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

• Autor(a) principal: Resende, Rafael Tassinari
• Data de Publicação: 2016
• Outros Autores: Resende, Marcos Deon Vilela, Silva, Fabyano Fonseca, Azevedo, Camila Ferreira, Takahashi, Elizabete Keiko, Silva-Junior, Orzenil Bonfim, Grattapaglia, Dario
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://doi.org/10.1111/nph.14266; http://www.locus.ufv.br/handle/123456789/13874
• topic: Eucalyptus; Genome-wide association study (GWAS); Growth traits; Missing Heritability; Puccinia psidii rust; Regional heritability mapping (RHM); Single nucleotide polymorphism (SNP); Wood properties

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.