Comparative analysis of SNP data and hybrid taxa information by using a classificatory linear mixed model to study the genetic variation and heritability of initial height growth in selected poplar hybrids

Abstract

Advances in genomics increase the possibility of using SNP markers in selecting new parental genotypes for poplar breeding or new poplar varieties for commercial deployment. Here, we use a classificatory linear mixed model and a quantitative genetic approach for the combined analysis of SNP and phenotypic data from poplar hybrids at the beginning of their testing program. The main objective was to compare genetic parameters estimated based on two classification criteria: original (hybrid) taxa and SNP clustering. Height growth measurements obtained in three consecutive years from a poplar trial planted in 2002 in the center of Chile were included. In 2016, DNA was extracted from leaves of the same hybrids and genotyped by sequencing. An increasing number of clusters based on the similarity of SNP information was obtained. Broad sense heritability values observed at all levels of genomic clustering were larger than the only estimate obtained by using the original taxa classification. Thus, the method can help to predict a higher genetic gain in the early selection of poplars, based on initial height growth. The method did not affect the accuracy of the heritability estimation. The systematic increment in the intra-clonal covariance with the clustering level also suggests a highly positive genotype-by-time interaction effect at high levels of SNP clustering, which can also be positive for selection purposes. We concluded that the use of SNP clustering allowed the expression of larger genetic differences among hybrids in initial height growth, regardless of the original hybrid taxa.