MOESM2 of Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction

Additional file 2. Bayesian inference for the LD-CS model. This file provides derivation of full conditional distributions of parameters in the LD-CD model, and MCMC algorithm to get point estimates of parameter values

Information for the 50k MD and 600k HD panels in the analyses.

<p>Information for the 50k MD and 600k HD panels in the analyses.</p

Regression coefficient of true on estimated breeding values using BayesB, anteBayesB or BayesN with 1.0 or 0.2 Mb windows for two values of <i>π</i> or <i>π</i>_<i>i</i> corresponding to 300 QTL each being associated with either 2 (red) or 10 (blue) SNP markers.

<p>Results are separated for common (row 1) versus rare (row 2) QTL alleles with the MD 50k (column 1) versus HD 600k (column 2) SNP panel. Dots represent regression coefficients from each of the eight replicates, and the bar indicates the mean. Regression coefficients closer to one (dashed horizontal line) reflect less prediction bias.</p

The posterior mean of number of the SNPs (light bar) and windows (dark bar) with nonzero effects from BayesB, anteBayesB or BayesN with 1.0 or 0.2 Mb windows.

<p>Results are separated for <i>k</i> = 2 (row 1) versus <i>k</i> = 10 (row 2) SNPs associated with each of the 300 QTL with the MD 50k (column 1) versus HD 600k (column 2) SNP panel. The capped error bar indicates the standard deviation of the posterior means from 8 replicates of the scenario with common and 8 replicates of the scenario with rare QTL alleles. The red dashed line shows the number of QTL simulated, which was 300.</p

Average computing time in hours for BayesB, anteBayesB or BayesN with 1.0 or 0.2 Mb windows for two values of <i>π</i> or <i>π</i>_<i>i</i> corresponding to 300 QTL each being associated with either 2 (red) or 10 (blue) SNP markers.

<p>The capped error bar indicates the standard deviation from 8 replicates of common and 8 replicates of rare QTL scenarios.</p

Average linkage disequilibrium (LD) between any two SNPs within 0.2 Mb distance across the genome.

<p>The shaded areas indicate one standard deviation departures from the average. The average distance between adjacent SNPs for the MD 50k and HD 600k SNP panel are indicated as broken vertical lines.</p

The accuracy of prediction using BayesB, anteBayesB or BayesN with 1 or 0.2 Mb windows for two values of <i>π</i> or <i>π</i>_<i>i</i> corresponding to 300 QTL each being associated with either 2 (red) or 10 (blue) SNP markers.

<p>Results are separated for common (row 1) versus rare (row 2) QTL alleles with the MD 50k (column 1) versus HD 600k (column 2) SNP panel. Dots represent accuracies from each of the eight replicates, and the bar indicates the mean.</p

Prediction R-squared evaluated in testing data sets (average over 30 randomly drawn testing data sets, each having 500 individuals) by training and validation data sets and model.

<p>N-FHS = Number of records from Framingham, N-GEN = Number of records from GENEVA. G-BLUP uses 400 K SNPs, wG-BLUP uses 400 K SNPs, but the contribution of each SNP to the genomic relationship matrix was weighted using as weight, where is the SNP associated p-value reported by <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003608#pgen.1003608-LangoAllen1" target="_blank">[5]</a>.</p

Genomic relationships realized at markers (vertical axis) versus those realized at causal loci (horizontal axis).

<p>The plot displays realized relationships between one individual in TST and all the other individuals in TRN for GEN (right panel) and FHS (left panel). Genomic relationships computed using markers are given in the vertical axis and those computed using genotypes at causal loci are in the horizontal coordinate.</p

Regression coefficient (, see expression 6) between realized genomic relationships at markers and those realized at causal loci, by data set, type of relationship and simulation scenario.

1<p>: Relationship between the individual whose phenotype is predicted and those used for model training; coefficients, , were estimated for each individual in training datasets. q_5% and q_95% represent the 5% and 95% empirical percentiles of the estimated regression coefficients.</p