Relevant bibliographies by topics / Genome wide association studies

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Genome wide association studies'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 January 2025

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Journal articles on the topic "Genome wide association studies"

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Shaffer, J. R., E. Feingold, and M. L. Marazita. "Genome-wide Association Studies." Journal of Dental Research 91, no. 7 (May 4, 2012): 637–41. http://dx.doi.org/10.1177/0022034512446968.

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The genomic era of biomedical research has given rise to the genome-wide association study (GWAS) approach, which attempts to discover novel genes affecting an outcome by testing a large number ( i.e., hundreds of thousands to millions) of genetic variants for association. This article discusses the issues surrounding the GWAS approach with emphasis on the prospects and challenges relevant to the oral health research community.
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Al-Chalabi, A. "Genome-Wide Association Studies." Cold Spring Harbor Protocols 2009, no. 12 (December 1, 2009): pdb.top66. http://dx.doi.org/10.1101/pdb.top66.

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Guo, Xiuqing, and Jerome I. Rotter. "Genome-Wide Association Studies." JAMA 322, no. 17 (November 5, 2019): 1705. http://dx.doi.org/10.1001/jama.2019.16479.

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Kuniholm;, M. H., and D. B. Goldstein. "Replicating Genome-Wide Association Studies." Science 318, no. 5849 (October 19, 2007): 390c—391c. http://dx.doi.org/10.1126/science.318.5849.390c.

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Meschia, James F. "Stroke Genome-Wide Association Studies." Stroke 41, no. 4 (April 2010): 579–80. http://dx.doi.org/10.1161/strokeaha.109.576769.

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Welch, Carrie L. "Beyond Genome-Wide Association Studies." Arteriosclerosis, Thrombosis, and Vascular Biology 32, no. 2 (February 2012): 207–15. http://dx.doi.org/10.1161/atvbaha.111.232694.

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Christiani, David C. "Pulmonary Function: From Genome-Wide Association Studies to Genome-Wide Interaction Studies." American Journal of Respiratory and Critical Care Medicine 199, no. 5 (March 2019): 557–59. http://dx.doi.org/10.1164/rccm.201810-1986ed.

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Huang, Wenhui, Pengyuan Wang, Zhen Liu, and Liqing Zhang. "Identifying disease associations via genome-wide association studies." BMC Bioinformatics 10, Suppl 1 (2009): S68. http://dx.doi.org/10.1186/1471-2105-10-s1-s68.

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Li, Gengxin, and Hongjiang Zhu. "Genetic Studies: The Linear Mixed Models in Genome-wide Association Studies." Open Bioinformatics Journal 7, no. 1 (December 13, 2013): 27–33. http://dx.doi.org/10.2174/1875036201307010027.

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With the availability of high-density genomic data containing millions of single nucleotide polymorphisms and tens or hundreds of thousands of individuals, genetic association study is likely to identify the variants contributing to complex traits in a genome-wide scale. However, genome-wide association studies are confounded by some spurious associations due to not properly interpreting sample structure (containing population structure, family structure and cryptic relatedness). The absence of complete genealogy of population in the genome-wide association studies model greatly motivates the development of new methods to correct the inflation of false positive. In this process, linear mixed model based approaches with the advantage of capturing multilevel relatedness have gained large ground. We summarize current literatures dealing with sample structure, and our review focuses on the following four areas: (i) The approaches handling population structure in genome-wide association studies; (ii) The linear mixed model based approaches in genome-wide association studies; (iii) The performance of linear mixed model based approaches in genome-wide association studies and (iv) The unsolved issues and future work of linear mixed model based approaches.
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Nathan, David G., and Stuart H. Orkin. "Musings on genome medicine: genome wide association studies." Genome Medicine 1, no. 1 (2009): 3. http://dx.doi.org/10.1186/gm3.

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Dissertations / Theses on the topic "Genome wide association studies"

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Delahaye-Sourdeix, Manon. "Moving beyond Genome-Wide Association Studies." Thesis, Lyon 1, 2014. http://www.theses.fr/2014LYO10238.

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Les études d'association à grande échelle consistent à étudier la corrélation de plusieurs millions de polymorphismes nucléotidiques avec un risque de cancer chez des milliers d'individus, sans avoir besoin de connaissances préalables sur la fonction biologique de ces variants. Ces études ont été utiles pour établir des hypothèses étiologiques et comprendre l'architecture génétique sous-jacente de plusieurs maladies humaines. Cependant, la plupart des facteurs héréditaires de ces maladies restent inexpliqués. Une partie de cette variation pourrait venir de variants rares qui ne sont pas ciblés par les puces de génotypage actuelles ou encore de variants avec un effet plus modéré voire faible pour lesquels une détection par les études d'association actuelles n'est pas envisageable. Dans ce contexte et comme illustré dans cette thèse, les récentes études d'association peuvent maintenant servir de point de départ pour de nouvelles découvertes, en mettant en place des stratégies innovantes pour étudier à la fois les variants rares et les maladies rares. Nous avons plus particulièrement exploré ces techniques dans le cadre du cancer du poumon, des voies aérodigestives et du lymphome de Hodgkins. L'utilisation de la bioinformatique pour combiner les résultats des études avec d'autres sources d'information, l'intégration de différents types de données génomiques ainsi que l'investigation de la relation entre altérations germinales et somatiques représentent les principales opportunités poursuivies dans ce travail de thèse
Genome-wide association (GWA) studies consist in testing up to one million (or more) single nucleotide polymorphisms (SNPs) for their association with cancer risk in thousands of individuals, without requiring any prior knowledge on the functional significance of these variants. These studies have been valuable for establishing etiological hypotheses and understanding the underlying genetic architecture of human diseases. However, most of the heritable factors of these traits remain unexplained. Part of this variation may come from rarer variants that are not targeted by current genotyping arrays or variants with moderate to low effects for which detection by current GWA studies is impractical. In this context and as illustrated in this thesis, GWA studies can now serve as starting points towards further discoveries, looking for new strategies to study both rarer variants and rarer diseases. We have specifically explored these approaches in the context of lung cancer, head and neck cancer and Hodgkin's lymphoma. The use of bioinformatics to combine recent GWA study results with other sources of information, the integration of different types of genomic data as well as the investigation of the interrelationship between germline and somatic alterations represent the main opportunities pursued in this thesis work
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Liu, Jin. "Penalized methods in genome-wide association studies." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1242.

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Penalized regression methods are becoming increasingly popular in genome-wide association studies (GWAS) for identifying genetic markers associated with disease. However, standard penalized methods such as the LASSO do not take into account the possible linkage disequilibrium between adjacent markers. We propose a novel penalized approach for GWAS using a dense set of single nucleotide polymorphisms (SNPs). The proposed method uses the minimax concave penalty (MCP) for marker selection and incorporates linkage disequilibrium (LD) information by penalizing the difference of the genetic effects at adjacent SNPs with high correlation. A coordinate descent algorithm is derived to implement the proposed method. This algorithm is efficient and stable in dealing with a large number of SNPs. A multi-split method is used to calculate the p-values of the selected SNPs for assessing their significance. We refer to the proposed penalty function as the smoothed MCP (SMCP) and the proposed approach as the SMCP method. Performance of the proposed SMCP method and its comparison with a LASSO approach are evaluated through simulation studies, which demonstrate that the proposed method is more accurate in selecting associated SNPs. Its applicability to real data is illustrated using data from a GWAS on rheumatoid arthritis. Based on the idea of SMCP, we propose a new penalized method for group variable selection in GWAS with respect to the correlation between adjacent groups. The proposed method uses the group LASSO for encouraging group sparsity and a quadratic difference for adjacent group smoothing. We call it smoothed group LASSO, or SGL for short. Canonical correlations between two adjacent groups of SNPS are used as the weights in the quadratic difference penalty. Principal components are used to reduced dimensionality locally within groups. We derive a group coordinate descent algorithm for computing the solution path of the SGL. Simulation studies are used to evaluate the finite sample performance of the SGL and group LASSO. We also demonstrate its applicability on rheumatoid arthritis data.
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Yazdani, Akram. "Statistical Approaches in Genome-Wide Association Studies." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423743.

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Genome-wide association studies, GWAS, typically contain hundreds of thousands single nucleotide polymorphisms, SNPs, genotyped for few numbers of samples. The aim of these studies is to identify regions harboring SNPs or to predict the outcomes of interest. Since the number of predictors in the GWAS far exceeds the number of samples, it is impossible to analyze the data with classical statistical methods. In the current GWAS, the widely applied methods are based on single marker analysis that does assess association of each SNP with the complex traits independently. Because of the low power of this analysis for detecting true association, simultaneous analysis has recently received more attention. The new statistical methods for simultaneous analysis in high dimensional settings have a limitation of disparity between the number of predictors and the number of samples. Therefore, reducing the dimensionality of the set of SNPs is required. This thesis reviews single marker analysis and simultaneous analysis with a focus on Bayesian methods. It addresses the weaknesses of these approaches with reference to recent literature and illustrating simulation studies. To bypass these problems, we first attempt to reduce dimension of the set of SNPs with random projection technique. Since this method does not improve the predictive performance of the model, we present a new two-stage approach that is a hybrid method of single and simultaneous analyses. This full Bayesian approach selects the most promising SNPs in the first stage by evaluating the impact of each marker independently. In the second stage, we develop a hierarchical Bayesian model to analyze the impact of selected markers simultaneously. The model that accounts for related samples places the local-global shrinkage prior on marker effects in order to shrink small effects to zero while keeping large effects relatively large. The prior specification on marker effects, which is hierarchical representation of generalized double Pareto, improves the predictive performance. Finally, we represent the result of real SNP-data analysis through single-maker study and the new two-stage approach.
Lo Studio di Associazione Genome-Wide, GWAS, tipicamente comprende centinaia di migliaia di polimorfismi a singolo nucleotide, SNPs, genotipizzati per pochi campioni. L'obiettivo di tale studio consiste nell'individuare le regioni cruciali SNPs e prevedere gli esiti di una variabile risposta. Dal momento che il numero di predittori è di gran lunga superiore al numero di campioni, non è possibile condurre l'analisi dei dati con metodi statistici classici. GWAS attuali, i metodi negli maggiormente utilizzati si basano sull'analisi a marcatore unico, che valuta indipendentemente l'associazione di ogni SNP con i tratti complessi. A causa della bassa potenza dell'analisi a marcatore unico nel rilevamento delle associazioni reali, l'analisi simultanea ha recentemente ottenuto più attenzione. I recenti metodi per l'analisi simultanea nel multidimensionale hanno una limitazione sulla disparità tra il numero di predittori e il numero di campioni. Pertanto, è necessario ridurre la dimensionalità dell'insieme di SNPs. Questa tesi fornisce una panoramica dell'analisi a marcatore singolo e dell'analisi simultanea, focalizzandosi su metodi Bayesiani. Vengono discussi i limiti di tali approcci in relazione ai GWAS, con riferimento alla letteratura recente e utilizzando studi di simulazione. Per superare tali problemi, si è cercato di ridurre la dimensione dell'insieme di SNPs con una tecnica a proiezione casuale. Poiché questo approccio non comporta miglioramenti nella accuratezza predittiva del modello, viene quindi proposto un approccio in due fasi, che risulta essere un metodo ibrido di analisi singola e simultanea. Tale approccio, completamente Bayesiano, seleziona gli SNPs più promettenti nella prima fase valutando l'impatto di ogni marcatore indipendentemente. Nella seconda fase, viene sviluppato un modello gerarchico Bayesiano per analizzare contemporaneamente l'impatto degli indicatori selezionati. Il modello che considera i campioni correlati pone una priori locale-globale ristretta sugli effetti dei marcatori. Tale prior riduce a zero gli effetti piccoli, mentre mantiene gli effetti più grandi relativamente grandi. Le priori specificate sugli effetti dei marcatori sono rappresentazioni gerarchiche della distribuzione Pareto doppia; queste a priori migliorano le prestazioni predittive del modello. Infine, nella tesi vengono riportati i risultati dell'analisi su dati reali di SNP basate sullo studio a marcatore singolo e sul nuovo approccio a due stadi.
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Porretta'S, Luciano. "MODELS AND METHODS IN GENOME WIDE ASSOCIATION STUDIES." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/265314.

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The interdisciplinary field of systems biology has evolved rapidly over the last few years. Different disciplines have contributed to the development of both its experimental and theoretical branches.Although computational biology has been an increasing activity in computer science for more than a two decades, it has been only in the past few years that optimization models have been increasingly developed and analyzed by researchers whose primary background is Operations Research(OR). This dissertation aims at contributing to the field of computational biology by applying mathematical programming to certain problems in molecular biology.Specifically, we address three problems in the domain of Genome Wide Association Studies}:(i) the Pure Parsimony Haplotyping Under uncertatind Data Problem that consists in finding the minimum number of haplotypes necessary to explain a given set of genotypes containing possible reading errors; (ii) the Parsimonious Loss Of Heterozygosity Problem that consists of partitioning suspected polymorphisms from a set of individuals into a minimum number of deletion areas; (iii) and the Multiple Individuals Polymorphic Alu Insertion Recognition Problem that consists of finding the set of locations in the genome where ALU sequences are inserted in some individual(s).All three problems are NP-hard combinatorial optimization problems. Therefore, we analyse their combinatorial structure and we propose an exact approach to solution for each of them. The proposed models are efficient, accurate, compact, polynomial-sized and usable in all those cases for which the parsimony criterion is well suited for estimation.
Option Informatique du Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Barrett, Jeffrey C. "Design and analysis of genome-wide association studies." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:45790b5c-e50c-406a-bb3c-a96868b11a28.

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Despite many years of effort, linkage and candidate gene association studies have yielded disappointingly few risk loci for common human diseases such as diabetes, auto-immune disorders and cancers. Large sample sizes, increased understanding of the patterns of correlation in genetic variation, and plunging genotyping costs have enabled genome-wide association studies, which have good power to detect common risk alleles of modest effect. I present an evaluation of SNP choice in study design and show that overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first generation platforms all offer good levels of genome coverage (∼ 70%). I next describe the largest such project undertaken to date, the Wellcome Trust Case Control Consortium, which consisted of 2000 cases from each of seven common diseases and 3000 shared controls. It identified nearly two dozen new associations. I demonstrate the importance of careful data quality control, including both standard and unorthodox analyses. I next focus on the association results therein for Crohn’s disease. I present a replication experiment in over 1000 additional Crohn’s patients which unambiguously confirmed six previously published loci and four new loci. Next I describe, in a general context, several issues impeding the combination of genome-wide scans, including data annotation, population structure and differences in genotyping platform. Each of these problems is shown to be tractable with available methods, provided that these methods are applied prudently. I present the results of a meta-analysis of three genome-wide scans for Crohn’s disease. The data showed a striking excess of significant associations, and a replication experiment involving over 4000 independent Crohn’s patients verified twenty new risk loci. Finally, I discuss the early success of genome-wide association and its consequences for further understanding the biology of human disease.
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Li, Shengxu. "Genome-wide association studies of body mass index." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608974.

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Keildson, Sarah. "Model selection strategies in genome-wide association studies." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:bd97c2e3-10e3-4007-9b7b-199e99d04f94.

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Unravelling the genetic architecture of common diseases is a continuing challenge in human genetics. While genome-wide association studies (GWAS) have proven to be successful in identifying many new disease susceptibility loci, the extension of these studies beyond single-SNP methods of analysis has been limited. The incorporation of multi-locus methods of analysis may, however, increase the power of GWAS to detect genes of smaller effect size, as well as genes that interact with each other and the environment. This investigation carried out large-scale simulations of four multi-locus model selection techniques; namely forward and backward selection, Bayesian model averaging (BMA) and least angle regression with a lasso modification (lasso), in order to compare the type I error rates and power of each method. At a type I error rate of ~5%, lasso showed the highest power across varied effect sizes, disease frequencies and genetic models. Lasso penalized regression was then used to perform three different types of analysis on GWAS data. Firstly, lasso was applied to the Wellcome Trust Case Control Consortium (WTCCC) data and identified many of the WTCCC SNPs that had a moderate-strong association (p<10-5) type 2 diabetes (T2D), as well as some of the moderate WTCCC associations (p<10-4) that have since been replicated in a large-scale meta-analysis. Secondly, lasso was used to fine-map the 17q21 childhood asthma risk locus and identified putative secondary signals in the 17q21 region, that may further contribute to childhood asthma risk. Finally, lasso identified three potential interaction effects potentially contributing towards coronary artery disease (CAD) risk. While the validity of these findings hinges on their replication in follow-up studies, the results suggest that lasso may provide scientists with exciting new methods of dissecting, and ultimately understanding, the complex genetic framework underlying common human diseases.
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Parisi, Rosa. "Multi-locus statistical analysis of genome-wide association studies." Thesis, University of Leeds, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535123.

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Yeung, Ming-yiu, and 楊明耀. "Genome wide association studies of biliary atresia in Chinese." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43703847.

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Bhattacharya, Kanishka. "Gene x gene interactions in genome wide association studies." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6cb7ab29-90df-4d70-bc2f-531f874b79d0.

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Genome wide association studies (GWAS) have revolutionized our approach to mapping genetic determinants of complex human diseases. However, even with success from recent studies, we have typically been able to explain only a fraction of the trait heritability. GWAS are typically analysed by testing for the marginal effects of single variants. Consequently, it has been suggested that gene-gene interactions might contribute to the missing heritability of complex diseases. GWAS incorporating interaction effects have not been routinely applied because of statistical and computational challenges relating to the number of tests performed, genome-wide. To overcome this issue, I have developed novel methodology to allow rapid testing of pairwise interactions in GWAS of complex traits, implemented in the IntRapid software. Simulations demonstrated that the power of this approach was equivalent to computationally demanding exhaustive searches of the genome, but required only a fraction of the computing time. Application of IntRapid to GWAS of a range of complex human traits undertaken by the Wellcome Trust Case Control Consortium (WTCCC) identified several interaction effects at nominal significance, which warrant further investigation in independent studies. In an attempt to fine-map the identified interacting loci, I undertook imputation of the WTCCC genotype data up to the 1000 Genomes Project reference panel (Phase 1 integrated release, March 2012) in the neighbourhood of the lead SNPs. I modified the IntRapid software to take account of imputed genotypes, and identified stronger signals of interaction after imputation at the majority of loci, where the lead SNP often had moved by hundreds of kilobases. The X-chromosome is often overlooked in GWAS of complex human traits, primarily because of the difference in the distribution of genotypes in males and females. I have extended IntRapid to allow for interactions with the X chromosome by considering males and females separately, and combining effect estimates across the sexes in a fixed-effects meta-analysis. Application to genotype data from the WTCCC failed to identify any strong signals of association with the X-chromosome, despite known epidemiological differences between the sexes for the traits considered. The novel methods developed as part of this doctoral work enable a user friendly, computationally efficient and powerful way of implementing genome-wide gene-gene interaction studies. Further work would be required to allow for more complex interaction modelling and deal with the associated computational burden, particularly when using next-generation sequencing (NGS) data which includes a much larger set of SNPs. However, IntRapid is demonstrably efficient in exhaustively searching for pairwise interactions in GWAS of complex traits, potentially leading to novel insights into the genetic architecture and biology of human disease.
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Books on the topic "Genome wide association studies"

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Torkamaneh, Davoud, and François Belzile, eds. Genome-Wide Association Studies. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2237-7.

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Appasani, Krishnarao, ed. Genome-Wide Association Studies. Cambridge: Cambridge University Press, 2015. http://dx.doi.org/10.1017/cbo9781107337459.

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Tsunoda, Tatsuhiko, Toshihiro Tanaka, and Yusuke Nakamura, eds. Genome-Wide Association Studies. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8177-5.

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Gondro, Cedric, Julius van der Werf, and Ben Hayes, eds. Genome-Wide Association Studies and Genomic Prediction. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-447-0.

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Guan, Weihua, ed. Epigenome-Wide Association Studies. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1994-0.

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Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9443-0.

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Prabhu, Snehit. Computational Contributions Towards Scalable and Efficient Genome-wide Association Methodology. [New York, N.Y.?]: [publisher not identified], 2013.

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Zhang, Qiangfeng Cliff. Towards the integration of structural and systems biology: Structure-based studies of protein-protein interactions on a genome-wide scale. [New York, N.Y.?]: [publisher not identified], 2012.

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Gloyn, Anna L., and Mark I. McCarthy. Genetics in diabetes: Type 2 diabetes and related traits. Basel: Karger, 2014.

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International Association for Time Use Research. Conference. Time use studies world wide: A collection of papers presented at the 1989 Varna Conference of the International Association for Time Use Research. Edited by Gershuny Jonathan. Sofia, Bulgaria: Socioconsult Ltd., 1989.

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Book chapters on the topic "Genome wide association studies"

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Gondro, Cedric. "Genome Wide Association Studies." In Use R!, 73–103. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14475-7_3.

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Iles, Mark M. "Genome-Wide Association Studies." In Methods in Molecular Biology, 89–103. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60327-416-6_7.

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Dehghan, Abbas. "Genome-Wide Association Studies." In Methods in Molecular Biology, 37–49. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7868-7_4.

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Laird, Nan M., and Christoph Lange. "Genome Wide Association Studies." In Statistics for Biology and Health, 175–89. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7338-2_11.

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Zheng, Gang, Yaning Yang, Xiaofeng Zhu, and Robert C. Elston. "Genome-Wide Association Studies." In Analysis of Genetic Association Studies, 337–49. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2245-7_12.

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Sebastiani, Paola, and Nadia Solovieff. "Genome Wide Association Studies." In Problem Solving Handbook in Computational Biology and Bioinformatics, 159–75. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-09760-2_8.

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Yang, Tun-Hsiang, Mark Kon, and Charles DeLisi. "Genome-Wide Association Studies." In Methods in Molecular Biology, 233–51. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-107-3_15.

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Xu, Shizhong. "Genome-Wide Association Studies." In Quantitative Genetics, 347–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_19.

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Lysenko, Artem, Keith A. Boroevich, and Tatsuhiko Tsunoda. "Genotyping and Statistical Analysis." In Genome-Wide Association Studies, 1–20. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8177-5_1.

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Ozaki, Kouichi, and Toshihiro Tanaka. "Genetics of Coronary Disease." In Genome-Wide Association Studies, 21–36. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8177-5_2.

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Conference papers on the topic "Genome wide association studies"

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Ltaief, Hatem, Rabab Alomairy, Qinglei Cao, Jie Ren, Lotfi Slim, Thorsten Kurth, Benedikt Dorschner, Salim Bougouffa, Rached Abdelkhalak, and David E. Keyes. "Toward Capturing Genetic Epistasis from Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression." In SC24: International Conference for High Performance Computing, Networking, Storage and Analysis, 1–12. IEEE, 2024. https://doi.org/10.1109/sc41406.2024.00012.

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Etcheverry, Lorena, Adriana Marotta, and Raul Ruggia. "Data Quality Metrics for Genome Wide Association Studies." In 2010 21st International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2010. http://dx.doi.org/10.1109/dexa.2010.40.

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Lu, Wenjie, Yoshiji Yamada, and Jun Sakuma. "Efficient Secure Outsourcing of Genome-Wide Association Studies." In 2015 IEEE Security and Privacy Workshops (SPW). IEEE, 2015. http://dx.doi.org/10.1109/spw.2015.11.

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Schwarzerová, Jana. "Metabolite Genome-Wide Association Studies Of Arabidopsis Thaliana." In STUDENT EEICT 2021. Brno: Fakulta elektrotechniky a komunikacnich technologii VUT v Brne, 2021. http://dx.doi.org/10.13164/eeict.2021.41.

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Tahmasebi, Behrooz, Mohammad Ali Maddah-Ali, and Seyed Abolfazl Motahari. "Information Theory of Mixed Population Genome-Wide Association Studies." In 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018. http://dx.doi.org/10.1109/itw.2018.8613344.

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Al Kawam, Ahmad, Mustafa Alshawaqfeh, James Cai, Erchin Serpedin, and Aniruddha Datta. "Simulating Variance Heterogeneity in Quantitative Genome Wide Association Studies." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3107411.3110407.

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Johnson, Aaron, and Vitaly Shmatikov. "Privacy-preserving data exploration in genome-wide association studies." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487687.

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Liu, Jie, Elizabeth Burnside, Humberto Vidaillet, and David Page. "A collective ranking method for genome-wide association studies." In the ACM Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2382936.2382976.

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Dai, X., A. T. Hinsu, C. Dadousis, R. J. Pandit, M. Crotta, G. Limon, B. Fosso, et al. "584. Genome-wide association studies of chicken caecal microbiota." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_584.

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Zheng, Ming, and Mugui Zhuo. "Analysis of Corrections Methods in Genome-Wide Association Studies." In 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/emim-17.2017.88.

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Reports on the topic "Genome wide association studies"

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Cooley, Philip, Robert Clark, and Ralph Folsom. Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches. RTI Press, May 2014. http://dx.doi.org/10.3768/rtipress.2014.rr.0022.1405.

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Onteru, Suneel, Danielle Gorbach, Jennifer M. Young, Dorian J. Garrick, Jack C. M. Dekkers, and Max F. Rothschild. Genome Wide Association Studies for Residual Feed Intake Traits in Pigs. Ames (Iowa): Iowa State University, January 2013. http://dx.doi.org/10.31274/ans_air-180814-1220.

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Fernando, Rohan L., Jack C. M. Dekkers, and Dorian J. Garrick. Bayesian Methods for Genomic Prediction and Genome-Wide Association Studies combining Information on Genotyped and Non-Genotyped Individuals. Ames (Iowa): Iowa State University, January 2014. http://dx.doi.org/10.31274/ans_air-180814-1245.

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Gorbach, Danielle M., Bin Fan, Suneel K. Onteru, Xia Zhao, Zhi-Qiang Du, Dorian J. Garrick, Jack C. M. Dekkers, and Max F. Rothschild. Genome-Wide Association Studies for Important Economic Traits in Domestic Animals Using High Density SNP Genotyping. Ames (Iowa): Iowa State University, January 2010. http://dx.doi.org/10.31274/ans_air-180814-980.

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Breiman, Adina, Jan Dvorak, Abraham Korol, and Eduard Akhunov. Population Genomics and Association Mapping of Disease Resistance Genes in Israeli Populations of Wild Relatives of Wheat, Triticum dicoccoides and Aegilops speltoides. United States Department of Agriculture, December 2011. http://dx.doi.org/10.32747/2011.7697121.bard.

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Wheat is the most widely grown crop on earth, together with rice it is second to maize in total global tonnage. One of the emerging threats to wheat is stripe (yellow) rust, especially in North Africa, West and Central Asia and North America. The most efficient way to control plant diseases is to introduce disease resistant genes. However, the pathogens can overcome rapidly the effectiveness of these genes when they are wildly used. Therefore, there is a constant need to find new resistance genes to replace the non-effective genes. The resistance gene pool in the cultivated wheat is depleted and there is a need to find new genes in the wild relative of wheat. Wild emmer (Triticum dicoccoides) the progenitor of the cultivated wheat can serve as valuable gene pool for breeding for disease resistance. Transferring of novel genes into elite cultivars is highly facilitated by the availability of information of their chromosomal location. Therefore, our goals in this study was to find stripe rust resistant and susceptible genotypes in Israeli T. dicoccoides population, genotype them using state of the art genotyping methods and to find association between genetic markers and stripe rust resistance. We have screened 129 accessions from our collection of wild emmer wheat for resistance to three isolates of stripe rust. About 30% of the accessions were resistant to one or more isolates, 50% susceptible, and the rest displayed intermediate response. The accessions were genotyped with Illumina'sInfinium assay which consists of 9K single nucleotide polymorphism (SNP) markers. About 13% (1179) of the SNPs were polymorphic in the wild emmer population. Cluster analysis based on SNP diversity has shown that there are two main groups in the wild population. A big cluster probably belongs to the Horanum ssp. and a small cluster of the Judaicum ssp. In order to avoid population structure bias, the Judaicum spp. was removed from the association analysis. In the remaining group of genotypes, linkage disequilibrium (LD) measured along the chromosomes decayed rapidly within one centimorgan. This is the first time when such analysis is conducted on a genome wide level in wild emmer. Such a rapid decay in LD level, quite unexpected for a selfer, was not observed in cultivated wheat collection. It indicates that wild emmer populations are highly suitable for association studies yielding a better resolution than association studies in cultivated wheat or genetic mapping in bi-parental populations. Significant association was found between an SNP marker located in the distal region of chromosome arm 1BL and resistance to one of the isolates. This region is not known in the literature to bear a stripe rust resistance gene. Therefore, there may be a new stripe rust resistance gene in this locus. With the current fast increase of wheat genome sequence data, genome wide association analysis becomes a feasible task and efficient strategy for searching novel genes in wild emmer wheat. In this study, we have shown that the wild emmer gene pool is a valuable source for new stripe rust resistance genes that can protect the cultivated wheat.
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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Abasht, Behnam, and Susan J. Lamont. Genome-Wide Association Study of Fatness in Chickens. Ames (Iowa): Iowa State University, January 2007. http://dx.doi.org/10.31274/ans_air-180814-892.

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Saatchi, Mahdi, Dorian J. Garrick, and Jeremy Taylor. Genome-Wide Association Study of Feed Efficiency in Beef Cattle. Ames (Iowa): Iowa State University, January 2014. http://dx.doi.org/10.31274/ans_air-180814-1141.

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de Oliveira, Gerson A., José Bento Sterman Ferraz, and Dorian J. Garrick. Genome Wide Association Study for Heifer Pregnancy in Nellore Cattle. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-576.

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Grumet, R., J. Burger, Y. Tadmor, A. Gur, C. Barry, A. Schäffer, and M. Petreikov. Cucumis fruit surface biology: Genetic analysis of fruit exocarp features in melon (C. melo) and cucumber (C. sativus). Israel: United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134155.bard.

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The fruit surface (exocarp) is a unique tissue with multiple roles influencing fruit growth and development, disease susceptibility, crop yield, post-harvest treatments, shipping and storage quality, and food safety. Furthermore, highly visible exocarp traits are the consumer's first exposure to the fruit, serving to identify fruit type, variety, attractiveness, and market value. Cucurbit fruit, including the closely related Cucumis species, melon (C. melo) and cucumber (C. sativus), exhibit tremendous diversity for fruit surface properties that are not present in model species. In this project, we identified genetic factors influencing Cucumis fruit surface morphology with respect to important quality determinants such as exocarp and flesh color, cuticle deposition, and surface netting. We employed a combination of approaches including: genome-wide association studies (GWAS) utilizing an extensive melon population and the U.S. Plant Introduction (PI) collection for cucumber to identify genomic regions associated with natural variation in fruit surface traits; bulked segregant RNA-seq (BSR-seq) analysis of bi-parental F2:3 or RIL (recombinant inbred line) populations to genomic regions and candidate genes segregating for fruit surface traits; and comparison of syntenic genomic regions and identification of homologous candidate genes. Candidate genes were examined for sequence and/or expression differences during fruit development that correspond with phenotypic differences. Primary outcomes of the work included identification of candidate genes influencing cuticle deposition, epidermal cell structure, surface netting, and intensity of rind and flesh color. Parallel studies identified mutations within the cucumber and melon homologs of the transcription factor WIN1 (WAX INDUCER1) as a significant factor influencing these surface properties. Additional QTL (quantitative trait loci) were identified in both species, and candidate genes in melon include a novel beta-glucosidase involved in lignin production and an integral membrane protein potentially involved in cuticle metabolism. Genetic resources and biochemical approaches have been developed to study cuticle and wax deposition in both species: segregating populations of melon were developed and sequenced for bulked segregant analysis and samples collected for metabolic analysis; an isolation procedure was developed for lipid droplets from cucumber peel and metabolomic analyses have been initiated. Genetic studies in melon identified mutations in a candidate gene (APRR2), associated with light immature rind, and further indicated that this gene is also associated with color intensity of both mature rinds and flesh, making it a good target for breeding. GWAS studies utilizing the cucumber core diversity population are being performed to identify additional sources of variation for fruit surface properties, map QTL, and examine for synteny with melon. Collectively these studies identified genetic regions associated with important quality traits and contributed to our understanding of underlying biological processes associated with fruit surface development. Knowledge of genetic control of these characteristics can facilitate more efficient breeding for important fruit surface traits.
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