Genotypes, Networks, Phenotypes: Moving Toward Plant Systems Genetics

Abstract

One of the central goals in biology is to understand how and how much of the phenotype of an organism is encoded in its genome. Although many genes that are crucial for organismal processes have been identified, much less is known about the genetic bases underlying quantitative phenotypic differences in natural populations. We discuss the fundamental gap between the large body of knowledge generated over the past decades by experimental genetics in the laboratory and what is needed to understand the genotype-to-phenotype problem on a broader scale. We argue that systems genetics, a combination of systems biology and the study of natural variation using quantitative genetics, will help to address this problem. We present major advances in these two mostly disconnected areas that have increased our understanding of the developmental processes of flowering time control and root growth. We conclude by illustrating and discussing the efforts that have been made toward systems genetics specifically in plants.

A Small RNA-Mediated Regulatory Network in Arabidopsis thaliana Demonstrates Connectivity Between phasiRNA Regulatory Modules and Extensive Co-Regulation of Transcription by miRNAs and phasiRNAs

“…Additionally, an expanded set of sRNA-mRNA interactions identified in studies such as this can facilitate approaches to metabolic pathway discovery using coregulated (instead of colocalized) set of genes (Schläpfer et al, 2017). This latter suggestion stems from systems biology approaches for whole genome studies that have proven useful for the de novo identification of pathways and/or gene clusters, closing the genotype to phenotype gap (Ogura and Busch, 2016).…”

Gene expression networks in the Drosophila Genetic Reference Panel

“…Systems genetics analysis seeks to determine how naturally occurring molecular variation gives rise to genetic variation in organismal phenotypes by examining genetic variation in gene expression (expression quantitative trait loci [eQTLs]) and other intermediate molecular phenotypes (Sieberts and Schadt 2007;Chen et al 2008;Emilsson et al 2008;Rockman 2008;Cookson et al 2009;Mackay et al 2009;Civelek and Lusis 2014;Albert and Kruglyak 2015;Gibson et al 2015;Ogura and Busch 2016;Schughart and Williams 2017). Polymorphic variants associated with variation in gene expression are classified as cis-or trans-eQTLs depending on whether they are proximal or distal to the gene encoding the transcript, respectively.…”

“…Polymorphic variants associated with variation in gene expression are classified as cis-or trans-eQTLs depending on whether they are proximal or distal to the gene encoding the transcript, respectively. Genetic variation in gene expression is pervasive; cis-eQTLs can have large effects on gene expression that are detectable in small samples; and variants associated with human diseases and quantitative traits tend to be enriched for cis-eQTLs (Sieberts and Schadt 2007;Chen et al 2008;Emilsson et al 2008;Rockman 2008;Cookson et al 2009;Mackay et al 2009;Nicolae et al 2010;Civelek and Lusis 2014;Albert and Kruglyak 2015;Gibson et al 2015;Ogura and Busch 2016;Boyle et al 2017;Schughart and Williams 2017). eQTLs with both cis-and trans-effects can be assembled into directed transcriptional networks of regulator and target genes (Liu et al 2008;Bryois et al 2014;Fagny et al 2017).…”

A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

“…While existing reductionist-based approaches aimed at characterizing individual significant QTLs are powerful for genetic dissection, it has become increasingly clear that complex traits, especially morphological integration between different but developmentally coordinated traits, may also be controlled by QTL-QTL interactions that coalesce into a highly intricate but coordinated network. A wealth of literature supporting network thinking has arisen from medical research (Barabási et al, 2011;Chan and Loscalzo, 2012), but in recent years a consensus has been reached on the necessity of using holistic, system-oriented approaches to study plant complex traits (Ogura and Busch, 2016;Lavarenne et al, 2018. Approaches for inferring various regulatory networks from genomic, proteomic, and transcriptomic data have been well developed and widely used as a routine approach for modern biological research (Mizrachi et al, 2017). However, the characterization of QTL interaction networks remains largely unexplored, mainly because no powerful statistical methods have been developed.…”