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Journal articles on the topic 'Genetic mapping'

To see the other types of publications on this topic, follow the link: Genetic mapping.

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Salava, J., Y. Wang, B. Krška, J. Polák, P. Komínek, R. W. Miller, W. M. Dowler, G. L. Reighard, and A. G. Abbott. "Molecular genetic mapping in apricot." Czech Journal of Genetics and Plant Breeding 38, No. 2 (July 30, 2012): 65–68. http://dx.doi.org/10.17221/6113-cjgpb.

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A genetic linkage map for apricot (Prunus armeniaca L.) has been constructed using amplified fragment length polymorphism (AFLP) markers in 80 BC1 individuals derived from a cross LE-3246 × Vestar. From 26 different primer combinations, a total of 248 AFLP markers were scored, of which, 40 were assigned to 8 linkage groups covering 315.8 cM of the apricot nuclear genome. The average interval between these markers was 7.7 cM. One gene (PPVres1) involved in resistance to PPV (Plum pox virus) was mapped. Two AFLP markers (EAA/MCAG8 and EAG/MCAT14) were found to be closely associated with the PPVres1 locus (4.6 cM resp. 4.7 cM). These markers are being characterized and they will be studied for utilization in apricot breeding with marker-assisted selection (MAS).

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2

Bo, W., Z. Wang, F. Xu, G. Fu, Y. Sui, W. Wu, X. Zhu, D. Yin, Q. Yan, and R. Wu. "Shape mapping: genetic mapping meets geometric morphometrics." Briefings in Bioinformatics 15, no. 4 (March 4, 2013): 571–81. http://dx.doi.org/10.1093/bib/bbt008.

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3

BORMAN, STU. "MAPPING HUMAN GENETIC VARIATION." Chemical & Engineering News 83, no. 8 (February 21, 2005): 13. http://dx.doi.org/10.1021/cen-v083n008.p013.

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4

Dzau, Victor J., Howard J. Jacob, Klaus Lindpainter, Detlev Ganten, and Eric S. Lander. "Genetic mapping in hypertension." Journal of Vascular Surgery 15, no. 5 (May 1992): 930–31. http://dx.doi.org/10.1016/0741-5214(92)90757-y.

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5

Gulsen, Osman. "Genetic mapping in plants." Journal of Biotechnology 161 (November 2012): 7–8. http://dx.doi.org/10.1016/j.jbiotec.2012.07.171.

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6

Malke, Horst. "Genetic and Physical Mapping." Bioelectrochemistry and Bioenergetics 29, no. 3 (February 1993): 373–74. http://dx.doi.org/10.1016/0302-4598(93)85015-l.

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7

Ebersberger, I., P. Galgoczy, S. Taudien, S. Taenzer, M. Platzer, and A. von Haeseler. "Mapping Human Genetic Ancestry." Molecular Biology and Evolution 24, no. 10 (July 21, 2007): 2266–76. http://dx.doi.org/10.1093/molbev/msm156.

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8

Hutchinson, Anna, Jennifer Asimit, and Chris Wallace. "Fine-mapping genetic associations." Human Molecular Genetics 29, R1 (August 3, 2020): R81—R88. http://dx.doi.org/10.1093/hmg/ddaa148.

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Abstract Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further ‘fine-mapping’ step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.

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9

Ryma, Guefrouchi, and Kholladi Mohamed-Khireddine. "Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping." Journal of Information Technology Research 12, no. 4 (October 2019): 153–70. http://dx.doi.org/10.4018/jitr.2019100108.

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Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

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10

Mynett-Johnson, Lesley A., and Patrick McKeon. "The molecular genetics of affective disorders: An overview." Irish Journal of Psychological Medicine 13, no. 4 (December 1996): 155–61. http://dx.doi.org/10.1017/s0790966700004444.

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AbstractObjective: Genetic mapping, the method of comparing an inheritance pattern of a disease to that of a chromosomal region, has brought about a revolution in the field of human inherited diseases. Diseases which exhibit a more complex pattern of inheritance now afford the next challange in the application of genetic mapping to the field of human disease. This article aims to review the application of genetic mapping to affective disorders.Method: Review of literature concerning the molecular genetics of affective disorders.Findings: This article describes the evidence for a genetic role in affective disorders, reviews the research to date and describes the difficulties arising out of the complex nature of these disorders.Conclusions: Although progress to date in psychiatric genetics has been somewhat disappointing, the combined approach of using all the genetic tools currently available on large collections of affected individuals and families should enable the genetic basis of affective disorders to be identified.

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11

Wilson, Garnett, and Malcolm Heywood. "Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings." Genetic Programming and Evolvable Machines 8, no. 2 (May 10, 2007): 187–220. http://dx.doi.org/10.1007/s10710-007-9027-9.

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12

Rieseberg and Buerkle. "Genetic Mapping in Hybrid Zones." American Naturalist 159, no. 3 (2002): S36. http://dx.doi.org/10.2307/3078920.

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13

Vieira, E. A., R. O. Nodari, A. C. M. Dantas, J. P. H. J. Ducroquet, M. Dalbó, and C. V. Borges. "Genetic mapping of Japanese plum." Cropp Breeding and Applied Biotechnology 5, no. 1 (March 30, 2005): 29–37. http://dx.doi.org/10.12702/1984-7033.v05n01a04.

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14

Weeks, Daniel E., Mark Lathrop, and Jurg Ott. "Multipoint Mapping under Genetic Interference." Human Heredity 43, no. 2 (1993): 86–97. http://dx.doi.org/10.1159/000154123.

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15

Zahn, L. M. "Mapping genetic adaptations to pollution." Science 354, no. 6317 (December 8, 2016): 1245–46. http://dx.doi.org/10.1126/science.354.6317.1245-e.

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16

Norton, Nadine, Sarah Dwyer, Michael C. O'Donovan, and Nigel M. Williams. "Genetic mapping approaches in neuropsychiatry." Psychiatry 4, no. 12 (December 2005): 22–26. http://dx.doi.org/10.1383/psyt.2005.4.12.22.

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17

Waldron, Denise. "CRISP(e)R genetic mapping." Nature Reviews Genetics 17, no. 7 (May 16, 2016): 375. http://dx.doi.org/10.1038/nrg.2016.68.

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18

Rieseberg, Loren H., and C. Alex Buerkle. "Genetic Mapping in Hybrid Zones." American Naturalist 159, S3 (March 2002): S36—S50. http://dx.doi.org/10.1086/338371.

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19

Davis, Brian K. "On mapping the genetic code." Journal of Theoretical Biology 259, no. 4 (August 2009): 860–62. http://dx.doi.org/10.1016/j.jtbi.2009.05.009.

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20

Beier, David R., and Bruce J. Herron. "Genetic Mapping and ENU Mutagenesis." Genetica 122, no. 1 (September 2004): 65–69. http://dx.doi.org/10.1007/s10709-004-1437-5.

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21

Smith, M. J., and P. N. Goodfellow. "Gene mapping and genetic diseases." Current Opinion in Cell Biology 1, no. 3 (June 1989): 460–65. http://dx.doi.org/10.1016/0955-0674(89)90006-9.

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22

Chakraborti, Nirupam. "Editorial: Mapping the Genetic Constellation." Materials and Manufacturing Processes 28, no. 7 (July 3, 2013): 707. http://dx.doi.org/10.1080/10426914.2013.784397.

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23

Altshuler, D., M. J. Daly, and E. S. Lander. "Genetic Mapping in Human Disease." Science 322, no. 5903 (November 7, 2008): 881–88. http://dx.doi.org/10.1126/science.1156409.

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24

Sadowski, J., P. Gaubier, M. Delseny, and C. F. Quiros. "Genetic and physical mapping in." MGG Molecular & General Genetics 251, no. 3 (1996): 298. http://dx.doi.org/10.1007/s004380050170.

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25

Ott, Jurg, and Helen Donis-Keller. "Statistical Methods in Genetic Mapping." Genomics 22, no. 2 (July 1994): 496–97. http://dx.doi.org/10.1006/geno.1994.1421.

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26

Matise, Tara Cox, Helen Onis-Keller, and Jurg Ott. "Statistical Methods in Genetic Mapping." Genomics 36, no. 1 (August 1996): 223–25. http://dx.doi.org/10.1006/geno.1996.0456.

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27

Vallada, Homero, Michael Gill, Shin Nanko, Michael Owen, Robin Murray, and David Collier. "Genetic mapping on chromosome 22." Schizophrenia Research 9, no. 2-3 (April 1993): 126–27. http://dx.doi.org/10.1016/0920-9964(93)90197-q.

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28

Olson, Jane M., John S. Witte, and Robert C. Elston. "Genetic mapping of complex traits." Statistics in Medicine 18, no. 21 (November 15, 1999): 2961–81. http://dx.doi.org/10.1002/(sici)1097-0258(19991115)18:21<2961::aid-sim206>3.0.co;2-u.

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29

Dirlewanger, E., and C. Bodo. "Molecular genetic mapping of peach." Euphytica 77, no. 1-2 (February 1994): 101–3. http://dx.doi.org/10.1007/bf02551470.

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30

Aytasheva, Z. G., Kh M. Kassymkanova, V. B. Turekhanova, T. Kiss, E. D. Dzhangalina, G. K. Jangulova, B. A. Zhumabaeva, and L. P. Lebedeva. "Multiple premises for research-integrated blended education via mapping genetic resources." International Journal of Biology and Chemistry 10, no. 2 (2017): 28–33. http://dx.doi.org/10.26577/2218-7979-2017-10-2-28-33.

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31

ZENG, ZHAO-BANG, CHEN-HUNG KAO, and CHRISTOPHER J. BASTEN. "Estimating the genetic architecture of quantitative traits." Genetical Research 74, no. 3 (December 1999): 279–89. http://dx.doi.org/10.1017/s0016672399004255.

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Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.

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32

Kassie, Fentanesh C., Joël R. Nguepjop, Hermine B. Ngalle, Dekoum V. M. Assaha, Mesfin K. Gesese, Wosene G. Abtew, Hodo-Abalo Tossim, et al. "An Overview of Mapping Quantitative Trait Loci in Peanut (Arachis hypogaea L.)." Genes 14, no. 6 (May 28, 2023): 1176. http://dx.doi.org/10.3390/genes14061176.

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Quantitative Trait Loci (QTL) mapping has been thoroughly used in peanut genetics and breeding in spite of the narrow genetic diversity and the segmental tetraploid nature of the cultivated species. QTL mapping is helpful for identifying the genomic regions that contribute to traits, for estimating the extent of variation and the genetic action (i.e., additive, dominant, or epistatic) underlying this variation, and for pinpointing genetic correlations between traits. The aim of this paper is to review the recently published studies on QTL mapping with a particular emphasis on mapping populations used as well as traits related to kernel quality. We found that several populations have been used for QTL mapping including interspecific populations developed from crosses between synthetic tetraploids and elite varieties. Those populations allowed the broadening of the genetic base of cultivated peanut and helped with the mapping of QTL and identifying beneficial wild alleles for economically important traits. Furthermore, only a few studies reported QTL related to kernel quality. The main quality traits for which QTL have been mapped include oil and protein content as well as fatty acid compositions. QTL for other agronomic traits have also been reported. Among the 1261 QTL reported in this review, and extracted from the most relevant studies on QTL mapping in peanut, 413 (~33%) were related to kernel quality showing the importance of quality in peanut genetics and breeding. Exploiting the QTL information could accelerate breeding to develop highly nutritious superior cultivars in the face of climate change.

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33

King, Graham J. "Progress of Apple Genetic Mapping in Europe." HortScience 30, no. 4 (July 1995): 749B—749. http://dx.doi.org/10.21273/hortsci.30.4.749b.

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The progress of the European Apple Genome Mapping Project will be described. Five populations segregating for a range of agronomic genes have been established in six European countries. Isozyme systems, RFLPs, RAPDs, and other PCR-based markers are being used to construct a unified genetic linkage map. Genotypic and phenotypic measurements have been precisely defined and standardized among participants. Phenotypic measurements for many agronomic traits are being replicated in different geographical locations over several years. Statistical and genetic analyses are aimed at defining components of genetic variation that account for “genes” manipulated by apple breeders. The segregation of fungal and insect resistance genes, tree habit, juvenility, budbreak, and many fruit characters has been scored. Markers have been identified linked to and flanking scab and mildew resistance genes. RAPD markers have been converted to codominant PCR-based markers for selection purposes. The JoinMap program has been extended for linkage analysis of crosses between heterozygous parents. A method for mapping QTLs in outcrossing species has been developed, together with software that is able to contend with dominant markers and missing data. Associated research is being carried out on the genetics and diversity of fungal resistance genes, fruit quality, and the socioeconomic aspects of apple production. The relational database, APPLE-STORE, has been designed and implemented for combined management of agronomic and genetic information. Synteny of linkage groups between Malus and Prunus has been established.

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34

Ommen, G. J. B. van, and P. L. Pearson. "Long-range mapping in the research and diagnosis of genetic disease." Genome 31, no. 2 (January 15, 1989): 730–36. http://dx.doi.org/10.1139/g89-131.

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This paper reviews current genetic and molecular biological methods that may be used in the so-called "reverse genetics" approach. These methods are the mapping, isolation, and study of the chromosomal DNA containing a previously unidentified gene responsible for a genetic disease, beginning with its chromosomal localization. In principle, the reverse genetics methodology follows the same path for different diseases studied. An overall outline of the steps to be undertaken is given and discussed. Several stages are illustrated with reference to current research in the fields of Duchenne muscular dystrophy, Huntington's disease, and polycystic kidney disease.Key words: human genetic disease, Duchenne muscular dystrophy, Huntington disease, polycystic kidney disease, reverse genetics.

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35

Varshney, R. K., U. Hähnel, T. Thiel, N. Stein, L. Altschmied, P. Langridge, and A. Graner. "Genetic and Physical Mapping of Genic Microsatellites in Barley (Hordeum vulgare L.)." Czech Journal of Genetics and Plant Breeding 41, No. 4 (November 21, 2011): 153–59. http://dx.doi.org/10.17221/3661-cjgpb.

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Due to the availability of sequence data from large-scale EST (expressed sequence tag) projects, it has become feasible to develop microsatellite or simple sequence repeat (SSR) markers from genes. A set of 111 090 barley ESTs (corresponding to 55.9 Mb of sequence) was employed for the identification of microsatellites with the help of a PERL5 script called MISA. As a result, a total of 9 564 microsatellites were identified in 8 766 ESTs (SSR-ESTs). Cluster analysis revealed the presence of 2 823 non-redundant SSR-ESTs in this set. From these 754 primer pairs were designed and analysed in a set of seven genotypes including the parents of three mapping populations. Finally, 185 microsatellite (EST-SSRs) loci were placed onto the barley genetic map. These markers show a uniform distribution on all the linkage groups ranging from 21 markers (on 7H) to 35 markers (3H). The polymorphism information content (PIC) for the developed markers ranged from 0.24 to 0.78 with an average of 0.48. For the assignment of these markers to BAC clones, a PCR-based strategy was established to screen the “Morex”-BAC library. By using this strategy BAC addresses were obtained for a total of 127 mapped EST-SSRs, which may provide at least two markers located on a single BAC. This observation is indicative of an uneven distribution of genes and may lead to the identification of gene-rich regions in the barley genome.  

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36

Stepanyan, Ivan V., and Michail Y. Lednev. "Parametric Multispectral Mappings and Comparative Genomics." Symmetry 14, no. 12 (November 29, 2022): 2517. http://dx.doi.org/10.3390/sym14122517.

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This article describes new algorithms that allow for viewing genetic sequences in the form of their multispectral images. We presented examples of the construction of such mappings with a demonstration of the practical problems of comparative genomics. New DNA visualization tools seem promising, thanks to their informativeness and representativeness. The research illustrates how a novel sort of multispectral mapping, based on decomposition in several parametric spaces, can be created for comparative genetics. This appears to be a crucial step in the investigation of the genetic coding phenomenon and in practical activities, such as forensics, genetic testing, genealogical analysis, etc. The article gives examples of multispectral parametric sets for various types of coordinate systems. We build mappings using binary sub-alphabets of purine/pyrimidine and keto/amino. We presented 2D and 3D renderings in different characteristic spaces: structural, integral, cyclic, spherical, and third-order spherical. This research is based on the method previously developed by the author for visualizing genetic information based on new molecular genetic algorithms. One of the types of mappings, namely two-dimensional, is an object of discrete geometry, a symmetrical square matrix of high dimension. The fundamental properties of symmetry, which are traced on these mappings, allow us to speak about the close connection between the phenomenon of genetic coding and symmetry when using the developed mathematical apparatus for representing large volumes of complexly organized molecular genetic information.

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37

Schwander, F., E. Zyprian, and R. Töpfer. "GENETIC MAPPING OF ACIDITY-RELEVANT TRAITS." Acta Horticulturae, no. 1082 (April 2015): 315–19. http://dx.doi.org/10.17660/actahortic.2015.1082.43.

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38

Fletcher, Michael. "Mapping genetic variants to cellular contexts." Nature Genetics 54, no. 7 (July 2022): 921. http://dx.doi.org/10.1038/s41588-022-01136-6.

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39

Lewis, M. T., and J. F. FELDMAN. "Genetic mapping of the bd locus." Fungal Genetics Reports 45, no. 1 (July 1, 1998): 21. http://dx.doi.org/10.4148/1941-4765.1255.

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40

Rieseberg, Loren H. "Mapping footprints of past genetic exchange." Science 366, no. 6465 (October 31, 2019): 570–71. http://dx.doi.org/10.1126/science.aaz1576.

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41

ARNHEIM, N., H. LI, and X. CUI. "Genetic mapping by single sperm typing." Animal Genetics 22, no. 2 (April 24, 2009): 105–15. http://dx.doi.org/10.1111/j.1365-2052.1991.tb00652.x.

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42

Drezner, Zvi, and George A. Marcoulides. "Mapping the convergence of genetic algorithms." Journal of Applied Mathematics and Decision Sciences 2006 (September 3, 2006): 1–16. http://dx.doi.org/10.1155/jamds/2006/70240.

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This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. The procedure is illustrated with a hybrid genetic algorithm applied to the quadratic assignment problem. Results provide valuable insight into how population members are selected as the number of generations increases and how genetic algorithms approach stagnation after many generations.

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43

Cloney, Ross. "CRISPR-based mapping of genetic interactions." Nature Reviews Genetics 18, no. 5 (April 3, 2017): 272. http://dx.doi.org/10.1038/nrg.2017.25.

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44

Li, Jinming, Stephanie L. Sherman, Neil Lamb, and Hongyu Zhao. "Multipoint Genetic Mapping with Trisomy Data." American Journal of Human Genetics 69, no. 6 (December 2001): 1255–65. http://dx.doi.org/10.1086/324578.

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45

Heckel, D. G. "Comparative Genetic Linkage Mapping in Insects." Annual Review of Entomology 38, no. 1 (January 1993): 381–408. http://dx.doi.org/10.1146/annurev.en.38.010193.002121.

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46

LONG, FEI, YING QING CHEN, JAMES M. CHEVERUD, and RONGLING WU. "Genetic mapping of allometric scaling laws." Genetical Research 87, no. 3 (June 2006): 207–16. http://dx.doi.org/10.1017/s0016672306008172.

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Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling relationships between rate and size (power laws). A statistical model for mapping specific quantitative trait loci (QTLs) that are responsible for allometric scaling laws has been developed. We present an improved model for allometric mapping of QTLs based on a more general allometry equation. This improved model includes two steps: (1) use model II regression analysis to estimate the parameters underlying universal allometric scaling laws, and (2) substitute the estimated allometric parameters in the mixture-based mapping model to obtain the estimation of QTL position and effects. This model has been validated by a real example for a mouse F2 progeny, in which two QTLs were detected on different chromosomes that determine the allometric relationship between growth rate and body weight.

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47

Schlaggar, Bradley L. "Mapping Genetic Influences on Cortical Regionalization." Neuron 72, no. 4 (November 2011): 499–501. http://dx.doi.org/10.1016/j.neuron.2011.10.024.

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48

Gorovitz, Samuel. "Genetic therapy: Mapping the ethical challanges." Philosophia 25, no. 1-4 (April 1997): 83–97. http://dx.doi.org/10.1007/bf02380027.

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49

Jin, Yuan, Sabrina Allan, Lauren Baber, Eric K. Bhattarai, Teresa M. Lamb, and Wayne K. Versaw. "Rapid genetic mapping in Neurospora crassa." Fungal Genetics and Biology 44, no. 6 (June 2007): 455–65. http://dx.doi.org/10.1016/j.fgb.2006.09.002.

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50

Vinod, K. K. "Kosambi and the genetic mapping function." Resonance 16, no. 6 (June 2011): 540–50. http://dx.doi.org/10.1007/s12045-011-0060-x.

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