Literature DB >> 35441688

Genetic mapping and prediction of flowering time and plant height in a maize Stiff Stalk MAGIC population.

Kathryn J Michel1, Dayane C Lima1, Hope Hundley2, Vasanth Singan2, Yuko Yoshinaga2, Chris Daum2, Kerrie Barry2, Karl W Broman3, C Robin Buell4,5, Natalia de Leon1,6, Shawn M Kaeppler1,6,7.   

Abstract

The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930s. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a 6-parent multiparent advanced generation intercross population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in 2 field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to 2 testers, respectively, and evaluated as hybrids in 2 locations in 2018 and 2019 using an incomplete block design. Markers were derived from a practical haplotype graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 635 of the DH lines and 2 sets of 570 and 471 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55-0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49-0.55). Overall, this finding is consistent with the overlapping and nonoverlapping significant quantitative trait loci found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available.
© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  MPP; Multiparent Advanced Generation Inter-Cross (MAGIC); Multiparental Populations; genomic prediction; maize; multiparent population; quantitative trait loci

Mesh:

Year:  2022        PMID: 35441688      PMCID: PMC9157087          DOI: 10.1093/genetics/iyac063

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.402


  63 in total

1.  Prediction of hybrid performance in maize using molecular markers and joint analyses of hybrids and parental inbreds.

Authors:  Tobias A Schrag; Jens Möhring; Albrecht E Melchinger; Barbara Kusterer; Baldev S Dhillon; Hans-Peter Piepho; Matthias Frisch
Journal:  Theor Appl Genet       Date:  2009-11-15       Impact factor: 5.699

2.  Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects.

Authors:  A E Melchinger; H F Utz; C C Schön
Journal:  Genetics       Date:  1998-05       Impact factor: 4.562

3.  Empirical threshold values for quantitative trait mapping.

Authors:  G A Churchill; R W Doerge
Journal:  Genetics       Date:  1994-11       Impact factor: 4.562

4.  ZmCCT and the genetic basis of day-length adaptation underlying the postdomestication spread of maize.

Authors:  Hsiao-Yi Hung; Laura M Shannon; Feng Tian; Peter J Bradbury; Charles Chen; Sherry A Flint-Garcia; Michael D McMullen; Doreen Ware; Edward S Buckler; John F Doebley; James B Holland
Journal:  Proc Natl Acad Sci U S A       Date:  2012-06-18       Impact factor: 11.205

5.  Advantages and pitfalls in the application of mixed-model association methods.

Authors:  Jian Yang; Noah A Zaitlen; Michael E Goddard; Peter M Visscher; Alkes L Price
Journal:  Nat Genet       Date:  2014-02       Impact factor: 38.330

6.  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

7.  Barley whole exome capture: a tool for genomic research in the genus Hordeum and beyond.

Authors:  Martin Mascher; Todd A Richmond; Daniel J Gerhardt; Axel Himmelbach; Leah Clissold; Dharanya Sampath; Sarah Ayling; Burkhard Steuernagel; Matthias Pfeifer; Mark D'Ascenzo; Eduard D Akhunov; Pete E Hedley; Ana M Gonzales; Peter L Morrell; Benjamin Kilian; Frank R Blattner; Uwe Scholz; Klaus F X Mayer; Andrew J Flavell; Gary J Muehlbauer; Robbie Waugh; Jeffrey A Jeddeloh; Nils Stein
Journal:  Plant J       Date:  2013-08-24       Impact factor: 6.417

8.  Discovery of QTL Alleles for Grain Shape in the Japan-MAGIC Rice Population Using Haplotype Information.

Authors:  Daisuke Ogawa; Yasunori Nonoue; Hiroshi Tsunematsu; Noriko Kanno; Toshio Yamamoto; Jun-Ichi Yonemaru
Journal:  G3 (Bethesda)       Date:  2018-11-06       Impact factor: 3.154

9.  The Practical Haplotype Graph, a platform for storing and using pangenomes for imputation.

Authors:  P J Bradbury; T Casstevens; S E Jensen; L C Johnson; Z R Miller; B Monier; M C Romay; B Song; E S Buckler
Journal:  Bioinformatics       Date:  2022-06-24       Impact factor: 6.931

10.  Reciprocal Genetics: Identifying QTL for General and Specific Combining Abilities in Hybrids Between Multiparental Populations from Two Maize (Zea mays L.) Heterotic Groups.

Authors:  Héloïse Giraud; Cyril Bauland; Matthieu Falque; Delphine Madur; Valérie Combes; Philippe Jamin; Cécile Monteil; Jacques Laborde; Carine Palaffre; Antoine Gaillard; Philippe Blanchard; Alain Charcosset; Laurence Moreau
Journal:  Genetics       Date:  2017-09-28       Impact factor: 4.562

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