SummaryCrop improvement and the dissection of complex genetic traits require germplasm diversity. Although this necessary phenotypic variability exists in diverse maize, most research is conducted using a small subset of inbred lines. An association population of 302 lines is now available -a valuable research tool that captures a large proportion of the alleles in cultivated maize. Provided that appropriate statistical models correcting for population structure are included, this tool can be used in association analyses to provide high-resolution evaluation of multiple alleles. This study describes the population structure of the 302 lines, and investigates the relationship between population structure and various measures of phenotypic and breeding value. On average, our estimates of population structure account for 9.3% of phenotypic variation, roughly equivalent to a major quantitative trait locus (QTL), with a high of 35%. Inclusion of population structure in association models is critical to meaningful analyses. This new association population has the potential to identify QTL with small effects, which will aid in dissecting complex traits and in planning future projects to exploit the rich diversity present in maize.

Thanks to genome-scale diversity data, present-day studies can provide a detailed view of how natural and cultivated species adapt to their environment and particularly to environmental gradients. However, due to their sensitivity, up-to-date studies might be more sensitive to undocumented demographic effects such as the pattern of migration and the reproduction regime. In this study, we provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection. We simulated 100 populations along a selective gradient and explored different migration models, sampling schemes and rates of self-fertilization. We investigated the power and robustness of eight methods to detect loci potentially under selection: three designed to detect genotype-environment correlations and five designed to detect adaptive differentiation (based on F(ST) or similar measures). We show that genotype-environment correlation methods have substantially more power to detect selection than differentiation-based methods but that they generally suffer from high rates of false positives. This effect is exacerbated whenever allele frequencies are correlated, either between populations or within populations. Our results suggest that, when the underlying genetic structure of the data is unknown, a number of robust methods are preferable. Moreover, in the simulated scenario we used, sampling many populations led to better results than sampling many individuals per population. Finally, care should be taken when using methods to identify genotype-environment correlations without correcting for allele frequency autocorrelation because of the risk of spurious signals due to allele frequency correlations between populations.

African rice (Oryza glaberrima) was domesticated independently from Asian rice. The geographical origin of its domestication remains elusive. Using 246 new whole-genome sequences, we inferred the cradle of its domestication to be in the Inner Niger Delta. Domestication was preceded by a sharp decline of most wild populations that started more than 10,000 years ago. The wild population collapse occurred during the drying of the Sahara. This finding supports the hypothesis that depletion of wild resources in the Sahara triggered African rice domestication. African rice cultivation strongly expanded 2,000 years ago. During the last 5 centuries, a sharp decline of its cultivation coincided with the introduction of Asian rice in Africa. A gene, PROG1, associated with an erect plant architecture phenotype, showed convergent selection in two rice cultivated species, Oryza glaberrima from Africa and Oryza sativa from Asia. In contrast, a shattering gene, SH5, showed selection signature during African rice domestication, but not during Asian rice domestication. Overall, our genomic data revealed a complex history of African rice domestication influenced by important climatic changes in the Saharan area, by the expansion of African agricultural society, and by recent replacement by another domesticated species.

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Climate changes will have an impact on food production and will require costly adaptive responses. Adapting to a changing environment will be particularly challenging in sub-Saharan Africa where climate change is expected to have a major impact. However, one important phenomenon that is often overlooked and is poorly documented is the ability of agro-systems to rapidly adapt to environmental variations. Such an adaptation could proceed by the adoption of new varieties or by the adaptation of varieties to a changing environment. In this study, we analyzed these two processes in one of the driest agro-ecosystems in Africa, the Sahel. We performed a detailed study in Niger where pearl millet is the main crop and covers 65% of the cultivated area. To assess how the agro-system is responding to recent recurrent drought, we analyzed samples of pearl millet landraces collected in the same villages in 1976 and 2003 throughout the entire cultivated area of Niger. We studied phenological and morphological differences in the 1976 and 2003 collections by comparing them over three cropping seasons in a common garden experiment. We found no major changes in the main cultivated varieties or in their genetic diversity. However, we observed a significant shift in adaptive traits. Compared to the 1976 samples, samples collected in 2003 displayed a shorter lifecycle, and a reduction in plant and spike size. We also found that an early flowering allele at the PHYC locus increased in frequency between 1976 and 2003. The increase exceeded the effect of drift and sampling, suggesting a direct effect of selection for earliness on this gene. We conclude that recurrent drought can lead to selection for earlier flowering in a major Sahelian crop. Surprisingly, these results suggest that diffusion of crop varieties is not the main driver of short term adaptation to climatic variation.

Estimation of long-term effective population size (N e ) from polymorphism data alone requires an independent knowledge of mutation rate. Microsatellites provide the opportunity to estimate N e because their high mutation rate can be estimated from observed mutations. We used this property to estimate N e in allotetraploid wheat Triticum turgidum at four stages of its history since its domestication. We estimated the mutation rate of 30 microsatellite loci. Allele-specific mutation rates were predicted from the number of repeats of the alleles. Effective population sizes were calculated from the diversity parameter ϭ 4N e . We demonstrated from simulations that the unbiased estimator of based on Nei's heterozygosity is the most appropriate for estimating N e because of a small variance and a relative robustness to variations in the mutation model compared to other estimators. We found a N e of 32,500 individuals with a 95% confidence interval of [20,739; 45,991] in the wild ancestor of wheat, 12,000 ([5790; 19,300]) in the domesticated form, 6000 ([2831; 9556]) in landraces, and 1300 ([689; 2031]) in recent improved varieties. This decrease illustrates the successive bottlenecks in durum wheat. No selective effect was detected on our loci, despite a complete loss of polymorphism for two of them.

While there has been progress in our understanding of the origin and history of agriculture in sub-Saharan Africa, a unified perspective is still lacking on where and how major crops were domesticated in the region. Here, we investigated the domestication of African yam (Dioscorea rotundata), a key crop in early African agriculture. Using whole-genome resequencing and statistical models, we show that cultivated yam was domesticated from a forest species. We infer that the expansion of African yam agriculture started in the Niger River basin. This result, alongside with the origins of African rice and pearl millet, supports the hypothesis that the vicinity of the Niger River was a major cradle of African agriculture.

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1. So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.