Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx.

Paulina Ballesta,Fabyano Fonseca Silva,Freddy Mora,David Bush

doi:10.3390/plants9010099

Abstract

High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this study, a high-density SNP array (60 K) developed for commercial Eucalyptus was used to genotype a breeding population of Eucalyptus cladocalyx, yielding only ~3.9 K informative SNPs. Traditional Bayesian genomic models were investigated to predict flowering, stem quality and growth traits by considering the following effects: (i) polygenic background and all informative markers (GS model) and (ii) polygenic background, QTL-genotype effects (determined by GWAS) and SNP markers that were not associated with any trait (GSq model). The estimates of pedigree-based heritability and genomic heritability varied from 0.08 to 0.34 and 0.002 to 0.5, respectively, whereas the predictive ability varied from 0.19 (GS) and 0.45 (GSq). The GSq approach outperformed GS models in terms of predictive ability when the proportion of the variance explained by the significant marker-trait associations was higher than those explained by the polygenic background and non-significant markers. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species, or when high-density marker panels are not available.

Highlights

A major focus of modern quantitative genetics is on assessing the association between polymorphic markers with phenotypic variations of complex traits
The ~3.9 K single nucleotide polymorphism (SNP) (~6% of the total SNP array) that were retained after filtering for minor allele frequency (MAF) and missing data were located in all eleven chromosomes of Eucalyptus, with an average of ~353 SNPs per chromosome, a density of 6
Polygenic background, quantitative trait loci (QTL)-genotype effects and SNP markers that were not associated with any trait (GSq model). ĥ2a corresponds to the pedigree-based estimated heritability. ĥ2m is the heritability estimate based on a set of markers that were not found to be significantly associated with a trait (GSq) or all SNP markers (GS), ĥ2q represents the heritability estimates based on a set of

Summary

Introduction

A major focus of modern quantitative genetics is on assessing the association between polymorphic markers with phenotypic variations of complex traits. In this sense, genotype–phenotype studies for quantitative traits at the genome level usually require high-density genetic marker panels, i.e., a large number of markers throughout the genome and large population sizes to obtain sufficient power and prediction resolution [1,2]. Plants 2020, 9, 99 array, it does not perform as well in terms of genome coverage or number of available SNPs for species which are more-distantly related to those for which the chip was developed [13]. Marker panels could be considered as either low- or high-density depending on the genome size, the extent of linkage disequilibrium (LD)

Objectives

Methods

Results

Discussion

Conclusion