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Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.

The development of climate-smart varieties requires the identification of haplotypes influencing traits of agronomic interest with well-defined effects across environments. For that we need advanced crossing schemes, multi-environment characterization, and state of the art statistical methodology. In this contribution, we present a sorghum multi-reference back-cross nested association mapping population composed of 3901 lines produced by crossing 24 diverse parents to three elite parents from West and Central Africa (WCA-BCNAM). This population is potentially one of the most relevant sorghum resources for research and breeding purposes in that region. The population was characterized in environments contrasting for photoperiod, rainfall, temperature, and soil fertility. To analyse this multi-parental, multi- environment design, we developed a new statistical methodology for QTL detection and parental effect estimation. In addition, envirotyping data were integrated in our models to determine the influence of environmental covariables on the genetic effects. We used this strategy to analyse the genetic architecture of flowering time and plant height, which represent key adaptation mechanisms. Our results allowed a better characterisation of well-known genomic regions impacting flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod sensitive and candidate gene Elf3 being insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. The strategy implemented in this work illustrates the need of synergy between genetics, breeding, and statistical genetics to harness the potential of sorghum in a changing climate. The resources and tools developed in this project should serve the whole sorghum community, for example by providing relevant elite parents for breeding programs.