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Network analysis of wood properties, gene expression levels and genotypes of natural Populus trichocarpa accessions

ABSTRACT: High-throughput approaches have been widely applied to elucidate the genetic underpinnings of industrially important wood properties. Each single wood trait was found to be highly polygenic in its nature. However, gene hierarchies need to be assessed with the most important gene variants controlling a specific trait within the complex network of interacting secondary cell wall characteristics that defines the overall wood phenotype. We tested the available genetic and genomic information in an integrative approach to effectively predict wood properties in Populus trichocarpa.Nine-yr-old natural P. trichocarpa trees including accessions with high contrasts in six traits related to wood chemistry and ultrastructure were profiled for gene expression on 49K Nimblegen array elements and for 28,831 polymorphic SNPs. Pre-selected transcripts and SNPs with high statistical dependency on the phenotypic trait were used in a Bayesian network learning procedure with stepwise K2 algorithm to infer phenotype-centric networks. Transcripts were pre-selected at much lower logBF threshold than SNPs and were not accommodated in the networks. Complexity of phenotype-centric networks with 100% predictive accuracy ranged from 7 (glucose) to 39 SNPs (alpha-cellulose). Pleiotropic gene actions were accommodated in genetic networks of correlated traits; such gene variants help to understand independent evolution of trait values, and they represent new tools to support the maximization of correlated response to selection.

ORGANISM(S): Populus trichocarpa

PROVIDER: GSE44606 | GEO | 2013/02/26

SECONDARY ACCESSION(S): PRJNA190708

REPOSITORIES: GEO

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Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (?eQTLs?) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3?UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3?UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Illumina gene expression microarrays for 60 normal human livers. Expression quantitative trait loci were identified by genome wide association mapping.

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Network analysis of wood properties, gene expression levels and genotypes of natural Populus trichocarpa accessions

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