Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 481295
Title Marker-Based Estimation of Heritability in Immortal Populations
Author(s) Kruijer, W.T.; Boer, M.P.; Malosetti, M.; Flood, P.J.; Engel, B.; Kooke, R.; Keurentjes, J.J.B.; Eeuwijk, F.A. van
Source Genetics 199 (2015)2. - ISSN 0016-6731 - p. 379 - 398.
DOI https://doi.org/10.1534/genetics.114.167916
Department(s) Mathematical and Statistical Methods - Biometris
Biometris
Horticulture & Product Physiology
Horticultural Supply Chains
Laboratory of Plant Physiology
Laboratory of Genetics
PE&RC
WIAS
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) genome-wide association - multi-environment trials - quantitative trait loci - plant-breeding trials - linear mixed models - arabidopsis-thaliana - missing heritability - complex traits - selection - prediction
Abstract Heritability is a central parameter in quantitative genetics, both from 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 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 G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at 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 GWAS 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.
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