ORCID Profile
0000-0003-0832-3472
Current Organisation
Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen
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Publisher: Wiley
Date: 07-2016
DOI: 10.3835/PLANTGENOME2016.02.0016
Abstract: Hybrid breeding in barley ( Hordeum vulgare L.) offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two‐way and 408 three‐way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP) array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five‐fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA) effects was moderate, amounting to 0.56 and 0.48 for two‐ and three‐way hybrids, respectively. The potential of GCA‐based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross‐validated prediction abilities of 0.51 and 0.58 for two‐ and three‐way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two‐way hybrids as training population and the three‐way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley.
Publisher: Springer Science and Business Media LLC
Date: 11-03-2015
Publisher: Elsevier BV
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 21-08-2019
DOI: 10.1007/S00122-019-03413-1
Abstract: A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.
Publisher: Wiley
Date: 07-2016
DOI: 10.3835/PLANTGENOME2015.04.0020
Abstract: A set of 585 informative single‐nucleotide polymorphism (SNP) loci was used to genotype both a panel of erse accessions and a set of recombinant inbred lines (RILs) bred from the cross Zhongpin03‐5373 (ZP resistant to SCN) × Zhonghuang13 (ZH susceptible). The SNP loci are mostly sited within genic sequence in regions of the soybean [ Glycine max (L.) Merr.] genome thought to harbor genes determining resistance to the soybean cyst nematode (SCN, Heterodera glycines Ichinohe). The three strongest quantitative trait nucleotides (QTNs) identified by association mapping (AM) involved the genes Glyma18 g02590 (a component of the multigene locus rhg1‐b ), Glyma11 g35820 and Glyma11 g35810 (an rhg1‐b paralog), as well as some other loci with smaller effects. The linkage mapping (LM) analysis performed using the RILs revealed two putative quantitative trait loci (QTL): one mapping to rhg1‐b and the other to an rhg1‐b paralog both of these loci were also identified by AM. The former locus explained 25.5% of the phenotypic variance for SCN resistance and the latter 5.8%. In combination, the two major loci acted nonadditively, providing a high level of SCN resistance.
Publisher: Springer Science and Business Media LLC
Date: 12-2014
Publisher: Public Library of Science (PLoS)
Date: 05-04-2023
DOI: 10.1371/JOURNAL.PONE.0283989
Abstract: Direct seeding has been widely adopted as an economical and labor-saving technique in rice production, though problems such as low seedling emergence rate, emergence irregularity and poor lodging resistance are existing. These problems are currently partially overcome by increasing seeding rate, however it is not acceptable for hybrid rice due to the high seed cost. Improving direct seeding by breeding is seen as the ultimate solution to these problems. For hybrid breeding, identifying superior hybrids among a massive number of hybrids from crossings between male and female parental populations by phenotypic evaluation is tedious and costly. Contrastingly, genomic selection rediction (GS/GP) could efficiently detect the superior hybrids capitalizing on genomic data, which holds a great potential in plant hybrids breeding. In this study, we utilized 402 rice inbred varieties and 401 hybrids to investigate the effectiveness of GS on rice mesocotyl length, a representative indicative trait of direct seeding suitability. Several GP methods and training set designs were studied to seek the optimal scenario of hybrid prediction. It was shown that using half-sib hybrids as training set with the phenotypes of all parental lines being fitted as a covariate could optimally predict mesocotyl length. Partitioning the molecular markers into trait-associated and -unassociated groups based on genome-wide association study using all parental lines and hybrids could further improve the prediction accuracy. This study indicates that GS could be an effective and efficient method for hybrid breeding for rice direct seeding.
Publisher: Research Square Platform LLC
Date: 05-04-2021
DOI: 10.21203/RS.3.RS-365517/V1
Abstract: Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a partial phenotyping strategy that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the partial genomic phenotyping strategy in a wheat dataset. Whether the partial phenotyping strategy resulted in more selection response depended on the correlations of phenotypes between environments. The partial phenotyping strategy consistently showed statistically significant higher simulated responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. Our results indicate that genomics-based partial phenotyping can improve selection response at middle stages of crop breeding programs.
Publisher: Springer Science and Business Media LLC
Date: 30-08-2017
DOI: 10.1007/S00299-017-2200-6
Abstract: Polymorphic probes identified via a sequence-based approach are suitable to infer the genotypes of recombinant inbred lines from hybridisation intensities of GeneChip
Publisher: Frontiers Media SA
Date: 20-11-2018
Publisher: Public Library of Science (PLoS)
Date: 27-02-2019
Publisher: Springer Science and Business Media LLC
Date: 08-09-2015
DOI: 10.1007/S00122-015-2602-1
Abstract: Fusarium head blight and Septoria tritici blotch resistances are complex traits and can be improved efficiently by genomic selection modeling main and epistatic effects. Enhancing the resistance against Fusarium head blight (FHB) and Septoria tritici blotch (STB) is of central importance for a sustainable wheat production. Our study is based on a large experimental data set of 2325 inbred lines genotyped with 12,642 SNP markers and phenotyped in multi-environmental trials for FHB and STB resistance as well as for plant height. Our objectives were to (1) investigate the impact of plant height on FHB and STB severity, (2) examine the potential of marker-assisted selection, and (3) study the prediction ability of genomic selection modeling main and epistatic effects. We observed low correlations between plant height and FHB (r = -0.15 P < 0.05) as well as STB severity (r = -0.17 P < 0.05) suggesting negligible morphological resistances. Cross-validation in combination with association mapping revealed absence of large effect QTL impeding an efficient pyramiding of different resistance loci through marker-assisted selection. The prediction ability of genomic selection was high amounting to 0.6 for FHB and 0.5 for STB resistance. Therefore, genomic selection is a promising tool to improve FHB and STB resistance in wheat.
Publisher: Springer Science and Business Media LLC
Date: 19-12-2016
DOI: 10.1007/S00122-016-2840-X
Abstract: Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and presence of local epistatic effects. The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a erse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they in idually explained only 2.5% of the genetic variation. Despite this, we found no stable marker-trait associations when validating in independent subsets. A two-dimensional scan for marker-marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.
Publisher: Springer Science and Business Media LLC
Date: 10-08-2017
Publisher: Springer Science and Business Media LLC
Date: 08-01-2016
DOI: 10.1007/S00122-015-2655-1
Abstract: Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable in iduals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.
Publisher: Wiley
Date: 28-11-2014
DOI: 10.1111/AGE.12236
Abstract: Imputation of missing genotypes, in particular from low density to high density, is an important issue in genomic selection and genome-wide association studies. Given the marker densities, the most important factors affecting imputation accuracy are the size of the reference population and the relationship between in iduals in the reference (genotyped with high-density panel) and study (genotyped with low-density panel) populations. In this study, we investigated the imputation accuracies when the reference population (genotyped with Illumina BovineSNP50 SNP panel) contained sires, halfsibs, or both sires and halfsibs of the in iduals in the study population (genotyped with Illumina BovineLD SNP panel) using three imputation programs (fimpute v2.2, findhap v2, and beagle v3.3.2). Two criteria, correlation between true and imputed genotypes and missing rate after imputation, were used to evaluate the performance of the three programs in different scenarios. Our results showed that fimpute performed the best in all cases, with correlations from 0.921 to 0.978 when imputing from sires to their daughters or between halfsibs. In general, the accuracies of imputing between halfsibs or from sires to their daughters were higher than were those imputing between non-halfsibs or from sires to non-daughters. Including both sires and halfsibs in the reference population did not improve the imputation performance in comparison with when only including halfsibs in the reference population for all the three programs.
Location: Australia
Location: Germany
Location: China
No related grants have been discovered for Sang He.