Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes

In the past decades, genomic prediction has had a large impact on plant breeding. Given the current advances of high-throughput phenotyping and sequencing technologies, it is increasingly common to observe a large number of traits, in addition to the target trait of interest. This raises the important question whether these additional or “secondary” traits can be used to improve genomic prediction for the target trait. With only a small number of secondary traits, this is known to be the case, given sufficiently high heritabilities and genetic correlations. Here we focus on the more challenging situation with a large number of secondary traits, which is increasingly common since the arrival of high-throughput phenotyping. In this case, secondary traits are usually incorporated through additional relatedness matrices. This approach is however infeasible when secondary traits are not measured on the test set, and cannot distinguish between genetic and non-genetic correlations. An alternative direction is to extend the classical selection indices using penalized regression. So far, penalized selection indices have not been applied in a genomic prediction setting, and require plot-level data in order to reliably estimate genetic correlations. Here we aim to overcome these limitations, using two novel approaches. Our first approach relies on a dimension reduction of the secondary traits, using either penalized regression or random forests (LS-BLUP/RF-BLUP). We then compute the bivariate GBLUP with the dimension reduction as secondary trait. For simulated data (with available plot-level data), we also use bivariate GBLUP with the penalized selection index as secondary trait (SI-BLUP). In our second approach (GM-BLUP), we follow existing multi-kernel methods but replace secondary traits by their genomic predictions, with the advantage that genomic prediction is also possible when secondary traits are only measured on the training set. For most of our simulated data, SI-BLUP was most accurate, often closely followed by RF-BLUP or LS-BLUP. In real datasets, involving metabolites in Arabidopsis and transcriptomics in maize, no method could substantially improve over univariate prediction when secondary traits were only available on the training set. LS-BLUP and RF-BLUP were most accurate when secondary traits were available also for the test set.

Linking genes with ecological strategies in Arabidopsis thaliana

Arabidopsis thaliana is the most prominent model system in plant molecular biology and genetics. Although its ecology was initially neglected, collections of various genotypes revealed a complex population structure, with high levels of genetic diversity and substantial levels of phenotypic variation. This helped identify the genes and gene pathways mediating phenotypic change. Population genetics studies further demonstrated that this variation generally contributes to local adaptation. Here, we review evidence showing that traits affecting plant life history, growth rate, and stress reactions are not only locally adapted, they also often co-vary. Co-variation between these traits indicates that they evolve as trait syndromes, and reveals the ecological diversification that took place within A. thaliana. We argue that examining traits and the gene that control them within the context of global summary schemes that describe major ecological strategies will contribute to resolve important questions in both molecular biology and ecology.

Evolutionary and ecological functional genomics, from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.

Association genetics of acetophenone defence against spruce budworm in mature white spruce

BACKGROUND

Outbreaks of spruce budworm (SBW, Choristoneura fumiferana Clem.) cause major recurrent damage in boreal conifers such as white spruce (Picea glauca [Moench] Voss) and large losses of forest biomass in North America. Although defensive phenolic compounds have recently been linked to chemical resistance against SBW, their genetic basis remains poorly understood in forest trees, especially in conifers. Here, we used diverse association genetics approaches to discover genes and their variants that may control the accumulation of acetophenones, and dissect the genetic architecture of these defence compounds against SBW in white spruce mature trees.

RESULTS

Out of 4747 single nucleotide polymorphisms (SNPs) from 2312 genes genotyped in a population of 211 unrelated individuals, genetic association analyses identified 35 SNPs in 33 different genes that were significantly associated with the defence traits by using single-locus, multi-locus and multi-trait approaches. The multi-locus approach was particularly effective at detecting SNP-trait associations that explained a large fraction of the phenotypic variance (from 20 to 43%). Significant genes were regulatory including the NAC transcription factor, or they were involved in carbohydrate metabolism, falling into the binding, catalytic or transporter activity functional classes. Most of them were highly expressed in foliage. Weak positive phenotypic correlations were observed between defence and growth traits, indicating little or no evidence of defence-growth trade-offs.

CONCLUSIONS

This study provides new insights on the genetic architecture of tree defence traits, contributing to our understanding of the physiology of resistance mechanisms to biotic factors and providing a basis for the genetic improvement of the constitutive defence of white spruce against SBW.

Antisense transcription represses Arabidopsis seed dormancy QTL DOG1 to regulate drought tolerance

Plants have developed multiple strategies to sense the external environment and to adapt growth accordingly. Delay of germination 1 (DOG1) is a major quantitative trait locus (QTL) for seed dormancy strength in Arabidopsis thaliana that is reported to be expressed exclusively in seeds. DOG1 is extensively regulated, with an antisense transcript (asDOG1) suppressing its expression in seeds. Here, we show that asDOG1 shows high levels in mature plants where it suppresses DOG1 expression under standard growth conditions. Suppression is released by shutting down antisense transcription, which is induced by the plant hormone abscisic acid (ABA) and drought. Loss of asDOG1 results in constitutive high-level DOG1 expression, conferring increased drought tolerance, while inactivation of DOG1 causes enhanced drought sensitivity. The unexpected role of DOG1 in environmental adaptation of mature plants is separate from its function in seed dormancy regulation. The requirement of asDOG1 to respond to ABA and drought demonstrates that antisense transcription is important for sensing and responding to environmental changes in plants.

Balancing selection maintains diversity in a cold tolerance gene in broadly distributed live oaks

Cold poses major physiological challenges to plants, especially long-lived trees. In trees occurring along variable temperature clines, the expected direction and consequences of selection on cold acclimation ability and freezing tolerance are not straightforward. Here we estimated selection in cold acclimation genes at two evolutionary timescales in all seven species of the American live oaks (Quercus subsection Virentes). Two cold response candidate genes were chosen: ICE1, a key gene in the cold acclimation pathway, and HOS1, which modulates cold response by negatively regulating ICE1. Two housekeeping genes, GAPDB and CHR11, were also analyzed. At the shallow evolutionary timescale, we demonstrate that HOS1 experienced recent balancing selection in the two most broadly distributed species, Q. virginiana and Q. oleoides. At a deeper evolutionary scale, a codon-based model of evolution revealed the signature of negative selection in ICE1. In contrast, three positively selected codons have been identified in HOS1, possibly a signature of the diversification of Virentes into warmer climates from a freezing adapted lineage of oaks. Our findings indicate that evolution has favored diversity in cold tolerance modulation through balancing selection in HOS1 while maintaining core cold acclimation ability, as evidenced by purifying selection in ICE1.