Figure 1.
Accuracy of five different marker-based methods to estimate heritability– review of empirical and simulation studies.
The efficiency was assessed in a review of 24 empirical studies (A) or 15 simulation studies (B), comparing heritability estimates using pedigree or one of the following methods: 1 - Ritland; 2 - relatedness classes; 3 - reconstructed pedigrees; 4 - marker-based animal model or 5 - genomic selection. Details of the number of studies for each method are given in Table 1. The bias was measured as marker -
pedigree in A and as E(
– h2) in B, where h2 is the simulated parameter. The horizontal line shows the median bias for each method. The bottom and top of the box show the 25th and 75th percentiles. The vertical dashed lines show the maximum and minimum biases and the circles are outliers.
Table 1.
Summary of studies comparing estimates of quantitative genetics parameters using pedigree-free methods.
Table 2.
Summary of simulation studies comparing estimates of quantitative genetics parameters using pedigree-free methods.
Figure 2.
Simulation results testing the accuracy of pedigree or marker-based methods to estimate heritability.
This figure shows the correlation between the heritability simulated and heritability estimates obtained using pedigree-based animal models (A), marker-based animal models (B) or marker-based relatedness coefficients truncated before the analysis (C). Each dot stands for a simulated population, with 90% selfing (in grey) or complete outcrossing (in black). Circles stand for means across 20 replicates and solid lines show the 95% confidence intervals, as estimated by Asreml (and averaged across replicates). The dashed lines represent y = x.
Figure 3.
Higher mean and larger variance in pairwise relatedness coefficients in selfing compared to outcrossing populations.
Regression between pairwise Loiselle coefficients estimated using 1500 SNP and ΦA. The population comprised 500 individuals with 90% selfing (grey crosses) or complete outcrossing (black circles). The legend indicates the slope of the regression of ΦA against Loiselle and the correlation coefficient r. The variance in relatedness was 0.0026 in the outcrossing population and 0.0108 in the selfing population (within the range of variances observed in wild populations, see Table 1 and [23]).
Figure 4.
Relatednesses at causal and marker loci are more closely correlated in selfing than in outcrossing populations.
Regression between pairwise Loiselle coefficients estimated using 1500 SNPs and pairwise Loiselle coefficients estimated using the allele frequency at QTLs determining the phenotypic trait. Outcrossing populations are shown in black and selfing populations (selfing rate 90%) in grey. The legend indicates the slope of the regression and the correlation coefficient r. The slope is expected to be close to one if the relatedness at causal loci is accurately predicted by the relatedness at observed SNPs.
Figure 5.
Heritability estimates become more accurate with the number of marker loci used to estimate relatedness.
Influence of the number of loci used to estimate pairwise relatedness coefficients (Loiselle coefficients) on the bias in heritability estimates, when using a marker-based animal model. Each dot stands for a simulated population of 500 individuals, with complete outcrossing (panel A, in black) or 90% selfing (panel B, in grey). Panel C shows the results when marker-based relatedness coefficients are truncated before the analysis. Large circles stands for the average heritability over the 20 replicated simulations. The confidence intervals estimated in Asreml for each replicate were averaged over the 20 replicates and are shown as solid lines. The dashed line stands for the simulated heritability.