Find Paper, Faster
Example:10.1021/acsami.1c06204 or Chem. Rev., 2007, 107, 2411-2502
Genomic signatures of domestication and adaptation during geographical expansions of rice cultivation
Plant Biotechnology Journal  (IF9.803),  Pub Date : 2021-10-18, DOI: 10.1111/pbi.13730
Xiaoming Zheng, Hongbo Pang, Junrui Wang, Xuefeng Yao, Yue Song, Fei Li, Danjing Lou, Jinyue Ge, Zongyao Zhao, Weihua Qiao, Sung Ryul Kim, Guoyou Ye, Kenneth M. Olsen, ChunMing Liu, Qingwen Yang

Rice (Oryza sativa L.), a global staple food now grown on all inhabited continents, was domesticated from its wild progenitor, O. rufipogon Griff., in tropical and subtropical regions of Asia (Oka, 1988). After domestication, the expansions of rice landraces into the present-day range required a diverse array of adaptations to local environments, which included changes in daylight sensitivity, expanded thermal tolerance (for excess cold and heat), adaptations to water availability (drought and waterlogging), and resistance to biotic stresses (Garris et al., 2005; Glaszmann, 1987). Although a large amount of genomic data has been available for wild and cultivated rice varieties, and genetic characterizations of important agronomic traits were obtained in the past two decades (Gutaker et al., 2020; Huang et al., 2011, 2012; Wang et al., 2016), a complete landscape of genomic variations underlying regional adaptations remains elusive.

We selected 185 wild rice (O. rufipogon) and 743 cultivated rice varieties, which represent 33 major rice-growing regions worldwide (Figure 1a). Of them, all 185 wild rice varieties and 371 cultivated rice varieties (203 japonica and 168 indica varieties) were newly sequenced (see methods and material on The sequencing depth for these varieties was >5, which is for the first time to sequence such a large amount of wild rice varieties at this sequencing depth. Through a combination of principal component analysis (PCA), we confirmed five cultivated rice subgroups, including aus, indica (ind), tropical japonica (trj), temperate japonica (tej), and aromatic (aro) (Figure 1b). In particular, the aus and ind varieties, cultivated primarily in tropical and subtropical regions of Asia, were derived from the wild rice clade designated as OR-I. The trj and tej varieties, both originated from the OR-J clade of wild rice, were cultivated primarily in tropical, high-elevation regions of Southeast Asia (trj) and colder regions of Northeast Asia (tej).

Figure 1
Open in figure viewerPowerPoint
Molecular evolution of genomic regions associated with rice adaptation. (a) Sampling locations and frequencies of wild and cultivated rice varieties. (b) PCA plots of rice varieties. (c) Regional Manhattan plots of geographical locations and adaptation-association for temperature and day length. The red horizontal line indicates the empirical probability (defined by –log10p > 3) of the top 300 for both absolute ρ and Bayes factor across the three chains. (d) Local Manhattan plots, gene positions and LD heatmaps show the regions surrounding the strong peaks of the candidate genes (COLDF). (e) Significantly associated SNPs in the genomic location of COLDF. (f) Geographical distribution of rice varieties with COLDFtej and COLDFother. The colour shading scale indicates minimum temperature in May. (g) Chilling tolerance response of rice varieties with the two COLDF alleles. (h) The frequency of the two COLDF alleles in wild and cultivated rice subgroups. (i–j) Mutations in the COLDF genomic region for TILLING (coldf-1; i) and gene editing (coldf-2; j) mutants and showed increased chilling sensitivity. (k) The cold tolerance response and survival rate of 14 varieties with COLDFtej and 19 accessions with COLDFother.

To understand the selection on critical traits and the recent rapid speciation, three robust indicators of selective sweeps were examined to identify top outliers: (i) maximum differentiation from wild rice; (ii) maximum negative residuals of diversity/differentiation metrices based on the Hudson–Kreitman–Aguade test; and (iii) top scoring windows from a site frequency spectrum composite likelihood ratio test based on the length of haplotype. Using high-stringency cut-offs, considerable overlaps in outliers were observed among these methods. We identified 131 genomic regions with strong selection signals. Within individual rice subgroups, the numbers of targeted regions specific to tej, trj, ind, and aus were 20, 11, 20, and 12, respectively, including 791, 1477, 2647, and 2786 genes, respectively. Some of them were strong candidates for selection imposed by human preferences and local environmental conditions.

To identify loci associated with local adaptation, we collected temperature and day-length information in May and August of the last 68 years across the sampling sites in the Figure 1a and performed a genotype-environment association analysis based on a Bayesian analysis based on allele frequency data (Coop et al., 2010). As shown in Manhattan plots (Figure 1c,d), 128 significant association signals/loci (defined by –log10p > 3) were identified. Among these significant association signals/loci, 25 were located in genic regions (exonic or intronic), including eight for day length (OsLFL1, RNC3, OsDof2, APG, RFT1, Hd1, Ghd7, and ROC4; Figure 1c) and one for temperature sensitivity (COLD1; Figure 1c). The remaining 103 loci were located in non-genic regions, more than half of which (59, or 57.28%) coincided with top selective sweep candidate regions detected in the four cultivated rice subgroups, including 32 specifics to tej, 16 to trj, 8 to aus, and 3 to ind. These were strong candidates as targets of selection for local adaptation.

Based on the adaptation-associated results, the top five significant sites were found on chromosome (Chr.) 8 (Figure 1d). We only identified a non-synonymous SNP (C/A) with a significant association peak (P = 1.31 × 10−22). This SNP was located in the coding region of LOC_Os08g36000 on Chr. 8 (+22 689 418 bp, C > A; +40 aa, Val > Phe) (Figure 1e). The encoded protein contained an F-box and two LRR domains. Many genes in this family respond to biotic and abiotic stresses, including temperature and hormones (Yan et al., 2010). The ‘C’ haplotype (Haptej) was almost fixed in the tej subgroup, with a frequency of 0.985, whereas the ‘A’ haplotype (Hapother) predominated in other subgroups included aus, ind, trj, and wild rice varieties, with a frequency of 0.961 (Figure 1f,h). To address the function of the Haptej allele, we performed cold treatments on 14 cultivated rice varieties with the Haptej allele and 19 cultivated rice varieties with the Hapother allele in a growth chamber, and the results showed that under cold stress (4 °C for 48 h), the survival rate was higher in rice varieties with the Haptej allele (93.25%) than those with the Hapother allele (10.12%; P = 1.66 × 10−13, two-tailed t-test) (Figure 1g,h,k). We therefore named the gene CHILLING-TOLERANCE DIVERGENCE F-box (COLDF).

To further characterize the function of COLDF, we obtained TILLING and CRISPR mutants of the gene in the ZH11 background (O. sativa ssp. japonica var. Zhonghua 11) (Figure 1i,j). The TILLING mutant carried a G to A mutation in the coding region (+22 688 780 bp, G > A; +256 aa, Ala > Thr), and the knockout mutant carried a 22-bp insertion in the coding region, designated as coldf-1 and coldf-2, respectively. Fourteen-day-old seedlings of the homozygous coldf-1 and coldf-2 mutants were subjected to cold stress at 4 °C for 48 h, and then returned to normal growth conditions at 30 °C for recovery. Results showed that these coldf seedlings were susceptible to cold stress, with a survival rate of 37% and 12% in coldf-1 and coldf-2, respectively, in comparison with a survival rate of 82% in ZH11 (Figure 1i,j; P = 1.07 × 10−10, two-tailed t-test for coldf-1 and P = 1.94 × 10−12, two-tailed t-test for coldf-2). These results suggest that the Haptej of COLDF is responsible for enhanced cold tolerance in the tej cultivars. Our findings provide insight into the mechanistic basis of rice domestication and improvement by identification of adaptation-associated loci, which would help in mitigating these effects by facilitating rapid development of improved varieties that are better able to tolerate the stresses accompanying the changing climate.