PMID- 32079240
OWN - NLM
STAT- MEDLINE
DCOM- 20201207
LR  - 20201214
IS  - 1422-0067 (Electronic)
IS  - 1422-0067 (Linking)
VI  - 21
IP  - 4
DP  - 2020 Feb 17
TI  - Genomic Prediction for Grain Yield and Yield-Related Traits in Chinese Winter 
      Wheat.
LID - 10.3390/ijms21041342 [doi]
LID - 1342
AB  - Genomic selection (GS) is a strategy to predict the genetic merits of individuals 
      using genome-wide markers. However, GS prediction accuracy is affected by many 
      factors, including missing rate and minor allele frequency (MAF) of genotypic 
      data, GS models, trait features, etc. In this study, we used one wheat population 
      to investigate prediction accuracies of various GS models on yield and 
      yield-related traits from various quality control (QC) scenarios, missing 
      genotype imputation, and genome-wide association studies (GWAS)-derived markers. 
      Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two 
      major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and 
      three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross 
      validation was used to estimate the prediction accuracy. The results indicated 
      that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided 
      better prediction accuracy. Under this QC scenario, prediction accuracies were 
      further calculated for imputed and GWAS-derived markers. It was observed that the 
      accuracies of the six traits were related to their heritability and genetic 
      architecture, as well as the GS prediction model. Moore-Penrose generalized 
      inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) 
      resulted in higher prediction accuracies than other GS models across traits. 
      Imputation of missing genotypic data had marginal effect on prediction accuracy, 
      while GWAS-derived markers improved the prediction accuracy in most cases. These 
      results demonstrate that QC on missing rate and MAF had positive impact on the 
      predictability of GS models. We failed to identify one single combination of QC 
      scenarios that could outperform the others for all traits and GS models. However, 
      the balance between marker number and marker quality is important for the 
      deployment of GS in wheat breeding. GWAS is able to select markers which are 
      mostly related to traits, and therefore can be used to improve the prediction 
      accuracy of GS.
FAU - Ali, Mohsin
AU  - Ali M
AD  - National Key Facility for Crop Gene Resources and Genetic Improvement, and 
      Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), 
      Beijing 100081, China.
FAU - Zhang, Yong
AU  - Zhang Y
AD  - National Key Facility for Crop Gene Resources and Genetic Improvement, and 
      Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), 
      Beijing 100081, China.
FAU - Rasheed, Awais
AU  - Rasheed A
AD  - International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, 
      12 Zhongguancun South Street, Beijing 100081, China.
AD  - Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan.
FAU - Wang, Jiankang
AU  - Wang J
AD  - National Key Facility for Crop Gene Resources and Genetic Improvement, and 
      Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), 
      Beijing 100081, China.
FAU - Zhang, Luyan
AU  - Zhang L
AD  - National Key Facility for Crop Gene Resources and Genetic Improvement, and 
      Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), 
      Beijing 100081, China.
LA  - eng
GR  - 31861143003/National Natural Science Foundation of China/
GR  - 2014CB138105/National Basic Research Program of China (973 Program)/
PT  - Journal Article
DEP - 20200217
PL  - Switzerland
TA  - Int J Mol Sci
JT  - International journal of molecular sciences
JID - 101092791
RN  - 0 (DNA, Plant)
SB  - IM
MH  - DNA, Plant/genetics/isolation & purification
MH  - Data Analysis
MH  - Edible Grain/*genetics
MH  - Gene Frequency
MH  - Genetic Variation
MH  - Genome-Wide Association Study
MH  - *Genomics
MH  - Genotype
MH  - Linkage Disequilibrium
MH  - Models, Genetic
MH  - Phenotype
MH  - Polymorphism, Single Nucleotide
MH  - *Quantitative Trait Loci
MH  - Selection, Genetic
MH  - Triticum/*genetics
PMC - PMC7073225
OTO - NOTNLM
OT  - genomic selection
OT  - minor allele frequency
OT  - missing data
OT  - wheat
COIS- The authors declare no conflict of interest.
EDAT- 2020/02/23 06:00
MHDA- 2020/12/15 06:00
PMCR- 2020/02/01
CRDT- 2020/02/22 06:00
PHST- 2020/01/11 00:00 [received]
PHST- 2020/02/06 00:00 [revised]
PHST- 2020/02/14 00:00 [accepted]
PHST- 2020/02/22 06:00 [entrez]
PHST- 2020/02/23 06:00 [pubmed]
PHST- 2020/12/15 06:00 [medline]
PHST- 2020/02/01 00:00 [pmc-release]
AID - ijms21041342 [pii]
AID - ijms-21-01342 [pii]
AID - 10.3390/ijms21041342 [doi]
PST - epublish
SO  - Int J Mol Sci. 2020 Feb 17;21(4):1342. doi: 10.3390/ijms21041342.