Association genetics of wood physical traits in the conifer white spruce and relationships with gene expression

Abstract

Marker-assisted selection holds promise for highly influencing tree breeding, especially for wood traits, by considerably reducing breeding cycles and increasing selection accuracy. In this study, we used a candidate gene approach to test for associations between 944 single-nucleotide polymorphism markers from 549 candidate genes and 25 wood quality traits in white spruce. A mixed-linear model approach, including a weak but nonsignificant population structure, was implemented for each marker-trait combination. Relatedness among individuals was controlled using a kinship matrix estimated either from the known half-sib structure or from the markers. Both additive and dominance effect models were tested. Between 8 and 21 single-nucleotide polymorphisms (SNPs) were found to be significantly associated (P ≤ 0.01) with each of earlywood, latewood, or total wood traits. After controlling for multiple testing (Q ≤ 0.10), 13 SNPs were still significant across as many genes belonging to different families, each accounting for between 3 and 5% of the phenotypic variance in 10 wood characters. Transcript accumulation was determined for genes containing SNPs associated with these traits. Significantly different transcript levels (P ≤ 0.05) were found among the SNP genotypes of a 1-aminocyclopropane-1-carboxylate oxidase, a β-tonoplast intrinsic protein, and a long-chain acyl-CoA synthetase 9. These results should contribute toward the development of efficient marker-assisted selection in an economically important tree species.

Figure 1.—

Distribution of genes into tissue-preferential transcript accumulation classes. (A–F) Each chart represents a different set of gene sequences (Xy, xylem; Ph, phloem; Ne, needles). (A) Whole-trancriptome microarray (25,094 genes). (B) Genes containing a SNP significantly associated with wood property traits (13 genes). (C) Genes containing a SNP submitted to association analysis (549 genes). (D–F) Genes containing a SNP significantly associated with wood traits at P ≤ 0.05: (D) MFA (60 genes), (E) tracheid cell diameter in radial direction (RCD) (57 genes), and (F) DEN (48 genes).

Figure 2.—

Patterns of intralocus linkage disequilibrium (LD) for white spruce estimated using the squared allelic correlation coefficient between each pair of SNPs within candidate genes.

Figure 3.—

Plot of the mean of L(K) over 20 runs for each of K clusters ranging from 1 to 7 as a function of the number of clusters. A constant of 15,500 was removed on the y-axis.

Figure 4.—

Genetic clustering of the 469 white spruce individuals of the discovery population using the program STRUCTURE. Vertical bars represent individuals, and shaded and solid areas represent proportional membership of each individual in each of the two clusters. Individuals are ranked by latitude and longitude.

Figure 5.—

Number of significant (P ≤ 0.01) SNP associations before correction for multiple testing for eight early- (EW), late- (LW), and total wood (TW) traits (see Table 1) in white spruce. Associations were tested using an additive (ADD) or dominant (DOM) effects mixed linear model.

Figure 6.—

Relationship between SNP genotypic classes and transcript accumulation. (A) Estimated genotypic effects of a significant SNP (PGWD1-1094) for earlywood average ring width (and TW-ARW, see Table 2) in the additive effects model. (B) Transcript levels of the corresponding gene determined by qPCR.

Figure 6.—

Relationship between SNP genotypic classes and transcript accumulation. (A) Estimated genotypic effects of a significant SNP (PGWD1-1094) for earlywood average ring width (and TW-ARW, see Table 2) in the additive effects model. (B) Transcript levels of the corresponding gene determined by qPCR.