Regional heritability mapping and genome‐wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus

Abstract

Although genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64-89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP-trait associations. RHM and GWAS QTLs individually explained 5-15% and 4-6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.

Independent and Joint‐GWASfor growth traits inEucalyptusby assembling genome‐wide data for 3373 individuals across four breeding populations

“…Various studies attempted GWAS for growth traits in forest trees, namely in Populus (Porth et al ., ; Allwright et al ., ; Du et al ., ; Fahrenkrog et al ., ), Pinus (Bartholomé et al ., ; Lu et al ., ) and Eucalyptus (Cappa et al ., ; Müller et al ., ; Resende et al ., ). Despite the considerably large number of individuals used in our study for each population and for the combined dataset, our results suggested that much larger numbers will be necessary to identify discrete regions capturing larger fractions of the genetic variance of complex traits as indicated by simulations (Spencer et al ., ; Visscher et al ., ).…”

“…Although less genetic variation is in principle available in such structured populations, the associations detected in genetically improved material should be considerably more relevant to further breeding, as their effect would be relevant in an already elite background. Following an equivalent rationale, three studies have reported GWAS for growth and wood quality traits in breeding populations of Eucalyptus (Cappa et al ., ; Müller et al ., ; Resende et al ., ). Interestingly, the results were on par with those described earlier in natural populations, in which few associations explaining small fractions of the genetic variation were detected.…”

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus

“…On the other hand, although genetic variation available in breeding populations is in principle more limited, associations detected in genetically improved material should be more relevant to breeding. A recent GWAS in a Eucalyptus breeding population reported promising results using a regional heritability mapping, an approach able to capture both common and rare allelic effects that individually contribute too little variance to be detected by conventional GWAS [58]. The availability of GWAS data could be valuable to improve genomic predictions accuracies by assigning locus- or trait-specific priors to genomic prediction models [65], as recently shown in rice [66].…”

Clonal selection ofEucalyptus grandis x Eucalyptus globulusfor productivity, adaptability, and stability, using SNP markers

“…As such, the SNP database reported in this article could be associated with new phenotypic data. Furthermore, the quantitative traits exhibited by the population may be associated with their corresponding genes through Genomic Wide Association (GWAS) and Regional Heritability Mapping (RHM) studies (Resende et al, 2017) The clustering analysis based on heterozygosity and similarity were associated with the top ten ranked clones for Tres Bocas, Algorta, the two environments together and HMPRGV (Figure 3). Clones that classified in the top ten for the four rankings are shown in green and clones that were in the top ten in at least in three of the four classifications are shown in yellow.…”

“…Selection based on GWP focuses on the simultaneous prediction of the genetic effects of thousands of dispersed markers in the genome, making it possible to infer large to small effects on traits for most loci, thus explaining almost all quantitative traits (Meuwissen et al, 2001;Isik, 2014). In addition, these molecular markers support a wide range of applications, including the study of high resolution genetic maps (Bartholomé et al, 2015), molecular diversity among species (Hudson et al, 2015), QTL detection (Resende et al, 2017), and inbreeding depression (Hedrick et al, 2016).…”