Article,
Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce
BMC Genomics, 24 (1): 147-- (2023)
DOI: 10.1186/s12864-023-09250-3
Abstract
Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait
loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing
the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method
to capture Mendelian segregation effects. Using QTLs detected in a genome‑wide association study (GWAS) may
improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full‑sib families using a newly
developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB)
and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as
growth and wood quality traits, only 2 – 13 associations were observed and the PVE of the strongest effects ranged
from 1.2\% to 2.0\%. GP using approximately 100 preselected SNPs, based on the smallest p‑values from GWAS showed
the greatest predictive ability (PA) for the trait BB. For the other traits, a preselection of 2000–4000 SNPs, was found to
offer the best model fit according to the Akaike information criterion being minimized. But PA‑magnitudes from GP
using such selections were still similar to that of GP using all markers. Analyses on both real‑life and simulated data
also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP
provided that the PVE of the QTL was ≥ 2.5\%
