted genetic elements (such as female limited chromosomes or cytoplasmic elements) and nuclear elements over the optimal sex ratio of an individual’s offspring due to the fact that maternally transmitted elements benefit from a female biased sex ratio (as they are transmitted through the matriline) while nuclear elements benefit from an equal sex ratio. I am investigating a maternally transmitted genetic element in a sexual booklouse, Lipsocelis nr. bostrychophila (Insecta; Psocodea) that manipulates reproduction such that all females carrying it produce exclusively female offspring. This is expected to affect L. nr. bostrychophila evolution in a number of ways. I investigated the ecology of L. nr. bostrychophila to gain a better understanding of whether and how the selfish reproductive manipulator (designated the distorting element) persists over time. I found that the distorting element is able to persist in L. nr. bostrychophila populations, both in the wild and in the laboratory, and this is partially due to the fact that females that carry the distorting element have a shorter lifespan and do not produce as many offspring as females that do not carry the element. This helps to counteract the advantage that females carrying the distorting element would otherwise have due to the fact that they do not produce male offspring. Additionally, I found that females that do not carry the distorting element also produce a female biased sex ratio. This also likely mediates the persistence of the distorting element in wild and laboratory L. nr. bostrychophila populations, and is particularly interesting in that I found that other wild Liposcelis species also exhibit female biased sex ratios. This suggests that L. nr. bostrychophila populations likely exhibited female bias sex ratios before the distorting element arose in this species. I also assessed the effect that the distorting element has had on the genomic evolution of L. nr. bostrychophila. I found that females that carry the distorting element have radically different mitochondria from females that do not carry it, leading me to speculate that the reduced longevity in females that carry the distorting element may be a consequence of impaired mitochondrial function. Finally, I found that all L. nr. bostrychophila individuals have unusual mitochondria, with females that carry the distorting element having five mitochondrial minichromosomes and females that do not carry the distorting element having seven (rather than the single chromosome typical in animals). These findings contribute to the growing body of evidence suggesting that genomic conflict is an important force shaping species’ evolution, supporting the importance of investigating the evolutionary forces at play within as well as between individuals. / Graduate / 2018-12-16 / 0329 / 0369 / 0353

The challenge in finding genes in eukaryotic organisms using computational methods is an ongoing problem in the biology. Based on various genomic signals found in eukaryotic genomes, this problem can be divided into many different sub­-problems such as identification of transcription start sites, translation initiation sites, splice sites, poly (A) signals, etc. Each sub-­problem deals with a particular type of genomic signals and various computational methods are used to solve each sub-­problem. Aggregating information from all these individual sub-­problems can lead to a complete annotation of a gene and its component signals. The fundamental principle of most of these computational methods is the mapping principle – building an input-­output model for the prediction of a particular genomic signal based on a set of known input signals and their corresponding output signal. The type of input signals used to build the model is an essential element in most of these computational methods. The common factor of most of these methods is that they are mainly based on the statistical analysis of the basic nucleotide sequence string composition. 4 Our study is based on a novel approach to predict genomic signals in which uniquely generated structural profiles that combine compressed physicochemical properties with topological and compositional properties of DNA sequences are used to develop machine learning predictive models. The compression of the physicochemical properties is made using principal component analysis transformation. Our ideas are evaluated through prediction models of canonical splice sites using support vector machine models. We demonstrate across several species that the proposed methodology has resulted in the most accurate splice site predictors that are publicly available or described. We believe that the approach in this study is quite general and has various applications in other biological modeling problems.

Horizontal gene transfer, well characterized as the transfer of genomic material between organisms contributes hugely in the evolution and speciation of bacteria. The transfer of such material brings about bacteria that are virulent and also in possession of genes that render them resistant to antibiotics. This helps to spread about and recombine genes of their kind to other bacteria. Horizontally acquired genomic elements exhibit compositional features that are deviant from the rest of the other genes in a recipient genome. They possess features such as unusual GC%, atypical codon usage, oligonucleotide usage bias and direct repeats at their flanks that can be used to distinguish them from native genes in a genome. This work focused on the developments of statistical and computational methods to aid with the detection of genes that have undergone horizontal transfer, to help track down genes that could be of medical and environmental importance. Therefore, SeqWord Gene Island Sniffer (SWGIS), a statistically driven computational tool for the prediction of genomic islands, and GEI-DB, a comprehensive database of horizontally transferred genomic elements were established. The SWGIS tool allows the precise predictions of precise inserts of horizontally acquired gene clusters in prokaryotic genomic sequences. Thus, the GEI-DB stores all the foreign genomic inserts that have been detected in the study, together with their annotations and evolutionary measures, such as groups of genomic islands that share similarities in DNA and amino acids features. Copyright / Dissertation (MSc)--University of Pretoria, 2009. / Biochemistry / unrestricted

Charles University Faculty of Pharmacy in Hradec Králové Department of Pharmacology and Toxicology Student: Martin Svoreň Supervisor: PharmDr. Martina Čečková, Ph.D. Specialized supervisor: PD Dr. Bernd Evert Title of diploma thesis: Analysis of genomic regions bound and regulated by Ataxin-3 Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease, is a dominantly inherited neurodegenerative disease. In SCA3, the disease protein ataxin-3 (ATXN3) contains an abnormally long polyglutamine (polyQ) tract encoded by CAG repeat expansion. ATXN3 binds DNA and interacts with transcriptional regulators pointing toward a direct role of ATXN3 in transcription. It is conceivable that mutant ATXN3 triggers multiple, interconnected pathogenic cascades leading to neurotoxicity, however, the principal molecular pathomechanism remains elusive. Here, PCR analyses of 16 ATXN3-bound genomic regions recently identified by next generation sequencing of immunoprecipitated ATXN3-bound chromatin fragments confirmed enriched binding of ATXN3 to 5 genomic regions next to genes encoding CCAAT/enhancer binding protein delta (CEBPD), period circadian clock-2 (PER2), phosphatase and tensin homolog (PTEN), serine protease inhibitor family F2 (SERPINF2) and thrombospondin-1 (THBS1). To investigate putative...

Submitted to the Faculty of the School of Informatics in parial fulfillment of the requirements for the degree of Master of Schience in Bioinformatics Indiana University August 2006 / Decoding the human genome resulted in generating large amount of data that need to be analyzed and given a biological meaning. The field of Life Schiences is highly information driven. The genomic data are mainly the gene expression data that are obtained from measurement of mRNA levels in an organism. Efficiently processing large amount of gene expression data has been possible with the help of high throughput technology. Research studies working on microarray data has led to the possibility of finding disease biomarkers. Carrying out biomarker discovery experiments has been greatly facilitated with the emergence of various analytical and visualization tools as well as annotation databases. These tools and databases are often termed as 'bioinformatics services'. The main purpose of this research was to develop SIBIOS (Bystem for Integration of Bioinformatics Services) as a platform to carry out microarray experiments for the purpose of biomarker discovery. Such experiments require the understanding of the current procedures adopted by researchers to extract biologically significant genes. In the course of this study, sample protocols were built for the purpose of biomarker discovery. A case study on the BCR-ABL subtype of ALL was selected to validate the results. Different approaches for biomarker discovery were explored and both statistical and mining techniques were considered. Biological annotation of the results was also carried out. The final task was to incorporate the new proposed sample protocols into SIBIOS by providing the workflow capabilities and therefore enhancing the system's characteristics to be able to support biomarker discovery workflows.

Indiana University-Purdue University Indianapolis (IUPUI) / The Fanconi anemia (FA) pathway consists of 21 genes that maintain genomic stability and prevent cancer. Biallelic mutations within this network cause Fanconi anemia, an inherited bone marrow failure and cancer predisposition syndrome. Heterozygous inborn mutations in FA genes increase risk of breast/ovarian cancers, and somatic mutations occur in malignancies in non-Fanconi patients. Understanding the tumor suppressive functions of FA signaling is important for the study of Fanconi anemia, inherited cancers, and sporadic cancers. The FA network functions as a genome guardian throughout the cell cycle. In addition to the well-established roles of FA proteins in interphase DNA replication/repair, the FA pathway controls mitosis by regulating the spindle assembly checkpoint (SAC) to ensure proper chromosome segregation. The SAC consists of several tumor suppressors, including Mad2, and SAC impairment predisposes to aneuploidy and cancer. However, the in vivo contribution of SAC dysfunction to malignant transformation of FA-deficient cells remains unknown. Furthermore, the mechanisms by which FA proteins regulate the SAC are unclear. To test whether SAC dysfunction drives genomic instability and tumorigenesis in FA, we generated a novel FA-SAC model by intercrossing Fancc-/- and Mad2+/- mice. The intercrossed mice displayed heightened aneuploidy secondary to exacerbated SAC dysfunction. Importantly, these mice were prone to developing hematologic malignancies, particularly leukemia, faithfully recapitulating the clinical phenotype of Fanconi anemia. Upon establishing SAC dysfunction as a driver of tumorigenesis in FA, we next explored the mechanism by which FANCC regulates the SAC. We demonstrated that the mitotic kinase CDK1 phosphorylates FANCC to regulate subcellular localization and SAC function of FANCC during mitosis. Our study highlights the essential role of compromised chromosome segregation in the development of leukemia due to impaired FA signaling. This work furthers our knowledge of FANCC signaling at the SAC, and has implications for future use of mitotic-centered therapies for FA-associated tumors. / 2 years

Quantitative genetics is the study of continuously distributed traits and their ge- netic components. Recent developments in DNA sequencing technologies and computational systems allow researchers to conduct large scale in silico studies. However, going from raw DNA reads to genomic prediction of quantitative traits with the help of neural networks is a long and error-prone process. In the course of this thesis, many steps involved in this process will be assessed in depth. Chap- ter 2 will feature a study that compares the landscape of chloroplast genome as- sembly tools. Chapter 3 will present a software to perform genome-wide associa- tion studies using modern tools, which allow GWAS-Flow to outperform current state of the art software packages. Chapter 4 will give an in depth introduc- tion to machine learning and the nature of quantitative traits and will combine those to genomic prediction with artificial neural networks and compares the re- sults to those of algorithms based on linear mixed models. Finally, in Chapter 5 the results from the previous chapters are summarized and used to elucidate the complex nature of studies concerning quantitative genetics. / Quantitative Genetik beschäftigt sich mit kontinuierlich verteilten Merkmalen und deren genetischer Komponenten. In den letzten Jahren gab es vielfältige Entwicklungen in der Computertechnik und der Genomik, insbesondere der DNA Sequenzierung, was Forschern erlaubt großflächig angelegte in silico Studien durchzuführen. Jedoch ist es ein komplexer Prozess von rohen Sequenzdaten bis zur genomischen Vorhersage mit Hilfe von neuronalen Netzwerken zu kommen. Im Rahmen der vorliegenden Studien werden viele Schritte, die an diesem Prozess beteiligt sind beleuchtet. Kapitel 2 wird einen Vergleich zwischen einer Vielzahl an Werkzeugen zur Assemblierung von Chloroplasten Genomen ziehen. Kapitel 3 stellt eine neu entwickelte Software zur genom-weiten Assoziationskartierung vor, die bisherigen Programmen überlegen ist. Kapitel 4 stellt maschinelles Lernen und die genetischen Komponenten von quantitativen Merkmalen vor und bringt diese im Kontext der genomischen Vorhersagen zusammen. Zum Schluss in Kapitel 5 werden die vorherigen Ergebnisse im Gesamtkontext der quantitativen Genetik erläutert.