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Journal articles on the topic 'Genetic mapping'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Salava, J., Y. Wang, B. Krška, et al. "Molecular genetic mapping in apricot." Czech Journal of Genetics and Plant Breeding 38, No. 2 (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&nbsp;individuals derived from a cross LE-3246 &times; 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 assoc

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2

Bo, W., Z. Wang, F. Xu, et al. "Shape mapping: genetic mapping meets geometric morphometrics." Briefings in Bioinformatics 15, no. 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 (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 (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 (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 (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 (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. W

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9

Mynett-Johnson, Lesley A., and Patrick McKeon. "The molecular genetics of affective disorders: An overview." Irish Journal of Psychological Medicine 13, no. 4 (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 i

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10

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 (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 similar

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11

Kassie, Fentanesh C., Joël R. Nguepjop, Hermine B. Ngalle, et al. "An Overview of Mapping Quantitative Trait Loci in Peanut (Arachis hypogaea L.)." Genes 14, no. 6 (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 populatio

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12

ZENG, ZHAO-BANG, CHEN-HUNG KAO, and CHRISTOPHER J. BASTEN. "Estimating the genetic architecture of quantitative traits." Genetical Research 74, no. 3 (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 architectu

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13

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

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14

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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15

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 (2005): 29–37. http://dx.doi.org/10.12702/1984-7033.v05n01a04.

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16

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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17

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

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18

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

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19

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

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20

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

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21

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

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22

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

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23

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

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24

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

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25

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

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26

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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27

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

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28

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

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29

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

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30

Olson, Jane M., John S. Witte, and Robert C. Elston. "Genetic mapping of complex traits." Statistics in Medicine 18, no. 21 (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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31

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

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32

Ommen, G. J. B. van, and P. L. Pearson. "Long-range mapping in the research and diagnosis of genetic disease." Genome 31, no. 2 (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 muscul

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33

Stepanyan, Ivan V., and Michail Y. Lednev. "Parametric Multispectral Mappings and Comparative Genomics." Symmetry 14, no. 12 (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 phenomen

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34

Aytasheva, Z. G., Kh M. Kassymkanova, V. B. Turekhanova, et al. "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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35

King, Graham J. "Progress of Apple Genetic Mapping in Europe." HortScience 30, no. 4 (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

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36

Kassem, Moulay Abdelmajid. "QTL Mapping of Seed Quality Traits in Crops." Plants 14, no. 3 (2025): 482. https://doi.org/10.3390/plants14030482.

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37

Cornely, Pierre-Richard Jean. "Genetic optimization mapping applied to medical image segmentation." Journal of Geography and Cartography 2, no. 1 (2019): 37. http://dx.doi.org/10.24294/jgc.v2i1.830.

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A number of important optimization problems have been classified as mapping applied towards segmentation of important features. The segmentation of important features can be formulated as configurational mapping problems by representing mapping configurations as solutions to problems of interest. One example of such configuration mapping is found in image segmentation where an image can be represented as unique subsets of a complete image and then evolved through mapping to become a segment of specific interest within an image. An effective segmentation mapping algorithm must determine the spe

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38

Mitchell-Olds, T. "Interval mapping of viability loci causing heterosis in Arabidopsis." Genetics 140, no. 3 (1995): 1105–9. http://dx.doi.org/10.1093/genetics/140.3.1105.

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Abstract The genetic basis of heterosis has implications for many problems in genetics and evolution. Heterosis and inbreeding depression affect human genetic diseases, maintenance of genetic variation, evolution of breeding systems, agricultural productivity, and conservation biology. Despite decades of theoretical and empirical studies, the genetic basis of heterosis has remained unclear. I mapped viability loci contributing to heterosis in Arabidopsis. An overdominant factor with large effects on viability mapped to a short interval on chromosome I. Homozygotes had 50% lower viability than

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39

Teneva, A., K. Dimitrov, Caro Petrovic, et al. "Molecular genetics and SSR markers as a new practice in farm animal genomic analysis for breeding and control of disease disorders." Biotehnologija u stocarstvu 29, no. 3 (2013): 405–29. http://dx.doi.org/10.2298/bah1303405t.

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Molecular genetics investigates the genetic makeup of individuals at the DNA level. That includes the identification and mapping of molecular genetic markers and genetic polymorphisms. Molecular genetic markers (DNA markers) are one of the most powerful means for the genomic analysis and allow the connection of hereditary traits with genomic variation. Molecular marker technology has developed rapidly over the last decade and two shapes of specific DNA based marker, Simple Sequence Repeats (SSRs), also known as microsatellites, and Single Nucleotide Polymorphisms (SNPs) prevail applications in

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40

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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41

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

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42

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

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43

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

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44

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

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45

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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46

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

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47

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

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48

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

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49

LONG, FEI, YING QING CHEN, JAMES M. CHEVERUD, and RONGLING WU. "Genetic mapping of allometric scaling laws." Genetical Research 87, no. 3 (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 estim

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50

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

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