Whole-Genome Sequencing of 117 Chromosome Segment Substitution Lines for Genetic Analyses of Complex Traits in Rice

Rice is one of the most important food crops in Asia. Genetic analyses of complex traits and molecular breeding studies in rice greatly rely on the construction of various genetic populations. Chromosome segment substitution lines (CSSLs) serve as a powerful genetic population for quantitative trait locus (QTL) mapping in rice. Moreover, CSSLs containing target genomic regions can be used as improved varieties in rice breeding. In this study, we developed a set of CSSLs consisting of 117 lines derived from the recipient ‘Huanghuazhan’ (HHZ) and the donor ‘Basmati Surkb 89–15’ (BAS). The 117 lines were extensively genotyped by whole-genome resequencing, and a high-density genotype map was constructed for the CSSL population. The 117 CSSLs covered 99.78% of the BAS genome. Each line contained a single segment, and the average segment length was 6.02 Mb. Using the CSSL population, we investigated three agronomic traits in Shanghai and Hangzhou, China, and a total of 25 QTLs were detected in both environments. Among those QTLs, we found that RFT1 was the causal gene for heading date variance between HHZ and BAS. RFT1 from BAS was found to contain a loss-of-function allele based on yeast two-hybrid assay, and its causal variation was a P to S change in the 94th amino acid of the RFT1 protein. The combination of high-throughput genotyping and marker-assisted selection (MAS) is a highly efficient way to construct CSSLs in rice, and extensively genotyped CSSLs will be a powerful tool for the genetic mapping of agronomic traits and molecular breeding for target QTLs/genes.

Identification of Heterotic Loci with Desirable Allelic Interaction to Increase Yield in Rice

Heterosis denotes the superiority of a hybrid plant over its parents. The use of heterosis has contributed significantly to yield improvement in crops. However, the genetic and molecular bases on heterosis are not fully understood. A large number of heterotic loci were identified for 12 yield-related traits in one parental population of chromosome segment substitution lines (CSSLs) and two test populations, which were interconnected by CSSLs derived from two rice genome-sequenced cultivars, Nipponbare and Zhenshan 97. Seventy-five heterotic loci were identified in both homozygous background of Zhenshan 97 and heterogeneous background of an elite hybrid cultivar Shanyou 63. Among the detected loci, at least 11 were colocalized in the same regions encompassing previously reported heterosis-associated genes. Furthermore, a heterotic locus Ghd8NIP for yield advantage was verified using transgenic experiments. Various allelic interaction at Ghd8 exhibited different heterosis levels in hetero-allelic combinations of five near-isogenic lines that contain a particular allele. The significant overdominance effects from some hetero-allelic combinations were found to improve yield heterosis in hybrid cultivars. Our findings support the role of allelic interaction at heterotic loci in the improvement of yield potential, which will be helpful for dissecting the genetic basis of heterosis and provide an optional strategy for the allele replacement in molecular breeding programs in hybrid rice.

Addressing Iron and Zinc Micronutrient Malnutrition Through Nutrigenomics in Pearl Millet: Advances and Prospects

Iron (Fe) and zinc (Zn) micronutrient deficiencies are significant health concerns, particularly among the underprivileged and resource-poor people in the semi-arid tropics globally. Pearl millet is regarded as a climate-smart crop with low water and energy footprints. It thrives well under adverse agro-ecologies such as high temperatures and limited rainfall. Pearl millet is regarded as a nutri-cereal owing to health-promoting traits such as high grain Fe and Zn content, metabolizable energy, high antioxidant and polyphenols, high proportion of slowly digestible starches, dietary fibers, and favorable essential amino acid profile compared to many cereals. Higher genetic variability for grain Fe and Zn content has facilitated considerable progress in mapping and mining QTLs, alleles and genes underlying micronutrient metabolism. This has been made possible by developing efficient genetic and genomic resources in pearl millet over the last decade. These include genetic stocks such as bi-parental RIL mapping populations, association mapping panels, chromosome segment substitution lines (CSSLs) and TILLING populations. On the genomics side, considerable progress has been made in generating genomic markers, such as SSR marker repository development. This was followed by the development of a next-generation sequencing-based genome-wide SNP repository. The circa 1,000 genomes re-sequencing project played a significant role. A high-quality reference genome was made available by re-sequencing of world diversity panel, mapping population parents and hybrid parental lines. This mini-review attempts to provide information on the current developments on mapping Fe and Zn content in pearl millet and future outlook.

Combining Genome and Gene Co-expression Network Analyses for the Identification of Genes Potentially Regulating Salt Tolerance in Rice

Salinity stress tolerance is a complex polygenic trait involving multi-molecular pathways. This study aims to demonstrate an effective transcriptomic approach for identifying genes regulating salt tolerance in rice. The chromosome segment substitution lines (CSSLs) of “Khao Dawk Mali 105 (KDML105)” rice containing various regions of DH212 between markers RM1003 and RM3362 displayed differential salt tolerance at the booting stage. CSSL16 and its nearly isogenic parent, KDML105, were used for transcriptome analysis. Differentially expressed genes in the leaves of seedlings, flag leaves, and second leaves of CSSL16 and KDML105 under normal and salt stress conditions were subjected to analyses based on gene co-expression network (GCN), on two-state co-expression with clustering coefficient (CC), and on weighted gene co-expression network (WGCN). GCN identified 57 genes, while 30 and 59 genes were identified using CC and WGCN, respectively. With the three methods, some of the identified genes overlapped, bringing the maximum number of predicted salt tolerance genes to 92. Among the 92 genes, nine genes, OsNodulin, OsBTBZ1, OsPSB28, OsERD, OsSub34, peroxidase precursor genes, and three expressed protein genes, displayed SNPs between CSSL16 and KDML105. The nine genes were differentially expressed in CSSL16 and KDML105 under normal and salt stress conditions. OsBTBZ1 and OsERD were identified by the three methods. These results suggest that the transcriptomic approach described here effectively identified the genes regulating salt tolerance in rice and support the identification of appropriate QTL for salt tolerance improvement.

Identification and Validation of QTLs for Yield and Yield Components under Long-Term Salt Stress Using IR64 CSSLs in the Genetic Background of Koshihikari and Their Backcross Progenies

Unraveling the complex genetic bases and mechanisms underlying salt tolerance is of great importance for developing salt-tolerant varieties. In this study, we evaluated 42 chromosome segment substitution lines (CSSLs) carrying chromosome segments from IR64 on the genetic background of Koshihikari under salt stress. Two CSSLs, SL2007 and SL2038, produced higher plant dry weight and grain yield than did Koshihikari under the stress condition. These CSSLs also showed lower Na+ and Cl− accumulation in the leaf and whole plant at the full heading stage, which might be related to the higher grain yield and yield components. To understand the genetic control of its grain yield and yield components, a SL2007/Koshihikari F2 population was generated for quantitative trait locus (QTL) analysis. Six QTLs for grain yield and yield-related traits were detected on chromosome 2. Using near-isogenic lines (NILs) from a SL2007/Koshihikari F5 population, qSTGY2.2 was delimited to a 2.5 Mb region and novel qSTPN2 was delimited to a 0.6 Mb region. We also detected a novel QTL, qSTGF2, for grain filling, which was considered an important contributor to grain yield under salt stress in this CSSL. Our results provide insights into mechanisms conferring grain yield under salinity stress and new genetic resources for cloning and breeding.

Identification of QTLs for Heat Tolerance at Flowering Stage Using Chromosome Segment Substitution Lines in Rice

High temperature is a major stress in rice production. Although considerable progresses have been made on investigating heat tolerance (HT) in rice, the genetic basis of HT at heading stage remains largely unknown. In this study, a novel set of chromosome segment substitution lines (CSSLs) consisting of 113 lines derived from a heat-resistant indica variety N22 and a heat-sensitive indica variety 9311 was developed and used for the analysis of genetic basis of HT. The heat sensitivity index (HSI) calculated based on seed-setting rates under natural and high temperature environments was used to evaluate the influence of HT at rice heading stage. Totally, five QTLs associated with HT were detected based on seed-setting rate (SSR) evaluation; these were named qSSR6-1, qSSR7-1, qSSR8-1, qSSR9-1 and qSSR11-1 located on chromosomes 6, 7, 8, 9 and 11, respectively. Heat-tolerant alleles of the QTLs were all derived from N22. Among them, qSSR9-1 overlapped with QTLs identified previously, while the remaining QTLs were found novel. Especially, qSSR7-1 explained a high phenotypic variation of 26.35% with a LOD score of 10.75, thus deserved to be further validated. These findings will increase our understanding of the genetic mechanism underlying HT and facilitate the breeding of heat-tolerant rice varieties.

QTL mapping for starch paste viscosity of rice (Oryza sativa L.) using chromosome segment substitution lines derived from two sequenced cultivars with the same Wx allele

Background The eating and cooking qualities (ECQs) of rice (Oryza sativa L.) are key characteristics affecting variety adoption and market value. Starch viscosity profiles tested by a rapid visco analyzer (RVA) offer a direct measure of ECQs and represent the changes in viscosity associated with starch gelatinization. RVA profiles of rice are controlled by a complex genetic system and are also affected by the environment. Although Waxy (Wx) is the major gene controlling amylose content (AC) and ECQs, there are still other unknown genetic factors that affect ECQs. Results Quantitative trait loci (QTLs) for starch paste viscosity in rice were analyzed using chromosome segment substitution lines (CSSLs) developed from the two cultivars 9311 and Nipponbare, which have same Wx-b allele. Thus, the effect of the major locus Wx was eliminated and the other locus associated with the RVA profile could be identified. QTLs for seven parameters of the starch RVA profile were tested over four years in Nanjing, China. A total of 310 QTLs were identified (from 1 to 55 QTLs per trait) and 136 QTLs were identified in more than one year. Among them, 6 QTLs were stalely detected in four years and 26 QTLs were detected in at least three years including 13 pleiotropic loci, controlling 2 to 6 RVA properties simultaneously. These stable QTL hotspots were co-located with several known starch synthesis-related genes (SSRGs). Sequence alignments showed that nucleotide and amino acid sequences of most SSRGs were different between the two parents. Finally, we detected stable QTLs associated with multiple starch viscosity traits near Wx itself, supporting the notion that additional QTLs near Wx control multiple characteristic values of starch viscosity. Conclusions By eliminating the contribution from the major locus Wx, multiple QTLs associated with the RVA profile of rice were identified, several of which were stably detected over four years. The complexity of the genetic basis of rice starch viscosity traits might be due to their pleiotropic effects and the multiple QTL hot spots. Minor QTLs controlling starch viscosity traits were identified by using the chromosome segment substitution strategy. Allele polymorphism might be the reason that QTLs controlling RVA profile characteristics were detected in some known SSRG regions.

Fine Mapping of A QTL Locus (QNFSP07-1) And Analysis of Candidate Genes For Four-Seeded Pods In Soybean

The number of four-seeded pods is a quantitative trait in soybean [Glycine max (L.) Merr.] and is closely related to yield in terms of breeding. In this study, individuals with high ratio of four-seed pods which from chromosome segment substitution lines (CSSLs) that can be stably inherited were selected as the parent, and Suinong 14 (SN14) was used as recurrent parent to construct secondary mapping population via marker-assisted selection. The initial QTL mapping interval was 0.67 Mb and was located on Gm07. Based on the initial QTL mapping results, individuals that were heterozygous at the interval (36116118-37399738 bp) were screened, and the heterozygous individuals were subjected to inbreeding to obtain 13 F3 populations, with a target interval of 321 kb. Gene annotation was performed on the finely mapped interval, and 27 genes were obtained. Glyma.07G200900, Glyma.07G201200 were identified as candidate genes. qRT-PCR was used to measure the expression of the candidate genes at different developmental stages of soybean. The expression levels of the 2 candidate genes in terms of cell division (axillary buds, COTs, EMs) were higher than those in terms of cell expansion (MM, LM), and these genes play a positive regulatory role in the formation of four-seeded pods. Haplotype analysis shows that Glyma.07G201200 has two excellent haplotypes. Those results provide the information for gene map-based cloning and molecular marker assisted breeding of the number of four-seeded pod in soybean.

Identification of QTLs Associated with Two-seed Pod Length and Width in Soybean

Background: Two-seed pod length and width (TSPL and TSPW, respectively) are the traits underlying seed size, which is an important factor influencing soybean yield. Methods: A population comprising 213 chromosome segment substitution lines from a cross between ‘Suinong14’ (SN14) and ZYD00006 was used for a quantitative trait locus (QTL) analysis. The QTLs were identified on the basis of the phenotypes from 2016 to 2019. Additionally, IciMapping 4.2 was used to analyze the phenotypic and genetic data. Genes were annotated using the KEGG and Phytozome databases. Result: Five QTLs for TSPL and four QTLs for TSPW were identified. One QTL on chromosome 17 was detected for TSPL in 2017 and 2018 as well for TSPW in 2018 and 2019. Analyses of the additive × additive epistatic effects of QTLs revealed six stable loci pairs for epistatic effects on the two traits. On the basis of an alignment of the parental gene sequences and the gene annotation information, Glyma.04G188800, Glyma.11G164700, Glyma.13G132700, Glyma.17G156100 and Glyma.13G133200 were selected as candidate genes for TSPL, whereas Glyma.13G174400, Glyma.13G174700, Glyma.16G012500, Glyma.17G156100, Glyma.19G161700 and Glyma.19G161800 were selected as candidate genes for TSPW. These results may be relevant for future attempts to modify soybean seed traits.