{"entity": "publication", "iuid": "86aa459ca48c418cbf4991eda32b3d72", "timestamp": "2026-07-22T14:29:24.209Z", "links": {"self": {"href": "https://publications.scilifelab.se/publication/86aa459ca48c418cbf4991eda32b3d72.json"}, "display": {"href": "https://publications.scilifelab.se/publication/86aa459ca48c418cbf4991eda32b3d72"}}, "title": "Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce.", "authors": [{"family": "Chen", "given": "Zhi-Qiang", "initials": "ZQ"}, {"family": "Klingberg", "given": "Adam", "initials": "A"}, {"family": "Hallingb\u00e4ck", "given": "Henrik R", "initials": "HR"}, {"family": "Wu", "given": "Harry X", "initials": "HX"}], "type": "journal article", "published": "2023-03-27", "journal": {"title": "BMC Genomics", "issn": "1471-2164", "volume": "24", "issue": "1", "pages": "147", "issn-l": "1471-2164"}, "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 \u2265 2.5%.", "doi": "10.1186/s12864-023-09250-3", "pmid": "36973641", "labels": {"Bioinformatics Support, Infrastructure and Training": "Service", "Bioinformatics Support for Computational Resources": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC10041705"}, {"db": "pii", "key": "10.1186/s12864-023-09250-3"}], "notes": [], "created": "2023-05-17T08:57:46.051Z", "modified": "2024-01-16T13:48:33.802Z"}