title: Marker-based estimation of heritability in immortal populations.
abstract: Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.
authors.affiliation: Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands willem.kruijer@wur.nl.
authors.forename: Willem
authors.initials: W
authors.lastname: Kruijer
authors.affiliation: Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands.
authors.forename: Martin P
authors.initials: MP
authors.lastname: Boer
authors.affiliation: Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands.
authors.forename: Marcos
authors.initials: M
authors.lastname: Malosetti
authors.affiliation: Laboratory of Genetics, Wageningen University and Research Centre, 6700AH Wageningen, The Netherlands Horticulture and Product Physiology, Wageningen University and Research Centre, 6700AP Wageningen, The Netherlands.
authors.forename: Pádraic J
authors.initials: PJ
authors.lastname: Flood
authors.affiliation: Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands.
authors.forename: Bas
authors.initials: B
authors.lastname: Engel
authors.affiliation: Laboratory of Plant Physiology, Wageningen University and Research Centre, 6700AR Wageningen, The Netherlands Laboratory of Genetics, Wageningen University and Research Centre, 6700AH Wageningen, The Netherlands.
authors.forename: Rik
authors.initials: R
authors.lastname: Kooke
authors.affiliation: Laboratory of Genetics, Wageningen University and Research Centre, 6700AH Wageningen, The Netherlands Swammerdam Institute for Life Sciences, University of Amsterdam, 1090GE Amsterdam, The Netherlands.
authors.forename: Joost J B
authors.initials: JJ
authors.lastname: Keurentjes
authors.affiliation: Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands.
authors.forename: Fred A
authors.initials: FA
authors.lastname: van Eeuwijk
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D001947:Breeding; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D005819:Genetic Markers; D005828:Genetics, Population; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D005838:Genotype; D040582:Inheritance Patterns; D008957:Models, Genetic; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software
chemical_list: D005819:Genetic Markers
keywords: Arabidopsis thaliana; GWAS; genomic prediction; marker-based estimation of heritability; one- vs. two-stage approaches