期刊
NEW PHYTOLOGIST
卷 223, 期 1, 页码 293-309出版社
WILEY
DOI: 10.1111/nph.15777
关键词
adaptation; drought tolerance; genome-wide association studies (GWAS); leaf morphology; pleiotropy; Populus
资金
- Center for Bioenergy Innovation (CBI)
- Bioenergy Science Center
- Office of Biological and Environmental Research in the DOE Office of Science
- USDA/DOE Joint Feedstocks for Bioenergy Program, USDA-NIFA, 'Structural Polymorphisms as Causes of Heterosis in Populus' [2013-67009-21008]
- US Department of Energy [DE-AC05-00OR22725]
- Office of Science of the US Department of Energy [DE-AC05-00OR22725, DE-AC02-05CH11231]
Genome-wide association studies (GWAS) have great promise for identifying the loci that contribute to adaptive variation, but the complex genetic architecture of many quantitative traits presents a substantial challenge. We measured 14 morphological and physiological traits and identified single nucleotide polymorphism (SNP)-phenotype associations in a Populus trichocarpa population distributed from California, USA to British Columbia, Canada. We used whole-genome resequencing data of 882 trees with more than 6.78 million SNPs, coupled with multitrait association to detect polymorphisms with potentially pleiotropic effects. Candidate genes were validated with functional data. Broad-sense heritability (H-2) ranged from 0.30 to 0.56 for morphological traits and 0.08 to 0.36 for physiological traits. In total, 4 and 20 gene models were detected using the single-trait and multitrait association methods, respectively. Several of these associations were corroborated by additional lines of evidence, including co-expression networks, metabolite analyses, and direct confirmation of gene function through RNAi. Multitrait association identified many more significant associations than single-trait association, potentially revealing pleiotropic effects of individual genes. This approach can be particularly useful for challenging physiological traits such as water-use efficiency or complex traits such as leaf morphology, for which we were able to identify credible candidate genes by combining multitrait association with gene co-expression and co-methylation data.
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