Literature DB >> 23860723

High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations.

Michael Stange1, H Friedrich Utz, Tobias A Schrag, Albrecht E Melchinger, Tobias Würschum.   

Abstract

High-density genotyping is extensively exploited in genome-wide association mapping studies and genomic selection in maize. By contrast, linkage mapping studies were until now mostly based on low-density genetic maps and theoretical results suggested this to be sufficient. This raises the question, if an increase in marker density would be an overkill for linkage mapping in biparental populations, or if important QTL mapping parameters would benefit from it. In this study, we addressed this question using experimental data and a simulation based on linkage maps with marker densities of 1, 2, and 5 cM. QTL mapping was performed for six diverse traits in a biparental population with 204 doubled haploid maize lines and in a simulation study with varying QTL effects and closely linked QTL for different population sizes. Our results showed that high-density maps neither improved the QTL detection power nor the predictive power for the proportion of explained genotypic variance. By contrast, the precision of QTL localization, the precision of effect estimates of detected QTL, especially for small and medium sized QTL, as well as the power to resolve closely linked QTL profited from an increase in marker density from 5 to 1 cM. In conclusion, the higher costs for high-density genotyping are compensated for by more precise estimates of parameters relevant for knowledge-based breeding, thus making an increase in marker density for linkage mapping attractive.

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Year:  2013        PMID: 23860723     DOI: 10.1007/s00122-013-2155-0

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  22 in total

1.  Efficient construction of high-density linkage map and its application to QTL analysis in barley.

Authors:  K Hori; T Kobayashi; A Shimizu; K Sato; K Takeda; S Kawasaki
Journal:  Theor Appl Genet       Date:  2003-07-01       Impact factor: 5.699

2.  New insights into the genetics of in vivo induction of maternal haploids, the backbone of doubled haploid technology in maize.

Authors:  Vanessa Prigge; Xiaowei Xu; Liang Li; Raman Babu; Shaojiang Chen; Gary N Atlin; Albrecht E Melchinger
Journal:  Genetics       Date:  2011-11-30       Impact factor: 4.562

3.  Statistical properties of QTL linkage mapping in biparental genetic populations.

Authors:  H Li; S Hearne; M Bänziger; Z Li; J Wang
Journal:  Heredity (Edinb)       Date:  2010-05-12       Impact factor: 3.821

4.  Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize.

Authors:  Christian Riedelsheimer; Jan Lisec; Angelika Czedik-Eysenberg; Ronan Sulpice; Anna Flis; Christoph Grieder; Thomas Altmann; Mark Stitt; Lothar Willmitzer; Albrecht E Melchinger
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-21       Impact factor: 11.205

Review 5.  Mapping QTL for agronomic traits in breeding populations.

Authors:  Tobias Würschum
Journal:  Theor Appl Genet       Date:  2012-05-22       Impact factor: 5.699

6.  Comparison of biometrical approaches for QTL detection in multiple segregating families.

Authors:  Wenxin Liu; Jochen C Reif; Nicolas Ranc; Giovanni Della Porta; Tobias Würschum
Journal:  Theor Appl Genet       Date:  2012-05-23       Impact factor: 5.699

7.  Molecular mapping of QTLs for resistance to Gibberella ear rot, in corn, caused by Fusarium graminearum.

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Journal:  Genome       Date:  2005-06       Impact factor: 2.166

8.  Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers.

Authors:  Huihui Yu; Weibo Xie; Jia Wang; Yongzhong Xing; Caiguo Xu; Xianghua Li; Jinghua Xiao; Qifa Zhang
Journal:  PLoS One       Date:  2011-03-03       Impact factor: 3.240

9.  Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines.

Authors:  Christian Riedelsheimer; Frank Technow; Albrecht E Melchinger
Journal:  BMC Genomics       Date:  2012-09-04       Impact factor: 3.969

10.  MaizeGDB: The maize model organism database for basic, translational, and applied research.

Authors:  Carolyn J Lawrence; Lisa C Harper; Mary L Schaeffer; Taner Z Sen; Trent E Seigfried; Darwin A Campbell
Journal:  Int J Plant Genomics       Date:  2008
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  32 in total

1.  Reconstruction of Genome Ancestry Blocks in Multiparental Populations.

Authors:  Chaozhi Zheng; Martin P Boer; Fred A van Eeuwijk
Journal:  Genetics       Date:  2015-06-04       Impact factor: 4.562

2.  Developmental Pleiotropy Shaped the Roots of the Domesticated Common Bean (Phaseolus vulgaris).

Authors:  Jugpreet Singh; Salvador A Gezan; C Eduardo Vallejos
Journal:  Plant Physiol       Date:  2019-05-06       Impact factor: 8.340

3.  Quantitative trait loci mapping for Gibberella ear rot resistance and associated agronomic traits using genotyping-by-sequencing in maize.

Authors:  Aida Z Kebede; Tsegaye Woldemariam; Lana M Reid; Linda J Harris
Journal:  Theor Appl Genet       Date:  2015-09-24       Impact factor: 5.699

4.  Genetic mapping of host resistance to the Pyrenophora teres f. maculata isolate 13IM8.3.

Authors:  Abdullah Fahad Alhashel; Roshan Sharma Poudel; Jason Fiedler; Craig H Carlson; Jack Rasmussen; Thomas Baldwin; Timothy L Friesen; Robert S Brueggeman; Shengming Yang
Journal:  G3 (Bethesda)       Date:  2021-12-08       Impact factor: 3.542

5.  Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population.

Authors:  Yang Bian; Qin Yang; Peter J Balint-Kurti; Randall J Wisser; James B Holland
Journal:  BMC Genomics       Date:  2014-12-05       Impact factor: 3.969

6.  Punctuated distribution of recombination hotspots and demarcation of pericentromeric regions in Phaseolus vulgaris L.

Authors:  Mehul S Bhakta; Valerie A Jones; C Eduardo Vallejos
Journal:  PLoS One       Date:  2015-01-28       Impact factor: 3.240

7.  Construction of high-quality recombination maps with low-coverage genomic sequencing for joint linkage analysis in maize.

Authors:  Chunhui Li; Yongxiang Li; Peter J Bradbury; Xun Wu; Yunsu Shi; Yanchun Song; Dengfeng Zhang; Eli Rodgers-Melnick; Edward S Buckler; Zhiwu Zhang; Yu Li; Tianyu Wang
Journal:  BMC Biol       Date:  2015-09-21       Impact factor: 7.431

8.  Ensemble Learning of QTL Models Improves Prediction of Complex Traits.

Authors:  Yang Bian; James B Holland
Journal:  G3 (Bethesda)       Date:  2015-08-13       Impact factor: 3.154

9.  Extracting genotype information of Arabidopsis thaliana recombinant inbred lines from transcript profiles established with high-density oligonucleotide arrays.

Authors:  Renate Schmidt; Anastassia Boudichevskaia; Hieu Xuan Cao; Sang He; Rhonda Christiane Meyer; Jochen Christoph Reif
Journal:  Plant Cell Rep       Date:  2017-08-30       Impact factor: 4.570

10.  QTL mapping and comparative genome analysis of agronomic traits including grain yield in winter rye.

Authors:  Bernd Hackauf; Stefan Haffke; Franz Joachim Fromme; Steffen R Roux; Barbara Kusterer; Dörthe Musmann; Andrzej Kilian; Thomas Miedaner
Journal:  Theor Appl Genet       Date:  2017-05-31       Impact factor: 5.699

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