Phenotypic variation and correlation analysis of RISLs
Phenotypic variation of the RISLs and two parents are illustrated in Fig. 2. All four traits of 387 RISLs displayed continuous distributions and transgressive segregation across four environments, indicating their quantitative inheritance. Coefficient of variation (CV) for GYP, GNP, and NP ranged from 23.8-27.9%, 17.8-25.7%, and 21.9-27.6% in four conditions, respectively, which meant a wide range of variation among the RISL. According to the results of CV, GYP, GNP, and NP were vulnerable to environmental influences. In particular, the CV of 1000-grain weight (TGW) ranged from 6.2-9.5%, which indicated that TGW was a stable quantitative trait.
The correlation among the four yield-related traits is shown in Fig. 3. GYP was positively correlated with NP and GNP while NP and GNP were negatively correlated. TGW was negatively correlated with GNP and NP. However, there was no significant correlation between TGW and GYP. The high yield performance of Nei2You No.6 may be mainly caused by the increase in GNP and NP.
Population sequencing and linkage map construction
In total, 221,340,292 high-quality reads were generated for Nei2B and 303,049,072 high-quality reads for R8006, with average sequencing depths of 60.48-fold and 79.38-fold, respectively. A total of 1,589,836 single nucleotide polymorphisms (SNPs) were identified between Nei2B and R8006.All of 387 RISLs were used for whole-genome sequencing and resulted in a total of 1.05T of clean data, with approximately 8.10-fold depth for each RISL. Using a sliding-window method, a total of 59,890 recombination breakpoints were generated, with an average of 155.16 breakpoints per line. We preliminary obtained 3,273 bin markers with 930,361 high-quality SNPs. After filtering out segregation distortion and low coverage markers, 3,203 effective markers were selected and then used for linkage analysis to construct a genetic linkage map (Fig. 4). The length of bin markers ranged from 50 kb to 1.4 Mb, with a mean of 119 kb. The number of SNPs and bins of each chromosome are shown in Table 1. The total genetic distance of the map was 1951.1 cM, with an average linkage distance of 0.61 cM between adjacent markers. The largest linkage group was Chr.1 with 419 bin markers and a length of 263.6 cM and the smallest Chr. 9 with 163 bin markers and a length of 69.3 cM. The ratio of linkage distance to physical distance ranged from 2.8 to 6.1 cM/Mb, with a mean ratio of 5.0 cM/Mb.
Coverage of genotypic difference in paired RISLs
To verify that if the construction of RISL population was successful, phylogenetic analysis was performed. The phylogeny showed that 387 RISLs were clustered in pairs (Additional file 1: Figure S1). Then, we compared the genome of each paired sister line. To determine the coverage of genotypic difference, we counted the different bins between each of paired RISLs in F15 generation (Fig. 5 and Additional file 2: Table S1). Each bin was covered by 77 paired RISLs on average, with the maximum of 119 paired and minimum of 54 paired. The maximum different bins between paired lines was 52.1%, and minimum of that was 8.6%, and the average different bins accounted as 39.1%. In F15 generation, the heterozygous region ranged from 0 to 27.5% with an average of 2.6%. This meant that the genome is not nearly homozygous until F15 generation. Some lines with high heterozygous rate may be caused by cross-pollination in high generation, and the homozygous degree of the whole population conforms to the stable genetic population. Therefore, we can conclude that the heterozygosity exists longer than in theory.
QTL analysis using the RISL population
As a result of QTL analysis, a total of 43 QTL were identified on all of twelve chromosomes, nine of which were repeatedly detected in multiple environments. Detailed information about all 43 QTL are summarized in Additional file 3: Table S2 and Fig. 6.
Eight QTL for GYP were detected on chromosomes 1, 6, 9, 10, and 12, and the total PVE ranged from 0.0 to 10.9% of the total phenotypic variation across four environments. qGYP-6b was detected in LS15 and FY15 with contributions to phenotypic variance of 4.3% and 4.2%, respectively. The other 7 QTL were detected in one environment. The positive alleles for five out of eight QTL for GYP were derived from Nei2B.
With PVE varying from 3.2 to 5.4%, four QTL related to NP were identified on chromosomes 3, 5, 7, and 9, respectively. In detail, qNP-7 was detected in two environments, which explained 7.1% and 3.7% of the phenotypic variance in LS15 and FY15, respectively. qNP-3, qNP-5, and qNP-9 were detected in only one environment. The positive alleles of qNP-3 and qNP-9 were from R8006 while qNP-5 and qNP-7 from Nei2B.
For GNP, 13 QTL were detected on chromosomes 1, 3, 4, 5, 6, 7, 8, and 12 with the phenotypic variance explained by QTL ranged from 3.2 to 10.2%. The total of PVE (%) of These 13 QTL for GNP ranged from 8.6 to 24.0%. qGNP-6c was repletely detected in FY16 and LS16 environments and explained 4.8% and 2.7% of phenotypic variance, respectively.
In addition, a total of 18 QTL associated with TGW were distributed on chromosomes 1, 2, 3, 5, 6, 7, 8, 10, and 12, totally explaining 13.9 to 37.4% of the phenotypic variation. The phenotypic variance explained by each QTL ranged from 2.4 to 6.1%, indicating multiple minor-effect genes for TGW in Nei2You No.6. All of these 13 QTL associated with TGW inherited from the big grain parent Nei2B. Among these, six loci were detected in multiple environments, such as qTGW-1a, qTGW-5, qTGW-7, qTGW-10b, qTGW-10c, and qTGW-12.
For QTL validation, sister lines were selected for phenotyping in Fuyang, 2018 (Fig. 7). For NP, lines Q277 and Q278 shared the same genetic background harboring on QTL other than qNP-7 detected were selected. The phenotypic data showed a significant difference between Q277 and Q278 in NP, which led to a significant improvement in GYP (Fig. 7a, b). Similarly, lines Q239 and Q240 differed in qNP-7 also showed a significant difference in NP.
For qTGW-1a, lines Q124 and Q125 shared the same genetic background besides the target region. The phenotypic data showed that there was a significant difference between Q124 and Q125 in TGW (Fig. 7c). For qTGW-7, it showed a similar result as qTGW-1a (Q378 vs Q379, Fig. 7d). For qTGW-10b and qTGW-10c, lines Q317 and Q318 showed significant difference in TGW (Fig. 7e). The qTGW-12 was validated using lines Q70 and Q71 for TGW (Fig. 7f). The phenotypic date showed that there was a significant difference between Q70 and Q71 in TGW. Similarly, lines Q146-Q147 and Q380-Q381 also contained qTGW-12 and had a significant difference in TGW.