A pattern is a relatively short sequence that represents a phenomenon in a set of sequences. Not all short sequences are patterns; only those that are statistically significant are referred to as patterns or motifs. Pattern discovery methods analyze sequences and attempt to identify and characterize meaningful patterns. This thesis extends the application of pattern discovery algorithms to a new problem domain - Single Nucleotide Polymorphism (SNP) classification. SNPs are single base-pair (bp) variations in the genome, and are probably the most common form of genetic variation. On average, one in every thousand bps may be an SNP. The function of most SNPs, especially those not associated with protein sequence changes, remains unclear. However, genome-wide linkage analyses have associated many SNPs with disorders ranging from Crohn’s disease, to cancer, to quantitative traits such as height or hair color. As a result, many groups are working to predict the functional effects of individual SNPs. In contrast, very little research has examined the causes of SNPs: Why do SNPs occur where they do? This thesis addresses this problem by using pattern discovery algorithms to study DNA non-coding sequences. The hypothesis is that short DNA patterns can be used to predict SNPs. For example, such patterns found in the SNP sequence might block the DNA repair mechanism for the SNP, thus causing SNP occurrence. In order to test the hypothesis, a model is developed to predict SNPs by using pattern discovery methods. The results show that SNP prediction with pattern discovery methods is weak (50 2%), whereas machine learning classification algorithms can achieve prediction accuracy as high as 68%. To determine whether the poor performance of pattern discovery is due to data characteristics (such as sequence length or pattern length) or to the specific biological problem (SNP prediction), a survey was conducted by profiling eight representative pattern discovery methods at multiple parameter settings on 6,754 real biological datasets. This is the first systematic review of pattern discovery methods with assessments of prediction accuracy, CPU usage and memory consumption. It was found that current pattern discovery methods do not consider positional information and do not handle short sequences well (<150 bps), including SNP sequences. Therefore, this thesis proposes a new supervised pattern discovery classification algorithm, referred to as Weighted-Position Pattern Discovery and Classification (WPPDC). The WPPDC is able to exploit positional information to identify positionally-enriched motifs, and to select motifs with a high information content for further classification. Tree structure is applied to WPPDC (referred to as T-WPPDC) in order to reduce algorithmic complexity. Compared to pattern discovery methods T-WPPDC not only showed consistently superior prediction accuracy and but generated patterns with positional information. Machine-learning classification methods (such as Random Forests) showed comparable prediction accuracy. However, unlike T-WPPDC, they are classification methods and are unable to generate SNP-associated patterns.

A pattern is a relatively short sequence that represents a phenomenon in a set of sequences. Not all short sequences are patterns; only those that are statistically significant are referred to as patterns or motifs. Pattern discovery methods analyze sequences and attempt to identify and characterize meaningful patterns. This thesis extends the application of pattern discovery algorithms to a new problem domain - Single Nucleotide Polymorphism (SNP) classification. SNPs are single base-pair (bp) variations in the genome, and are probably the most common form of genetic variation. On average, one in every thousand bps may be an SNP. The function of most SNPs, especially those not associated with protein sequence changes, remains unclear. However, genome-wide linkage analyses have associated many SNPs with disorders ranging from Crohn’s disease, to cancer, to quantitative traits such as height or hair color. As a result, many groups are working to predict the functional effects of individual SNPs. In contrast, very little research has examined the causes of SNPs: Why do SNPs occur where they do? This thesis addresses this problem by using pattern discovery algorithms to study DNA non-coding sequences. The hypothesis is that short DNA patterns can be used to predict SNPs. For example, such patterns found in the SNP sequence might block the DNA repair mechanism for the SNP, thus causing SNP occurrence. In order to test the hypothesis, a model is developed to predict SNPs by using pattern discovery methods. The results show that SNP prediction with pattern discovery methods is weak (50 2%), whereas machine learning classification algorithms can achieve prediction accuracy as high as 68%. To determine whether the poor performance of pattern discovery is due to data characteristics (such as sequence length or pattern length) or to the specific biological problem (SNP prediction), a survey was conducted by profiling eight representative pattern discovery methods at multiple parameter settings on 6,754 real biological datasets. This is the first systematic review of pattern discovery methods with assessments of prediction accuracy, CPU usage and memory consumption. It was found that current pattern discovery methods do not consider positional information and do not handle short sequences well (<150 bps), including SNP sequences. Therefore, this thesis proposes a new supervised pattern discovery classification algorithm, referred to as Weighted-Position Pattern Discovery and Classification (WPPDC). The WPPDC is able to exploit positional information to identify positionally-enriched motifs, and to select motifs with a high information content for further classification. Tree structure is applied to WPPDC (referred to as T-WPPDC) in order to reduce algorithmic complexity. Compared to pattern discovery methods T-WPPDC not only showed consistently superior prediction accuracy and but generated patterns with positional information. Machine-learning classification methods (such as Random Forests) showed comparable prediction accuracy. However, unlike T-WPPDC, they are classification methods and are unable to generate SNP-associated patterns.

No description available.

Previous studies of the grey wolf (Canis lupus) using microsatellites have showed strong population structure despite the high mobility of individuals. I re-assessed the structure of North American grey wolves by genotyping 132 wolves at a genome-wide set of >26 000 single nucleotide polymorphisms (SNPs), and found less population structure, a strong pattern of isolation by distance, and determined that gene flow between subpopulations relates to prey specialization. To assess how accurately smaller data sets assign individuals, I analyzed sub-sets of SNPs and found that small marker sets varied greatly in estimates of subpopulation assignment, and showed high discordance with assignments determined when using all 26k markers. Finally, using a genome scan to detect natural selection I identified SNPs in three genes that may have undergone directional selection, contain variation with observed phenotypic consequences in other mammal species and may be related to adaptation in grey wolves. / Systematics and Evolution

Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-04-17T15:24:58Z No. of bitstreams: 2 TESE Manuela de Souza.pdf: 8389907 bytes, checksum: 5002b2436d9b695b176799e52ad52b6a (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-04-17T15:24:58Z (GMT). No. of bitstreams: 2 TESE Manuela de Souza.pdf: 8389907 bytes, checksum: 5002b2436d9b695b176799e52ad52b6a (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-04-19 / FACEPE / Doenças neuropsiquiátricas afetam cerca de 450 milhões de pessoas em todo mundo e dentre estas patologias os Transtornos do Humor (TH) e Doença de Alzheimer (DA) são as mais comuns. Em relação à sua etiologia as doenças neuropsiquiátricas são resultados de variações em um grupo de genes e de fatores ambientais. Pesquisas recentes vêm mostrando associação positiva entre variações genéticas em genes envolvidos nos sistemas de neurotransmissores com o desenvolvimento de doenças neuropsiquiátricas. Por isso, os estudos sobre polimorfismos genéticos, nas doenças psiquiátricas são de grande importância para a compreensão dos mecanismos moleculares envolvidos e podem auxiliar no diagnóstico das mesmas. Neste cenário, mutações encontradas no DNA têm sido amplamente estudadas, a fim de elucidar aspectos genéticos relacionados às neuropatologias. Os polimorfismos do tipo SNPs (Polimorfismo de Base Única) e INDELs (inserções e deleções de fragmentos de DNA) têm se destacado devido as fortes associações com os TH e DA. Diversos métodos de biologia molecular têm sido utilizados para detectar estes tipos de polimorfismos, os experimentos moleculares geram grande quantidade de dados a serem analisados, fazendo-se necessário a utilização de ferramentas computacionais para se extrair informações a partir desses dados gerados. Assim, o objetivo desse estudo foi o uso de bioinformática e de genotipagem em larga escala na busca de novos polimorfismos genético em TH e aplicação de ferramentas computacionais em banco de dados de GWAS referente à DA. Para obter os resultados referentes à TH optamos por utilizar o software CLCbio Workbench®, sequenciamento automatizado mega Bace 1000 e experimentos preliminares da técnica de DNA pooling. Já para a DA, utilizamos o teste de associação e método de regressão linear do software PLINK e o pacote genetics da linguagem de programação R, para correlacionar os níveis da proteína βamiloide no plasma e líquido cefalorraquidiano e um total de 598.821 SNPs, ambos os dados oriundos do banco de dados ADNI (Alzheimer’s Disease Neuroimaging Initiative). Após uma sequência de passos in silico identificamos variações anteriormente descritas e novos polimorfismos candidatos à fisiopatologia dos TH, na fase de validação dessas variações, por meio de sequenciamento, falsos positivos foram frequentemente identificados, sendo descartados após a verificação na cadeia complementar. Apenas o SNP rs14068, localizado no exon 2 do gene GABRA5 foi validado em amostras de pacientes com TH. No estudo referente à DA, 5 SNPs nas regiões dos genes TOMM40, PAMR1, TRIM9 e CCDC112 e 3 SNPs em regiões de intron atingiram associação significativa, levantando a possibilidade de estejam relacionados a fisiopatologia da DA.

Made available in DSpace on 2018-08-01T21:35:21Z (GMT). No. of bitstreams: 1 tese_12338_Tese - Paulo Cilas Morais Lira Junior.pdf: 2589044 bytes, checksum: 296741ac94e07c977802b7850599cabc (MD5) Previous issue date: 2018-03-19 / O câncer de ovário (CaOV) configura como um câncer letal. Fatores genéticos contribuindo para o risco de desenvolvimento do CaOV têm sido investigados através dos estudos de associação ampla do genoma (GWAS), identificando loci de risco em diferentes regiões dos cromossomos, dentre eles o locus 8q21. Nesse estudo, realizamos uma análise funcional sistemática de nove SNPs candidatos para a causalidade ao CaOV no locus proximal ao gene CHMP4C. Após a caracterização da região para prováveis elementos regulatórios e genes associados, testamos os nove SNPs candidatos para atividade alelo específica para regiões com atividade de enhancer, como também testes para identificar prováveis fatores de transcrição. O SNP candidato localizado na região codificante do gene CHMP4C foi testado para instabilidade da proteína. Três SNPs foram identificados com funcionalidade alelo específica: rs35094336, rs137960856, rs1116683632. Este estudo elucidou o campo funcional da região 8q21 associado ao CaOV e identificou SNPs funcionais como possíveis mecanismos de associação ao risco de desenvolvimento da doença.

Indiana University-Purdue University Indianapolis (IUPUI) / Developmental changes in the liver significantly impact drug disposition. Due to the emergence of microRNAs as important regulators of drug disposition, we hypothesize that age-dependent change in microRNA expression and genetic variants in microRNA target sites contribute to variability in drug disposition. In human liver tissues, expression of 533 microRNAs and over 14,000 genes were measured. In all, 114 microRNAs were upregulated and 72 downregulated from fetal to pediatric, and 2 and 3, respectively, from pediatric to adult. Among these microRNAs, 99 microRNA-mRNA interactions were predicted or have previously been validated to target drug disposition genes and over 1,000 significant negative correlations were observed between miRNA-mRNA pairs. We validated these interactions using various cell culture models. Genetic variants in the promoter and coding regions of drug disposition genes have also been shown to alter enzyme expression and/or activity. However, these variants do not account for all variability in enzyme activity. Emerging evidence has shown that variants in the 3’UTR may explain variable drug response by altering microRNA regulation. Five 3’UTR variants were associated with significantly altered CYP2B6 activity in healthy human volunteers. The rs70950385 (AG>CA) variant was associated with decreased CYP2B6 activity among normal metabolizers. In vitro luciferase assays confirmed that the CA allele altered miR 1275 targeting of CYP2B6 mRNA. Due to the large number of 3’UTR variants predicted to alter microRNA regulation, a high-throughput method, PASSPORT-seq, was developed to test over 100 3’UTR variants simultaneously in different cell lines. Thirty-eight variants resulted in FDR-significant altered expression between wild-type and variant sequences. Our data suggest a mechanism for the marked changes in hepatic gene expression between the fetal and pediatric developmental periods, support a role for these age dependent microRNAs in regulating drug disposition, and provide strong evidence that 3’UTR variants are also an important source of variability in drug disposition.

No description available.

Taste impacts the palatability and intake of food, which is influenced by several factors such as cultural and genetic factors. Individual variations in taste perception may be important risk factors for poor eating habits and development of obesity. The differences in taste perception which impact dietary intake may lead to better understanding of obesity development and prevention of diet-related diseases. Obesity is one of the main causes for various health conditions in the United States as well as in the world. Genetic inheritance plays an important role in individual variations to taste and food choices. This study explored associations between two single nucleotide polymorphisms (SNPs, rs713598 and rs10246939) in the TAS2R38 bitter taste receptor gene, dietary intake, and body fat percentage. Five hundred presumably healthy students aged 18-25 years, including 86 (17%) males and 414 (83%) females from Mississippi State University participated in the study. Saliva was collected for genetic analysis, participants completed dietary history questionnaires and body composition was measured using bioelectrical impedance analysis. All statistical analysis of data was conducted using SPSS software to examine associations between SNPs, food intake, and percentage of body fat. Our results did not show a significant association between the SNPs; rs713598 and rs10246939 in the TAS2R38 bitter taste receptor gene and dietary intake of vegetables and fruits as well as percentage of body fat in this group of participants. However, alcohol and caffeine intakes were significantly different between genotypes in rs713598; p< 0.01, p< 0.05, respectively.

Different taste preferences and genetic variations may lead to particular food patterns that contribute to nutrient-related health outcomes such as hypertension. The objective of this study was to investigate single polymorphism of taste genes and salt taste perception in order to determine whether single nucleotide polymorphisms (SNPs) in the salt taste receptor genes (SCNN1B, TRPV1) affect salt taste perception in hypertensive participants. A cross-sectional study of 253 adults age 20-82 from each group, hypertensive (49%) and normotensive (51%), were enrolled. Salt taste recognition threshold, food preference score, and salt taste receptor genotype were determined. The hypertensive group had a higher salt taste recognition threshold than the normotensive group. However, there was no correlation between salt taste recognition threshold and salty food preference. Results also provide evidence that the polymorphism TRPV1, rs4790522 with AA genotype is associated with a lower sensitivity threshold of salt taste.