Plant Cell Physiol. 2019 Jun 1;60(6):1342-1353. doi: 10.1093/pcp/pcz050.
Zhao Y(1)(2), Xie P(1)(2), Guan P(1)(2), Wang Y(1), Li Y(1), Yu K(1), Xin M(1),
Hu Z(1), Yao Y(1), Ni Z(1), Sun Q(1), Xie C(1), Peng H(1).
Author information:
(1)State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis
and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China
Agricultural University, Beijing 100193, PR China.
(2)These authors contributed equally to this work.
Spike brittleness represents an important domestication trait in crops. Although
the brittle rachis of wild wheat was cloned, however, the molecular mechanism
underlying spike brittleness is yet to be elucidated. Here, we identified a
single dominant brittle rachis gene Br-Ab on chromosome arm 3AbS using an F2
population of diploid wheat and designated Btr1-Ab. Sequence analysis of the
Btr1-A gene in 40 diploid wheat accessions, 80 tetraploid wheat accessions and
38 hexaploid wheat accessions showed that two independent mutations (Ala119Thr
for diploid and Gly97* for polyploids) in the Btr1-A coding region resulting in
the nonbrittle rachis allele. Overexpression of Btr1-Ab in nonbrittle hexaploid
wheat led to brittle rachis in transgenic plants. RNA-Seq analysis revealed that
Btr1-A represses the expression of cell wall biosynthesis genes during wheat
rachis development. In addition, we found that Btr1-A can modify spike
morphology and reduce threshability, grain size and thousand grain weight in
transgenic wheat. These results demonstrated that Btr1-A reduces cell wall
synthesis in rachis nodes, resulting in natural spikelet shattering, and that
the transition from Btr1-A to btr1-A during wheat domestication had profound
effects on evolution of spike morphology and yield-related traits.
DOI: 10.1093/pcp/pcz050
PMID: 30994893
regions for locus-specific trade-offs for grain weight and grain number.
Theor Appl Genet. 2018 Apr;131(4):985-998. doi: 10.1007/s00122-017-3037-7. Epub
2017 Dec 7.
Sukumaran S(1), Lopes M(2), Dreisigacker S(3), Reynolds M(3).
Author information:
(1)Global Wheat Program, International Maize and Wheat Improvement Center
(CIMMYT), Apdo. Postal 6-641, Mexico City, 06600, Mexico. s.sukumaran@cgiar.org.
(2)CIMMYT, P.O. Box 39, Emek, Ankara, 06511, Turkey.
(3)Global Wheat Program, International Maize and Wheat Improvement Center
(CIMMYT), Apdo. Postal 6-641, Mexico City, 06600, Mexico.
GWAS on multi-environment data identified genomic regions associated with
trade-offs for grain weight and grain number. Grain yield (GY) can be dissected
into its components thousand grain weight (TGW) and grain number (GN), but
little has been achieved in assessing the trade-off between them in spring
wheat. In the present study, the Wheat Association Mapping Initiative (WAMI)
panel of 287 elite spring bread wheat lines was phenotyped for GY, GN, and TGW
in ten environments across different wheat growing regions in Mexico, South
Asia, and North Africa. The panel genotyped with the 90 K Illumina Infinitum SNP
array resulted in 26,814 SNPs for genome-wide association study (GWAS).
Statistical analysis of the multi-environmental data for GY, GN, and TGW
observed repeatability estimates of 0.76, 0.62, and 0.95, respectively. GWAS on
BLUPs of combined environment analysis identified 38 loci associated with the
traits. Among them four loci-6A (85 cM), 5A (98 cM), 3B (99 cM), and 2B
(96 cM)-were associated with multiple traits. The study identified two loci that
showed positive association between GY and TGW, with allelic substitution
effects of 4% (GY) and 1.7% (TGW) for 6A locus and 0.2% (GY) and 7.2% (TGW) for
2B locus. The locus in chromosome 6A (79-85 cM) harbored a gene TaGW2-6A. We
also identified that a combination of markers associated with GY, TGW, and GN
together explained higher variation for GY (32%), than the markers associated
with GY alone (27%). The marker-trait associations from the present study can be
used for marker-assisted selection (MAS) and to discover the underlying genes
for these traits in spring wheat.
DOI: 10.1007/s00122-017-3037-7
PMID: 29218375 [Indexed for MEDLINE]
Genes Genet Syst. 2019 Apr 9;94(1):35-49. doi: 10.1266/ggs.18-00045. Epub 2019
Jan 10.
Yoshioka M(1), Takenaka S(1)(2), Nitta M(1), Li J(1), Mizuno N(1), Nasuda S(1).
Author information:
(1)Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto
University.
(2)Department of Plant Life Science, Faculty of Agriculture, Ryukoku University.
We investigated the genetic diversity of the core collection of hexaploid wheat
accessions in the Japanese wheat gene bank, NBRP-Wheat, with a focus on grain
morphology. We scanned images of grains in the core collection, which consists
of 189 accessions of Triticum aestivum, T. spelta, T. compactum, T.
sphaerococcum, T. macha and T. vavilovii. From the scanned images, we recorded
six metric characters (area size, perimeter length, grain length, grain width,
length to width ratio and circularity) using the software package SmartGrain
ver. 1.2. Statistical analyses of the collected data along with hundred-grain
weight revealed that T. aestivum has the largest diversity in grain morphology.
Principal component analysis of these seven characters demonstrated that two
principal components (PCcore1 and PCcore2) explain more than 96% of the
variation in the core collection accessions. The correlation coefficients
between the principal components and characters indicate that PCcore1 is related
to grain size and PCcore2 to grain shape. From a genome-wide association study,
we found a total of 15 significant marker-trait associations (MTAs) for grain
morphological characters. More interestingly, we found mutually exclusive MTAs
for PCcore1 and PCcore2 on 18 and 13 chromosomes, respectively. The results
suggest that grain morphology in hexaploid wheat is determined by two factors,
grain size and grain shape, which are under the control of multiple genetic
loci.
DOI: 10.1266/ggs.18-00045
PMID: 30626760
Theor Appl Genet. 2019 Nov;132(11):3115-3128. doi: 10.1007/s00122-019-03410-4.
Epub 2019 Aug 9.
Wang X(1), Dong L(1), Hu J(1), Pang Y(1), Hu L(1), Xiao G(1), Ma X(1), Kong
X(2), Jia J(2), Wang H(3), Kong L(4).
Author information:
(1)State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop
Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018,
China.
(2)Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of
Agriculture, The National Key Facility for Crop Gene Resources and Genetic
Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural
Sciences, Beijing, 100081, China.
(3)State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop
Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018,
China. wanghongwei@sdau.edu.cn.
(4)State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop
Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018,
China. lkong@sdau.edu.cn.
In this study, four wheat recombinant inbred line populations constructed by crossing the modern variety
Yanzhan 1 with three semi-wild wheat varieties (i.e., Chayazheda, Yutiandaomai,
and Yunnanxiaomai from Xinjiang, Tibet, and Yunnan, respectively) and one exotic
accession Hussar from Great Britain were investigated for grain weight and eight
morphological traits in seven environments. Eighty-eight QTLs for all measured
traits were totally identified through nested association mapping utilizing
14,643 high-quality polymorphic single nucleotide polymorphism (SNP) markers
generated by 90 K SNP array. Among them, 64 (72.7%) QTLs have the most favorable
alleles donated by semi-wild wheat varieties. For 14 QTL clusters affecting at
least two grain morphological traits, nine QTL clusters were located in similar
position with known genes/QTL, and the other five were novel. Three important
novel QTLs (i.e., qTGW-1B.1, qTGW-1B.2, and qTGW-1A.1) were further validated in
a natural wheat population via haplotype analysis. The favorable haplotypes for
these three QTLs might be used in marker-assisted selection for the improvement
of wheat yield by modifying morphological traits.
DOI: 10.1007/s00122-019-03410-4
PMCID: PMC6791957
PMID: 31399755
Genes (Basel). 2018 Dec 17;9(12):636. doi: 10.3390/genes9120636.
Avni R(1), Oren L(2)(3), Shabtay G(4), Assili S(5)(6), Pozniak C(7), Hale I(8),
Ben-David R(9), Peleg Z(10), Distelfeld A(11).
Author information:
(1)School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv
6997801, Israel. razavni@post.tau.ac.il.
(2)School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv
6997801, Israel. leahoren1988@gmail.com.
(3)The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew
University of Jerusalem, Rehovot 7610001, Israel. leahoren1988@gmail.com.
(4)School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv
6997801, Israel. gaishabtay@yahoo.com.
(5)The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew
University of Jerusalem, Rehovot 7610001, Israel. siwar.assili@mail.huji.ac.il.
(6)The Institute of Plant Sciences, Agriculture Research Organization
(ARO)-Volcani, Rishon LeZion 7505101, Israel. siwar.assili@mail.huji.ac.il.
(7)University of Saskatchewan, Saskatoon SK S7N 5A8, Canada.
zvi.peleg@mail.huji.ac.il.
(8)Department of Agriculture, Nutrition, and Food Systems, University of New
Hampshire, Durham, NH, USA. curtis.pozniak@usask.ca.
(9)The Institute of Plant Sciences, Agriculture Research Organization
(ARO)-Volcani, Rishon LeZion 7505101, Israel. Iago.Hale@unh.edu.
(10)The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew
University of Jerusalem, Rehovot 7610001, Israel. roib@volcani.agri.gov.il.
(11)School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv
6997801, Israel. adistel@tauex.tau.ac.il.
The domestication and subsequent genetic improvement of wheat led to the
development of large-seeded cultivated wheat species relative to their
smaller-seeded wild progenitors. While increased grain weight (GW) continues to
be an important goal of many wheat breeding programs, few genes underlying this
trait have been identified despite an abundance of studies reporting
quantitative trait loci (QTL) for GW. Here we perform a QTL analysis for GW
using a population of recombinant inbred lines (RILs) derived from the cross
between wild emmer wheat accession 'Zavitan' and durum wheat variety 'Svevo'.
Identified QTLs in this population were anchored to the recent Zavitan reference
genome, along with previously published QTLs for GW in tetraploid wheat. This
genome-based, meta-QTL analysis enabled the identification of a locus on
chromosome 6A whose introgression from wild wheat positively affects GW. The
locus was validated using an introgression line carrying the 6A GW QTL region
from Zavitan in a Svevo background, resulting in >8% increase in GW compared to
Svevo. Using the reference sequence for the 6A QTL region, we identified a wheat
ortholog to OsGRF4, a rice gene previously associated with GW. The coding
sequence of this gene (TtGRF4-A) contains four single nucleotide polymorphisms
(SNPs) between Zavitan and Svevo, one of which reveals the Zavitan allele to be
rare in a core collection of wild emmer and completely absent from the
domesticated emmer genepool. Similarly, another wild emmer accession (G18-16)
was found to carry a rare allele of TtGRF4-A that also positively affects GW and
is characterized by a unique SNP absent from the entire core collection. These
results exemplify the rich genetic diversity of wild wheat, posit TtGRF4-A as a
candidate gene underlying the 6A GW QTL, and suggest that the natural Zavitan
and G18-16 alleles of TtGRF4-A have potential to increase wheat yields in
breeding programs.
DOI: 10.3390/genes9120636
PMCID: PMC6315823
PMID: 30562998
Theor Appl Genet. 2017 Sep;130(9):1765-1771. doi: 10.1007/s00122-017-2953-x.
Epub 2017 Aug 1.
Li W(1), Yang B(2).
Author information:
(1)Department of Biology and Microbiology, South Dakota State University,
Brookings, SD, 57007, USA. Wanlong.li@sdstate.edu.
(2)Department of Genetics, Development and Cell Biology, Iowa State University,
Ames, IA, 50011, USA.
Identifying and mapping grain size candidate genes in the wheat genome greatly
empowers reverse genetics approaches to improve grain yield potential of wheat.
Grain size (GS) or grain weight is believed to be a major driving force for
further improvement of wheat yield. Although the large, polyploid genome of
wheat poses an obstacle to identifying GS determinants using map-based cloning,
a translational genomics approach using GS regulators identified in the model
plants rice and Arabidopsis as candidate genes appears to be effective and
supports a hypothesis that a conserved genetic network regulates GS in rice and
wheat. In this review, we summarize the progress in the studies on GS in the
model plants and wheat and identify 45 GS candidate loci in the wheat genome. In
silico mapping of these GS loci in the diploid wheat and barley genomes showed
(1) several gene families amplified in the wheat lineage, (2) a significant
number of the GS genes located in the proximal regions surrounding the
centromeres, and (3) more than half of candidate genes to be negative
regulators, or their expression negatively related by microRNAs. Identifying and
mapping the wheat GS gene homologs will not only facilitate candidate gene
analysis, but also open the door to improving wheat yield using reverse genetics
approaches by mining desired alleles in landraces and wild ancestors and to
developing novel germplasm by TILLING and genome editing technologies.
DOI: 10.1007/s00122-017-2953-x
PMID: 28765985
BMC Plant Biol. 2019 Dec 16;19(1):553. doi: 10.1186/s12870-019-2015-4.
Luján Basile SM(1), Ramírez IA(2), Crescente JM(3)(4), Conde MB(3), Demichelis
M(3), Abbate P(2), Rogers WJ(1)(4), Pontaroli AC(2)(4), Helguera M(3), Vanzetti
LS(5)(6).
Author information:
(1)Laboratorio de Biología Funcional y Biotecnología (BIOLAB)-INBIOTEC-CONICET,
Facultad de Agronomía, UNCPBA., Av. República de Italia, Azul, 7300, Argentina.
(2)Unidad Integrada Balcarce Facultad de Ciencias Agrarias, Universidad Nacional
de Mar del Plata - Estación Experimental Agropecuaria Balcarce, Instituto
Nacional de Tecnología, Ruta 226, km 73.5, Balcarce, 24105, Argentina.
(3)Laboratorio de Biotecnología, EEA INTA Marcos Juárez, Grupo Biotecnología y
Recursos Genéticos, Instituto Nacional de Tecnología Agropecuaria, Ruta 12 s/n,
Marcos Juárez, 2580, Argentina.
(4)Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)., Buenos
Aires, Argentina.
(5)Laboratorio de Biotecnología, EEA INTA Marcos Juárez, Grupo Biotecnología y
Recursos Genéticos, Instituto Nacional de Tecnología Agropecuaria, Ruta 12 s/n,
Marcos Juárez, 2580, Argentina. vanzetti.leonardo@inta.gob.ar.
(6)Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)., Buenos
Aires, Argentina. vanzetti.leonardo@inta.gob.ar.
To better understand the genetic basis and relationships of adaptation and yield related traits, we used a collection of 102 Argentinean hexaploid wheat cultivars genotyped with the 35k SNPs array,
grown from two to six years in three different locations. Based on SNPs data and
gene-related molecular markers, we performed a haplotype block characterization
of the germplasm and a genome-wide association study (GWAS).
RESULTS: The genetic structure of the collection revealed four subpopulations,
reflecting the origin of the germplasm used by the main breeding programs in
Argentina. The haplotype block characterization showed 1268 blocks of different
sizes spread along the genome, including highly conserved regions like the 1BS
chromosome arm where the 1BL/1RS wheat/rye translocation is located. Based on
GWAS we identified ninety-seven chromosome regions associated with heading date,
plant height, thousand grain weight, grain number per spike and fruiting
efficiency at harvest (FEh). In particular FEh stands out as a promising trait
to raise yield potential in Argentinean wheats; we detected fifteen
haplotypes/markers associated with increased FEh values, eleven of which showed
significant effects in all three evaluated locations. In the case of adaptation,
the Ppd-D1 gene is consolidated as the main determinant of the life cycle of
Argentinean wheat cultivars.
CONCLUSION: This work reveals the genetic structure of the Argentinean hexaploid
wheat germplasm using a wide set of molecular markers anchored to the Ref Seq
v1.0. Additionally GWAS detects chromosomal regions (haplotypes) associated with
important yield and adaptation components that will allow improvement of these
traits through marker-assisted selection.
DOI: 10.1186/s12870-019-2015-4
PMCID: PMC6916457
PMID: 31842779
Front Plant Sci. 2019 Jun 4;10:717. doi: 10.3389/fpls.2019.00717. eCollection
2019.
Khalid M(1)(2), Afzal F(1), Gul A(1), Amir R(1), Subhani A(3), Ahmed Z(4),
Mahmood Z(4), Xia X(2), Rasheed A(2)(5)(6), He Z(2)(5).
Author information:
(1)Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of
Science and Technology (NUST), Islamabad, Pakistan.
(2)Institute of Crop Sciences, National Wheat Improvement Centre, Chinese
Academy of Agricultural Sciences (CAAS), Beijing, China.
(3)Barani Agriculture Research Institute (BARI), Chakwal, Pakistan.
(4)Crop Science Institute, National Agricultural Research Centre, Islamabad,
Pakistan.
(5)International Maize and Wheat Improvement Centre (CIMMYT), CAAS, Beijing,
China.
(6)Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
Modern breeding imposed selection for improved productivity that largely
influenced the frequency of superior alleles underpinning traits of breeding
interest. Therefore, molecular diagnosis for the allelic variations of such
genes is important to manipulate beneficial alleles in wheat molecular breeding.
We analyzed a diversity panel largely consisted of advanced lines derived from
synthetic hexaploid wheats for allelic variation at 87 functional genes or loci
of breeding importance using 124 high-throughput KASP markers. We also developed
two KASP markers for water-soluble carbohydrate genes (TaSST-D1 and TaSST-A1)
associated with plant height and thousand grain weight (TGW) in the diversity
panel. KASP genotyping results indicated that beneficial alleles for genes
underpinning flowering time (Ppd-D1 and Vrn-D3), thousand grain weight
(TaCKX-D1, TaTGW6-A1, TaSus1-7B, and TaCwi-D1), water-soluble carbohydrates
(TaSST-A1), yellow-pigment content (Psy-B1 and Zds-D1), and root lesion
nematodes (Rlnn1) were fixed in diversity panel with frequency ranged from 96.4
to 100%. The association analysis of functional genes with agronomic and
biochemical traits under well-watered (WW) and water-limited (WL) conditions
revealed that 21 marker-trait associations (MTAs) were consistently detected in
both moisture conditions. The major developmental genes such as Vrn-A1, Rht-D1,
and Ppd-B1 had the confounding effect on several agronomic traits including
plant height, grain size and weight, and grain yield in both WW and WL
conditions. The accumulation of favorable alleles for grain size and weight
genes additively enhanced grain weight in the diversity panel. Graphical
genotyping approach was used to identify accessions with maximum number of
favorable alleles, thus likely to have high breeding value. These results
improved our knowledge on the selection of favorable and unfavorable alleles
through unconscious selection breeding and identified the opportunities to
deploy alleles with effects in wheat breeding.
DOI: 10.3389/fpls.2019.00717
PMCID: PMC6558208
PMID: 31214230
Theor Appl Genet. 2019 Feb;132(2):419-429. doi: 10.1007/s00122-018-3229-9. Epub
2018 Nov 13.
Sestili F(1), Pagliarello R(1), Zega A(2), Saletti R(2), Pucci A(1), Botticella
E(1), Masci S(1), Tundo S(1), Moscetti I(1), Foti S(2), Lafiandra D(3).
Author information:
(1)Department of Agriculture and Forest Sciences, University of Tuscia, Via S.
Camillo de Lellis, 01100, Viterbo, Italy.
(2)Department of Chemical Sciences, University of Catania, Viale A. Doria 6,
95125, Catania, Italy.
(3)Department of Agriculture and Forest Sciences, University of Tuscia, Via S.
Camillo de Lellis, 01100, Viterbo, Italy. lafiandr@unitus.it.
Knocking down GW2 enhances grain size by regulating genes encoding the synthesis
of cytokinin, gibberellin, starch and cell wall. Raising crop yield is a
priority task in the light of the continuing growth of the world's population
and the inexorable loss of arable land to urbanization. Here, the RNAi approach
was taken to reduce the abundance of Grain Weight 2 (GW2) transcript in the
durum wheat cultivar Svevo. The effect of the knockdown was to increase the
grains' starch content by 10-40%, their width by 4-13% and their surface area by
3-5%. Transcriptomic profiling, based on a quantitative real-time PCR platform,
revealed that the transcript abundance of genes encoding both cytokinin
dehydrogenase 1 and the large subunit of ADP-glucose pyrophosphorylase was
markedly increased in the transgenic lines, whereas that of the genes encoding
cytokinin dehydrogenase 2 and gibberellin 3-oxidase was reduced. A proteomic
analysis of the non-storage fraction extracted from mature grains detected that
eleven proteins were differentially represented in the transgenic compared to
wild-type grain: some of these were involved, or at least potentially involved,
in cell wall development, suggesting a role of GW2 in the regulation of cell
division in the wheat grain.
DOI: 10.1007/s00122-018-3229-9
PMID: 30426174 [Indexed for MEDLINE]
BMC Plant Biol. 2017 Nov 14;17(Suppl 1):181. doi: 10.1186/s12870-017-1121-4.
Teplyakova S(1)(2), Lebedeva M(2)(3), Ivanova N(2), Horeva V(2), Voytsutskaya
N(2), Kovaleva O(2), Potokina E(4)(5)(6).
Author information:
(1)Saint Petersburg State University, Universitetskaya emb.7/9, St. Petersburg,
199034, Russia.
(2)N.I. Vavilov Institute of Plant Genetic Resources (VIR), Bolshaya Morskaya,
42-44, 190000, St. Petersburg, Russia.
(3)Saint Petersburg State Forest Technical University, Institutskiy per, 5,
194021, St. Petersburg, Russia.
(4)Saint Petersburg State University, Universitetskaya emb.7/9, St. Petersburg,
199034, Russia. e.potokina@vir.nw.ru.
(5)N.I. Vavilov Institute of Plant Genetic Resources (VIR), Bolshaya Morskaya,
42-44, 190000, St. Petersburg, Russia. e.potokina@vir.nw.ru.
(6)Saint Petersburg State Forest Technical University, Institutskiy per, 5,
194021, St. Petersburg, Russia. e.potokina@vir.nw.ru.
We investigated the effect of the 7-bp deletion in exon 1 of HvGA20ox2
gene (sdw1.d mutation) on the variation of yield-related and malting quality
traits in the population of DHLs derived from cross of medium tall barley Morex
and semi-dwarf barley Barke. Segregation of plant height, flowering time,
thousand grain weight, grain protein content and grain starch was evaluated in
two diverse environments separated from one another by 15° of latitude. The 7-bp
deletion in HvGA20ox2 gene reduced plant height by approximately 13 cm and
delayed flowering time by 3-5 days in the barley segregating DHLs population
independently on environmental cue. On other hand, the sdw1.d mutation did not
affect significantly either grain quality traits (protein and starch content) or
thousand grain weight.
DOI: 10.1186/s12870-017-1121-4
PMCID: PMC5688404
PMID: 29143605
Theor Appl Genet. 2019 Nov;132(11):3191-3200. doi: 10.1007/s00122-019-03418-w.
Epub 2019 Sep 12.
Xu D(1)(2), Wen W(1), Fu L(1), Li F(1), Li J(1), Xie L(1), Xia X(1), Ni Z(2), He
Z(3), Cao S(4).
Author information:
(1)Institute of Crop Science, National Wheat Improvement Center, Chinese Academy
of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081,
China.
(2)Department of Plant Genetics & Breeding/State Key Laboratory for
Agrobiotechnology, China Agricultural University, 2 Yuanmingyuan West Road,
Beijing, 100094, China.
(3)Institute of Crop Science, National Wheat Improvement Center, Chinese Academy
of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081,
China. zhhecaas@163.com.
(4)Institute of Crop Science, National Wheat Improvement Center, Chinese Academy
of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081,
China. shcao8@163.com.
Genetic dissection uncovered a major QTL QTKW.caas-4BS corresponding with a
483 kb deletion that included genes ZnF, EamA and Rht-B1. This deletion was
associated with increased grain weight and semi-dwarf phenotype. Previous
studies identified quantitative trait loci (QTL) for thousand kernel weight
(TKW) in the region spanning the Rht-B1 locus in wheat (Triticum aestivum L.).
We recently mapped a major QTL QTKW.caas-4BS for TKW spanning the Rht-B1 locus
in a recombinant inbred line (RIL) population derived from Doumai/Shi 4185 using
the wheat 90K array. The allele from Doumai at QTKW.caas-4BS significantly
increased TKW and kernel number per spike, and conferred semi-dwarf trait, which
was beneficial to improve grain yield without a penalty to lodging. To further
dissect QTKW.caas-4BS, we firstly re-investigated the genotypes and phenotypes
of the RILs and confirmed the QTL using cleaved amplified polymorphic sequence
(CAPS) markers developed from flanking SNP markers IWA102 and IWB54814. The
target sequences of the CAPS markers were used as queries to BLAST the wheat
reference genome RefSeq v1.0 and hit an approximate 10.4 Mb genomic region.
Based on genomic mining and SNP loci from the wheat 660K SNP array in the above
genomic region, we developed eight new markers and narrowed QTKW.caas-4BS to a
genetic interval of 1.5 cM. A 483 kb deletion in Doumai corresponded with
QTKW.caas-4BS genetically, including three genes ZnF, EamA and Rht-B1. The other
15 genes with either differential expressions and/or sequence variations between
parents were also potential candidate genes for QTKW.caas-4BS. The findings not
only provide a toolkit for marker-assisted selection of QTKW.caas-4BS but also
defined candidate genes for further functional analysis.
DOI: 10.1007/s00122-019-03418-w
PMID: 31515582
Theor Appl Genet. 2018 May;131(5):1073-1090. doi: 10.1007/s00122-018-3059-9.
Epub 2018 Feb 22.
Liu K(1), Sun X(1), Ning T(1), Duan X(1), Wang Q(1), Liu T(1), An Y(1), Guan
X(1), Tian J(2), Chen J(3).
Author information:
(1)State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology
and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of
Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University,
Tai'an, 271018, People's Republic of China.
(2)State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology
and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of
Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University,
Tai'an, 271018, People's Republic of China. jctian9666@163.com.
(3)State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology
and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of
Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University,
Tai'an, 271018, People's Republic of China. cjs777777@126.com.
Coincident regions on chromosome 4B for GW, on 5A for SD and TSS, and on 3A for
SL and GNS were detected through an integration of a linkage analysis and a
genome-wide association study (GWAS). In addition, six stable QTL clusters on
chromosomes 2D, 3A, 4B, 5A and 6A were identified with high PVE% on a composite
map. The panicle traits of wheat, such as grain number per spike and 1000-grain
weight, are closely correlated with grain yield. Superior and effective alleles
at loci related to panicles developments play a crucial role in the progress of
molecular improvement in wheat yield breeding. Here, we revealed several notable
allelic variations of seven panicle-related traits through an integration of
genome-wide association mapping and a linkage analysis. The linkage analysis was
performed using a recombinant inbred line (RIL) population (173 lines of F8:9)
with a high-density genetic map constructed with 90K SNP arrays, Diversity
Arrays Technology (DArT) and simple sequence repeat (SSR) markers in five
environments. Thirty-five additive quantitative trait loci (QTL) were
discovered, including eleven stable QTLs on chromosomes 1A, 2D, 4B, 5B, 6B, and
6D. The marker interval between EX_C101685 and RAC875_C27536 on chromosome 4B
exhibited pleiotropic effects for GW, SL, GNS, FSN, SSN, and TSS, with the
phenotypic variation explained (PVE) ranging from 5.40 to 37.70%. In addition,
an association analysis was conducted using a diverse panel of 205 elite wheat
lines with a composite map (24,355 SNPs) based on the Illumina Infinium assay in
four environments. A total of 73 significant marker-trait associations (MTAs)
were detected for panicle traits, which were distributed across all wheat
chromosomes except for 4D, 5D, and 6D. Consensus regions between
RAC875_C27536_611 and Tdurum_contig4974_355 on chromosome 4B for GW in multiple
environments, between QTSS5A.7-43 and BS00021805_51 on 5A for SD and TSS, and
between QSD3A.2-164 and RAC875_c17479_359 on 3A for SL and GNS in multiple
environments were detected through linkage analysis and a genome-wide
association study (GWAS). In addition, six stable QTL clusters on chromosomes
2D, 3A, 4B, 5A, and 6A were identified with high PVE% on a composite map. This
study provides potentially valuable information on the dissection of
yield-component traits and valuable genetic alleles for molecular-design
breeding or functional gene exploration.
DOI: 10.1007/s00122-018-3059-9
PMID: 29470622
Theor Appl Genet. 2020 Jan;133(1):239-257. doi: 10.1007/s00122-019-03454-6. Epub
2019 Oct 4.
Tura H(1), Edwards J(1)(2), Gahlaut V(3), Garcia M(4), Sznajder B(1), Baumann
U(1), Shahinnia F(1)(5), Reynolds M(6), Langridge P(1)(7), Balyan HS(3), Gupta
PK(3), Schnurbusch T(1)(8), Fleury D(1).
Author information:
(1)School of Agriculture, Food and Wine, Waite Campus, University of Adelaide,
PMB1, Glen Osmond, SA, 5064, Australia.
(2)Australian Grain Technologies, 20 Leitch Road, Roseworthy, SA, Australia.
(3)Department of Genetics and Plant Breeding, Ch. Charan Singh University,
Meerut, India.
(4)School of Agriculture, Food and Wine, Waite Campus, University of Adelaide,
PMB1, Glen Osmond, SA, 5064, Australia. melissa.garcia@adelaide.edu.au.
(5)Institute for Crop Science and Plant Breeding, Bavarian State Research Center
for Agriculture, Am Gereuth 8, 85354, Freising, Germany.
(6)International Maize and Wheat Improvement Center (CIMMYT), Int. AP 6-641,
06600, Mexico, D.F., Mexico.
(7)Julius-Kühn-Institute, Königin-Louise-Str 19, 14195, Berlin, Germany.
(8)Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK),
Corrensstr. 3, 06466, Gatersleben, Germany.
Genetic control of grain yield and phenology was examined in the Excalibur/Kukri
doubled haploid mapping population grown in 32 field experiments across the
climatic zones of southern Australia, India and north-western Mexico where the
wheat crop experiences drought and heat stress. A total of 128 QTL were
identified for four traits: grain yield, thousand grain weight (TGW), days to
heading and grain filling duration. These QTL included 24 QTL for yield and 27
for TGW, showing significant interactions with the environment (Q * E). We also
identified 14 QTL with a significant, small main effects on yield across
environments. The study focussed on a region of chromosome 1B where two main
effect QTL were found for yield and TGW without the confounding effect of
phenology. Excalibur was the source of favourable alleles: QYld.aww-1B.2 with a
peak at 149.5-150.1 cM and QTgw.aww-1B at 168.5-171.4 cM. We developed near
isogenic lines (NIL) for the interval including QYld.aww-1B.2 and QTgw.aww-1B
and evaluated them under semi-controlled conditions. Significant differences in
four pairs of NIL were observed for grain yield but not for TGW, confirming a
positive effect of the Excalibur allele for QYld.aww-1B.2. The interval
containing QYld.aww-1B.2 was narrowed down to 2.9 cM which corresponded to a
2.2 Mbp genomic region on the chromosome 1B genomic reference sequence of cv.
Chinese Spring and contained 39 predicted genes.
DOI: 10.1007/s00122-019-03454-6
PMID: 31586227
Genes (Basel). 2019 Apr 18;10(4):307. doi: 10.3390/genes10040307.
Irshad A(1), Guo H(2), Zhang S(3), Gu J(4), Zhao L(5), Xie Y(6), Xiong H(7),
Zhao S(8), Ding Y(9), Ma Y(10), Liu L(11).
Author information:
(1)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. ahsanirshad@126.com.
(2)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. guohuijun@caas.cn.
(3)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China.
zhangshunlin1994@163.com.
(4)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. guijiayu@caas.cn.
(5)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. zhaolinshu@caas.cn.
(6)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. xieyongdun@caas.cn.
(7)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. xionghongchun@caas.cn.
(8)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. zhaoshirong@caas.cn.
(9)Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National
Engineering Laboratory of Crop Molecular Breeding/National Center of Space
Mutagenesis for Crop Improvement, Beijing 100081, China. dingyuping@caas.cn.
(10)Institute of Crop Sciences, Chinese Academy of Agricultural
Sciences/National Engineering Laboratory of Crop Molecular Breeding/National
Center of Space Mutagenesis for Crop Improvement, Beijing 100081, China.
mayouzhi@caas.cn.
(11)Institute of Crop Sciences, Chinese Academy of Agricultural
Sciences/National Engineering Laboratory of Crop Molecular Breeding/National
Center of Space Mutagenesis for Crop Improvement, Beijing 100081, China.
liuluxiang@caas.cn.
Wheat is a staple food commodity grown worldwide, and wheat starch is a valuable
source of energy and carbon that constitutes 80% of the grain weight.
Manipulation of genes involved in starch synthesis significantly affects wheat
grain weight and yield. TaSSIV plays an important role in starch synthesis and
its main function is granule formation. To mine and stack more favorable
alleles, single nucleotide polymorphisms (SNPs) of TaSSIV-A, B, and D were
investigated across 362 wheat accessions by Ecotype-Targeting Induced Local
Lesions IN Genome (EcoTILLING). As a result, a total of 38 SNPs in the amplified
regions of three TaSSIV genes were identified, of which 10, 15, and 13 were in
TaSSIV-A, B, and D, respectively. These 38 SNPs were evaluated by using KASP and
six SNPs showed an allele frequency >5% whereas the rest were <5%, i.e.,
considered to be minor alleles. In the Chinese mini core collection, three
haplotypes were detected for TaSSIV-A and three for TaSSIV-B. The results of an
association study in the Chinese mini core collection with thousand grain weight
(TGW) and spike length (SPL) showed that Hap-2-1A was significantly associated
with TGW and Hap-3-1B with SPL. Allelic frequency and geographic distribution
indicated that the favored haplotype (Hap-2-1A) has been positively selected in
Chinese wheat breeding. These results suggested that the Kompetitive Allele
Specific PCR (KASP) markers can be applied in starch improvement to ultimately
improve wheat yield by marker assisted selection in wheat breeding.
DOI: 10.3390/genes10040307
PMCID: PMC6523294
PMID: 31003564
Planta. 2019 Dec;250(6):1967-1981. doi: 10.1007/s00425-019-03278-0. Epub 2019
Sep 16.
Cao P(1), Liang X(1)(2), Zhao H(1)(2), Feng B(3), Xu E(1), Wang L(4), Hu
Y(5)(6).
Author information:
(1)Key Laboratory of Plant Molecular Physiology, CAS Center for Excellence in
Molecular Plant Sciences, Institute of Botany, Chinese Academy of Sciences,
Beijing, 100093, China.
(2)University of Chinese Academy of Sciences, Beijing, 100049, China.
(3)Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041,
China.
(4)Henan Science and Technology University, Luoyang, 471023, Henan, China.
(5)Key Laboratory of Plant Molecular Physiology, CAS Center for Excellence in
Molecular Plant Sciences, Institute of Botany, Chinese Academy of Sciences,
Beijing, 100093, China. huyuxin@ibcas.ac.cn.
(6)National Center for Plant Gene Research, Beijing, 100093, China.
huyuxin@ibcas.ac.cn.
Totally, 48 loci responsible for six spike-related traits were identified in
wheat, and a major locus QGl-4A for grain length was mapped and validated for
marker-assisted selection in breeding. Wheat yield is determined by the number
of spikes, number of grains per spike (GN), and one-thousand kernel weight
(TKW), among which GN and TKW are greatly related to the spike development and
thus the spike-related traits, including spike length (SL), number of spikelet
per spike (SN), grain length (GL) and grain width (GW). To identify the key loci
governing the spike-related traits (SL, SN, GN, TKW, GL and GW), we conducted
the quantitative trait loci (QTL) analysis combined with wheat 660K SNP chip and
Kompetitive allele-specific PCR (KASP) assay, using the F2 and F2:3 populations
derived from Luohan6 (LH6) with big spike and grain and Zhengmai366 with small
spike and grain, and identified a total of 48 QTLs on 18 chromosomes. Moreover,
a major stable QTL for GL on chromosome 4A, designated as QGl-4A, was mapped
into a 0.37 cM interval between KASP markers Xib4A-10 and Xib4A-12,
corresponding to 20 Mb physical region in the Chinese Spring genome. This QTL
explained 17.30% and 5.12% of the phenotypic variation for GL in the F2 and F2:3
populations. Further association analysis of flanking markers Xib4A-10 and
Xib4A-12 in 192 wheat varieties showed that these two markers could be used for
marker-assisted selection in breeding. These results provide valuable
information for map-based cloning of the target genes involved in the regulation
of spike-related traits in common wheat.
DOI: 10.1007/s00425-019-03278-0
PMID: 31529397
Theor Appl Genet. 2020 Mar;133(3):857-872. doi: 10.1007/s00122-019-03511-0. Epub
2019 Dec 16.
Yang L(1), Zhao D(1), Meng Z(2), Xu K(3), Yan J(3), Xia X(1), Cao S(1), Tian
Y(1), He Z(1)(4), Zhang Y(5).
Author information:
(1)Institute of Crop Sciences, National Wheat Improvement Center, Chinese
Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing,
100081, China.
(2)Shangqiu Academy of Agricultural and Forestry Sciences, 10 Shengli Road,
Shangqiu, 476000, Henan Province, China.
(3)Institute of Cotton Research, CAAS, 38 Huanghe Dadao, Anyang, 455000, Henan
Province, China.
(4)International Maize and Wheat Improvement Center (CIMMYT), China Office, c/o
CAAS, Beijing, 100081, China.
(5)Institute of Crop Sciences, National Wheat Improvement Center, Chinese
Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing,
100081, China. zhangyong05@caas.cn.
We identified four chromosome regions harboring QTL for grain yield-related
traits, and breeder-friendly KASP markers were developed and validated for
marker-assisted selection. Identification of major stable quantitative trait
loci (QTL) for grain yield-related traits is important for yield potential
improvement in wheat breeding. In the present study, 266 recombinant inbred
lines (RILs) derived from a cross between Zhongmai 871 (ZM871) and its sister
line Zhongmai 895 (ZM895) were evaluated for thousand grain weight (TGW), grain
length (GL), grain width (GW), and grain number per spike (GNS) in 10
environments and for grain filling rate in six environments. Sixty RILs, with 30
higher and 30 lower TGW, respectively, were genotyped using the wheat 660 K SNP
array for preliminary QTL mapping. Four genetic regions on chromosomes 1AL, 2BS,
3AL, and 5B were identified to have a significant effect on TGW-related traits.
A set of Kompetitive Allele Specific PCR markers were converted from the SNP
markers on the above target chromosomes and used to genotype all 266 RILs. The
mapping results confirmed the QTL named Qgw.caas-1AL, Qgl.caas-3AL,
Qtgw.caas-5B, and Qgl.caas-5BS on the targeted chromosomes, explaining
5.0-20.6%, 5.7-15.7%, 5.5-17.3%, and 12.5-20.5% of the phenotypic variation for
GW, GL, TGW, and GL, respectively. A novel major QTL for GNS on chromosome 5BS,
explaining 5.2-15.2% of the phenotypic variation, was identified across eight
environments. These QTL were further validated using BC1F4 populations derived
from backcrosses ZM871/ZM895//ZM871 (121 lines) and ZM871/ZM895//ZM895 (175
lines) and 186 advanced breeding lines. Collectively, selective genotyping is a
simple, economic, and effective approach for rapid QTL mapping and can be
generally applied to genetic mapping studies for important agronomic traits.
DOI: 10.1007/s00122-019-03511-0
PMID: 31844965
PLoS One. 2020 Feb 14;15(2):e0229159. doi: 10.1371/journal.pone.0229159.
eCollection 2020.
Sun L(1), Huang S(1), Sun G(2), Zhang Y(1), Hu X(1), Nevo E(3), Peng J(4), Sun
D(1)(5).
Author information:
(1)College of Plant Science and Technology, Huazhong Agricultural University,
Wuhan, Hubei, China.
(2)Biology Department, Saint Mary's University, Halifax, Nova Scotia, Canada.
(3)Institute of Evolution, University of Haifa, Mount Carmel, Haifa, Israel.
(4)Germplasm Enhancement Department, Huazhi Biotech Institute, Changsa, Hunan,
China.
(5)Hubei Collaborative Innovation Center for Grain Industry, Jingzhou, Hubei,
China.
Durum wheat, genetic resource with favorable alleles is considered as natural
gene pool for wheat breeding. Kernel size and weight are important factors
affecting grain yield in crops. Here, association analysis was performed to
dissect the genetic constitution of kernel-related traits in 150 lines collected
from 46 countries and regions using a set of EST-derived and genome-wide SNP
markers with five consecutive years of data. Total 109 significant associations
for eight kernel-related traits were detected under a mix linear model,
generating 54 unique SNP markers distributed on 13 of 14 chromosomes. Of which,
19 marker-trait associations were identified in two or more environments,
including one stable and pleiotropic SNP BE500291_5_A_37 on chromosome 5A
correlated with six kernel traits. Although most of our SNP loci were overlapped
with the previously known kernel weight QTLs, several novel loci for kernel
traits in durum were reported. Correlation analysis implied that the moderate
climatic variables during growth and development of durum are needed for the
large grain size and high grain weight. Combined with our previous studies, we
found that chromosome 5A might play an important role in durum growth and
development.
DOI: 10.1371/journal.pone.0229159
PMCID: PMC7021289
PMID: 32059028