Biology subjects

Chen, Z.-q.

Publications and source records attributed to Chen, Z.-q..

6 recordsLinked to original sources

bioRxiv2022

Incorporating genome-wide association study results into genomic prediction enhances the predictive ability of growth and phenology traits in Norway spruce

Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) effects by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects, especially for an oligogenic trait. Using QTLs detected in the genome-wide association study (GWAS) could improve genomic prediction, including informative marker selection and adding a QTL with the largest effect size as a fixed effect. Here, we performed GWAS and genomic selection studies in a population with 904 clones from 32 full-sib families using a newly developed 50k SNP Norway spruce array. In total, GWAS identified 41 SNPs associated with budburst stage (BB) and the SNP with the largest effect size explained 5.1% of the phenotypic variation (PVE). For the other five traits like growth and wood quality traits, only 2 - 13 SNPs were detected and PVE of the strongest effects ranged from 1.2% to 2.0%. GP with approximately 100 preselected SNPs based on the smallest p-values from GWAS showed the largest predictive ability (PA) for the oligogenic trait BB. But for the other polygenic traits, approximate 2000-4000 preselected SNPs, indicated by the smallest Akaike information criterion to offer the best model fit, still resulted in PA being similar to that of GP models using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was [≥]2.5%.

genetics↗

bioRxiv2022

Teasing apart the joint effect of demography and natural selection in the birth of a contact zone

Vast population movements induced by recurrent climatic cycles have shaped the genetic structure of plant species. This is especially true in Scandinavia that was repeatedly glaciated. During glacial periods trees were confined to refugia, south and east of the ice sheet, from which they recolonized Scandinavia as the ice melted away. This multi-pronged recolonization led to large contact zones in most species. We leverage large genomic data from 5000 trees to reconstruct the demographic history of Norway spruce (Picea abies) and test for the presence of natural selection during the recolonization process and the establishment of the contact zone. Sweden is today made up of two large genetic clusters, a southern one originating from the Baltics and a Northern one originating from Northern Russia. The contact zone delineating these two clusters closely matches the limit between two major climatic regions. This suggests that natural selection contributed to the establishment and the maintenance of the contact zone. To test this hypothesis we first used Approximate Bayesian Computation; an Isolation-with migration model with genome-wide linked selection fits the data better than a purely neutral one. Secondly, we identified loci characterized by both extreme allele frequency differences between geographic regions and association to the variables defining the climatic zones. These loci, many of which are related to phenology, form clusters present on all linkage groups. Altogether, the current genetic structure reflects the joint effect of climatic cycles, recolonization and selection on the establishment of strong local adaptation and contact zones. Significance StatementUnderstanding how past climatic events, human actions and evolutionary forces contributed to the present distribution of genetic diversity is crucial to predict their reaction to the current climate crisis. Vast distribution shifts induced by past environmental changes, local ecological processes, natural selection and human transfers contributed to the current distribution of Norway spruce across Northern Europe. Genome-wide polymorphisms from thousands of individuals show that Scandinavia was recolonized after the Last Glacial from both south and north. This two-pronged recolonization established a contact zone between two genetic clusters that matches the limit between two major climate zones. The contact zone is shaped and maintained by natural selection on a large number of loci that form blocks of co-adapted loci spread genome-wide.

evolutionary biology↗

bioRxiv2020

Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce

Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle, but the efforts to develop routines for operational breeding are so far limited. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce (Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments. GS predictive abilities were comparable with those based on pedigree-based prediction. The highest PAs were reached with at least 80-90% of the dataset used as training set. Use of different statistical methods had no significant impact on the estimated PAs. We also compared the abilities to predict density, MFA and MOE of 19 year old trees with models trained on data from coring at different ages and to different depths into the stem. 78-95% of the maximal PAs obtained from coring to the pith at high age were reached by using data possible to obtain by drilling 3-5 rings towards the pith at tree age 10-12, thereby shortening the cycle and reducing the impact on the tree.

plant biology↗

bioRxiv2019

Genomic prediction accuracies and abilities for growth and wood quality traits of Scots pine, using genotyping-by-sequencing (GBS) data

Higher genetic gains can be achieved through genomic selection (GS) by shortening time of progeny testing in tree breeding programs. Genotyping-by-sequencing (GBS), combined with two imputation methods, allowed us to perform the current genomic prediction study in Scots pine (Pinus sylvestris L.). 694 individuals representing 183 full-sib families were genotyped and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic prediction models. In addition, the impact on the predictive ability (PA) and prediction accuracy to estimate genomic breeding values was evaluated by assigning different ratios of training and validation sets, as well as different subsets of SNP markers. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed higher PAs and prediction accuracies than Bayesian LASSO (BL). A subset of approximately 4000 markers was sufficient to provide the same PAs and accuracies as the full set of 8719 markers. Furthermore, PAs were similar for both pedigree- and genomic-based estimations, whereas accuracies and heritabilities were slightly higher for pedigree-based estimations. However, prediction accuracies of genomic models were sufficient to achieve a higher selection efficiency per year, varying between 50-87% compared to the traditional pedigree-based selection.

genetics↗

bioRxiv2018

Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as genotyping platform in Norway spruce

BackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as a genotyping platform. We used 116,765 high quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (GxE), genetic composition, size of the training and validation set, relatedness, and the number of SNPs on the accuracy and predictive ability (PA) of GS.\n\nResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11-14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased more than 30% using one site as training for GS prediction to the second site, indicating that GxE for tree height should be accommodated in model fitting. Using half-sib family structure instead of full-sib led a significant reduction in GS accuracy and PA. The full-sib family structure only needed 750 makers to reach similar accuracy and PA as 100,000 markers required for half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000-8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all makers.\n\nConclusionsThe study indicates GS would be efficient in reducing generation time of a breeding cycle in conifer tree breeding program that requires a long-term progeny testing. Sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G x E effect when a strong GxE is detected.

genetics↗

bioRxiv2017

Cell Lysate Microarray for Mapping the Network of Genetic Regulators for Histone Marks

Protein, as the major executer for cell progresses and functions, its abundance and the level of post-translational modifications, are tightly monitored by regulators. Genetic perturbation could help us to understand the relationships between genes and protein functions. Herein, we developed a cell lysate microarray on kilo-conditions (CLICK) from 4,837 yeast knockout (YKO) strains and 322 temperature-sensitive mutant strains to explore the impact of the genome-wide interruption on certain protein. Taking histone marks as examples, a general workflow was established for the global identification of upstream regulators. Through a single CLICK array test, we obtained a series of regulators for H3K4me3 which covers most of the known regulators in Saccharomyces cerevisiae. We also noted that several group of proteins that are linked to negatively regulation of H3K4me3. Further, we discovered that Cab4p and Cab5p, two key enzymes of CoA biosynthesis, play central roles in histone acylation. Because of its general applicability, CLICK array could be easily adopted to rapid and global identification of upstream protein/enzyme(s) that regulate/modify the level of a protein or the posttranslational modification of a non-histone protein.

systems biology↗