RESUMEN

Chlorophyll is an essential component that captures light energy to drive photosynthesis. Chlorophyll content can affect photosynthetic activity and thus yield. Therefore, mining candidate genes of chlorophyll content will help increase maize production. Here, we performed a genome-wide association study (GWAS) on chlorophyll content and its dynamic changes in 378 maize inbred lines with extensive natural variation. Our phenotypic assessment showed that chlorophyll content and its dynamic changes were natural variations with a moderate genetic level of 0.66/0.67. A total of 19 single-nucleotide polymorphisms (SNPs) were found associated with 76 candidate genes, of which one SNP, 2376873-7-G, co-localized in chlorophyll content and area under the chlorophyll content curve (AUCCC). Zm00001d026568 and Zm00001d026569 were highly associated with SNP 2376873-7-G and encoded pentatricopeptide repeat-containing protein and chloroplastic palmitoyl-acyl carrier protein thioesterase, respectively. As expected, higher expression levels of these two genes are associated with higher chlorophyll contents. These results provide a certain experimental basis for discovering the candidate genes of chlorophyll content and finally provide new insights for cultivating high-yield and excellent maize suitable for planting environment.

Asunto(s)

RESUMEN

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs' ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.

Asunto(s)

RESUMEN

Drought seriously threats the growth and development of Gossypium hirsutum L. To dissect the genetic basis for drought tolerance in the G. hirsutum L. germplasm, a population, consisting of 188 accessions of G. hirsutum races and a cultivar (TM-1), was genotyped using the Cotton80KSNP biochip, and 51,268 high-quality single-nucleotide polymorphisms (SNPs) were obtained. Based on the phenotypic data of eight drought relative traits from four environments, we carried out association mapping with five models using GAPIT software. In total, thirty-six SNPs were detected significantly associated at least in two environments or two models. Among these SNPs, 8 and 28 (including 24 SNPs in 5 peak regions) were distributed in the A and D subgenome, respectively; eight SNPs were found to be distributed within separate genes. An SNP, TM73079, located on chromosome D10, was simultaneously associated with leaf fresh weight, leaf wilted weight, and leaf dry weight. Another nine SNPs, TM47696, TM33865, TM40383, TM10267, TM59672, TM59675, TM59677, TM72359, and TM72361, on chromosomes A13, A10, A12, A5, D6, and D9, were localized within or near previously reported quantitative trait loci for drought tolerance. Moreover, 520 genes located 200 kb up- and down-stream of 36 SNPs were obtained and analyzed based on gene annotation and transcriptome sequencing. The results showed that three candidate genes, Gh_D08G2462, Gh_A03G0043, and Gh_A12G0369, may play important roles in drought tolerance. The current GWAS represents the first investigation into mapping QTL for drought tolerance in G. hirsutum races and provides important information for improving cotton cultivars.

RESUMEN

Tuber shape is one of the most important quality traits in potato appearance. Since poor or irregular shape results in higher costs for processing and influences the consumers' willingness to purchase, breeding for shape uniformity and shallow eye depth is highly important. Previous studies showed that the major round tuber shape controlling locus, the Ro locus, is located on chromosome 10. However, fine mapping and cloning of tuber shape genes have not been reported. In this study, the analyses of tissue sectioning and transcriptome sequencing showed that the developmental differences between round and elongated tuber shapes begin as early as the hook stage of the stolon. To fine map tuber shape genes, a high-density genetic linkage map of the Ro region on chromosome 10 based on a diploid segregating population was constructed. The total length of the genetic linkage map was 25.8 cM and the average marker interval was 1.98 cM. Combined with phenotypic data collected from 2014 to 2017, one major quantitative trait locus (QTL) for tuber shape was identified, which explained 61.7-72.9% of the tuber shape variation. Through the results of genotyping and phenotypic investigation of recombinant individuals, Ro was fine mapped in a 193.43 kb interval, which contained 18 genes. Five candidate genes were preliminarily predicted based on tissue sections and transcriptome sequencing. This study provides an important basis for cloning Ro gene(s).

Asunto(s)

RESUMEN

The sorghum-sudangrass hybrid is a vital gramineous herbage.The F2 population was obtained to clarify genetic regularities among the traits of sorghum-sudangrass hybrids by bagging and selfing in the F1 generation using 'scattered ear sorghum' and 'red hull sudangrass.' This hybrid combines the characteristics of the strong resistance of parents, high yield, and good palatability and has clear heterosis. A thorough understanding of the genetic mechanisms of yield traits in sorghum-sudangrass hybrids is essential in improving their yield. Therefore, we conducted quantitative trait locus (QTL) mapping for plant height, stem diameter, tiller number, leaf number, leaf length, leaf width, and fresh weight of each plant in three different environments, using a high-density genetic linkage map based on single nucleotide polymorphism markers previously constructed by our team. A total of 55 QTLs were detected, uniformly distributed over the 10 linkage groups (LGs), with logarithm of odds values ranging between 2.5 and 7.1, which could explain the 4.9-52.44% phenotypic variation. Furthermore, 17 yield-related relatively high-frequency QTL (RHF-QTL) loci were repeatedly detected in at least two environments, with an explanatory phenotypic variation of 4.9-30.97%. No RHF-QTLs were associated with the tiller number. The genes within the confidence interval of RHF-QTL were annotated, and seven candidate genes related to yield traits were screened. Three QTL sites overlapping or adjacent to previous studies were detected by comparative analysis. We also found that QTL was enriched and that qLL-10-1 and qFW-10-4 were located at the same location of 25.81 cM on LG10. The results of this study provide a foundation for QTL fine mapping, candidate gene cloning, and molecular marker-assisted breeding of sorghum-sudangrass hybrids.

RESUMEN

KEY MESSAGE: We fine-mapped QBp.caas-3BL for black point resistance in an interval of 1.7 Mb containing five high-confidence annotated genes and developed a KASP marker suitable for selection of QBp.caas-3BL. Wheat black point, which occurs in most wheat-growing regions of the world, is detrimental to grain appearance, processing and nutrient quality. Mining and characterization of genetic loci for black point resistance are helpful for breeding resistant wheat cultivars. We previously identified a major QTL QBp.caas-3BL in a recombinant inbred line (RIL) population of Linmai 2/Zhong 892 across five environments. Here we confirmed the QTL in two additional environments. The genetic region of QBp.caas-3BL was enriched with newly developed markers. Using four sets of near isogenic lines, QBp.caas-3BL was narrowed down to a physical interval of approximately 1.7 Mb, including five annotated genes according to IWGSC reference genome. TraesCS3B02G404300, TraesCS3B02G404600 and TraesCS3B02G404700 were predicted as candidate genes based on the analyses of sequence polymorphisms and differential expression. We also converted a SNP of TraesCS3B02G404700 into a breeding-applicable KASP marker and verified its efficacy for marker-assisted breeding in a panel of germplasm. The findings not only lay a foundation for map-based cloning of QBp.caas-3BL but also provide a useful marker for selection of resistant cultivars genotypes in wheat breeding.

Asunto(s)

RESUMEN

Rice blast is one of the most widespread and devastating diseases in rice production. Tremendous success has been achieved in the identification and characterization of genes and quantitative trait loci (QTLs) conferring seedling blast resistance, however, genetic studies on panicle blast resistance have lagged far behind. In this study, two advanced backcross inbred sister lines (MSJ13 and MSJ18) were obtained in the process of introducing Pigm into C134S and showed significant differences in the panicle blast resistance. One F2 population derived from the crossing MSJ13/MSJ18 was used to QTL mapping for panicle blast resistance using genotyping by sequencing (GBS) method. A total of seven QTLs were identified, including a major QTL qPBR10-1 on chromosome 10 that explains 24.21% of phenotypic variance with LOD scores of 6.62. Furthermore, qPBR10-1 was verified using the BC1F2 and BC1F3 population and narrowed to a 60.6-kb region with six candidate genes predicted, including two genes encoding exonuclease family protein, two genes encoding hypothetical protein, and two genes encoding transposon protein. The nucleotide variations and the expression patterns of the candidate genes were identified and analyzed between MSJ13 and MSJ18 through sequence comparison and RT-PCR approach, and results indicated that ORF1 and ORF2 encoding exonuclease family protein might be the causal candidate genes for panicle blast resistance in the qPBR10-1 locus. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01268-3.

RESUMEN

Chlorophyll is the most important pigment for plant photosynthesis that plays an important role in crop growth and production. In this study, the chlorophyll content trait was explored to improve sugarcane yield. Two hundred and eighty-five F1 progenies from the cross YT93-159 × ROC22 with significantly different chlorophyll contents were included as test materials. The chlorophyll content of the +1 leaves during elongation phase was measured using a SPAD-502 meter through a three-crop cycle (plant cane, first ratoon, and second ratoon). Linkage analysis was conducted on a high-density genetic map constructed based on the sugarcane 100K SNP chip. In addition, Fv/Fm, plant height, stalk diameter, brix data were collected on plant cane during the elongation and maturation phases. The results showed that the +1 leaf SPAD values, which can be used as an important reference to evaluate the growth potential of sugarcane, were significantly and positively correlated with the Fv/Fm during elongation phase, as well as with plant height, stalk diameter, and brix during maturity phase (P < 0.01). The broad sense heritability (H 2) of the chlorophyll content trait was 0.66 for plant cane crop, 0.67 for first ratoon crop, and 0.73 for second ratoon crop, respectively, indicating that this trait was mainly controlled by genetic factors. Thirty-one quantitative trait loci (QTL) were detected by QTL mapping. Among them, a major QTL, qCC-R1, could account for 12.95% of phenotypic variation explained (PVE), and the other 30 minor QTLs explained 2.37-7.99% PVE. Twenty candidate genes related to chlorophyll content were identified in the QTLs plus a 200-Kb extension region within either sides, of which four were homologous genes involved in the chlorophyll synthesis process and the remaining 16 played a certain role in chlorophyll catabolic pathway, chloroplast organization, or photosynthesis. These results provide a theoretical reference for analyzing the genetic mechanism of chlorophyll synthesis and subsequent improvement of photosynthetic characteristics in sugarcane.

RESUMEN

BACKGROUND: Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. RESULTS: According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. CONCLUSION: Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.

Asunto(s)

RESUMEN

Rapeseed (Brassica napus L.) is one of the most important oil crops in the world. However, the yield and quality of rapeseed were largely decreased by clubroot (Plasmodiophora brassicae Woronin). Therefore, it is of great importance for screening more resistant germplasms or genes and improving the resistance to P. brassicae in rapeseed breeding. In this study, a massive resistant identification for a natural global population was conducted in two environments with race/pathotype 4 of P. brassicae which was the most predominant in China, and a wide range of phenotypic variation was found in the population. In addition, a genome-wide association study of 472 accessions for clubroot resistance (CR) was performed with 60K Brassica Infinium SNP arrays for the first time. In total, nine QTLs were detected, seven of which were novel through integrative analysis. Furthermore, additive effects in genetic control of CR in rapeseed among the above loci were found. By bioinformatic analyses, the candidate genes of these loci were predicted, which indicated that TIR-NBS gene family might play an important role in CR. It is believable that the results presented in our study could provide valuable information for understanding the genetic mechanism and molecular regulation of CR.

RESUMEN

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.