A major gene controlling grain cadmium (Cd) concentration, designated as Cdu-B1, has been mapped to the long arm of chromosome 5B, but the genetic factor(s) conferring the low Cd phenotype are currently unknown. Genetic mapping of markers linked to Cdu-B1 in a population of recombinant inbred substitution lines (RSLs) revealed that the gene(s) associated with variation in Cd concentration reside(s) in wheat deletion bin 5BL9 between fraction breakpoints 0.76 and 0.79, and linked to two candidate genes; PCS2 (phytochelatin synthetase) and Xwg644, which codes for a known ABC (ATP-binding cassette) protein. Genetic mapping and quantitative trait locus (QTL) analysis of grain Cd concentration was performed in a doubled haploid (DH) population and revealed that these genes were not associated with Cdu-B1. Two expressed sequence markers (ESMs), and five sequence tagged site (STS) markers were identified that co-segregated with Cdu-B1, and explained >80% of the phenotypic variation in grain Cd concentration. A gene coding for a P1B-ATPase, designated as OsHMA3 (heavy metal associated), has recently been associated with phenotypic variation in grain Cd concentration in rice. Mapping of the orthologous gene to OsHMA3 in the DH population revealed complete linkage with Cdu-B1 and was designated as HMA3-B1. Fine mapping of Cdu-B1 in >4000 F2 plants localized Cdu-B1 to a 0.14 cM interval containing HMA3-B1. Two bacterial artificial chromosomes (BACs) containing full-length coding sequence for HMA3-B1 and HMA3-A1 (homoeologous copy from the A genome) were identified and sequenced. Sequencing of HMA3-B1 from high and low Cd accumulators of durum wheat revealed a 17 bp duplication in high accumulators that results in predicted pre-mature stop codon and thus, a severely truncated protein. Several DNA markers linked to Cdu-B1, including HMA3-B1, were successfully converted to high throughput markers and were evaluated for practical use in breeding programs. These markers were successful at classifying a collection of 96 genetically diverse cultivars and breeding lines into high and low Cd accumulators and will have broad application in breeding programs targeting selection for low grain Cd concentrations. Current results support HMA3-B1 as a candidate gene responsible for phenotypic differences in grain Cd concentrations in durum wheat.

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.

Genome-wide association studies (GWAS) have successfully identified thousands of variants robustly associated with hundreds of complex traits, but the biological mechanisms driving these results remain elusive. Functional annotation, describing the roles of known genes and regulatory elements, provides additional information about associated variants. This dissertation explores the potential of these annotations to explain the biology behind observed GWAS results. The first project develops a random-effects approach to genetic fine mapping of trait-associated loci. Functional annotation and estimates of the enrichment of genetic effects in each annotation category are integrated with linkage disequilibrium (LD) within each locus and GWAS summary statistics to prioritize variants with plausible functionality. Applications of this method to simulated and real data show good performance in a wider range of scenarios relative to previous approaches. The second project focuses on the estimation of enrichment by annotation categories. I derive the distribution of GWAS summary statistics as a function of annotations and LD structure and perform maximum likelihood estimation of enrichment coefficients in two simulated scenarios. The resulting estimates are less variable than previous methods, but the asymptotic theory of standard errors is often not applicable due to non-convexity of the likelihood function. In the third project, I investigate the problem of selecting an optimal set of tissue-specific annotations with greatest relevance to a trait of interest. I consider three selection criteria defined in terms of the mutual information between functional annotations and GWAS summary statistics. These algorithms correctly identify enriched categories in simulated data, but in the application to a GWAS of BMI the penalty for redundant features outweighs the modest relationships with the outcome yielding null selected feature sets, due to the weaker overall association and high similarity between tissue-specific regulatory features. All three projects require little in the way of prior hypotheses regarding the mechanism of genetic effects. These data-driven approaches have the potential to illuminate unanticipated biological relationships, but are also limited by the high dimensionality of the data relative to the moderate strength of the signals under investigation. These approaches advance the set of tools available to researchers to draw biological insights from GWAS results.

Genome-wide association studies (GWAS) of common diseases have identified hundreds of genomic regions harboring disease-associated variants. Translating these findings into an improved understanding of human disease requires identifying the causal variants(s) and gene(s) in the implicated regions which, to date, has only been accomplished for a small number of associations. Several factors complicate the identification of mutations playing a causal role in disease. First, GWAS arrays survey only a subset of known variation. The true causal mutation may not have been directly assayed in the GWAS and may be an unknown, novel variant. Moreover, the regions identified by GWAS may contain several genes and many tightly linked variants with equivalent association signals, making it difficult to decipher causal variants from association data alone. Finally, in many cases the variants with strongest association signals map to non-coding regions that we do not yet know how to interpret and where it remains challenging to predict a variants likely phenotypic impact. Here, we present a framework for the genetic and functional study of intergenic regions identified through GWAS and describe application of this framework to chromosome 9p21: a non-coding region with associations to type 2 diabetes (T2D), myocardial infarction (MI), aneurysm, glaucoma, and multiple cancers. First, we compare methods for genetic fine-mapping of GWAS associations, including methods for creating a more comprehensive catalog of variants in implicated regions and methods for capturing these variants in case- control cohorts. Next, we describe an approach for using massively parallel reporter assays (MPRA) to systematically identify regulatory elements and variants across disease-associated regions. On chromosome 9p21, we fine-map the T2D and MI associations and identify, for each disease, a collection of common variants with equivalent association signals. Using MPRA, we identify hundreds of regulatory elements on chromosome 9p21 and multiple variants (including MI- and T2D-associated variants) with evidence for allelic effects on regulatory activity that can serve as a foundation for further study. More generally, the methods presented here have broad potential application to the many intergenic regions identified through GWAS and can help to uncover the mechanisms by which variants in these regions influence human disease.

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One of the main biological goals of recent years is to determine the genes in the human genome that cause disease. Recent technological advances have realised genome-wide association studies, which have uncovered numerous genetic regions implicated with human diseases. The current approach to analysing data from these studies is based on testing association at single SNPs but this is widely accepted as underpowered to detect rare and poorly tagged variants. In this thesis we propose several novel approaches to analysing large-scale association data, which aim to improve upon the power offered by traditional approaches. We combine an established imputation framework with a sophisticated disease model that allows for multiple disease causing mutations at a single locus. To evaluate our methods, we have developed a fast and realistic method to simulate association data conditional on population genetic data. The simulation results show that our methods remain powerful even if the causal variant is not well tagged, there are haplotypic effects or there is allelic heterogeneity. Our methods are further validated by the analysis of the recent WTCCC genome-wide association data, where we have detected confirmed disease loci, known regions of allelic heterogeneity and new signals of association. One of our methods also has the facility to identify the high risk haplotype backgrounds that harbour the disease alleles, and therefore can be used for fine-mapping. We believe that the incorporation of our methods into future association studies will help progress the understanding genetic diseases.

The cost of feed remains an important factor affecting the profitability of beef production, and the difficulty of recording feed intake is a major limitation in an industry-wide selection program. Novel genomics approaches offer opportunities to select for efficient cattle. Therefore, the main objective of this work was to identify genetic markers responsible for genetic variation in feed efficiency traits as well as to understand the molecular basis of feed efficiency traits. The candidate gene approach revealed new single nucleotide polymorphisms (SNPs) in the Cholecystokinin B receptor (CCKBR) and pancreatic anionic trypsinogen (TRYP8) genes that showed strong evidence of association with feed efficiency traits. An in silico approach was proposed as a cost-effective method for SNP discovery. SNPs within genes Pyruvate carboxylase, ATPaseH+, UBQEI, UCP2, and PTI showed evidence of association with carcass traits without negatively affecting feed efficiency traits. The polymorphisms within genes CCKBR and TRYP8 were associated with pancreas mass and pancreatic exocrine secretion. A fine-mapping study on 1,879 SNPs revealed 807 SNPs with significant associations corresponding to 1,012 genes. These 807 SNPs represented a genomic heritability of 0.32 and 89% of the genetic variance of residual feed intake (RFI). Genomic breeding values estimated from the SNP set (807) were highly correlated (0.96) to the breeding values estimated from a mixed animal model. The 10 most influential SNPs were located in chromosomes 16, 17, 9, 11, 12, 20, 15, and 19. Enrichment analysis for the identified genes (1,012) suggested 110 biological processes and 141 pathways contributed to variation in RFI. The 339 newly identified SNPs corresponding to 180 genes identified by fine-mapping were tested for association with feed efficiency, growth, and carcass traits. Strong evidence of associations for RFI was located on chromosomes 8, 15, 16, 18, 19, 21, and 28. Combing validated SNPs from fine-mapping and the candidate gene approach may help develop a DNA test panel for commercial use and increase our understanding of the biological basis of feed efficiency in beef cattle. / The Ministry of Higher Education of Egypt

abstract: All biological processes like cell growth, cell differentiation, development, and aging requires a series of steps which are characterized by gene regulation. Studies have shown that gene regulation is the key to various traits and diseases. Various factors affect the gene regulation which includes genetic signals, epigenetic tracks, genetic variants, etc. Deciphering and cataloging these functional genetic elements in the non-coding regions of the genome is one of the biggest challenges in precision medicine and genetic research. This thesis presents two different approaches to identifying these elements: TreeMap and DeepCORE. The first approach involves identifying putative causal genetic variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. TreeMap performs an organized search for individual and multiple causal variants using a tree guided nested machine learning method. DeepCORE on the other hand explores novel deep learning techniques that models the relationship between genetic, epigenetic and transcriptional patterns across tissues and cell lines and identifies co-operative regulatory elements that affect gene regulation. These two methods are believed to be the link for genotype-phenotype association and a necessary step to explaining various complex diseases and missing heritability. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2020

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