Christensen, O. F. | Search bioRxiv

bioRxiv2022

Metabolomic-genomic prediction drastically improves prediction accuracy of breeding values in crop breeding

Metabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. Objective was to investigate heritabilities and accuracies of genetic evaluation of malting quality (MQ) traits by integrating both genomic and metabolomic information. In total, 2,430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO). Plot heritabilities ranged from 0.060 to 0.279 for GBLUP, and increased to range from 0.123 to 0.283 for MGBLUP. LOLO scheme yielded higher accuracies than LOYO scheme, and MGBLUP yielded higher accuracies than GBLUP regardless of cross-validation scheme. Author SummaryMetabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. We carried out a study on 2,430 plots of 562 malting spring barley lines and 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample, to investigate the prediction of breeding values of five malting quality traits by integrating genomic and metabolomic information using a metabolomics-genomic model. Results showed an increase in prediction accuracy for this model compared to a genomic model.

genomics↗

bioRxiv2016

Metafounders are Fst fixation indices and reduce bias in single step genomic evaluations

BACKGROUNDMetafounders are pseudo-individuals that condense the genetic heterozygosity and relationships within and across base pedigree populations, i.e. ancestral populations. This work addresses estimation and usefulness of metafounder relationships in Single Step GBLUP.\n\nRESULTSWe show that the ancestral relationship parameters are proportional to standardized covariances of base allelic frequencies across populations, like Fst fixation indexes. These covariances of base allelic frequencies can be estimated from marker genotypes of related recent individuals, and pedigree. Simple methods for estimation include naive computation of allele frequencies from marker genotypes or a method of moments equating average pedigree-based and marker-based relationships. Complex methods include generalized least squares or maximum likelihood based on pedigree relationships. To our knowledge, methods to infer Fst coefficients and Fst differentiation have not been developed for related populations.\n\nA compatible genomic relationship matrix constructed as a crossproduct of {-1,0,1} codes, and equivalent (up to scale factors) to an identity by state relationship matrix at the markers, is derived. Using a simulation with a single population under selection, in which only males and youngest animals were genotyped, we observed that generalized least squares or maximum likelihood gave accurate and unbiased estimates of the ancestral relationship parameter (true value: 0.40) whereas the other two (naive and method of moments) were biased (estimates of 0.43 and 0.35). We also observed that genomic evaluation by Single Step GBLUP using metafounders was less biased in terms of accurate genetic trend (0.01 instead of 0.12 bias), slightly overdispersed (0.94 instead of 0.99) and as accurate (0.74) than the regular Single Step GBLUP. Single Step GBLUP using metafounders also provided consistent estimates of heritability.\n\nCONCLUSIONSEstimation of metafounder relationship can be achieved using BLUP-like methods with pedigree and markers. Inclusion of metafounder relationships improves bias of genomic predictions with no loss in accuracy.

genetics↗