Screening of RAPD Markers Linked to the Photoperiod-Sensitivity Gene in Rice Chromosome 6 Using Bulked Segregant Analysis

Bulked segregant analysis was used to determine randomly amplifiedpolymorphic DNA (RAPD) markers in a specific interval in themiddle of chromosome 6 of rice for tagging the photoperiod sensitivitygene.Two pools of F₂ individuals (japonica cv. Nipponbare and indicacv. Kasalath) were constructed according to the genotypes ofthree restriction fragment length polymorphism (RFLP) markerslocated at both ends and the middle of the targeted interval.Then another pair of pools were constructed based on the "graphicalgenotype," which was made with our high density linkage map.RAPD analysis was performed using these DNA pools as templates,and polymorphic fragments were detected and mapped. Using 80primers, either singlyor pairwise, we tested 2,404 primer pairsand established 14 markers tightly linked to the photoperiodsensitivitygene. The obtained RAPD markers were converted intosequence-tagged sites bycloning and sequencing of the polymorphicfragments and they can be used directlyfor construction of physicalmaps. This bulked segregant method can be applied for any speciesand any region of interest in which detailed linkage maps orphysical maps are needed.     

Indel‐seq: a fast‐forward genetics approach for identification of trait‐associated putative candidate genomic regions and its application in pigeonpea (Cajanus cajan)

Identification of candidate genomic regions associated with target traits using conventional mapping methods is challenging and time‐consuming. In recent years, a number of single nucleotide polymorphism (SNP)‐based mapping approaches have been developed and used for identification of candidate/putative genomic regions. However, in the majority of these studies, insertion–deletion (Indel) were largely ignored. For efficient use of Indels in mapping target traits, we propose Indel‐seq approach, which is a combination of whole‐genome resequencing (WGRS) and bulked segregant analysis (BSA) and relies on the Indel frequencies in extreme bulks. Deployment of Indel‐seq approach for identification of candidate genomic regions associated with fusarium wilt (FW) and sterility mosaic disease (SMD) resistance in pigeonpea has identified 16 Indels affecting 26 putative candidate genes. Of these 26 affected putative candidate genes, 24 genes showed effect in the upstream/downstream of the genic region and two genes showed effect in the genes. Validation of these 16 candidate Indels in other FW‐ and SMD‐resistant and FW‐ and SMD‐susceptible genotypes revealed a significant association of five Indels (three for FW and two for SMD resistance). Comparative analysis of Indel‐seq with other genetic mapping approaches highlighted the importance of the approach in identification of significant genomic regions associated with target traits. Therefore, the Indel‐seq approach can be used for quick and precise identification of candidate genomic regions for any target traits in any crop species.     

Using single‐cell genomics to understand developmental processes and cell fate decisions

High‐throughput ‐omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision‐making is inherently a unicellular process to which “bulk” ‐omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single‐cell methods bridge this gap, allowing high‐throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single‐cell gene expression data and highlight areas of developmental biology where single‐cell techniques have made important contributions. These include understanding of cell‐to‐cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.     

Discovering Candidate Chromosomal Regions Linked to Kernel Size-Related Traits via QTL Mapping and Bulked Sample Analysis in Maize

Kernel size-related traits, including kernel length, kernel width, and kernel thickness, are critical components in determining yield and kernel quality in maize (Zea mays L.). Dissecting the phenotypic characteristics of these traits, and discovering the candidate chromosomal regions for these traits, are of potential importance for maize yield and quality improvement. In this study, a total of 139 F2:3 family lines derived from EHel and B73, a distinct line with extremely low ear height (EHel), was used for phenotyping and QTL mapping of three kernel size-related traits, including 10-kernel length (KL), 10-kernel width (KWid), and 10-kernel thickness (KT). The results showed that only one QTL for KWid, i.e., qKWid9 on Chr9, with a phenotypic variation explained (PVE) of 13.4% was detected between SNPs of AX-86298371 and AX-86298372, while no QTLs were detected for KL and KT across all 10 chromosomes. Four bulked groups of family lines, i.e., Groups I to IV, were constructed with F2:3 family lines according to the phenotypic comparisons of KWid between EHel and B73. Among these four groups, Group I possessed a significantly lower KWid than EHel (P = 0.0455), Group II was similar to EHel (P = 0.34), while both Group III and Group IV were statistically higher than EHel (P < 0.05). Besides, except Group IV exhibited a similar KWid to B73 (P = 0.11), KWid of Groups I to III were statistically lower than B73 (P < 0.00). By comparing the bulked genotypes of the four groups to EHel and B73, a stable chromosomal region on Chr9 between SNPs of AX-86298372 to AX-86263154, entirely covered by qKWid9, was identified to link KWid with the positive allele of increasing phenotypic effect to KWid from B73, similar to that of qKWid9. A large amount of enzyme activity and macromolecule binding-related genes were annotated within this chromosomal region, suggesting qKWid9 as a potential QTL for KWid in maize.     

Apple fruit acidity is genetically diversified by natural variations in three hierarchical epistatic genes: MdSAUR37, MdPP2CH and MdALMTII

Many efforts have been made to map quantitative trait loci (QTL s) to facilitate practical marker‐assisted selection (MAS ) in plants. In the present study, using MapQTL and BSA‐seq (bulk segregant analysis using next generation sequencing) with two independent pedigree‐based populations, we identified four major genome‐wide QTL s responsible for apple fruit acidity. Candidate genes were screened in major QTL regions, and three functional gene markers, including a non‐synonymous A/G single‐nucleotide polymorphism (SNP ) in the coding region of MdPP 2CH , a 36‐bp insertion in the promoter of MdSAUR 37 and a previously reported SNP in MdALMTII , were validated to influence the malate content of apple fruits. In addition, MdPP 2CH inactivated three vacuolar H⁺‐ATP ases (MdVHA ‐A3, MdVHA ‐B2 and MdVHA ‐D2) and one aluminium‐activated malate transporter (MdALMTII ) via dephosphorylation and negatively influenced fruit malate accumulation. The dephosphotase activity of MdPP 2CH was suppressed by MdSAUR 37, which implied a higher hierarchy of genetic interaction. Therefore, the MdSAUR 37/MdPP 2CH /MdALMTII chain cascaded hierarchical epistatic genetic effects to precisely determine apple fruit malate content. An A/G SNP (?1010) on the MdMYB 44 promoter region from a major QTL (qtl08.1) was closely associated with fruit malate content. The predicted phenotype values (PPV s) were estimated using the tentative genotype values of the gene markers, and the PPV s were significantly correlated with the observed phenotype values. Our findings provide an insight into plant genome‐based selection in apples and will aid in conducting research to understand the fundamental physiological basis of quantitative genetics.     

Next‐generation sequencing for identification of candidate genes for Fusarium wilt and sterility mosaic disease in pigeonpea (Cajanus cajan)

To map resistance genes for Fusarium wilt (FW) and sterility mosaic disease (SMD) in pigeonpea, sequencing‐based bulked segregant analysis (Seq‐BSA) was used. Resistant (R) and susceptible (S) bulks from the extreme recombinant inbred lines of ICPL 20096 × ICPL 332 were sequenced. Subsequently, SNP index was calculated between R‐ and S‐bulks with the help of draft genome sequence and reference‐guided assembly of ICPL 20096 (resistant parent). Seq‐BSA has provided seven candidate SNPs for FW and SMD resistance in pigeonpea. In parallel, four additional genotypes were re‐sequenced and their combined analysis with R‐ and S‐bulks has provided a total of 8362 nonsynonymous (ns) SNPs. Of 8362 nsSNPs, 60 were found within the 2‐Mb flanking regions of seven candidate SNPs identified through Seq‐BSA. Haplotype analysis narrowed down to eight nsSNPs in seven genes. These eight nsSNPs were further validated by re‐sequencing 11 genotypes that are resistant and susceptible to FW and SMD. This analysis revealed association of four candidate nsSNPs in four genes with FW resistance and four candidate nsSNPs in three genes with SMD resistance. Further, In silico protein analysis and expression profiling identified two most promising candidate genes namely C.cajan_01839 for SMD resistance and C.cajan_03203 for FW resistance. Identified candidate genomic regions/SNPs will be useful for genomics‐assisted breeding in pigeonpea.     

RNA structure: Merging chemistry and genomics for a holistic perspective

The advent of deep sequencing technology has unexpectedly advanced our structural understanding of molecules composed of nucleic acids. A significant amount of progress has been made recently extrapolating the chemical methods to probe RNA structure into sequencing methods. Herein we review some of the canonical methods to analyze RNA structure, and then we outline how these have been used to probe the structure of many RNAs in parallel. The key is the transformation of structural biology problems into sequencing problems, whereby sequencing power can be interpreted to understand nucleic acid proximity, nucleic acid conformation, or nucleic acid‐protein interactions. Utilizing such technologies in this way has the promise to provide novel structural insights into the mechanisms that control normal cellular physiology and provide insight into how structure could be perturbed in disease.     

Rapid identification of lettuce seed germination mutants by bulked segregant analysis and whole genome sequencing

Lettuce (Lactuca sativa) seeds exhibit thermoinhibition, or failure to complete germination when imbibed at warm temperatures. Chemical mutagenesis was employed to develop lettuce lines that exhibit germination thermotolerance. Two independent thermotolerant lettuce seed mutant lines, TG01 and TG10, were generated through ethyl methanesulfonate mutagenesis. Genetic and physiological analyses indicated that these two mutations were allelic and recessive. To identify the causal gene(s), we applied bulked segregant analysis by whole genome sequencing. For each mutant, bulked DNA samples of segregating thermotolerant (mutant) seeds were sequenced and analyzed for homozygous single‐nucleotide polymorphisms. Two independent candidate mutations were identified at different physical positions in the zeaxanthin epoxidase gene (ABSCISIC ACID DEFICIENT 1/ZEAXANTHIN EPOXIDASE, or ABA1/ZEP) in TG01 and TG10. The mutation in TG01 caused an amino acid replacement, whereas the mutation in TG10 resulted in alternative mRNA splicing. Endogenous abscisic acid contents were reduced in both mutants, and expression of the ABA1 gene from wild‐type lettuce under its own promoter fully complemented the TG01 mutant. Conventional genetic mapping confirmed that the causal mutations were located near the ZEP/ABA1 gene, but the bulked segregant whole genome sequencing approach more efficiently identified the specific gene responsible for the phenotype.