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

Abstract

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 independentmore » 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.« less

Authors:
 [1];  [1];  [1];  [2];  [1]
  1. Cornell Univ., Ithaca, NY (United States)
  2. Cornell Univ., Ithaca, NY (United States); United States Dept. of Agriculture-Agricultural Research Service, Ithaca, NY (United States)
Publication Date:
Research Org.:
Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
OSTI Identifier:
1545796
Grant/Contract Number:  
AR0000598
Resource Type:
Accepted Manuscript
Journal Name:
G3
Additional Journal Information:
Journal Volume: 9; Journal Issue: 9; Journal ID: ISSN 2160-1836
Publisher:
Genetics Society of America
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Endophenotypes; Transcriptome-Wide Association Studies; Genome-Wide Association Studies; Fisher's Combined Test; Variance Partitioning; Natural Variation

Citation Formats

Kremling, Karl A. G., Diepenbrock, Christine H., Gore, Michael A., Buckler, Edward S., and Bandillo, Nonoy B. ­­­­Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays. United States: N. p., 2019. Web. doi:10.1534/g3.119.400549.
Kremling, Karl A. G., Diepenbrock, Christine H., Gore, Michael A., Buckler, Edward S., & Bandillo, Nonoy B. ­­­­Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays. United States. doi:10.1534/g3.119.400549.
Kremling, Karl A. G., Diepenbrock, Christine H., Gore, Michael A., Buckler, Edward S., and Bandillo, Nonoy B. Tue . "­­­­Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays". United States. doi:10.1534/g3.119.400549.
@article{osti_1545796,
title = {­­­­Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays},
author = {Kremling, Karl A. G. and Diepenbrock, Christine H. and Gore, Michael A. and Buckler, Edward S. and Bandillo, Nonoy B.},
abstractNote = {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.},
doi = {10.1534/g3.119.400549},
journal = {G3},
number = 9,
volume = 9,
place = {United States},
year = {2019},
month = {7}
}

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Works referenced in this record:

Maize association population: a high-resolution platform for quantitative trait locus dissection
journal, November 2005