A fast genomic selection approach for large genomic data

Abstract

We propose a novel computational method for genomic selection that combines identical-by-state (IBS)-based Haseman-Elston (HE) regression and best linear prediction (BLP), called HE-BLP. Genomic best linear unbiased prediction (GBLUP) has been widely used in whole-genome prediction for breeding programs. To determine the total genetic variance of a training population, a linear mixed model (LMM) should be solved via restricted maximum likelihood (REML), whose computational complexity is the cube of the sample size. We proposed a novel computational method combining identical-by-state (IBS)-based Haseman-Elston (HE) regression and best linear prediction (BLP), called HE-BLP. With this method, the total genetic variance can be estimated by solving a simple HE linear regression, which has a computational complex of the sample size squared; therefore, it is suitable for large-scale genomic data, except those with which environmental effects need to be estimated simultaneously, because it does not allow for this estimation. In Monte Carlo simulation studies, the estimated heritability based on HE was identical to that based on REML, and the prediction accuracy via HE-BLP and traditional GBLUP was also quite similar when quantitative trait loci (QTLs) were randomly distributed along the genome and their effects followed a normal distribution. In addition, the kernel row number (KRN) trait in a maize IBM population was used to evaluate the performance of the two methods; the results showed similar prediction accuracy of breeding values despite slightly different estimated heritability via HE and REML, probably due to the underlying genetic architecture. HE-BLP can be a future genomic selection method choice for even larger sets of genomic data in certain special cases where environmental effects can be ignored. The software for HE regression and the simulation program is available online in the Genetic Analysis Repository (GEAR; https://github.com/gc5k/GEAR/wiki).

Genome-Wide SNP Data Revealed the Extent of Linkage Disequilibrium, Persistence of Phase and Effective Population Size in Purebred and Crossbred Buffalo Populations

“…Genomic selection (GS) has been widely used to estimate the breeding values in various fields, such as animal and plant breeding programs (Newell and Jannink, 2014; Liu and Chen, 2017; Weller et al, 2017). These breeding programs select their breeding animals or plants based on predicted genomic breeding values (GBVs).…”

A new genomic prediction method with additive-dominance effects in the least-squares framework

“…In contrast, REML is a model-based approach and the exact structure of the estimated variance, regardless of additive or dominance, remains elusive. Furthermore, as discussed in our previous study (Liu and Chen, 2017), the computational complex for HE is Oðn 2 Þ, proportional to the square of sample size, but for REML Oðn 3 Þ. The computational advantage of HE is important especially when the sample size is large.…”

“…In our previous study, we developed a fast genomic prediction approach (namely HEBLP, or HEBLP|A herein) combining identical-by-state (IBS)-based Haseman-Elston (HE) regression and best linear prediction (BLP). It can obtain the total additive genetic variance via a simple HE linear regression with reduced computation complexity, but only additive effects are included (Liu and Chen, 2017). The present study aims to develop the HEBLP with both the additive and dominance effects (HEBLP|AD) and to evaluate its predictive performance in the simulated and a real Arabidopsis thaliana F2 population.…”

Genomic Approaches for Climate Resilience Breeding in Oats

“…Two distinct lineages may be easily defined (Fu and Williams 2008;Loskutov 2008)and extant diploids may be assigned unambiguously to either the A or the C branch. The A and C lineage divergence may have occurred from 4-20 myr ago (Liu and Chen 2017;Fu 2018). There is less certainty about the origin of sub-genomes within extant polyploids, although there is now a consensus that a variant lineage of the A genomes, designated as the D, is found with C genome lineages in most extant tetraploids, with one of these DC species subsequently giving rise to today's ADC hexaploids (Yan et al 2016b).…”