Biology subjects

Garcia Gil, M. R.

Publications and source records attributed to Garcia Gil, M. R..

4 recordsLinked to original sources

bioRxiv2018

Genetic control of transition from juvenile to mature wood with respect to microfibril angle (MFA) in Norway spruce (Picea abies) and lodgepole pine (Pinus contorta)

Genetic control of microfibril angle (MFA) transition from juvenile to mature was evaluated in Norway spruce and lodgepole pine. Increment cores were collected at breast height from 5,618 trees in two 21-year-old Norway spruce progeny trials in southern Sweden, and from 823 trees in two 34-35 - year-old lodgepole pine progeny trials in northern Sweden. Radial variations in MFA from pith to bark were measured for each core using SilviScan. To estimate MFA transition from juvenile to mature, a threshold level of MFA 20{degrees} was considered and six different regression functions were fitted to the MFA profile of each tree after exclusion of outliers, following three steps. The narrow-sense heritability estimates (h2) obtained for MFA transition were highest based on the slope function, ranging from 0.21 to 0.23 for Norway spruce and from 0.34 to 0.53 for lodgepole pine, while h2 were mostly non-significant based on the logistic function, under all exclusion methods. Results of this study indicate that it is possible to select for an earlier MFA transition from juvenile to mature in Norway spruce and lodgepole pine selective breeding programs, as the genetic gains ({triangleup}G) obtained in direct selection of this trait were very high in both species.

genomics↗

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↗

bioRxiv2018

An ultra-dense haploid genetic map for evaluating the highly fragmented genome assembly of Norway spruce (Picea abies)

Norway spruce (Picea abies (L.) Karst.) is a conifer species of substanital economic and ecological importance. In common with most conifers, the P. abies genome is very large ([~]20 Gbp) and contains a high fraction of repetitive DNA. The current P. abies genome assembly (v1.0) covers approximately 60% of the total genome size but is highly fragmented, consisting of >10 million scaffolds. The genome annotation contains 66,632 gene models that are at least partially validated (www.congenie.org), however, the fragmented nature of the assembly means that there is currently little information available on how these genes are physically distributed over the 12 P. abies chromosomes. By creating an ultra-dense genetic linkage map, we anchored and ordered scaffolds into linkage groups, which complements the fine-scale information available in assembly contigs. Our ultra-dense haploid consensus genetic map consists of 21,056 markers derived from 14,336 scaffolds that contain 17,079 gene models (25.6% of the validated gene models) that we have anchored to the 12 linkage groups. We used data from three independent component maps, as well as comparisons with previously published Picea maps to evaluate the accuracy and marker ordering of the linkage groups. We demonstrate that approximately 3.8% of the anchored scaffolds and 1.6% of the gene models covered by the consensus map have likely assembly errors as they contain genetic markers that map to different regions within or between linkage groups. We further evaluate the utility of the genetic map for the conifer research community by using an independent data set of unrelated individuals to assess genome-wide variation in genetic diversity using the genomic regions anchored to linkage groups. The results show that our map is sufficiently dense to enable detailed evolutionary analyses across the P. abies genome.

genetics↗