The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.

Stress resilience in crop plants:strategic thinking to address local food production problems

There are many ways to assess or define the stress resilience of crop production, but ultimately the resilience of systems (and communities), i.e., an ability to survive and prosper, is driven by profitability. Here we review challenges for those who seek to bring about beneficial change in practice or policy as we translate novel crop science research findings into impacts on the food supply chain. While advances in plant and crop science are relevant to this challenge, the context of application is crucial here and this will mean that many other considerations, discussed below, will potentially moderate the impact on crop growth and yield of what could be the introduction of very significant breakthroughs in genetic gain. This paper considers opportunities for plant scientists seeking to address the world’s growing food security challenge by exploiting new understanding of the basis of crop stress resilience. Ultimately the local challenge is to increase the resilience of cropping systems and rural communities

Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits

A recombinant inbred line (RIL) population was evaluated in seven field experiments representing four environments: water stress at flowering (WS) and well-watered (WW) conditions in Mexico and Zimbabwe. The QTLs were identified for each trait in each individual experiment (single-experiment analysis) as well as per environment, per water regime across locations and across all experiments (joint analyses). For the six target traits (male flowering, anthesis-to-silking interval, grain yield, kernel number, 100-kernel fresh weight and plant height) 81, 57, 51 and 34 QTLs were identified in the four step-wise analyses, respectively. Despite high values of heritability, the phenotypic variance explained by QTLs was reduced, indicating epistatic interactions. About 80, 60 and 6% of the QTLs did not present significant QTL-by-environment interactions (QTL×E) in the joint analyses per environment, per water regime and across all experiments. The expression of QTLs was quite stable across years at a given location and across locations under the same water regime. However, the stability of QTLs decreased drastically when data were combined across water regimes, reflecting a different genetic basis of the target traits in the drought and well-watered trials. Several clusters of QTLs for different traits were identified by the joint analyses of the WW (chromosomes 1 and 8) and WS (chromosomes 1, 3 and 5) treatments and across water regimes (chromosome 1). Those regions are clear targets for future marker-assisted breeding, and our results confirm that the best approach to breeding for drought tolerance includes selection under water stres

Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize

Identifying quantitative trait loci (QTL) of sizeable effects that are expressed in diverse genetic backgrounds across contrasting water regimes particularly for secondary traits can significantly complement the conventional drought tolerance breeding efforts. We evaluated three tropical maize biparental populations under water-stressed and well-watered regimes for drought-related morpho-physiological traits, such as anthesis-silking interval (ASI), ears per plant (EPP), stay-green (SG) and plant-to-ear height ratio (PEH). In general, drought stress reduced the genetic variance of grain yield (GY), while that of morpho-physiological traits remained stable or even increased under drought conditions. We detected consistent genomic regions across different genetic backgrounds that could be target regions for marker-assisted introgression for drought tolerance in maize. A total of 203 QTL for ASI, EPP, SG and PEH were identified under both the water regimes. Meta-QTL analysis across the three populations identified six constitutive genomic regions with a minimum of two overlapping traits. Clusters of QTL were observed on chromosomes 1.06, 3.06, 4.09, 5.05, 7.03 and 10.04/06. Interestingly, a ~8-Mb region delimited in 3.06 harboured QTL for most of the morpho-physiological traits considered in the current study. This region contained two important candidate genes viz., zmm16 (MADS-domain transcription factor) and psbs1 (photosystem II unit) that are responsible for reproductive organ development and photosynthate accumulation, respectively. The genomic regions identified in this study partially explained the association of secondary traits with GY. Flanking single nucleotide polymorphism markers reported herein may be useful in marker-assisted introgression of drought tolerance in tropical maize

Analytical and Decision Support Tools for Genomics-Assisted Breeding

To successfully implement genomics-assisted breeding (GAB) in crop improvement programs, efficient and effective analytical and decision support tools (ADSTs) are ‘must haves’ to evaluate and select plants for developing next-generation crops. Here we review the applications and deployment of appropriate ADSTs for GAB, in the context of next-generation sequencing (NGS), an emerging source of massive genomic information. We discuss suitable software tools and pipelines for marker-based approaches (markers/haplotypes), including large-scale genotypic and phenotypic, data management, and molecular breeding approaches. Although phenotyping remains expensive and time consuming, prediction of allelic effects on phenotypes opens new doors to enhance genetic gain across crop cycles, building on reliable phenotyping approaches and good crop information systems, including pedigree information and target haplotypes

Identification of orthologous regions associated with tissue growth under water-limited conditions

Plant recovery from early season drought is related to the amount of biomass retained during stress and biomass production after the end of stress. Reduction in leaf expansion is one of the first responses to water deficit. It is assumed that the control of tissue development under water deficit contributes to traits such as early vigor, as well as maintenance of growth of reproductive organs. To dissect the underlying mechanisms controlling tissue expansion under water-limited conditions, we used a multilevel approach combining quantitative genetics and genomics. To identify orthologous genetic regions controlling tissue growth under water-limited conditions a series of QTL mapping and microarray gene expression studies were conducted in rice and maize. Results of differentially expressed genes from microarray experiments, QTLs and candidate genes related to growth in the different species are compared on consensus maps (within species) and then on synteny maps (between species), to identify common genetic regions between rice and maize