­­­­Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays

Kremling, Karl A. G.; Diepenbrock, Christine H.; Gore, Michael A.; Buckler, Edward S.; Bandillo, Nonoy B.

doi:10.1534/g3.119.400549

Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays

Journal Article · Tue Jul 23 00:00:00 EDT 2019 · G3

DOI:https://doi.org/10.1534/g3.119.400549· OSTI ID:1545796

Kremling, Karl A. G. ^[1]; Diepenbrock, Christine H. ^[2]; Gore, Michael A. ^[2]; Buckler, Edward S. ^[3]; Bandillo, Nonoy B. ^[2]

Cornell Univ., Ithaca, NY (United States); University of Illinois at Urbana-Champaign
Cornell Univ., Ithaca, NY (United States)
Cornell Univ., Ithaca, NY (United States); United States Dept. of Agriculture-Agricultural Research Service, Ithaca, NY (United States)

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genomewide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. As a result, this not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000598

OSTI ID:: 1545796

Journal Information:: G3, Journal Name: G3 Journal Issue: 9 Vol. 9; ISSN 2160-1836

Publisher:: Genetics Society of AmericaCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
Endophenotypes
Fisher's Combined Test
Genome-Wide Association Studies
Natural Variation
Transcriptome-Wide Association Studies
Variance Partitioning