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1
Brinza, Dumitru. "Discrete Algorithms for Analysis of Genotype Data." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/19.
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Accessibility of high-throughput genotyping technology makes possible genome-wide association studies for common complex diseases. When dealing with common diseases, it is necessary to search and analyze multiple independent causes resulted from interactions of multiple genes scattered over the entire genome. The optimization formulations for searching disease-associated risk/resistant factors and predicting disease susceptibility for given case-control study have been introduced. Several discrete methods for disease association search exploiting greedy strategy and topological properties of case-control studies have been developed. New disease susceptibility prediction methods based on the developed search methods have been validated on datasets from case-control studies for several common diseases. Our experiments compare favorably the proposed algorithms with the existing association search and susceptibility prediction methods.
2
Groth, Philip. "Knowledge management and discovery for genotype/phenotype data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2009. http://dx.doi.org/10.18452/16033.
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Die Untersuchung des Phänotyps bringt z.B. bei genetischen Krankheiten ein Verständnis der zugrunde liegenden Mechanismen mit sich. Aufgrund dessen wurden neue Technologien wie RNA-Interferenz (RNAi) entwickelt, die Genfunktionen entschlüsseln und mehr phänotypische Daten erzeugen. Interpretation der Ergebnisse solcher Versuche ist insbesondere bei heterogenen Daten eine große Herausforderung. Wenige Ansätze haben bisher Daten über die direkte Verknüpfung von Genotyp und Phänotyp hinaus interpretiert. Diese Dissertation zeigt neue Methoden, die Entdeckungen in Phänotypen über Spezies und Methodik hinweg ermöglichen. Es erfolgt eine Erfassung der verfügbaren Datenbanken und der Ansätze zur Analyse ihres Inhalts. Die Grenzen und Hürden, die noch bewältigt werden müssen, z.B. fehlende Datenintegration, lückenhafte Ontologien und der Mangel an Methoden zur Datenanalyse, werden diskutiert. Der Ansatz zur Integration von Genotyp- und Phänotypdaten, PhenomicDB 2, wird präsentiert. Diese Datenbank assoziiert Gene mit Phänotypen durch Orthologie über Spezies hinweg. Im Fokus sind die Integration von RNAi-Daten und die Einbindung von Ontologien für Phänotypen, Experimentiermethoden und Zelllinien. Ferner wird eine Studie präsentiert, in der Phänotypendaten aus PhenomicDB genutzt werden, um Genfunktionen vorherzusagen. Dazu werden Gene aufgrund ihrer Phänotypen mit Textclustering gruppiert. Die Gruppen zeigen hohe biologische Kohärenz, da sich viele gemeinsame Annotationen aus der Gen-Ontologie und viele Protein-Protein-Interaktionen innerhalb der Gruppen finden, was zur Vorhersage von Genfunktionen durch Übertragung von Annotationen von gut annotierten Genen zu Genen mit weniger Annotationen genutzt wird. Zuletzt wird der Prototyp PhenoMIX präsentiert, in dem Genotypen und Phänotypen mit geclusterten Phänotypen, PPi, Orthologien und weiteren Ähnlichkeitsmaßen integriert und deren Gruppierungen zur Vorhersage von Genfunktionen, sowie von phänotypischen Wörtern genutzt.In diseases with a genetic component, examination of the phenotype can aid understanding the underlying genetics. Technologies to generate high-throughput phenotypes, such as RNA interference (RNAi), have been developed to decipher functions for genes. This large-scale characterization of genes strongly increases phenotypic information. It is a challenge to interpret results of such functional screens, especially with heterogeneous data sets. Thus, there have been only few efforts to make use of phenotype data beyond the single genotype-phenotype relationship. Here, methods are presented for knowledge discovery in phenotypes across species and screening methods. The available databases and various approaches to analyzing their content are reviewed, including a discussion of hurdles to be overcome, e.g. lack of data integration, inadequate ontologies and shortage of analytical tools. PhenomicDB 2 is an approach to integrate genotype and phenotype data on a large scale, using orthologies for cross-species phenotypes. The focus lies on the uptake of quantitative and descriptive RNAi data and ontologies of phenotypes, assays and cell-lines. Then, the results of a study are presented in which the large set of phenotype data from PhenomicDB is taken to predict gene annotations. Text clustering is utilized to group genes based on their phenotype descriptions. It is shown that these clusters correlate well with indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. The clusters are then used to predict gene function by carrying over annotations from well-annotated genes to less well-characterized genes. Finally, the prototype PhenoMIX is presented, integrating genotype and phenotype data with clustered phenotypes, orthologies, interaction data and other similarity measures. Data grouped by these measures are evaluated for theirnpredictiveness in gene functions and phenotype terms.
3
Yang, Li. "A Goodness-of-fit Association Test for Whole Genome Sequencing Data." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/296.
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Although many genetic factors have been successfully identified for human diseases in genome-wide association studies (GWAS), genes discovered to date only account for a small proportion of overall genetic contributions to many complex traits. Association studies have difficulty in detecting the remaining true genetic variants that are either common variants with weak allelic effects, or rare variants that have strong allelic effects but are weakly associated at the population level. In this work we applied a goodness-of-fit test for detecting sets of common and rare variants associated with quantitative or binary traits by using whole genome sequencing (WGS) data. This test has been proved optimal for detecting weak and sparse signals in the literature, which fits the requirements for targeting the genetic components of missing heritability. Furthermore, this p-value-combining method allows one to incorporate different data and/or research results for meta-analysis. The method was used to simultaneously analyse the WGS and GWAS data of Genetic Analysis Workshop (GAW) 18 for detecting true genetic variants. The results show that goodness-of-fit test is comparable or better than the influential sequence kernel association test in many cases.
4
O'Connell, Jared Michael. "Statistical methods for genotype microarray data on large cohorts of individuals." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4e3328cf-0d8e-4587-b24d-9b59fa220f32.
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Genotype microarrays assay hundreds of thousands of genetic variants on an individual's genome. The availability of this high throughput genotyping capability has transformed the field of genetics over the past decade by enabling thousands of individuals to be rapidly assayed. This has lead to the discovery of hundreds of genetic variants that are associated with disease and other phenotypes in genome wide association studies (GWAS). These data have also brought with them a number of new statistical and computational challenges. This thesis deals with two primary analysis problems involving microarray data; genotype calling and haplotype inference. Genotype calling involves converting the noisy bivariate fluorescent signals generated by microarray data into genotype values for each genetic variant and individual. Poor quality genotype calling can lead to false positives and loss of power in GWAS so this is an important task. We introduce a new genotype calling method that is highly accurate and has the novel capability of fusing microarray data with next-generation sequencing data for greater accuracy and fewer missing values. Our new method compares favourably to other available genotype calling software. Haplotype inference (or phasing) involves deconvolving these genotypes into the two inherited parental chromosomes for an individual. The development of phasing methods has been a fertile field for statistical genetics research for well over ten years. Depending on the demography of a cohort, different phasing methods may be more appropriate than others. We review the popular offerings and introduce a new approach to try and unify two distinct problems; the phasing of extended pedigrees and the phasing of unrelated individuals. We conduct an extensive comparison of phasing methods on real and simulated data. Finally we demonstrate some preliminary results on extending methodology to sample sizes in the tens of thousands.
5
Pestana, Valeria. "Modeling drug response in cancer cell linesusing genotype and high-throughput“omics” data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166744.
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ROSA, Rogério dos Santos. "Associating genotype sequence properties to haplotype inference errors." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16011.
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Haplotype information has a central role in the understanding and diagnosis of certain illnesses, and also for evolution studies. Since that type of information is hard to obtain directly, computational methods to infer haplotype from genotype data have received great attention from the computational biology community. Unfortunately, haplotype inference is a very hard computational biology problem and the existing methods can only partially identify correct solutions. I present neural network models that use different properties of the data to predict when a method is more prone to make errors. I construct models for three different Haplotype Inference approaches and I show that our models are accurate and statistically relevant. The results of our experiments offer valuable insights on the performance of those methods, opening opportunity for a combination of strategies or improvement of individual approaches. I formally demonstrate that Linkage Disequilibrium (LD) and heterozygosity are very strong indicators of Switch Error tendency for four methods studied, and I delineate scenarios based on LD measures, that reveal a higher or smaller propension of the HI methods to present inference errors, so the correlation between LD and the occurrence of errors varies among regions along the genotypes. I present evidence that considering windows of length 10, immediately to the left of a SNP (upstream region), and eliminating the non-informative SNPs through Fisher’s Test leads to a more suitable correlation between LD and Inference Errors. I apply Multiple Linear Regression to explore the relevance of several biologically meaningful properties of the genotype sequences for the accuracy of the haplotype inference results, developing models for two databases (considering only Humans) and using two error metrics. The accuracy of our results and the stability of our proposed models are supported by statistical evidence.
Haplótipos têm um papel central na compreensão e diagnóstico de determinadas doenças e também para estudos de evolução. Este tipo de informação é difícil de obter diretamente, diante disto, métodos computacionais para inferir haplótipos a partir de dados genotípicos têm recebido grande atenção da comunidade de biologia computacional. Infelizmente, a Inferência de Halótipos é um problema difícil e os métodos existentes só podem predizer parcialmente soluções corretas. Foram desenvolvidos modelos de redes neurais que utilizam diferentes propriedades dos dados para prever quando um método é mais propenso a cometer erros. Foram calibrados modelos para três abordagens de Inferência de Haplótipos diferentes e os resultados validados estatisticamente. Os resultados dos experimentos oferecem informações valiosas sobre o desempenho e comportamento desses métodos, gerando condições para o desenvolvimento de estratégias de combinação de diferentes soluções ou melhoria das abordagens individuais. Foi demonstrado que Desequilíbrio de Ligação (LD) e heterozigosidade são fortes indicadores de tendência de erro, desta forma foram delineados cenários com base em medidas de LD, que revelam quando um método tem maior ou menor propensão de cometer erros. Foi identificado que utilizando janelas de 10 SNPs (polimorfismo de um único nucleotídeo), imediatamente a montante, e eliminando os SNPs não informativos pelo Teste de Fisher leva-se a uma correlação mais adequada entre LD e a ocorrência de erros. Por fim, foi aplicada análise de Regressão Linear para explorar a relevância de várias propriedades biologicamente significativas das sequências de genótipos para a precisão dos resultados de Inferência de Haplótipos, estimou-se modelos para duas bases de dados (considerando apenas humanos) utilizando duas métricas de erro. A precisão dos resultados e a estabilidade dos modelos propostos foram validadas por testes estatísticos.
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Liu, Lian. "Topics in measurement error and missing data problems." Thesis, [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1627.
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Rimal, Suraj. "POPULATION STRUCTURE INFERENCE USING PCA AND CLUSTERING ALGORITHMS." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2860.
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Genotype data, consisting large numbers of markers, is used as demographic and association studies to determine genes related to specific traits or diseases. Handling of these datasets usually takes a significant amount of time in its application of population structure inference. Therefore, we suggested applying PCA on genotyped data and then clustering algorithms to specify the individuals to their particular subpopulations. We collected both real and simulated datasets in this study. We studied PCA and selected significant features, then applied five different clustering techniques to obtain better results. Furthermore, we studied three different methods for predicting the optimal number of subpopulations in a collected dataset. The results of four different simulated datasets and two real human genotype datasets show that our approach performs well in the inference of population structure. NbClust is more effective to infer subpopulations in the population. In this study, we showed that centroid-based clustering: such as k-means and PAM, performs better than model-based, spectral, and hierarchical clustering algorithms. This approach also has the benefit of being fast and flexible in the inference of population structure.
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Strömstedt, Hallberg Simon, and Jonas Giek. "Simulerad effektivisering av genotypdataanalys genom poolade data." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-296223.
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Målet med projektet är att undersöka om det går att effektivisera hur man undersöker människors gener. Detta görs genom att skapa ett program i Java. Resultatet är ett program som sorterar genotypdata från 1000 Genomes Project och utvärderar nyttan av att undersöka genotyper från flera individer samtidigt.
10
Bosch, Puig Lluís. "Age-and genotype-related changes in intramuscular fat content and composition in pigs using longitudinal data." Doctoral thesis, Universitat de Lleida, 2011. http://hdl.handle.net/10803/77959.
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La present Tesi Doctoral s’emmarca en una línia d’investigació del Departament de
Producció Animal de la Universitat de Lleida, dedicada a la millora genètica de la
qualitat de la carn en bestiar porcí, en particular del contingut i composició del greix
intramuscular. La Tesi es composa de quatre estudis, centrant-se el primer d’ells en el
desenvolupament d’un mètode per a determinar el contingut i composició del greix
intramuscular a partir de biòpsies i mostres post-mortem petites amb les que després
es puguin portar a terme estudis en disseny longitudinal. La metodologia proposada
ha resultat útil, demostrant-se que, especialment per al contingut de greix
intramuscular, els espècimens petits del múscul objectiu són tan informatius com
mostres grans d’altres músculs. En el segon estudi s’ha investigat, mitjançant un
experiment amb dades longitudinals obtingudes segons la metodologia descrita
anteriorment, l’efecte de l’edat sobre el contingut i composició del greix
intramuscular i subcutani al llarg del cicle d’engreix en porcs de raça Duroc. Es
conclou que un retard en l’edat de sacrifici implica un augment del contingut de greix
intramuscular i d’àcid oleic, tot i que això s’aconsegueix a expenses de disminuir la
velocitat de creixement magre. Per altra part es demostra que el greix intramuscular i
el greix subcutani tenen comportaments diferents de creixement i composició i que la
quantitat de greix per si mateix també influeix en la seva composició. El que un porc
sigui més gras de l’esperat a una edat determinada és degut, en el cas del greix
intramuscular, a que ha augmentat el contingut de greix monoinsaturat, en especial
d’olèic, mentre que, en el del greix subcutani a que s’ha incrementat el contingut de
saturat. En els dos últims estudis s’examina si la variació al·lèlica en els gens IGF-1
(insulin-like growth factor-1) i LEP (leptina), així com la concentració de IGF-1 i
leptina en plasma, s’associen amb el contingut i la composició del greix
intramuscular i, en cas que així fos, si aquesta associació és funció de l’edat. Es posa
en evidència que els polimorfismes moleculars estudiats no són neutrals en relació al
contingut de greix intramuscular, però també que els seus efectes no són constants al
llarg del període de creixement. En aquest sentit, tant l’edat com l’estat
d’engrassament poden modificar-los.La presente Tesis Doctoral se emmarca en una línea de investigación del Departamento de Producción Animal de la Universidad de Lleida dedicada a la mejora genética de la calidad de la carne en porcino, en particular del contenido y la composición de la grasa intramuscular. La Tesis se compone de cuatro estudios, centrándose el primero de ellos en el desarrollo de un método para determinar el contenido y la composición de la grasa intramuscular a partir de biopsias y muestras post-mortem pequeñas con las que luego poder realizar estudios mediante diseños longitudinales. La metodología propuesta ha resultado útil, demostrándose que, especialmente para el contenido de grasa intramuscular, los especímenes pequeños del músculo objetivo son tan informativos como muestras grandes de otros músculos. En el segundo estudio se ha investigado mediante un experimento con datos longitudinales, obtenidos según la metodología descrita anteriormente, el efecto de la edad sobre el contenido y la composición de la grasa intramuscular y subcutánea durante el engorde de cerdos Duroc. Se concluye que un retraso en la edad de sacrificio comporta un aumento del contenido de grasa intramuscular y de ácido oleico, aunque ello se consigue a costa de disminuir la velocidad de crecimiento magro. Por otra parte, se demuestra que la grasa intramuscular y la grasa subcutánea tienen patrones distintos de crecimiento y composición y que la cantidad de grasa por sí misma influye en su composición. El que un cerdo sea más graso de lo esperado a una edad determinada es debido, en el caso de la grasa intramuscular, a que ha aumentado el contenido de grasa monoinsaturada, en especial de oleico, mientras que, en el de la subcutánea, a que se ha incrementado el de la saturada. En los dos últimos estudios se examina si la variación alélica en los genes IGF-1 (insulin-like growth factor-1) y LEP (leptina), así como la concentración de IGF-1 y leptina en plasma, se asocian con el contenido y la composición de la grasa intramuscular y, en caso de que así fuera, si tal asociación es función de la edad. Se constata que los polimorfismos moleculares estudiados no son neutrales respecto al contenido de grasa intramuscular, pero, también, que sus efectos no son constantes a lo largo del crecimiento. En este sentido, tanto la edad como el estado de engrasamiento pueden modificarlos.
This PhD is part of a line of research conducted in the Department of Animal Production of the Universitat de Lleida dedicated to the genetic improvement of pig meat quality, with particular reference to intramuscular fat content and composition. The PhD comprises four studies, with the first one focusing on the development of a method to jointly determine the content and composition of intramuscular fat from biopsies and small post-mortem samples and, in this way, to carry out studies with longitudinal data. It has been found that this particular methodology is useful and, in for intramuscular fat, small specimens of the target muscle are as informative as large samples of other muscles. In the second study the effect of age on the content and composition of the intramuscular and subcutaneous fat in the fattening period in Duroc pigs was investigated by an experiment using longitudinal data obtained following the methodology described above. It was concluded that a delay in the age of slaughter of the pig leads to an increase in intramuscular fat and oleic acid, although this comes at the cost of reducing the rate of lean growth. Moreover, it was proved that intramuscular and subcutaneous fat behaved differently in terms of fat accretion and composition and that the amount of fat itself affected composition. Whereas, for the intramuscular fat, values above the expected at a given age were because of increased monounsaturated fatty acid content, especially oleic acid, for the subcutaneous fat, they were due to the increased saturated fatty acid content. The final two studies considered whether allelic variation at the IGF-1 (insuline-like growth factor-1) and LEP (leptin) genes, as well as the concentration of IGF-1 and leptin in plasma, are associated to intramuscular fat content and composition and, if so, whether this is a function of age. It can be seen that the molecular polymorphisms studied are not neutral with regard to the content of intramuscular fat, but that their effects are not constant throughout the growing period. In this sense, both age and fatness can modify them.
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Wang, Yuker, Victoria Carlton, George Karlin-Neumann, Ronald Sapolsky, Li Zhang, Martin Moorhead, Zhigang Wang, et al. "High quality copy number and genotype data from FFPE samples using Molecular Inversion Probe (MIP) microarrays." BioMed Central, 2009. http://hdl.handle.net/10150/610039.
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BACKGROUND:A major challenge facing DNA copy number (CN) studies of tumors is that most banked samples with extensive clinical follow-up information are Formalin-Fixed Paraffin Embedded (FFPE). DNA from FFPE samples generally underperforms or suffers high failure rates compared to fresh frozen samples because of DNA degradation and cross-linking during FFPE fixation and processing. As FFPE protocols may vary widely between labs and samples may be stored for decades at room temperature, an ideal FFPE CN technology should work on diverse sample sets. Molecular Inversion Probe (MIP) technology has been applied successfully to obtain high quality CN and genotype data from cell line and frozen tumor DNA. Since the MIP probes require only a small (~40 bp) target binding site, we reasoned they may be well suited to assess degraded FFPE DNA. We assessed CN with a MIP panel of 50,000 markers in 93 FFPE tumor samples from 7 diverse collections. For 38 FFPE samples from three collections we were also able to asses CN in matched fresh frozen tumor tissue.RESULTS:Using an input of 37 ng genomic DNA, we generated high quality CN data with MIP technology in 88% of FFPE samples from seven diverse collections. When matched fresh frozen tissue was available, the performance of FFPE DNA was comparable to that of DNA obtained from matched frozen tumor (genotype concordance averaged 99.9%), with only a modest loss in performance in FFPE.CONCLUSION:MIP technology can be used to generate high quality CN and genotype data in FFPE as well as fresh frozen samples.
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Elom, Hilary, and Shimin Zheng. "The distribution of hepatitis c virus genotypes in US population. Data from NHANES 2006-2016." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/116.
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Background: Unlike other non-hepatitis c viral infection, hepatitis c viral infection is a non-vaccine preventable disease. Thus, effective treatment is an important part in the prevention of complication of chronic hepatitis c infection. The viral genotype plays a significant role in the choice of treatment regimen.
Aim: the purpose of this study is to estimate the prevalence of hepatitis c viral infection and the distribution of viral genotype in the US population.
Methods: Diagnosis of Hepatitis C viral infection was made by assaying the blood specimen collected from the study participants using Ampiclor monitor (Roche Diagnostic System, Inc Branchburg NJ), and genotype determined from the NS5b region. The data is from NHANSE 2006-2016. SAS v 9.4 software was used to perform the analysis.
Results: Of the 356 participants (2006-2016) who tested positive to Hepatitis C virus-RNA, 205 persons had genotype 1a, 1b (n=66), other forms of genotype 1 (n=1), genotype 2(n=41), genotype 3 (n=30), genotype 4 (n=1), genotype 6(n=1), undetermined genotype (n=8). Based on weighted analysis of person infected with genotype 1, 2, 3; genotype 1 was highest across all ages and gender (78.2%). Of 271 participants infected with genotype 1, there were 5.09% Mexican Americans, 3.94% other Hispanics, 56.58% non-Hispanic whites, 28.74% non-Hispanic black, and 5.65% other races including multiracial population.
Subjects aged 50 years or above was 27.7% less likely being infected with HCV genotype 1 vs 2 and 3, compared with younger individuals (adjusted Odds Ratio (95% confidence interval) (aOR): 0.72 (0.72-0.73)). Non-Hispanic black were about 13 times (aOR: 13.1 (13.0-13.2)) as likely to be infected with genotype 1 vs 2 and 3 as non-Hispanic white.
Conclusion: Hepatitis C virus genotype 1 is predominant among those infected with hepatitis c virus in the US population. Improvement in therapy targeting genotype 1 is essential to reduce the burden and complication of chronic hepatitis C in the United States.
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Andersson, Alfred. "Neural networks for imputation of missing genotype data : An alternative to the classical statistical methods in bioinformatics." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413635.
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In this project, two different machine learning models were tested in an attempt at imputing missing genotype data from patients on two different panels. As the integrity of the patients had to be protected, initial training was done on data simulated from the 1000 Genomes Project. The first model consisted of two convolutional variational autoencoders and the latent representations of the networks were shuffled to force the networks to find the same patterns in the two datasets. This model was unfortunately unsuccessful at imputing the missing data. The second model was based on a UNet structure and was more successful at the task of imputation. This model had one encoder for each dataset, making each encoder specialized at finding patterns in its own data. Further improvements are required in order for the model to be fully capable at imputing the missing data.
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Roshyara, Nab Raj, Holger Kirsten, Katrin Horn, Peter Ahnert, and Markus Scholz. "Impact of pre-imputation SNP-filtering on genotype imputation results." Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-151874.
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Background: Imputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE. Results: We considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of ompletely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality. Conclusion: Even a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.
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Tecle, Tesfaldet. "Biomolecular characterization of mumps virus genotypes with varying neurovirulence /." Stockholm : [Karolinska institutets bibl.], 2002. http://diss.kib.ki.se/2002/91-7349-234-5.
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Erdogan, Onur. "Predicting The Disease Of Alzheimer (ad) With Snp Biomarkers And Clinical Data Based Decision Support System Using Data Mining Classification Approaches." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614832/index.pdf.
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Single Nucleotide Polymorphisms (SNPs) are the most common DNA sequence variations where only a single nucleotide (A, T, C, G) in the human genome differs between individuals. Besides being the main genetic reason behind individual phenotypic differences, SNP variations have the potential to exploit the molecular basis of many complex diseases. Association of SNPs subset with diseases and analysis of the genotyping data with clinical findings will provide practical and affordable methodologies for the prediction of diseases in clinical settings. So, there is a need to determine the SNP subsets and patients&rsquoclinical data which is informative for the prediction or the diagnosis of the particular diseases. So far, there is no established approach for selecting the representative SNP subset and patients&rsquo
clinical data, and data mining methodology that is based on finding hidden and key patterns over huge databases. This approach have the highest potential for extracting the knowledge from genomic datasets and to select the number of SNPs and most effective clinical features for diseases that are informative and relevant for clinical diagnosis. In this study we have applied one of the widely used data mining classification methodology: &ldquo
decision tree&rdquo
for associating the SNP Biomarkers and clinical data with the Alzheimer&rsquo
s disease (AD), which is the most common form of &ldquo
dementia&rdquo
. Different tree construction parameters have been compared for the optimization, and the most efficient and accurate tree for predicting the AD is presented.
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Shabalina, Taisiia [Verfasser]. "Optimisation of genetic evaluations for longevity in Holstein dairy cattle through special consideration of health traits, SNP marker data and genotype by environment interactions / Taisiia Shabalina." Gießen : Universitätsbibliothek, 2021. http://d-nb.info/1233036637/34.
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Churchhouse, Claire. "Bayesian methods for estimating human ancestry using whole genome SNP data." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:0cae8a4a-6989-485b-a7cb-0a03fb86096d.
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The past five years has seen the discovery of a wealth of genetics variants associated with an incredible range of diseases and traits that have been identified in genome- wide association studies (GWAS). These GWAS have typically been performed in in- dividuals of European descent, prompting a call for such studies to be conducted over a more diverse range of populations. These include groups such as African Ameri- cans and Latinos as they are recognised as bearing a disproportionately large burden of disease in the U.S. population. The variation in ancestry among such groups must be correctly accounted for in association studies to avoid spurious hits arising due to differences in ancestry between cases and controls. Such ancestral variation is not all problematic as it may also be exploited to uncover loci associated with disease in an approach known as admixture mapping, or to estimate recombination rates in admixed individuals. Many models have been proposed to infer genetic ancestry and they differ in their accuracy, the type of data they employ, their computational efficiency, and whether or not they can handle multi-way admixture. Despite the number of existing models, there is an unfulfilled requirement for a model that performs well even when the ancestral populations are closely related, is extendible to multi-way admixture scenarios, and can handle whole- genome data while remaining computationally efficient. In this thesis we present a novel method of ancestry estimation named MULTIMIX that satisfies these criteria. The underlying model we propose uses a multivariate nor- mal to approximate the distribution of a haplotype at a window of contiguous SNPs given the ancestral origin of that part of the genome. The observed allele types and the ancestry states that we aim to infer are incorporated in to a hidden Markov model to capture the correlations in ancestry that we expect to exist between neighbouring sites. We show via simulation studies that its performance on two-way and three-way admixture is competitive with state-of-the-art methods, and apply it to several real admixed samples of the International HapMap Project and the 1000 Genomes Project.
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Piovesan, Pamela. "Validação cruzada com correção de autovalores e regressão isotônica nos modelos AMMI." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-16102007-113618/.
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Neste trabalho apresenta-se a aplicação dos modelos AMMI para um estudo detalhado do efeito das interações entre genótipos e ambientes em experimentos multiambientais. Através da decomposição da soma de quadrados dessas interações, busca-se selecionar o número de termos que explicam essa interação, descartando o ruído presente na mesma. Há duas maneiras para a escolha desses termos: validação cruzada e teste de hipóteses. O foco será na validação cruzada pela vantagem de ser um critério "preditivo" de avaliação. São apresentados dois métodos de validação cruzada, métodos esses esboçados por Eastment e Krzanowski (1982) e Gabriel (2002). Esses métodos utilizam a decomposição por valores singulares para obter os autovalores referentes à matriz de interações, cuja soma de quadrados nos dá exatamente a soma de quadrados da interação. Como esses autovalores são superestimados ou subestimados (ARAÚJO; DIAS, 2002), essas técnicas de validação serão aperfeiçoadas através da correção desses autovalores e, para reordená-los, será utilizada a regressão isotônica. Será realizado um estudo comparativo entre esses métodos, através de dados reais.This paper presents the application of AMMI models for a thorough study about the effect of the interaction between genotypes and environments in multi-environments experiments. Through the decomposition of the sum of squares of these interactions, one searches to select the number of terms that explains this interaction, discarding its noise in. There are two ways for choosing these terms: cross-validation and hypotheses test. The focus will be on the crossvalidation for its advantage of being one prediction criterion of evaluation. Two methods of cross-validation are presented , both outlined by Eastment and Krzanowski (1982) and Gabriel (2002). These methods use the decomposition by singular values in order to obtain eigenvalues referred to the matrix of interactions, whose sum of squares accurately gives us the sum of squares of the interation. As these eigenvalues either over- or underestimated (ARAÚJO; DIAS, 2002), these techniques of validation will be improved through the correction of these eigenvalues and, in order to rearrange them, isotonic regression will be used . A comparative study between these methods through real data will be carried out.
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Brown, Steven Richard. "A design of experiments approach for engineering carbon metabolism in the yeast Saccharomyces cerevisiae." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/26158.
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The proven ability to ferment Saccharomyces cerevisiae on a large scale presents an attractive target for producing chemicals and fuels from sustainable sources. Efficient and predominant carbon flux through to ethanol is a significant engineering issue in the development of this yeast as a multi-product cell chassis used in biorefineries. In order to evaluate diversion of carbon flux away from ethanol, combinatorial deletions were investigated in genes encoding the six isozymes of alcohol dehydrogenase (ADH), which catalyse the terminal step in ethanol production. The scarless, dominant and counter- selectable amdSYM gene deletion method was optimised for generation of a combinatorial ADH knockout library in an industrially relevant strain of S. cerevisiae. Current understanding of the individual ADH genes fails to fully evaluate genotype-by-genotype and genotype-by-environment interactions: rather, further research of such a complex biological process requires a multivariate mathematical modelling approach. Application of such an approach using the Design of Experiments (DoE) methodology is appraised here as essential for detailed empirical evaluation of complex systems. DoE provided empirical evidence that in S. cerevisiae: i) the ADH2 gene is not associated with producing ethanol under anaerobic culture conditions in combination with 25 g l-1 glucose substrate concentrations; ii) ADH4 is associated with increased ethanol production when the cell is confronted with a zinc-limited [1 μM] environment; and iii) ADH5 is linked with the production of ethanol, predominantly at pH 4.5. A successful metabolic engineering strategy is detailed which increases the product portfolio of S. cerevisiae, currently used for large-scale production of bioethanol. Heterologous expression of the cytochrome P450 fatty acid peroxygenase from Jeotgalicoccus sp., OleTJE, fused to the RhFRED reductase from Rhodococcus sp. NCIMB 978 converted free fatty acid precursors to C13, C15 and C17 alkenes (3.81 ng μl-1 total alkene concentration).
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Thibord, Florian. "Variation génétique et plasmatique des microARNs : impact sur les paramètres biologiques de l’hémostase OPTIMIR, a novel algorithm for integrating available genome-wide genotype data into miRNA sequence alignment analysis A Genome Wide Association Study on plasma FV levels identified PLXDC2 as a new modifier of the coagulation process." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS379.
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Les microARNs (miARNs) sont les membres d’une classe de petits ARNs non codants d’environ 22 nucléotides, dont le mécanisme principal est de réguler l’expression des gènes dans le cytoplasme. Leurs importance est telle qu’il est estimé que la majorité des gènes humains sont régulés par ces petits ARNs, et ils sont ainsi potentiellement impliqués dans le développement de nombreuse pathologies. La séquence des miARNs peut-être soumise à des variations post- transcriptionnelles et des variations génétiques générant alors des séquences isoformes appelées isomiRs. Afin de détécter et quantifier précisemment l’expression des miARNs à partir de données de séquençage, cette hétérogénéité intra-miARN, due aux isomiRs, doit être prise en compte, tout comme l’homogénéité inter-miARN due aux miARNs paralogues. Le pipeline optimiR, développé dans le cadre de cette thèse, permet de surmonter ces challenges grâce notamment à l’intégration de l’information génétique des échantillons analysés, ainsi qu’à une stratégie d’alignement originale, qui permettent de détecter les isomiRs tout en distinguant les miARNs paralogues. Les données analysées lors de cette thèse proviennent de la cohorte MARTHA, composée de patients ayant développé une thrombose veineuse (VTE), parfois avec récidive. L’expression normalisée de 162 miARNs obtenue pour 344 patients a ensuite été utilisée afin d’analyser: 1) les déterminants génétiques de l’expression de ces miARNs; 2) l’association des miARNs avec le risque de récidive pour la VTE; 3) les corrélations avec certains paramètres biologiques de l’hémostase. Collectivement, ces analyses m’ont permis d’identifier des microARNs d’intérêt pour la recherche sur la thrombose veineuse et sur l’hémostaseMicroRNAs (miRNA) are small non coding RNAs with an average size of 22 nucleotides, mainly known to regulate gene expression in the cytoplasm. These small RNAs are estimated to regulate the majority of human genes, and are potentially involved in several diseases. MiRNA sequences might contain genetic variants and can undergo post-transcriptional variations, which generate miRNA isoforms called isomiRs. In order to accurately detect and quantify miRNA expression, isomiRs as well as paralogous miRNAs must be accounted for. The optimiR pipeline developed during this project overcome these challenges by integrating genetic information and by implementing an original strategy based on local alignement. Sequencing data were obtained from the MARTHA cohort, which is composed of french unrelated patients who experienced venous thrombosis (VTE). Normalized expression of 162 miRNAs from 334 patients were used to analyze: 1) the genetic determinants of miRNA expression; 2) the association of miRNA expression levels with VTE recurence; 3) the correlations between miRNA expression levels and hemostatic traits. As a whole, these analyses allowed me to identify miRNAs of interest for the study of VTE and hemostasis
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Bresso, Emmanuel. "Organisation et exploitation des connaissances sur les réseaux d'intéractions biomoléculaires pour l'étude de l'étiologie des maladies génétiques et la caractérisation des effets secondaires de principes actifs." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0122/document.
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La compréhension des pathologies humaines et du mode d'action des médicaments passe par la prise en compte des réseaux d'interactions entre biomolécules. Les recherches récentes sur les systèmes biologiques produisent de plus en plus de données sur ces réseaux qui gouvernent les processus cellulaires. L'hétérogénéité et la multiplicité de ces données rendent difficile leur intégration dans les raisonnements des utilisateurs. Je propose ici des approches intégratives mettant en oeuvre des techniques de gestion de données, de visualisation de graphes et de fouille de données, pour tenter de répondre au problème de l'exploitation insuffisante des données sur les réseaux dans la compréhension des phénotypes associés aux maladies génétiques ou des effets secondaires des médicaments. La gestion des données sur les protéines et leurs propriétés est assurée par un système d'entrepôt de données générique, NetworkDB, personnalisable et actualisable de façon semi-automatique. Des techniques de visualisation de graphes ont été couplées à NetworkDB pour utiliser les données sur les réseaux biologiques dans l'étude de l'étiologie des maladies génétiques entrainant une déficience intellectuelle. Des sous-réseaux de gènes impliqués ont ainsi pu être identifiés et caractérisés. Des profils combinant des effets secondaires partagés par les mêmes médicaments ont été extraits de NetworkDB puis caractérisés en appliquant une méthode de fouille de données relationnelles couplée à Network DB. Les résultats permettent de décrire quelles propriétés des médicaments et de leurs cibles (incluant l'appartenance à des réseaux biologiques) sont associées à tel ou tel profil d'effets secondairesThe understanding of human diseases and drug mechanisms requires today to take into account molecular interaction networks. Recent studies on biological systems are producing increasing amounts of data. However, complexity and heterogeneity of these datasets make it difficult to exploit them for understanding atypical phenotypes or drug side-effects. This thesis presents two knowledge-based integrative approaches that combine data management, graph visualization and data mining techniques in order to improve our understanding of phenotypes associated with genetic diseases or drug side-effects. Data management relies on a generic data warehouse, NetworkDB, that integrates data on proteins and their properties. Customization of the NetworkDB model and regular updates are semi-automatic. Graph visualization techniques have been coupled with NetworkDB. This approach has facilitated access to biological network data in order to study genetic disease etiology, including X-linked intellectual disability (XLID). Meaningful sub-networks of genes have thus been identified and characterized. Drug side-effect profiles have been extracted from NetworkDB and subsequently characterized by a relational learning procedure coupled with NetworkDB. The resulting rules indicate which properties of drugs and their targets (including networks) preferentially associate with a particular side-effect profile
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Hartung, Karin. "Biometrical approaches for analysing gene bank evaluation data on barley (Hordeum spec.)." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:100-opus-2251.
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Yilmaz, Kutay. "Seeding Date and Genotype Maturity Interactions on Grain Sorghum [Sorghum bicolor –(L.) Moench] Performance In North Dakota." Thesis, North Dakota State University, 2020. https://hdl.handle.net/10365/32043.
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Grain sorghum [Sorghum bicolor (L.) Moench] varieties fail to reach maturity in North Dakota’s short and cool growing season. The study objective was to evaluate seeding date and white grain sorghum genotypes. A randomized complete block design study was conducted at Carrington, Oakes, and Prosper, ND, in 2018 and 2019. Genotypes included two commercial hybrids and four open-pollinated genotypes. Reaching heading and anthesis, hybrids required more heat units (GDDs), compared with the open-pollinated genotypes. Highest grain yield was obtained from the first and second seeding dates. Earlier-maturing open-pollinated genotypes maintained yield across seeding dates, whereas yield was reduced at later dates for the longer maturity hybrids. Hybrids produced the highest number of kernels per panicle at the first seeding date with fewer seeds at each successive seeding date. Although the open-pollinated genotypes out-yielded the hybrids at later seeding dates, the risk of lodging is too great to recommend their commercialization.
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Galván, Femenía Iván. "Compositional methodology and statistical inference of family relationships using genetic markers." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/672178.
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The present thesis is a compendium of three research articles produced between 2015 and 2019. The three articles are different contributions based on compositional statistical methodology and statistical inference of genetic
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relatedness. In the first work of this thesis, we review the classical graphical methods used to detect relatedness and introduce the analysis of Compositional Data for relatedness research. In the second article, we propose the analysis of identity by state genotype sharing data instead of the classical identity by state allele sharing data. The third article finishes the thesis with the development of the likelihood ratio approach to infer three-quarter siblings in genetic databases. To illustrate all the results of this doctoral thesis we use genetic markers from worldwide human population projects such as the Human Genome Diversity Project and the 1000 Genomes Project, as well as from a local prospective human cohort of the Genomes of Catalonia (GCAT)Aquesta tesi doctoral és un compendi de tres articles de recerca produïts entre el 2015-2019. Els tres articles són aportacions diferents basades en la metodologia de les dades composicionals i en la inferència estadística de relacions familiars. En el primer treball d'aquesta tesi, revisem els mètodes gràfics clàssics utilitzats per detectar relacions familiars i introduïm l'anàlisi de les dades composicionals per a la investigació de relacions familiars. En el segon, es proposa l'anàlisi de dades de genotips compartits idèntics per estat en lloc de les clàssiques dades d'al·lels compartits. El tercer article finalitza la tesi amb l'elaboració de la raó de versemblances per inferir tres quarts germans en bases de dades genètiques. Per il·lustrar els resultats, s'utilitzen marcadors genètics de projectes de població humana com el Projecte de la Diversitat del Genoma Humà, el Projecte 1000 Genomes i una cohort humana prospectiva local dels genomes de Catalunya (GCAT)
Programa de Doctorat en Tecnologia
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Osman, Mohammed A. "Effect of water stress on the physiology, growth, and morphology of three pearl millet genotypes." Diss., The University of Arizona, 1988. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_1988_11_sip1_w.pdf&type=application/pdf.
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Saïdou, Abdoul-Aziz. "Etude moléculaire, évolution et caractérisation de gènes impliqués dans l'adaptation du mil (Pennisetum glaucum L.) aux changements climatiques." Thesis, Montpellier, SupAgro, 2011. http://www.theses.fr/2011NSAM0002/document.
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L'évolution climatique a été marquée ces dernières décennies par des changements importants, notamment une augmentation de température et une variation de la pluviométrie. Une des conséquences du changement climatique est son impact actuel et futur sur l'agriculture et la sécurité alimentaire. En région sahélienne, la sécurité alimentaire repose essentiellement sur quelques céréales, parmi lesquelles le mil occupe une place fondamentale. Un des traits qui permettent l'adaptation au climat de cette espèce est la date de floraison, dont la variation permet d'accomplir le cycle de vie de la plante dans des saisons pluvieuses plus ou moins courtes. Les bases génétiques de ce caractère adaptatif sont encore peu connues. Nous avons développé une méthodologie de cartographie d'association phénotype-génotype, afin d'identifier des gènes impliqués dans la variation de ce caractère. Cette étude a permis d'identifier deux gènes candidats, PHYC et MADS11, associés à la date de floraison et à la variation morphologique chez le mil. Nous avons validé ces associations par des analyses QTLs. Pour PHYC, nous avons aussi étudié le pattern de déséquilibre de liaison sur une zone d'environ 80 kb autour du gène, et développé une approche de Markov Chain Monte Carlo (MCMC) pour comparer les gènes identifiés dans la région. Cette analyse suggère que les polymorphismes à l'intérieur de PHYC sont les meilleurs candidats expliquant l'effet phénotypique observée dans cette région du génome. La seconde partie de ce projet a été consacrée à l'examen méthodologique de la cartographie d'association pour l'étude des interactions entre le génotype et l'environnement. Les résultats de cette thèse ont été discutés notamment dans la perspective de gestion de l'impact du changement climatique sur le mil, céréale majeure des zones semi-aridesIn last decades, climate changes led to temperature increase and rainfall variation across the globe. One of the key consequences of these changes is their impact on agriculture and food security. In sahelian countries, food security relies on a few cereal crops, among which pearl millet plays a crucial role for population food supply. Sahel region is facing the impact of rainfall variability and drought since the 1970s. Flowering time variation is one of the main adaptations that allow pearl millet cultivation in drier and shorter rainy seasons. The genetic bases of this complex trait are still understudied. We developed an association mapping framework for the analysis of genotype-phenotype relationship in pearl millet. We successfully identified two genes associated with flowering time variation in pearl millet (PHYC and MADS11). We confirmed these associations using QTL studies. For PHYC, we also examined the pattern of linkage disequilibri um on a chromosomal region extending to 80 kb around the gene, and we developed a Markov Chain Monte Carlo approach (MCMC) to compare six genes identified in this region. Our results suggest that, among the polymorphisms observed in this region, polymorphisms in PHYC are the best candidate for a direct causative role. The second part of this project addressed methodological examination of association mapping framework to deal with genotype by environment interactions. The results of this work were discussed with regard to the challenge of pearl millet crop adaptation to climate change
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Pironti, Alejandro [Verfasser], and Thomas [Akademischer Betreuer] Lengauer. "Improving and validating data-driven genotypic interpretation systems for the selection of antiretroviral therapies / Alejandro Pironti ; Betreuer: Thomas Lengauer." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1122110626/34.
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Baker, George L. "Flavor formation and sensory perception of selected peanut genotypes (Arachis hypogea L.) as affected by storage water activity, roasting, and planting date." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000105.
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Thesis (Ph. D.)--University of Florida, 2002.Title from title page of source document. Document formatted into pages; contains xii, 130 p.; also contains graphics. Includes vita. Includes bibliographical references.
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Santos-Ciminera, Patricia Dantas Ciminera Patricia Dantas Santos Santos Patricia. "Molecular epidemiology of epidemic severe malaria caused by Plasmodium vivax in the state of Amazonas, Brazil /." Download the dissertation in PDF, 2005. http://www.lrc.usuhs.mil/dissertations/pdf/Santos2005.pdf.
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Luo, Yuqun. "Incorporation of Genetic Marker Information in Estimating Modelparameters for Complex Traits with Data From Large Complex Pedigrees." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039109696.
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Peña, Marisol Garcia. "Alternativas de análise para experimentos G × E multiatributo." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-04052016-111857/.
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Geralmente, nos experimentos genótipo por ambiente (G × E) é comum observar o comportamento dos genótipos em relação a distintos atributos nos ambientes considerados. A análise deste tipo de experimentos tem sido abordada amplamente para o caso de um único atributo. Nesta tese são apresentadas algumas alternativas de análise considerando genótipos, ambientes e atributos simultaneamente. A primeira, é baseada no método de mistura de máxima verossimilhança de agrupamento - Mixclus e a análise de componentes principais de 3 modos - 3MPCA, que permitem a análise de tabelas de tripla entrada, estes dois métodos têm sido muito usados na área da psicologia e da química, mas pouco na agricultura. A segunda, é uma metodologia que combina, o modelo de efeitos aditivos com interação multiplicativa - AMMI, modelo eficiente para a análise de experimentos (G × E) com um atributo e a análise de procrustes generalizada, que permite comparar configurações de pontos e proporcionar uma medida numérica de quanto elas diferem. Finalmente, é apresentada uma alternativa para realizar imputação de dados nos experimentos (G × E), pois, uma situação muito frequente nestes experimentos, é a presença de dados faltantes. Conclui-se que as metodologias propostas constituem ferramentas úteis para a análise de experimentos (G × E) multiatributo.Usually, in the experiments genotype by environment (G×E) it is common to observe the behaviour of genotypes in relation to different attributes in the environments considered. The analysis of such experiments have been widely discussed for the case of a single attribute. This thesis presents some alternatives of analysis, considering genotypes, environments and attributes simultaneously. The first, is based on the mixture maximum likelihood method - Mixclus and the three-mode principal component analysis, these two methods have been very used in the psychology and chemistry, but little in agriculture. The second, is a methodology that combines the additive main effects and multiplicative interaction models - AMMI, efficient model for the analysis of experiments (G×E) with one attribute, and the generalised procrustes analysis, which allows compare configurations of points and provide a numerical measure of how much they differ. Finally, an alternative to perform data imputation in the experiments (G×E) is presented, because, a very frequent situation in these experiments, is the presence of missing values. It is concluded that the proposed methodologies are useful tools for the analysis of experiments (G×E) multi-attribute.
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Liang, Bor-Cherng, and 梁博程. "Haplotype Decomposition and Reconstruction from Large Scale Genotype Data." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/19723834795990807865.
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碩士國立清華大學
資訊工程學系
92
In this thesis, we address the problem of haplotype decomposition and reconstruction. While focusing on large scale genotype data, we propose a new framework to determine the haplotype block partitions and to resolve the haplotype pair of each genotype. In implementing the decomposition scheme, we formulate a dynamic programming algorithm to minimize the total number of tag SNPs. For structuring the reconstruction method, we introduce an at-least-one perfect-phylogeny-tree model within each block, and use tiling blocks consisting of tag SNPs among blocks. It turns out that the two elements are well coupled and lead to an accurate and efficient haplotype reconstruction system. Our approach is closely related to the work of Eskin et al.. However, the perfect phylogeny model used in their scheme is restricted by only one perfect phylogeny tree within a block. We instead adopt a more flexible criterion that requires at least one perfect phylogeny tree. Furthermore, in dealing with the difficult problem of resolving whole haplotypes among blocks, we go further to take into account all blocks, whereas their work only considers two adjacent blocks. Specifically, the contributions of our work can be characterized by: (i) an at-least-one prefect-phylogeny-tree model, to fit the real genotype data and improve the accuracy of haplotype resolving within a block; (ii) an informative score function, to resolve a genotype into the most likely pair of haplotypes; (iii) tiling blocks consisting of tag SNPs, to make all of the choices resolvable; and (iii) mutual relation among blocks, to resolve whole haplotypes among blocks by considering all blocks, and to reduce the effects caused by a few erratic choices. We have also included various experimental results to illustrate the advantages of the proposed method. Keywords: Haplotype, tag SNPs, perfect phylogeny tree, tiling block
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Xing, L., X. Zhou, Yonghong Peng, R. Zhang, J. Hu, J. Yu, and B. Liu. "Integrating phenotype-genotype data for prioritization of candidate symptom genes." 2013. http://hdl.handle.net/10454/9755.
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NoSymptoms and signs (symptoms in brief) are the essential clinical manifestations for traditional Chinese medicine (TCM) diagnosis and treatments. To gain insights into the molecular mechanism of symptoms, this paper presents a network-based data mining method to integrate multiple phenotype-genotype data sources and predict the prioritizing gene rank list of symptoms. The result of this pilot study suggested some insights on the molecular mechanism of symptoms.
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Groth, Philip [Verfasser]. "Knowledge management and discovery for genotype-phenotype data / von Philip Groth." 2009. http://d-nb.info/1000377008/34.
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Schwender, Holger [Verfasser]. "Statistical analysis of genotype and gene expression data / by Holger Schwender." 2007. http://d-nb.info/997828072/34.
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Wang, Wen-Chang, and 王紋璋. "A Monte Carlo Method for Linkage Analysis with Sibship Genotype Data." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/04078523516426903881.
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博士國立中央大學
數學研究所
92
We propose a new Monte Carlo approach to the problem of calculating the conditional probability of inheritance patterns given sibship genotype data in multipoint linkage analysis. By limiting the study to sibships, we hope to have a linkage analysis method that can incorporate general crossover process model and can be used to examine the issue of genetic interference in the context of linkage studies. This thesis is separated into three parts. In Part I, we introduce the new Monte Carlo approach of multipoint linkage analysis. Our approach is mainly an application of importance sampling method. The crossover distribution used in this approach is estimated from the CEPH and Icelandic family genotype data. Estimation of this crossover distribution is described in Part III. To make the computation efficient, we show that the calculation of the probability of legal ordered parental genotype given sibship genotype and inheritance patterns can be carried out quickly by a straight-forward classification of inheritance patterns. To evaluate the performance of our method, we compare the performance of our method with that of GENEHUNTER in terms of the accuracy in calculating the conditional probability of IBD sharing for sib-pairs in CEPH families given the sibship genotype on chromosome 19. In Part II, we deal with the set of legal inheritance vectors for a sibship at one marker. We classify the sibships according to the genotype of the sibs into 9 classes, and list explicitly the set of legal inheritance vectors for each class. Because an inheritance pattern is legal at several markers if it is legal at everyone of these markers, results in Part II can be extended directly to the set of legal inheritance patterns for many markers. We use the result to reduce the time and memory needed in our Monte Carlo approach in Part I. In Part III, we provide a nonparametric estimate of the crossover distribution on the basis of the CEPH family genotype data, the Icelandic family genotype data, and the order of the markers where genotype data are taken. The only assumption employed in this approach is that, in one meiosis, there is at most one crossover point between markers close enough to each other. This estimated crossover distribution can be used in multipoint linkage analysis without the assumption of no interference.
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Schriek, Cornelis Arnold. "Analysis and standardization of marker genotype data for DNA fingerprinting applications." Diss., 2011. http://hdl.handle.net/2263/28908.
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Genetic polymorphisms can be seen as the occurrence of more than one form of a DNA- or protein sequence at a single locus in a group of organisms, where these different forms occur more frequently than can be attributed to mutation alone. The combination of genetic polymorphisms present in the genome of a particular individual is referred to as its genotype. A wide range of genotyping techniques have been developed to detect and visualize genetic polymorphisms. One such technique examines highly polymorphic repetitive DNA regions called microsatellites, also called “short tandem repeats” (STRs) and sometimes “simple sequence repeats” (SSRs) or “simple-sequence length polymorphisms” (SSLPs). A microsatellite region consists of a DNA sequence of identical units of usually 2-6 base pairs strung together to produce highly variable numbers of tandem repeats among individuals of a population. Microsatellite genotyping is a popular choice for many types of studies including individual identification, paternity testing, germplasm evaluation, genome mapping and diversity studies and can be used in many commercial, academic, social, and agricultural applications. There are, however, many obstacles in effectively managing and analysing microsatellite genotype data. Currently, researchers are struggling to effectively manage and analyse rapidly growing volumes of genotyping data. Management problems range from simply the lack of a secure, easily accessible central data repository to more complex issues like the merging and standardization of data from multiple sources into combined datasets. Due to these issues, genetic fingerprinting applications such as identity matching and relatedness studies can be challenging when data from different experiments or laboratories have to be combined into a central database. The main aim of this M.Sc study in Bioinformatics was to develop a bioinformatics resource for the management and analysis of genetic fingerprinting data from microsatellite marker genotyping studies, and to apply the software to the analysis of microsatellite marker data from ramets of Pinus patula clones with the purpose of analysing clonal identity in pine breeding programmes. The software resource developed here is called GenoSonic. It is a web application that provides users with a secure, easily accessible space where genotyping project data can be managed and analysed as a team. Users can upload and download large amounts of marker genotype data. Once uploaded to the system, DNA fingerprint data needs to be standardised before it can be used in further analyses. To do this, a two-step approach was implemented in GenoSonic. The first step is to assign standardized allele sizes to all of the input allele sizes of the microsatellite fingerprints automatically using a novel automated binning algorithm called CSMerge-1, which was designed specifically to bin data from multiple experiments. The second step is to manually verify the results from the automated binning function and add the verified data to a standardized dataset. Once the genetic fingerprints have been standardized, allele- and genotype frequencies can be viewed for any given marker. GenoSonic also provides functionalities for identity matching. One or more DNA fingerprints from unknown samples can be matched against a standardized dataset to establish identities or infer relatedness. Finally, GenoSonic implements a genetic distance tree construction function, which can be used to visualize relatedness among samples in a selected dataset. The bioinformatics resource developed in this study was applied to a microsatellite DNA fingerprinting project aimed at the re-establishment or confirmation of clonal identity of Pinus patula ramets from pine clonal seed orchards developed by a South African forestry company at one of their new agricultural estates in South Africa. The results from GenoSonic‟s automated binning function (CSMerge-1) and the results from the identity matching and tree construction exercise were compared to results obtained by human experts who have analysed the data manually. It was demonstrated that the results from GenoSonic equalled or surpassed the manual results in terms of accuracy and consistency, and far surpasses the manual effort in terms of the speed at which analyses could be completed. GenoSonic was developed with specific focus on reusability, and the ability to be modified or extended to solve future genotyping-related problems. This study not only provides a solution to current genotype data management and analysis needs of researchers, but is aimed at serving as a basic framework, or component library for future software development projects that may be required to address specific needs of researchers dealing with high-throughput genotyping data.Dissertation (MSc)--University of Pretoria, 2011.
Biochemistry
unrestricted
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Li, X., X. Zhou, Yonghong Peng, B. Liu, R. Zhang, J. Hu, J. Yu, C. Jia, and C. Sun. "Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes." 2014. http://hdl.handle.net/10454/10724.
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YesSymptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
NSFC Project (61105055, 81230086), China 973 Program (2014CB542903), The National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), the National S&T Major Special Project on Major New Drug Innovation (2012ZX09503-001-003), and the Fundamental Research Funds for the Central Universities.
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Chen, Sui-Pi, and 陳穗碧. "A Bayesian clustering approach for detecting gene-gene interactions in high-dimensional genotype data." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/38870357626630831310.
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博士國立交通大學
統計學研究所
100
The detection of susceptibility genes for complex disease is a major challenge for human geneticists. The phenomenon of epistasis, or gene-gene interactions, is particularly difficult to handle for traditional statistical techniques. Over the past few decades, three kind of approaches have been proposed to address this issue. First, a set of approaches modified logistic regression have the direct ability to interpret the result. But it is limited by the parametric model which does not describe the nonlinear relationship between the epistasis and the phenotype. Second, data-mining or machine-learning methods, such as MDR and CART, do not fit a single prespecified model, but rather they attempt to step through the space of possible models in a computationally efficient way to address the problem from the regression-based approach. However, as genomic technologies rapidly advance, the explosion of epistasis makers make exhaustive searches of multilocus combinations computationally infeasible. Bayesian model selection techniques offer an alternative approach for selecting loci and the interactions between them that are the best predictors of phenotype. A representative algorithm is Bayesian epistasis association mapping (BEAM). This paper applies a Bayesian formulation of a clustering procedure for identification of gene-gene interactions under case-control studies, called Bayesian clustering for detecting epistasis (BCDE) model. BCDE model uses the Dirichlet process mixtures to model SNP marker partitions and the Gibbs weighted Chinese restaurant sampling to simulate posterior distributions of these partitions. Unlike the representative Bayesian epistasis detection algorithm BEAM where markers are partitioned into three groups, BCDE model can be evaluated at any given partition, regardless of the number of groups. We further develop a permutation test to validate the disease association for SNP subsets identified by BCDE model, which can yield results that are more robust to model specification and prior assumptions. Performance of BCDE model and comparison with BEAM are examined on various simulated data and a schizophrenia SNP dataset.
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Ng, K. C. S., J. C. S. Ngabonziza, P. Lempens, Jong B. C. de, Leth F. van, and Conor J. Meehan. "Bridging the TB data gap: in silico extraction of rifampicin-resistant tuberculosis diagnostic test results from whole genome sequence data." 2019. http://hdl.handle.net/10454/17491.
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YesBackground: Mycobacterium tuberculosis rapid diagnostic tests (RDTs) are widely employed in routine laboratories and national surveys for detection of rifampicinresistant (RR)-TB. However, as next-generation sequencing technologies have become more commonplace in research and surveillance programs, RDTs are being increasingly complemented by whole genome sequencing (WGS). While comparison between RDTs is difficult, all RDT results can be derived from WGS data. This can facilitate continuous analysis of RR-TB burden regardless of the data generation technology employed. By converting WGS to RDT results, we enable comparison of data with different formats and sources particularly for low- and middle-income high TB-burden countries that employ different diagnostic algorithms for drug resistance surveys. This allows national TB control programs (NTPs) and epidemiologists to utilize all available data in the setting for improved RR-TB surveillance. Methods: We developed the Python-based MycTB Genome to Test (MTBGT) tool that transforms WGS-derived data into laboratory-validated results of the primary RDTs—Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and GenoscholarNTM+MDRTB II. The tool was validated through RDT results of RR-TB strains with diverse resistance patterns and geographic origins and applied on routine-derived WGS data. Results: The MTBGT tool correctly transformed the single nucleotide polymorphism (SNP) data into the RDT results and generated tabulated frequencies of the RDT probes as well as rifampicin-susceptible cases. The tool supplemented the RDT probe reactions output with the RR-conferring mutation based on identified SNPs. The MTBGT tool facilitated continuous analysis of RR-TB and Xpert probe reactions from different platforms and collection periods in Rwanda. Conclusion: Overall, the MTBGT tool allows low- and middle-income countries to make sense of the increasingly generated WGS in light of the readily available RDT.
Erasmus Mundus Joint Doctorate Fellowship grant 2016- 1346.
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Shields, Phil. "Estimating grain yield using spectral reflectance data in winter wheat genotypes." 1987. http://hdl.handle.net/2097/22227.
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Cai, Yimei. "Estimation of the seed dispersal distribution with genotypic data." 2007. http://purl.galileo.usg.edu/uga%5Fetd/cai%5Fyimei%5F200712%5Fphd.
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Farrell, John J. "The prediction of HLA genotypes from next generation sequencing and genome scan data." Thesis, 2014. https://hdl.handle.net/2144/14694.
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Genome-wide association studies have very successfully found highly significant disease associations with single nucleotide polymorphisms (SNP) in the Major Histocompatibility Complex for adverse drug reactions, autoimmune diseases and infectious diseases. However, the extensive linkage disequilibrium in the region has made it difficult to unravel the HLA alleles underlying these diseases. Here I present two methods to comprehensively predict 4-digit HLA types from the two types of experimental genome data widely available.
The Virtual SNP Imputation approach was developed for genome scan data and demonstrated a high precision and recall (96% and 97% respectively) for the prediction of HLA genotypes. A reanalysis of 6 genome-wide association studies using the HLA imputation method identified 18 significant HLA allele associations for 6 autoimmune diseases: 2 in ankylosing spondylitis, 2 in autoimmune thyroid disease, 2 in Crohn's disease, 3 in multiple sclerosis, 2 in psoriasis and 7 in rheumatoid arthritis. The EPIGEN consortium also used the Virtual SNP Imputation approach to detect a novel association of HLA-A*31:01 with adverse reactions to carbamazepine.
For the prediction of HLA genotypes from next generation sequencing data, I developed a novel approach using a naïve Bayes algorithm called HLA-Genotyper. The validation results covered whole genome, whole exome and RNA-Seq experimental designs in the European and Yoruba population samples available from the 1000 Genomes Project. The RNA-Seq data gave the best results with an overall precision and recall near 0.99 for Europeans and 0.98 for the Yoruba population. I then successfully used the method on targeted sequencing data to detect significant associations of idiopathic membranous nephropathy with HLA-DRB1*03:01 and HLA-DQA1*05:01 using the 1000 Genomes European subjects as controls.
Using the results reported here, researchers may now readily unravel the association of HLA alleles with many diseases from genome scans and next generation sequencing experiments without the expensive and laborious HLA typing of thousands of subjects. Both algorithms enable the analysis of diverse populations to help researchers pinpoint HLA loci with biological roles in infection, inflammation, autoimmunity, aging, mental illness and adverse drug reactions.
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Thornton-Wells, Tricia A. "Comparison of three clustering methods for dissecting trait heterogeneity in simulated genotypic data." Diss., 2005. http://etd.library.vanderbilt.edu/ETD-db/available/etd-07182005-122343/.
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Collier, Robert. "Empirically Evaluated Improvements to Genotypic Spatial Distance Measurement Approaches for the Genetic Algorithm." Thesis, 2012. http://hdl.handle.net/10214/3565.
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The ability to visualize a solution space can be very beneficial, and it is generally accepted that the objective of visualization is to aid researchers in gathering insight. However, insight cannot be gathered effectively if the source data is misrepresented. This dissertation begins by demonstrating that the adaptive landscape visualization in widespread usage frequently misrepresents the neighborhood structure of genotypic space and, consequently, will mislead users about the manner in which solution space is traversed by the genetic algorithm. Bernhard Riemann, the father of topology, explicitly noted that a measurement of the distance between entities should represent the manner in which one can be brought towards the other. Thus, the commonly used Hamming distance, for example, is not representative of traversals of genotypic space by the genetic algorithm – a representative measure must include consideration for both mutation and recombination. This dissertation separately explores the properties that mutational and recombinational distances should have, and ultimately establishes a measure that is representative of the traversals made by both operators simultaneously.
It follows that these measures can be used to enhance the adaptive landscape, by minimizing the discrepancy between the interpoint distances in genotypic space and the interpoint distances in the two-dimensional representation from which the landscape is extruded. This research also establishes a methodology for evaluating measures defining neighbourhood structures that are purportedly representative of traversals of genotypic space, by comparing them against an empirically generated norm. Through this approach it is conclusively demonstrated that the Hamming distance between genotypes is less representative than the proposed measures, and should not be used to define the neighbourhood structure from which visualizations would be constructed.
While the proposed measures do not distort the data or otherwise mislead the user, they do require a significant computational expense. Fortunately, the choice to use these measures is always made at the discretion of the user, with additional costs incurred when accuracy and representativity are of paramount importance. These measures will ultimately find further application in population diversity measurement, cluster analysis, and any other task where the representativity of the neighborhood structure of the genotypic space is vital.
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Lu, Kun-Chuan, and 呂坤泉. "Effects of Harvest Date on Pod Maturity, Yield and Quality of Four Peanut Genotypes." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/85856815036275153826.
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碩士國立中興大學
農藝學研究所
84
To study the effects of harvest date on pod maturity, yield and quality of peanut, two Spanish type (TN 11 and TNG 6) and two Virginia type (Li-chu-tzae and VB313 ) peanut genotypes were grown with two space-in-rows in the field of the Taiwan Agricultural Research Institute in the spring and fall crop seasons of 1995. The results were summarized as follows: 1.The first and second branches of peanuts set more pods than any of other branches. More pods set at the upper branches of Virginia type than Spanish type. Due to growth habits, some upper pods may have earlier maturity than lower pods. 2.Peanuts grown in 30 cm of space-in-row had higher pod weight and number per plant than those in 10 cm of space- in-row. However, peanuts grown in 10cm of space-in-row had significant higher pod and seed yield per hectare than those in 30cm of space-in-row. 3.The maturing pods usually were less than 70% for four genotypes grown in both spring and fall crop seasons. For the Spanish type peanuts, 108-115 days after first bloom stage in spring or 88-95 days after first bloom stage in fall is the most appropriate harvest date. As for the Virgina type peanuts, the most appropriate harvest date is about 115 days and 95 days after first bloom stage in sprint and fall, separately. 4.Higher oil content was found in the well maturing peanuts and in the spring season. However, protein content was slightly affected by the change of harvest date.
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Breazel, Ellen Hepfer. "Effect of common errors in microsatellite data on estimates of population differentiation and inferring genotypic structure of complex disease loci using genome-wide expression data." 2008. http://purl.galileo.usg.edu/uga%5Fetd/breazel%5Fellen%5Fh%5F200808%5Fphd.
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