Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer

Abstract

Most traits of agronomic importance are quantitative in nature, and genetic markers have been used for decades to dissect such traits. Recently, genomic selection has earned attention as next generation sequencing technologies became feasible for major and minor crops. Mixed models have become a key tool for fitting genomic selection models, but most current genomic selection software can only include a single variance component other than the error, making hybrid prediction using additive, dominance and epistatic effects unfeasible for species displaying heterotic effects. Moreover, Likelihood-based software for fitting mixed models with multiple random effects that allows the user to specify the variance-covariance structure of random effects has not been fully exploited. A new open-source R package called sommer is presented to facilitate the use of mixed models for genomic selection and hybrid prediction purposes using more than one variance component and allowing specification of covariance structures. The use of sommer for genomic prediction is demonstrated through several examples using maize and wheat genotypic and phenotypic data. At its core, the program contains three algorithms for estimating variance components: Average information (AI), Expectation-Maximization (EM) and Efficient Mixed Model Association (EMMA). Kernels for calculating the additive, dominance and epistatic relationship matrices are included, along with other useful functions for genomic analysis. Results from sommer were comparable to other software, but the analysis was faster than Bayesian counterparts in the magnitude of hours to days. In addition, ability to deal with missing data, combined with greater flexibility and speed than other REML-based software was achieved by putting together some of the most efficient algorithms to fit models in a gentle environment such as R.

Distinct genetic architectures for phenotype means and plasticities in Zea mays

“…The genetic correlations between the mean phenotype values, linear plasticities, and non-linear plasticities for each phenotype were calculated using sommer 51 . The kinship matrix used 973,965 SNPs (see “Genotype Processing”) and the “Normalized_IBS” option of TASSEL v5.0 52,53 …”

Massive phenotyping of multiple cranberry populations reveals novel QTLs for fruit anthocyanin content and other important chemical traits

“…In particular, we used the function mmer of the R package sommer (Covarrubias-Pazaran 2016) following the author recommendations. For the chemical data, we treated each year/month combination separately, and once calculated the BLUP, we averaged the BLUP values among years for each of the months studied.…”

Cell Membrane Stability and Association Mapping for Drought and Heat Tolerance in a Worldwide Wheat Collection

“…After removing SNP markers with missing values (>10%) and minor allele frequency (MAF < 5%), missing values were imputed using random forest regression [54]. The calculated r 2 among all pairs of SNPs loci were calculated and plotted using R software [55] and sommer package [56]. The calculated r 2 was used to estimate the rate of LD decay with genetic distance [57].…”