Dataset Information

Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis.

ABSTRACT:

Background

Genome-wide association studies (GWAS) identify loci underlying the variation of complex traits. One of the main limitations of GWAS is the availability of reliable phenotypic data, particularly for long-lived tree species. Although an extensive amount of phenotypic data already exists in breeding programs, accounting for its high heterogeneity is a great challenge. We combine spatial and factor-analytics analyses to standardize the heterogeneous data from 120 field experiments of 483,424 progenies of Norway spruce to implement the largest reported GWAS for trees using 134 605 SNPs from exome sequencing of 5056 parental trees.

Results

We identify 55 novel quantitative trait loci (QTLs) that are associated with phenotypic variation. The largest number of QTLs is associated with the budburst stage, followed by diameter at breast height, wood quality, and frost damage. Two QTLs with the largest effect have a pleiotropic effect for budburst stage, frost damage, and diameter and are associated with MAP3K genes. Genotype data called from exome capture, recently developed SNP array and gene expression data indirectly support this discovery.

Conclusion

Several important QTLs associated with growth and frost damage have been verified in several southern and northern progeny plantations, indicating that these loci can be used in QTL-assisted genomic selection. Our study also demonstrates that existing heterogeneous phenotypic data from breeding programs, collected over several decades, is an important source for GWAS and that such integration into GWAS should be a major area of inquiry in the future.

SUBMITTER: Chen ZQ

PROVIDER: S-EPMC8201819 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Publications

Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis.

Chen Zhi-Qiang ZQ Zan Yanjun Y Milesi Pascal P Zhou Linghua L Chen Jun J Li Lili L Cui BinBin B Niu Shihui S Westin Johan J Karlsson Bo B García-Gil Maria Rosario MR Lascoux Martin M Wu Harry X HX

Genome biology 20210613 1

<h4>Background</h4>Genome-wide association studies (GWAS) identify loci underlying the variation of complex traits. One of the main limitations of GWAS is the availability of reliable phenotypic data, particularly for long-lived tree species. Although an extensive amount of phenotypic data already exists in breeding programs, accounting for its high heterogeneity is a great challenge. We combine spatial and factor-analytics analyses to standardize the heterogeneous data from 120 field experiment ...[more]

PMID: 34120648

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Dataset Information

Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis.

Background

Results

Conclusion

Publications

Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis.

Similar Datasets

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