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Updated: Feb 3, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
Exploring the genetic basis of gene transcript abundance and metabolite levels in loblolly pine (Pinus taeda L.)
Mengmeng Lu1,2,3, Candace M Seeve4, Carol A Loopstra1,2
1Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA.
Background:
Identifying genetic variations that shape important complex traits is fundamental to the genetic improvement of important forest tree species, such as loblolly pine (Pinus taeda L.), which is one of the most commonly planted forest tree species in the southern U.S. Gene transcripts and metabolites are important regulatory intermediates that link genetic variations to higher-order complex traits such as wood development and drought response. A few prior studies have associated intermediate phenotypes including mRNA expression and metabolite levels with a limited number of molecular markers, but the identification of genetic variations that regulate intermediate phenotypes needs further investigation.
Results:
We identified 1841 single nucleotide polymorphisms (SNPs) associated with 191 gene expression mRNA phenotypes and 524 SNPs associated with 53 metabolite level phenotypes using 2.8 million exome-derived SNPs. The identified SNPs reside in genes with a wide variety of functions. We further integrated the identified SNPs and the associated expressed genes and metabolites into networks. We described the SNP-SNP interactions that significantly impacted the gene transcript abundance and metabolite level in the networks. Key loci and genes in the wood development and drought response networks were identified and analyzed.
Conclusions:
This work provides new candidate genes for research on the genetic basis of gene expression and metabolism linked to wood development and drought response in loblolly pine and highlights the efficiency of using association-mapping-based networks to discover candidate genes with important roles in complex biological processes.
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