Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS

Abstract

Understanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high‐quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom‐up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large‐scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding.

Characterization of Genetic Diversity and Genome-Wide Association Mapping of Three Agronomic Traits in Qingke Barley (Hordeum Vulgare L.) in the Qinghai-Tibet Plateau

“…The 308 accessions were genotyped using a newly developed genotyping-by-sequencing technology (tGBS) that eases the process of sorting high-quality GBS sequencing libraries and results in more accurate SNP calling (Ott et al, 2017 ; Li et al, 2019 ). Sequence reads were aligned to the Hordeum vulgare Hv IBSC PGSB v2 reference genome (Mascher et al, 2017 ) after de-barcoding and trimming.…”

Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

“…Sequencing-based genotyping enables to generate thousands of SNPs for separating breeding lines in a given population. Two methods of genotyping, RAD-seq (Miller et al, 2007;Baird et al, 2008) and tGBS (Ott et al, 2017), were widely used in breeding programs (Kim et al, 2016;Zheng et al, 2018;Li et al, 2019). RAD-seq can generate more SNP sites compared with tGBS, while SNP sites called by tGBS have higher read depths than RAD-seq.…”

Natural population re-sequencing detects the genetic basis of local adaptation to low temperature in a woody plant

“…Besides, we also evaluated the pairwise genome-wide fixation index (F ST ) values, the F ST between north group and south group was 0.102, slightly greater than 0.046 (the F ST between north group and east group) and 0.077 (theF ST between south group and east group) (Figure 2e), reflected that the relationship of north group and south group was further than the other. The little genetic differentiation and similar level of nucleotide diversity suggest that there might exit a weaker selection bottleneck during the natural evolution, as suggested in peach (Li et al, 2019).…”

“…These results reflected that the north group most likely to generate a number of genetic diversities during the nature selection to adapt to the climate of high latitudes. Compared to other major crops, the nucleotide diversity in paper mulberry is higher than that of cotton (1.3×10 -3 ) (Wang et al, 2017) and peach (1.3×10 -3 ) (Li et al, 2019), but relatively low compared with date palm (9.2×10 -3 ) (Hazzouri et al, 2015), wild mei (2.8×10 -3 ) (Zhang et al, 2018). Besides, we also evaluated the pairwise genome-wide fixation index (F ST ) values, the F ST between north group and south group was 0.102, slightly greater than 0.046 (the F ST between north group and east group) and 0.077 (theF ST between south group and east group) (Figure 2e), reflected that the relationship of north group and south group was further than the other.…”

“…GWAS analysis as an integrated approach should be taken into account, which has been applied to elucidate the genes responding to cold stress in model plants and common crops, such as rice (Shakiba et al, 2017), maize (Huang et al, 2013), winter faba bean (Sallam, Arbaoui, El-Esawi, Abshire, & Martsch, 2016) and so on, but GWAS analysis has not been applied to elucidate the genes responding to low-temperature in woody plants. Recently, the GWAS analysis between SNP and the original environment (EnvGWAS) also be used to illuminate the genetic basis of adaptive evolution (Li et al, 2019), which can help us reveal the genetic basis of local temperature adaptation. Therefore, to expose the molecular basis of low temperature adaptation in woody plants, GWAS analysis should be used to identify significant variations associated with low temperature adaptive traits of woody plants.…”