High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata

Abstract

Background: The growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F₁ populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes.

Results: Our mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285-4674 markers per map, with sizes ranging from c. 1643-2292 cM, and mean marker intervals of 0.7-2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1-3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F₁ population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits.

Conclusions: Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits.

Keywords: Chromosomal rearrangement; Quantitative trait loci; Radiata pine; Single-nucleotide polymorphisms; Within-family genomic prediction.

Fig. 1

Summary of the pipeline used for the construction of parental linkage maps in the Pinus radiata QTL and FWK populations

Fig. 2

QTL positions on the parental linkage maps of the Pinus radiata QTL population. A QTL positions in the 268,345 parental linkage map. B QTL positions in the 268,405 parental linkage map. Scale bars shows cM (Kosambi). Horizontal lines show the location of markers in the parental bin maps

Fig. 3

QTL positions on linkage group 4 in the 268345 and 268405 parental linkage maps in the Pinus radiata QTL population. Scale bar shows cM (Kosambi). Horizontal lines show the location of markers in the parental bin maps. Lines between groups show the location of a sub-set of homologous contigs between the parents

Fig. 4

Genomic predictive ability for diameter at breast height (DBH) and wood density (WD) as a function of training population size (A) and number of markers (B) used in random cross-validations of 86 Pinus radiata genotypes from the QTL population. Error bars correspond to standard deviations across 100 random cross-validations for each set of parameters. Analyses in (A) were based on all markers (M = 9353). The training population size in (B) was N = 43 (two-fold cross-validation) and subsets of markers were selected at random

Fig. 5

Genetic architecture of growth and wood quality traits in Pinus radiata. Relative proportions of markers with small, medium, and large effects (A) and standardised marker effect size distributions for diameter at breast height (DBH) and wood density (WD) as estimated using GCTB (B). Figure 5 (B) reports results from analyses across both populations. In Fig. 5 (A) WD* and DBH* were from analyses combined across populations, the remaining traits were from the QTL population only