Готові списки джерел за темами / Genetic data

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Добірка наукової літератури з теми "Genetic data"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 20 лютого 2023

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Статті в журналах з теми "Genetic data"

Volkova, T., E. Furta, O. Dmitrieva, and I. Shabalina. "Pattern Building Methods in Genetic Data Processing." Journal on Selected Topics in Nano Electronics and Computing 1, no. 2 (June 2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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Taylor, Mark J. "Data Protection, Shared (Genetic) Data and Genetic Discrimination." Medical Law International 8, no. 1 (December 2006): 51–77. http://dx.doi.org/10.1177/096853320600800103.

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Ross-Ibarra, Jeffrey. "Genetic Data Analysis II. Methods for Discrete Population Genentic Data." Economic Botany 56, no. 2 (April 2002): 216. http://dx.doi.org/10.1663/0013-0001(2002)056[0216:gdaimf]2.0.co;2.

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Slatkin, Montgomery, Wayne P. Maddison, and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Zoology 40, no. 2 (June 1991): 248. http://dx.doi.org/10.2307/2992265.

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Chase, Gary A., and Bruce S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Journal of the American Statistical Association 86, no. 413 (March 1991): 248. http://dx.doi.org/10.2307/2289745.

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Feytmans, E., and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Biometrics 47, no. 3 (September 1991): 1205. http://dx.doi.org/10.2307/2532683.

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Morton, N. E. "Genetic Data Analysis. Methods for Discrete Population Genetic Data." Journal of Medical Genetics 29, no. 3 (March 1, 1992): 216. http://dx.doi.org/10.1136/jmg.29.3.216.

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Slatkin, M., and W. P. Maddison. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Biology 40, no. 2 (June 1, 1991): 248–49. http://dx.doi.org/10.1093/sysbio/40.2.248.

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Butler, Amy W., Sarah Cohen-Woods, Anne Farmer, Peter McGuffin, and Cathryn M. Lewis. "Integrating Phenotypic Data For Depression." Journal of Integrative Bioinformatics 7, no. 3 (December 1, 2010): 290–99. http://dx.doi.org/10.1515/jib-2010-136.

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Анотація:

Abstract The golden era of molecular genetic research brings about an explosion of phenotypic, genotypic and sequencing data. Building on the common aims to exploit understanding of human diseases, it also opens up an opportunity for scientific communities to share and combine research data. Genome-wide association studies (GWAS) have been widely used to locate genetic variants, which are susceptible for common diseases. In the field of medical genetics, many international collaborative consortiums have been established to conduct meta-analyses of GWAS results and to combine large genotypic data sets to perform mega genetic analyses. Having an integrated phenotype database is significant for exploiting the full potential of extensive genotypic data. In this paper, we aim to share our experience gained from integrating four heterogeneous sets of major depression phenotypic data onto the MySQL platform. These data sets constitute clinical data which had been gathered for various genetic studies for the past decade. We also highlight in this report some generic data handling techniques, the costs and benefits regarding the use of integrated phenotype database within our own institution and under the consortium framework.

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Uzych, Leo. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (January 1996): 13–14. http://dx.doi.org/10.1097/00043764-199601000-00001.

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Дисертації з теми "Genetic data"

Qiao, Dandi. "Statistical Approaches for Next-Generation Sequencing Data." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10689.

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During the last two decades, genotyping technology has advanced rapidly, which enabled the tremendous success of genome-wide association studies (GWAS) in the search of disease susceptibility loci (DSLs). However, only a small fraction of the overall predicted heritability can be explained by the DSLs discovered. One possible explanation for this ”missing heritability” phenomenon is that many causal variants are rare. The recent development of high-throughput next-generation sequencing (NGS) technology provides the instrument to look closely at these rare variants with precision and efficiency. However, new approaches for both the storage and analysis of sequencing data are in imminent needs. In this thesis, we introduce three methods that could be utilized in the management and analysis of sequencing data. In Chapter 1, we propose a novel and simple algorithm for compressing sequencing data that leverages on the scarcity of rare variant data, which enables the storage and analysis of sequencing data efficiently in current hardware environment. We also provide a C++ implementation that supports direct and parallel loading of the compressed format without requiring extra time for decompression. Chapter 2 and 3 focus on the association analysis of sequencing data in population-based design. In Chapter 2, we present a statistical methodology that allows the identification of genetic outliers to obtain a genetically homogeneous subpopulation, which reduces the false positives due to population substructure. Our approach is computationally efficient that can be applied to all the genetic loci in the data and does not require pruning of variants in linkage disequilibrium (LD). In Chapter 3, we propose a general analysis framework in which thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multi-loci analysis, which has focused on the dimension reduction of data, the proposed approach profits from the availability of large numbers of genetic loci. Thus it will be especially relevant for whole-genome sequencing studies which commonly record several thousand loci per gene.

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Haroun, Paul. "Genetic algorithm and data visualization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0017/MQ37125.pdf.

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Lankhorst, Marc Martijn. "Genetic algorithms in data analysis." [S.l. : [Groningen] : s.n.] ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/142964662.

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Hiden, Hugo George. "Data-based modelling using genetic programming." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246137.

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Auton, Adam. "The estimation of recombination rates from population genetic data." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:dc38045b-725d-4afc-8c76-94769db3534d.

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Genetic recombination is an important process that generates new combinations of genes on which natural selection can operate. As such, an understanding of recombination in the human genome will provide insight into the evolutionary processes that have shaped our genetic history. The aim of this thesis is to use samples of population genetic data to explore the patterns of variation in the rate of recombination in the human genome. To do this I introduce a novel means of estimating recombination rates from population genetic data. The new, computationally efficient method incorporates a model of recombination hotspots that was absent in existing methods. I use samples from the International HapMap Project to obtain recombination rate estimates for the autosomal portion of the genome. Using these estimates, I demonstrate that recombination has a number of interesting relationships with other genome features such as genes, DNA repeats, and sequence motifs. Furthermore, I show that genes of differing function have significantly different rates of recombination. I explore the relationship between recombination and specific sequence motifs and argue that while sequence motifs are an important factor in determining the location of recombination hotspots, the factor that controls motif activity is unknown. The observation of many relationships between recombination and other genome features motivates an attempt to quantify the contributions to the recombination rate from specific features. I employ a wavelet analysis to investigate scale-specific patterns of recombination. In doing so, I reveal a number of highly significant correlations between recombination and other features of the genome at both the fine and broad scales, but find that relatively little of the variation in recombination rates can be explained. I conclude with a discussion of the results contained in the body of the thesis, and suggest a number of areas for future research.

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Agarwala, Vineeta. "Integrating empirical data and population genetic simulations to study the genetic architecture of type 2 diabetes." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11120.

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Most common diseases have substantial heritable components but are characterized by complex inheritance patterns implicating numerous genetic and environmental factors. A longstanding goal of human genetics research is to delineate the genetic architecture of these traits - the number, frequencies, and effect sizes of disease-causing alleles - to inform mapping studies, elucidate mechanisms of disease, and guide development of targeted clinical therapies and diagnostics. Although vast empirical genetic data has now been collected for common diseases, different and contradictory hypotheses have been advocated about features of genetic architecture (e.g., the contribution of rare vs. common variants). Here, we present a framework which combines multiple empirical datasets and simulation studies to enable systematic testing of hypotheses about both global and locus-specific complex trait architecture. We apply this to type 2 diabetes (T2D).

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Romano, Eduardo O. "Selection indices for combining marker genetic data and animal model information /." This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-09192009-040546/.

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Li, Xin. "Haplotype Inference from Pedigree Data and Population Data." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1259867573.

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Thesis(Ph.D.)--Case Western Reserve University, 2010
Title from PDF (viewed on 2009-12-30) Department of Electrical Engineering and Computer Science Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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Shenoy, U. Nagaraj. "Automatic Data Partitioning By Hierarchical Genetic Search." Thesis, Indian Institute of Science, 1996. http://hdl.handle.net/2005/172.

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CDAC
The introduction of languages like High Performance Fortran (HPF) which allow the programmer to indicate how the arrays used in the program have to be distributed across the local memories of a multi-computer has not completely unburdened the parallel programmer from the intricacies of these architectures. In order to tap the full potential of these architectures, the compiler has to perform this crucial task of data partitioning automatically. This would not only unburden the programmer but would make the programs more efficient since the compiler can be made more intelligent to take care of the architectural nuances. The topic of this thesis namely the automatic data partitioning deals with finding the best data partition for the various arrays used in the entire program in such a way that the cost of execution of the entire program is minimized. The compiler could resort to runtime redistribution of the arrays at various points in the program if found profitable. Several aspects of this problem have been proven to be NP-complete. Other researchers have suggested heuristic solutions to solve this problem. In this thesis we propose a genetic algorithm namely the Hierarchical Genetic Search algorithm to solve this problem.

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Al-Madi, Naila Shikri. "Improved Genetic Programming Techniques For Data Classification." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27097.

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Evolutionary algorithms are one category of optimization techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. Data mining is a relatively broad field that deals with the automatic knowledge discovery from databases and it is one of the most developed fields in the area of artificial intelligence. Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems. GP solves classification problems as an optimization tasks, where it searches for the best solution with highest accuracy. However, GP suffers from some weaknesses such as long execution time, and the need to tune many parameters for each problem. Furthermore, GP can not obtain high accuracy for multiclass classification problems as opposed to binary problems. In this dissertation, we address these drawbacks and propose some approaches in order to overcome them. Adaptive GP variants are proposed in order to automatically adapt the parameter settings and shorten the execution time. Moreover, two approaches are proposed to improve the accuracy of GP when applied to multiclass classification problems. In addition, a Segment-based approach is proposed to accelerate the GP execution time for the data classification problem. Furthermore, a parallelization of the GP process using the MapReduce methodology was proposed which aims to shorten the GP execution time and to provide the ability to use large population sizes leading to a faster convergence. The proposed approaches are evaluated using different measures, such as accuracy, execution time, sensitivity, specificity, and statistical tests. Comparisons between the proposed approaches with the standard GP, and with other classification techniques were performed, and the results showed that these approaches overcome the drawbacks of standard GP by successfully improving the accuracy and execution time.

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Книги з теми "Genetic data"

Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.

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Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.

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Genetic programming and data structures: Genetic programming + data structures = automatic programming! Boston: Kluwer Academic Publishers, 1998.

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Langdon, W. B. Genetic Programming and Data Structures. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5731-9.

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Genetic algorithms + data structures = evolution programs. 3rd ed. Berlin: Springer-Verlag, 1996.

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Genetic algorithms + data structures = evolution programs. 2nd ed. Berlin: Springer-Verlag, 1994.

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Genetic algorithms + data structures = evolution programs. Berlin: Springer-Verlag, 1992.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02830-8.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-07418-3.

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Lin, Shili, and Hongyu Zhao. Handbook on Analyzing Human Genetic Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-69264-5.

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Частини книг з теми "Genetic data"

Agbinya, Johnson I. "Genetic Algorithm." In Applied Data Analytics - Principles and Applications, 75–91. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337225-5.

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Sipper, Moshe. "Genetic Programming." In Encyclopedia of Machine Learning and Data Mining, 1. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_376-1.

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Sipper, Moshe. "Genetic Programming." In Encyclopedia of Machine Learning and Data Mining, 568. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_376.

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Eisen, Jonathan A. "The Genetic Data Environment." In Sequence Data Analysis Guidebook, 13–38. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:13.

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Yin, Yong, Ikou Kaku, Jiafu Tang, and JianMing Zhu. "Genetic Algorithm-based Fuzzy Nonlinear Programming." In Data Mining, 55–86. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-338-1_4.

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Isik, Fikret, James Holland, and Christian Maltecca. "Genetic Values." In Genetic Data Analysis for Plant and Animal Breeding, 141–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55177-7_5.

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Langdon, W. B. "Advanced Genetic Programming Techniques." In Genetic Programming and Data Structures, 43–59. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5731-9_3.

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Maulik, Ujjwal, Sanghamitra Bandyopadhyay, and Anirban Mukhopadhyay. "Data Mining Fundamentals." In Multiobjective Genetic Algorithms for Clustering, 51–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_3.

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Sebastiani, Paola. "Intelligent Data Analysis of Human Genetic Data." In Advances in Intelligent Data Analysis XI, 2–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34156-4_2.

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Lee, Soojung. "A Collaborative Filtering System Using Clustering and Genetic Algorithms." In Data Mining and Big Data, 154–61. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9563-6_16.

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Тези доповідей конференцій з теми "Genetic data"

Wineberg, Mark, and Sebastian Lenartowicz. "Reexpressing problematic optimization data." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3079190.

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Eggermont, Jeroen, Joost N. Kok, and Walter A. Kosters. "Genetic Programming for data classification." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.968104.

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Hong, Tzung-Pei, Chun-Hao Chen, and Vincent S. Tseng. "Genetic-Fuzzy Data Mining Techniques." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.157.

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Mansour, Nashat, Rouba Zantout, and Mirvat El-Sibai. "Mining breast cancer genetic data." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818131.

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Kurveyv, Mamta, D. K. Chitre, and Hemlata Patil. "Genetic algorithm for data mining." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1742162.

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Kumar, T. V. Vijay, Vikram Singh, and Ajay Kumar Verma. "Generating Distributed Query Processing Plans Using Genetic Algorithm." In 2010 International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2010. http://dx.doi.org/10.1109/dsde.2010.56.

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Attaoui, Mohammed Oualid, Hanene Azzag, Mustapha Lebbah, and Nabil Keskes. "Multi-objective data stream clustering." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389930.

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Benkrid, Soumia, Yacine Mestoui, Ladjel Bellatreche, and Carlos Ordonez. "A Genetic Optimization Physical Planner for Big Data Warehouses." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378196.

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Wang, Andong, Victor O. K. Li, and Jacqueline C. K. Lam. "Optimization of Urban Heating Network Design Using Genetic Algorithm." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622530.

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Kim, Sunhee, Young-Suk Lee, and Chang-Yong Lee. "Computational method of database construction for genetic variant calling." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020263.

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Звіти організацій з теми "Genetic data"

Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), February 2018. http://dx.doi.org/10.2172/1419729.

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1441274.

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Cawley, John, Euna Han, Jiyoon (June) Kim, and Edward Norton. Testing for Peer Effects Using Genetic Data. Cambridge, MA: National Bureau of Economic Research, August 2017. http://dx.doi.org/10.3386/w23719.

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Fluhr, Robert, and Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, December 2005. http://dx.doi.org/10.32747/2005.7696517.bard.

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Анотація:

Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was to be used to generate probes for establishing high-throughput alternative transcriptome analysis in the form of a splicing-specific oligonucleotide microarray. We achieved the first goal and established a database and web site termed Alternative Splicing In Plants (ASIP, http://www.plantgdb.org/ASIP/). We also thoroughly reviewed the extent of alternative splicing in plants (Arabidopsis and rice) and proposed mechanisms for transcript processing. We noted that the repertoire of plant alternative splicing differs from that encountered in animals. For example, intron retention turned out to be the major type. This surprising development was proven by direct RNA isolation techniques. We further analyzed EST databases available from many plants and developed a process to assess their alternative splicing rate. Our results show that the lager genome-sized plant species have enhanced rates of alternative splicing. We did advance gene prediction accuracy in plants by incorporating scoring for non-canonical introns. Our data and programs are now being used in the continuing annotation of plant genomes of agronomic importance, including corn, soybean, and tomato. Based on the gene annotation data developed in the early part of the project, it turned out that specific probes for different exons could not be scaled up to a large array because no uniform hybridization conditions could be found. Therefore, we modified our original objective to design and produce an oligonucleotide microarray for probing alternative splicing and realized that it may be reasonable to investigate the extent of alternative splicing using novel commercial whole genome arrays. This possibility was directly examined by establishing algorithms for the analysis of such arrays. The predictive value of the algorithms was then shown by isolation and verification of alternative splicing predictions from the published whole genome array databases. The BARD-funded work provides a significant advance in understanding the extent and possible roles of alternative splicing in plants as well as a foundation for advances in computational gene prediction.

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BONNEAU, François, Guillaume CAUMON, Judith SAUSSE, and Philippe RENARD. Genetic-like modeling of fracture network honoring connectivity data in the geothermal heat exchanger at Soultz-sous-Forêt (France). Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0094.

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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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Lers, Amnon, Majid R. Foolad, and Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7600014.bard.

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ABSTRACT Postharvest losses of fresh produce are estimated globally to be around 30%. Reducing these losses is considered a major solution to ensure global food security. Storage at low temperatures is an efficient practice to prolong postharvest performance of crops with minimal negative impact on produce quality or human health and the environment. However, many fresh produce commodities are susceptible to chilling temperatures, and the application of cold storage is limited as it would cause physiological chilling injury (CI) leading to reduced produce quality. Further, the primary CI becomes a preferred site for pathogens leading to decay and massive produce losses. Thus, chilling sensitive crops should be stored at higher minimal temperatures, which curtails their marketing life and in some cases necessitates the use of other storage strategies. Development of new knowledge about the biological basis for chilling tolerance in fruits and vegetables should allow development of both new varieties more tolerant to cold, and more efficient postharvest storage treatments and storage conditions. In order to improve the agricultural performance of modern crop varieties, including tomato, there is great potential in introgression of marker-defined genomic regions from wild species onto the background of elite breeding lines. To exploit this potential for improving tomato fruit chilling tolerance during postharvest storage, we have used in this research a recombinant inbred line (RIL) population derived from a cross between the red-fruited tomato wild species SolanumpimpinellifoliumL. accession LA2093 and an advanced Solanum lycopersicumL. tomato breeding line NCEBR-1, developed in the laboratory of the US co-PI. The original specific objectives were: 1) Screening of RIL population resulting from the cross NCEBR1 X LA2093 for fruit chilling response during postharvest storage and estimation of its heritability; 2) Perform a transcriptopmic and bioinformatics analysis for the two parental lines following exposure to chilling storage. During the course of the project, we learned that we could measure greater differences in chilling responses among specific RILs compared to that observed between the two parental lines, and thus we decided not to perform transcriptomic analysis and instead invest our efforts more on characterization of the RILs. Performing the transcriptomic analysis for several RILs, which significantly differ in their chilling tolerance/sensitivity, at a later stage could result with more significant insights. The RIL population, (172 lines), was used in field experiment in which fruits were examined for chilling sensitivity by determining CI severity. Following the field experiments, including 4 harvest days and CI measurements, two extreme tails of the response distribution, each consisting of 11 RILs exhibiting either high sensitivity or tolerance to chilling stress, were identified and were further examined for chilling response in greenhouse experiments. Across the RILs, we found significant (P < 0.01) correlation between field and greenhouse grown plants in fruit CI. Two groups of 5 RILs, whose fruits exhibited reproducible chilling tolerant/sensitive phenotypes in both field and greenhouse experiments, were selected for further analyses. Numerous genetic, physiological, biochemical and molecular variations were investigated in response to postharvest chilling stress in the selected RILs. We confirmed the differential response of the parental lines of the RIL population to chilling stress, and examined the extent of variation in the RIL population in response to chilling treatment. We determined parameters which would be useful for further characterization of chilling response in the RIL population. These included chlorophyll fluorescence Fv/Fm, water loss, total non-enzymatic potential of antioxidant activity, ascorbate and proline content, and expression of LeCBF1 gene, known to be associated with cold acclimation. These parameters could be used in continuation studies for the identification and genetic mapping of loci contributing to chilling tolerance in this population, and identifying genetic markers associated with chilling tolerance in tomato. Once genetic markers associated with chilling tolerance are identified, the trait could be transferred to different genetic background via marker-assisted selection (MAS) and breeding. The collaborative research established in this program has resulted in new information and insights in this area of research and the collaboration will be continued to obtain further insights into the genetic, molecular biology and physiology of postharvest chilling tolerance in tomato fruit. The US Co-PI, developed the RIL population that was used for screening and measurement of the relevant chilling stress responses and conducted statistical analyses of the data. Because we were not able to grow the RIL population under field conditions in two successive generations, we could not estimate heritability of response to chilling temperatures. However, we plan to continue the research, grow the RIL progeny in the field again, and determine heritability of chilling tolerance in a near future. The IS and US investigators interacted regularly and plan to continue and expand on this study, since combing the expertise of the Co-PI in genetics and breeding with that of the PI in postharvest physiology and molecular biology will have great impact on this line of research, given the significant findings of this one-year feasibility project.

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O'Neill, Sharman, Abraham Halevy, and Amihud Borochov. Molecular Genetic Analysis of Pollination-Induced Senescence in Phalaenopsis Orchids. United States Department of Agriculture, 1991. http://dx.doi.org/10.32747/1991.7612837.bard.

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The project investigated the molecular genetic and biochemical basis of pollination-induced senescence of Phalaenopsis flowers. This experimental system offered unique advantages in that senescence is strictly regulated by pollination, providing the basis to experimentally initiate and synchronize senescence in populations of flowers. The postpollination syndrome in the Phalaenopsis orchid system was dissected by investigating the temporal and spatial regulation of ACC synthase gene expression. In the stigma, pollen-borne auxin induces the expression of the auxin-regulated ACC synthase (PS-ACS2) gene, resulting in ACC synthesis within 1 h following pollination. Newly formed ACC is oxidized by basal constitutive ACC oxidase to ethylene, which then induces the expression of the ethylene-regulated ACC synthase(PS-ACS1) and oxidase (ACO1) genes for further autocatalytic production of ethylene. It is speculated that during the 6-h period following pollination, emasculation leads to the production or release of a sensitivity factor that sensitizes the cells of the stigma to ethylene. ACC and ethylene molecules are translocated from the stigma to the labellum and perianth where ethylene induces the expression of PS-ACS1 and ACO1 resulting in an increased production of ACC and ethylene. Organ-localized ethylene is responsible for inrolling and senescence of the labellum and perianth. The regulation of ethylene sensitivity and signal transduction events in pollinated flowers was also investigated. The increase in ethylene sensitivity appeared in both the flower column and the perianth, and was detected as early as 4 h after pollination. The increase in ethylene sensitivity following pollination was not dependent on endogenous ethylene production. Application of linoleic and linoleic acids to Phalaenopsis and Dendrobium flowers enhanced their senescence and promoted ethylene production. Several major lipoxygenase pathway products including JA-ME, traumatic acid, trans-2-hexenal and cis-3-hexenol, also enhanced flower senescence. However, lipoxygenase appears to not be directly involved in the endogenous regulation of pollination-induced Phalaenopsis and Dendrobium flower senescence. The data suggest that short-chain saturated fatty acids may be the ethylene "sensitivity factors" produced following pollination, and that their mode of action involves a decrease in the order of specific regions i the membrane lipid bilayer, consequently altering ethylene action. Examination of potential signal transduction intermediates indicate a direct involvement of GTP-binding proteins, calcium ions and protein phosphorylation in the cellular signal transduction response to ethylene following pollination. Modulations of cytosolic calcium levels allowed us to modify the flowers responsiveness to ethylene.

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de Miguel Beriain, Iñigo, Aliuska Duardo Sánchez, and José Antonio Castillo Parrilla. What Can We Do with the Data of Deceased People? A Normative Proposal. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64580.

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The health and genetic data of deceased people are a particularly important asset in the field of biomedical research. However, in practice, using them is compli- cated, as the legal framework that should regulate their use has not been fully developed yet. The General Data Protection Regulation (GDPR) is not applicable to such data and the Member States have not been able to agree on an alternative regulation. Recently, normative models have been proposed in an attempt to face this issue. The most well- known of these is posthumous medical data donation (PMDD). This proposal supports an opt-in donation system of health data for research purposes. In this article, we argue that PMDD is not a useful model for addressing the issue at hand, as it does not consider that some of these data (the genetic data) may be the personal data of the living relatives of the deceased. Furthermore, we find the reasons supporting an opt-in model less convincing than those that vouch for alternative systems. Indeed, we propose a normative framework that is based on the opt-out system for non-personal data combined with the application of the GDPR to the relatives’ personal data.

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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.

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