Biology subjects

Wu, H. X.

Publications and source records attributed to Wu, H. X..

4 recordsLinked to original sources

bioRxiv2018

Functional Quantitative Trait Loci (QTL) analysis for adaptive traits in a three-generation Scots pine pedigree

In forest tree breeding, QTL identification aims to accelerate the breeding cycle and increase the genetic gain of traits with economical and ecological value. In our study, both phenotypic data and predicted breeding values were used in the identification QTL linked to the adaptive value in a three-generation pedigree population, for the first time in a conifer species (Pinus sylvestris L.). A total of 11 470 open pollinated F2-progeny trees established at three different locations, were measured for growth and adaptive traits. Breeding values were predicted for their 360 mothers, originating from a single cross of two parents. A multilevel LASSO association analysis was conducted to detect QTL using genotypes of the mothers with the corresponding phenotypes and estimated breeding values (EBVs). Different levels of genotype-by-environment (GxE) effects among sites and ages were detected for survival and height. Moderate-to-low narrow sense heritabilities and EBVs accuracies were found for all traits and all sites. We identified 18 AFLPs and 12 SNPs to be associated with QTL for one or more traits. 62 QTL were significant with percentages of variance explained ranging from 1.7 to 18.9%, mostly for traits based on phenotypic data. Two SNP-QTL showed pleiotropic effects for traits related with survival, seed and flower production. Furthermore, we detected several QTL with significant effects across multiple ages, which could be considered as strong candidate loci for early selection. The lack of reproducibility of some QTL detected across sites may be due to environmental heterogeneity and QTL-by-environment effects.

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↗

bioRxiv2018

Genetic analysis using parent-progeny relationship for wood quality traits in Norway spruce (Picea abies (L.) Karst.)

Two-generations pedigree involving 524 plus trees and their open-pollinated (OP) progenies were jointly studied to estimate parent-progeny correlation and heritability. Three wood traits (wood density, MFA, and MOE) were determined by SilviS-can in one ramet per plus tree and 12 OP progenies. Three ramets per plus tree and 12 OP were also measured with two indirect methods, Pilodyn and Hitman. The overall correlation between OP-based breeding values and plus tree-based phenotypes was low to moderate for all traits. The correlations between the phenotypic values of the mother trees and the breeding values estimated on their half-sib pro-genies are low to moderate. Reasons for this may be experimental errors in progeny trials and lack of experimental design in archives, contributing to the parent and progeny correlation. The management practices in the archive may contribute more to such low correlation. Offspring progeny heritability estimates based on SilviScan measurements were higher than parent-offspring regression using one single ramet from the archive. Moreover, when three ramets were measured the parent-offspring regression heritability estimates were higher than those based solely on progeny data for the Pilodyn and Hitman on the standing trees. The standard error of the heritability estimates decreased with increasing progeny size.

plant biology↗

bioRxiv2017

Genomic prediction reveals significant non-additive effects for growth in hybrid Eucalyptus

Non-additive genetic effects can be effectively exploited in control-pollinated families with the availability of genome-wide markers. We used 41,304 SNP markers and compared pedigree vs. marker-based genetic models by analysing height, diameter, basic density and pulp yield for 338 Eucalyptus urophylla x E.grandis control-pollinated families represented by 949 informative individuals. We evaluated models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance). We showed that the models can capture a large proportion of the genetic variance from dominance and epistasis for growth traits as those components are typically not independent. We also show that we could partition genetic variances more precisely when using relationship matrices derived from markers compared to using only pedigree information. In addition, phenotypic prediction accuracies were only slightly increased by including dominance effects for growth traits since estimates of non-additive variances yielded rather high standard errors. This novel result improves our current understanding of the architecture of quantitative traits and recommends accounting for dominance variance when developing genomic selection strategies in hybrid Eucalyptus.

genetics↗