Kruijer Willem, Boer Martin P, Malosetti Marcos, Flood Pádraic J, Engel Bas, Kooke Rik, Keurentjes Joost J B, van Eeuwijk Fred A
Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands
Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands.
Genetics. 2015 Feb;199(2):379-98. doi: 10.1534/genetics.114.167916. Epub 2014 Dec 19.
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.
从进化和育种的角度来看，遗传力是数量遗传学中的一个核心参数。对于植物性状，传统上是通过比较基因型内和基因型间的变异性来估计遗传力的。这种方法估计的是广义遗传力，没有考虑不同的遗传相关性。随着高密度标记的出现，人们越来越关注基于标记的狭义遗传力估计，即使用混合模型，其中遗传相关性是根据遗传标记估计的。这种估计在人类遗传学中受到了广泛关注，但在植物性状方面很少有报道。一个主要障碍是，目前的方法和软件假设每个基因型只有一个表型值，因此需要基因型均值。我们在此提出的一种替代方法是在单株植物或小区水平上使用混合模型。通过统计论证、模拟和实际数据，我们研究了这两种方法的可行性以及它们如何影响使用最佳线性无偏预测器的基因组预测和全基因组关联研究。从基因型均值获得的遗传力估计值具有非常大的标准误差，有时在生物学上是不现实的。在单株植物或小区水平上的混合模型产生了更现实的估计值，对于模拟性状，标准误差缩小了多达13倍。使用这些混合模型也提高了基因组预测的准确性，准确性提高了多达49%。对于模拟性状的全基因组关联研究，使用单株植物数据几乎没有提高检测力。这种新方法适用于任何可以对单个基因型进行多次重复评分的复杂性状。这包括重要的农作物，以及细菌和真菌。
