Excavating the quantitative trait locus (QTL) associated with rice cooking quality, analyzing candidate genes, and improving cooking quality-associated traits of rice varieties by genetic breeding can effectively improve the taste of rice. In this study, we used the indica rice HZ, the japonica rice Nekken2 and 120 recombinant inbred lines (RILs) populations constructed from them as experimental materials to measure the gelatinization temperature (GT), gel consistency (GC) and amylose content (AC) of rice at the maturity stage. We combined the high-density genetic map for QTL mapping. A total of 26 QTLs associated with rice cooking quality (1 QTL associated with GT, 13 QTLs associated with GC, and 12 QTLs associated with AC) were detected, among which the highest likelihood of odd (LOD) value reached 30.24. The expression levels of candidate genes in the localization interval were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR), and it was found that the expression levels of six genes were significantly different from that in parents. It was speculated that the high expression of LOC_Os04g20270 and LOC_Os11g40100 may greatly increase the GC of rice, while the high expression of LOC_Os01g04920 and LOC_Os02g17500 and the low expression of LOC_Os03g02650 and LOC_Os05g25840 may reduce the AC. The results lay a molecular foundation for the cultivation of new high-quality rice varieties, and provide important genetic resources for revealing the molecular regulation mechanism of rice cooking quality.
Quantitative Trait Loci ; Oryza/genetics* ; Plant Breeding ; Cooking ; Genetic Association Studies

The chemical characterization analysis of a medical device often results in chemical substances with unknown toxicities. While identification of each individual toxicity could result in a time-consuming hurdle with tremendous labor and financial burden, quantitative structure-activity relationship (QSAR) is of great significance for toxicity risk assessment of such chemical substances. By establishing quantitative relationship between the molecular structures or active groups of similar chemical compounds with their biological activities, QSAR can be utilized to predict the toxicity of such target compounds with significantly reduced cost and time. In this article, the authors generally summarized the mechanisms of QSAR approaches, current applications of QSAR modeling in the field of medical device, an introduction of the characteristics of publicly and commercially-available QSAR software, and briefly explored future trends of QSAR modeling in medical device toxicological risk assessment. The utilization of QSAR would undoubtedly further advance the toxicological risk assessment of medical devices.
Quantitative Structure-Activity Relationship ; Risk Assessment ; Software

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.
Amifostine ; Computer Simulation ; Cytostatic Agents ; Drug Discovery ; Drug Therapy ; Humans ; Quantitative Structure-Activity Relationship ; Radiation Tolerance ; Radiotherapy ; United States Food and Drug Administration

OBJECTIVES: The molecular mechanisms underlying attention-deficit hyperactivity disorder (ADHD) remain unclear. Therefore, this study aimed to identify the genetic susceptibility loci for ADHD in Korean children with ADHD. We performed a case-control and a family-based genome-wide association study (GWAS), as well as genome-wide quantitative trait locus (QTL) analyses, for two symptom traits. METHODS: A total of 135 subjects (71 cases and 64 controls), for the case-control analysis, and 54 subjects (27 probands and 27 unaffected siblings), for the family-based analysis, were included. RESULTS: The genome-wide QTL analysis identified four single nucleotide polymorphisms (SNPs) (rs7684645 near APELA, rs12538843 near YAE1D1 and POU6F2, rs11074258 near MCTP2, and rs34396552 near CIDEA) that were significantly associated with the number of inattention symptoms in ADHD. These SNPs showed possible association with ADHD in the family-based GWAS, and with hyperactivity-impulsivity in genome-wide QTL analyses. Moreover, association signals in the family-based QTL analysis for the number of inattention symptoms were clustered near genes IL10, IL19, SCL5A9, and SKINTL. CONCLUSION: We have identified four QTLs with genome-wide significance and several promising candidates that could potentially be associated with ADHD (CXCR4, UPF1, SETD5, NALCN-AS1, ERC1, SOX2-OT, FGFR2, ANO4, and TBL1XR1). Further replication studies with larger sample sizes are needed.
Adolescent* ; Case-Control Studies ; Child ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Interleukin-10 ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Sample Size

Although pork quality traits are important commercially, genome-wide association studies (GWASs) have not well considered Landrace and Yorkshire pigs worldwide. Landrace and Yorkshire pigs are important pork-providing breeds. Although quantitative trait loci of pigs are well-developed, significant genes in GWASs of pigs in Korea must be studied. Through a GWAS using the PLINK program, study of the significant genes in Korean pigs was performed. We conducted a GWAS and surveyed the gene ontology (GO) terms associated with the backfat thickness (BF) trait of these pigs. We included the breed information (Yorkshire and Landrace pigs) as a covariate. The significant genes after false discovery rate (<0.01) correction were AFG1L, SCAI, RIMS1, and SPDEF. The major GO terms for the top 5% of genes were related to neuronal genes, cell morphogenesis and actin cytoskeleton organization. The neuronal genes were previously reported as being associated with backfat thickness. However, the genes in our results were novel, and they included ZNF280D, BAIAP2, LRTM2, GABRA5, PCDH15, HERC1, DTNBP1, SLIT2, TRAPPC9, NGFR, APBB2, RBPJ, and ABL2. These novel genes might have roles in important cellular and physiological functions related to BF accumulation. The genes related to cell morphogenesis were NOX4, MKLN1, ZNF280D, BAIAP2, DNAAF1, LRTM2, PCDH15, NGFR, RBPJ, MYH9, APBB2, DTNBP1, TRIM62, and SLIT2. The genes that belonged to actin cytoskeleton organization were MKLN1, BAIAP2, PCDH15, BCAS3, MYH9, DTNBP1, ABL2, ADD2, and SLIT2.
Actin Cytoskeleton ; Gene Ontology ; Genome-Wide Association Study* ; Korea ; Morphogenesis ; Neurons ; Quantitative Trait Loci ; Red Meat ; Swine*

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.
Alternative Splicing ; Arabidopsis* ; Dataset ; Phenotype ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci*

The human pregnane X receptor (hPXR) plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards hPXR. Heuristic method (HM)-Best Subset Modeling (BSM) and HM-Polynomial Neural Networks (PNN) were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain (AD) of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved (for HM-BSM, r (2)=0.881, q LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =0.655). The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to hPXR.
Computer Simulation ; Humans ; Models, Statistical ; Molecular Weight ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship ; Receptors, Steroid ; agonists ; chemistry ; Small Molecule Libraries ; chemistry ; Static Electricity

The shape and structure of granules are controlled by the granulation process, which is one of the main factors to determine the nature of the solid dosage forms. In this article, three kinds of granules of a traditional Chinese medicine for improving appetite and promoting digestion, namely, Jianwei Granules, were prepared using granulation technologies as pendular granulation, high speed stirring granulation, and fluidized bed granulation and the powder properties of them were investigated. Meanwhile, synchrotron radiation X-ray computed micro tomography (SR-µCT) was applied to quantitatively determine the irregular internal structures of the granules. The three-dimensional (3D) structure models were obtained by 3D reconstruction, which were more accurately to characterize the three-dimensional structures of the particles through the quantitative data. The models were also used to quantitatively compare the structural differences of granules prepared by different granulation processes with the same formula, so as to characterize how the production process plays a role in the pharmaceutical behaviors of the granules. To focus on the irregularity of the particle structure, the box counting method was used to calculate the fractal dimensions of the granules. The results showed that the fractal dimension is more sensitive to reflect the minor differences in the structure features than the conventional parameters, and capable to specifically distinct granules in structure. It is proved that the fractal dimension could quantitatively characterize the structural information of irregular granules. It is the first time suggested by our research that the fractal dimension difference (Df,c) between two fractal dimension parameters, namely, the volume matrix fractal dimension and the surface matrix fractal dimension, is a new index to characterize granules with irregular structures and evaluate the effects of production processes on the structures of granules as a new indicator for the granulating process control and optimization.
Drugs, Chinese Herbal ; analysis ; Fractals ; Medicine, Chinese Traditional ; Powders ; Quantitative Structure-Activity Relationship ; Synchrotrons ; Technology, Pharmaceutical ; Tomography, X-Ray Computed

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with complex diseases or traits. However, the exact biological functions of these loci are largely unknown. Recent functional annotation indicates that the majority of disease/trait associated loci are concentrated in regulatory DNA of human genome. Expression quantitative trait loci (eQTL) analyses, chromosome conformation capture related methods and genome editing methods (such as CRISPR/Cas9) may facilitate the functional study of these loci. Research on noncoding RNAs and rare variants may improve the functional understanding. These efforts may promise translation of GWAS findings to clinical practices.
Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Quantitative Trait Loci

OBJECTIVES: For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. METHODS: There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. RESULTS: We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. CONCLUSIONS: We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.
Humans ; Quantitative Structure-Activity Relationship* ; Reproducibility of Results ; Social Control, Formal