Natural variation and genomic prediction of growth, physiological traits, and nitrogen-use efficiency in perennial ryegrass under low-nitrogen stress

Abstract

Genomic prediction of nitrogen use efficiency (NUE) has not been studied in perennial grass species exposed to low N stress. The experiment was designed to conduct genomic prediction of physiological traits and NUE in 184 global accessions of perennial ryegrass (Lolium perenne L.) in response to the normal (7.5 mM) and low (0.75 mM) N supply. Significant variations in plant height (ΔHT), leaf fresh weight (LFW), leaf dry weight (LDW), chlorophyll index (Chl), chlorophyll fluorescence, leaf N and carbon (C) content, C/N ratio and NUE were observed in accessions after 21 days of both N treatments in a greenhouse, but to a greater extent under low N stress. Six genomic prediction models (BayesC, BL, BRR, RRBLUP, RKHS, RF) produced similar prediction accuracy of traits within the normal N or low N treatment, but prediction accuracy differed between the two N treatments. ΔHT, LFW, LDW, and C were slightly better predicted under the normal N with an average r = 0.26 in comparison with r = 0.22 under low N, while prediction accuracy for Chl, N, C/N, and NUE was significantly improved under low N stress with an average r = 0.45, compared to the r = 0.26 under normal N. The population panel contained three population structures, which generally had no effect on prediction accuracy. The moderate prediction accuracy for N, C and NUE under low N stress is promising, which indicates a feasible way that may be used to assess valuable germplasm for further conducting genomic prediction of NUE for enhancing breeding programs of perennial grass species grown under N-deficient conditions.

The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower

“…Improving a plant's capacity to acquire and use nitrogen more efficiently may be the key to improving performance in poor nutrient conditions (Han et al, 2015;Tegeder and Masclaux-Daubresse, 2018;Swarbreck et al, 2019). Similar to results from ryegrass (Zhao et al, 2020), sunflower was found to generally increase nitrogen utilization efficiency under nutrient limitation (Fig 2a). Moreover, contrary to RGR, the magnitude of this increase was unrelated to NUtE under benign conditions.…”

Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize (Zea mays. L) Under optimum and low nitrogen conditions

“…For instance, RR-BLUP or G-BLUP is now very commonly used to predict several complex traits in maize ( Guo et al, 2012 ; Hao et al, 2019 ; Beyene et al, 2021 ). Several NUE traits like chlorophyll index, chlorophyll fluorescence and leaf N content were also selected by genomic prediction before integrating into NUE breeding programs in ryegrass ( Zhao et al, 2020 ). In the present study, GS was performed on GY and other agronomic traits evaluated under optimum and low soil N stress conditions.…”

Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences

“…As a promising new breeding method, GS has been applied to the prediction of heterosis of various crops such as rice [11,12,16], corn [17], barley [18], wheat [19], ryegrass [20], and pumpkin [21]. The traits which were predicted in the different studies were not only limited to yield and yield components [16][17][18][19][20][21], but also those traits such as biotic-and abiotic-stress tolerances [22,23], nutrient utilization efficiency [24,25]. Compared with the previous GS studies on other crops, our research had the following characteristics.…”

“…Compared with the previous GS studies on other crops, our research had the following characteristics. First, the genetic information used in our study involves 4.44 million SNPs, which were 2-3 orders of magnitude more than the number of molecular markers used in the previous studies [16][17][18][19][20][21][22][23][24][25]. The previous studies either involved resequencing data of the crops with much smaller genomes such as rice [11,12,16], or only a small part of genome-wide SNPs of the crops with larger genomes such as barley [18] and corn [17].…”