Eggplant is an important horticultural crop and one of the most widely grown vegetables in the Solanaceae family. Eggplant fruit-related agronomic traits are complex quantitative traits with low efficiency and long cycle time for traditional breeding selection. With the rapid development of high-throughput sequencing technology and bioinformatics tools, genome-wide association study (GWAS) has shown great application potential in analyzing the genetic rules of complex agronomic traits related to eggplant fruits. This paper first reviews the progress of genome-wide association analysis in eggplant fruit shape, fruit color and other fruit-related agronomic traits. Subsequently, aiming at the problem of missing heritability, which is common in the genetic studies of eggplant quantitative traits, this paper puts forward the development strategies of eggplant GWAS in the future based on the hot spots of application of four GWAS strategies in the research of agronomics traits related to eggplant fruits. Lastly, the application of GWAS strategy in the field of eggplant molecular breeding is expected to provide a theoretical basis and reference for the future use of GWAS to analyze the genetic basis of various eggplant fruit-related traits and to select fruit materials that meet consumer needs.
Solanum melongena/genetics* ; Fruit/genetics* ; Genome-Wide Association Study ; Plant Breeding ; Agriculture ; Vegetables

BACKGROUND: Observational research has reported that systemic lupus erythematosus (SLE) is related to common female hormone-dependent cancers, but the underlying causal effect remains undefined. This study aimed to explore the causal association of these conditions by Mendelian randomization (MR) analysis. METHODS: We selected instrumental variables for SLE from genome-wide association studies (GWASs) conducted in European and East Asian populations. The genetic variants for female malignant neoplasms were obtained from corresponding ancestry GWASs. We utilized inverse variance weighted (IVW) as the primary analysis, followed by sensitivity analysis. Furthermore, we conducted multivariable MR (MVMR) to estimate direct effects by adjusting for the body mass index and estradiol. Finally, we implemented reverse direction MR analysis and gave a negative example to test the reliability of MR results. RESULTS: We found SLE was significantly negatively associated with overall endometrial cancer risk (odds ratio [OR] = 0.961, 95% confidence interval [CI] = 0.935-0.987, P  = 3.57E-03) and moderately inversely related to endometrioid endometrial cancer (ENEC) (OR = 0.965, 95% CI = 0.936-0.995, P  = 0.024) risk in the European population by IVW. We replicated these results using other MR models and detected a direct effect by MVMR (overall endometrial cancer, OR = 0.962, 95% CI = 0.941-0.983, P  = 5.11E-04; ENEC, OR = 0.964, 95% CI = 0.940-0.989, P  = 0.005). Moreover, we revealed that SLE was correlated with decreased breast cancer risk (OR = 0.951, 95% CI = 0.918-0.986, P  = 0.006) in the East Asian population by IVW, and the effect was still significant in MVMR (OR = 0.934, 95% CI = 0.859-0.976, P  = 0.002). The statistical powers of positive MR results were all >0.9. CONCLUSION This finding suggests a possible causal effect of SLE on the risk of overall endometrial cancer and breast cancer in European and East Asian populations, respectively, by MR analysis, which compensates for inherent limitations of observational research.
Female ; Humans ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Neoplasms, Hormone-Dependent ; Reproducibility of Results ; Endometrial Neoplasms ; Lupus Erythematosus, Systemic/genetics* ; Carcinoma, Endometrioid ; Breast Neoplasms ; Polymorphism, Single Nucleotide

BACKGROUND: Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population. METHODS: Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately. RESULTS: In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model. CONCLUSIONS In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.
Male ; Humans ; Female ; Coronary Artery Disease/genetics* ; Biological Specimen Banks ; East Asian People ; Risk Assessment/methods* ; Genetic Predisposition to Disease/genetics* ; Risk Factors ; Genome-Wide Association Study

Kawasaki disease (KD) is a systemic inflammatory vascular disorder that predominantly affects children and is the leading cause of acquired heart disease in children. Although the etiology of this disease remains unclear, genome-wide association and genome-wide linkage studies have shown that some susceptible genes and chromosomal regions are associated with the development and progression of KD. With the advancement of high-throughput DNA sequencing techniques, more and more genomic information related to KD is being discovered. Understanding the genes involved in the pathogenesis of KD may provide novel insights into the diagnosis and treatment of KD. By analyzing related articles and summarizing related research advances, this article mainly discusses the T cell activation-enhancing genes that have been confirmed to be closely associated with the development and progression of KD and reveals their association with the pathogenesis of KD and coronary artery lesions.
Child ; Humans ; Mucocutaneous Lymph Node Syndrome/complications* ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Polymorphism, Genetic ; Coronary Vessels/pathology* ; Polymorphism, Single Nucleotide

OBJECTIVES: Intracerebral hemorrhage (ICH) has the highest mortality and disability rates among various subtypes of stroke. Previous studies have shown that the gut microbiome (GM) is closely related to the risk factors and pathological basis of ICH. This study aims to explore the causal effect of GM on ICH and the potential mechanisms. METHODS: Genome wide association study (GWAS) data on GM and ICH were obtained from Microbiome Genome and International Stroke Genetics Consortium. Based on the GWAS data, we first performed Mendelian randomization (MR) analysis to evaluate the causal association between GM and ICH. Then, a conditional false discovery rate (cFDR) method was conducted to identify the pleiotropic variants. RESULTS: MR analysis showed that Pasteurellales, Pasteurellaceae, and Haemophilus were negatively correlated with the risk of ICH, whileVerrucomicrobiae, Verrucomicrobiales, Verrucomicrobiaceae, Akkermansia, Holdemanella, and LachnospiraceaeUCG010 were positively correlated with ICH. By applying the cFDR method, 3 pleiotropic loci (rs331083, rs4315115, and rs12553325) were found to be associated with both GM and ICH. CONCLUSIONS There is a causal association and pleiotropic variants between GM and ICH.
Humans ; Genome-Wide Association Study ; Gastrointestinal Microbiome/genetics* ; Genetic Predisposition to Disease ; Cerebral Hemorrhage/genetics* ; Stroke

OBJECTIVE: To investigate the causal relationship between neutrophil extracellular trap (NET) and sepsis based on Mendelian randomization analysis. METHODS: The genome wide association study (GWAS) dataset for the NET biomarker myeloperoxidase (MPO)-DNA complex based on Donkel et al. 's Rotterdam study (RS) and GWAS dataset for identifying sepsis from the UK biobank were selected to screen single nucleotide polymorphisms (SNPS) associated with MPO-DNA complex as instrumental variable (IV) for genetic variation, using MPO-DNA complex as exposure factor. Potential causal associations between MPO-DNA complex and the risk of occurrence of sepsis, 28-day death from sepsis, need for intensive care due to sepsis, and 28-day death from sepsis requiring intensive care were analyzed using a two-sample, one-way Mendelian randomization analysis primary analysis method of inverse analysis of variance (IVW). Potential pleiotropy was assessed using the MR Egger regression intercept test. Sensitivity analysis was performed using the "leave one out" test. RESULTS: The GWAS data were obtained from a European population of both sexes, and the screening criteria was based on the three main assumptions of Mendelian randomization, resulting in 22 SNP entering the Mendelian randomization analysis. The results of the Mendelian randomization causal association effect analysis using the IVW method showed that for every standard deviation increase in the level of the MPO-DNA complex, the risk of sepsis increased by approximately 18% [odds ratio (OR) = 1.18, 95% confidence interval (95%CI) was 1.07-1.29, P < 0.001], the risk of 28-day death from sepsis increased by approximately 51% (OR = 1.51, 95%CI was 1.27-1.81, P < 0.001), an increase of approximately 38% in the risk of occurrence of needing intensive care due to sepsis (OR = 1.38, 95%CI was 1.12-1.70, P = 0.002), and an increase of approximately 125% in the risk of 28-day death from sepsis requiring intensive care (OR = 2.25, 95%CI was 1.21-4.18, P = 0.01). MR Egger regression intercept test suggested that there was no horizontal pleiotropy in the included SNP, and the MR-PRESSO test did not find outliers. Sensitivity analysis suggested that the results of Mendelian randomization were robust. CONCLUSIONS Rising NET can increase the risk of sepsis onset, progression and death as derived from Mendelian randomization analysis.
Female ; Male ; Humans ; Extracellular Traps ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Sepsis/genetics* ; Nonoxynol ; DNA

Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.
Artificial Intelligence ; Data Science ; Genome-Wide Association Study ; Genomics ; Technology

Non-syndromic oral cleft (NSOC), a common birth defect, remains to be a critical public health problem in China. In the context of adjustment of childbearing policy for two times in China and the increase of pregnancy at older childbearing age, NSOC risk prediction will provide evidence for high-risk population identification and prenatal counseling. Genome-wide association study and second generation sequencing have identified multiple loci associated with NSOC, facilitating the development of genetic risk prediction of NSOC. Despite the marked progress, risk prediction models of NSOC still faces multiple challenges. This paper summarizes the recent progress in research of NSOC risk prediction models based on the results of extensive literature retrieval to provide some insights for the model development regarding research design, variable selection, model-build strategy and evaluation methods.
Humans ; Cleft Palate/genetics* ; Cleft Lip/genetics* ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Risk Factors ; Polymorphism, Single Nucleotide

Mendelian Randomization Analysis ; Bone Density ; Genome-Wide Association Study ; Minerals ; Life Expectancy ; Polymorphism, Single Nucleotide

The "Lübeck disaster", twins studies, adoptees studies, and other epidemiological observational studies have shown that host genetic factors play a significant role in determining the host susceptibility to Mycobacterium tuberculosis infection and pathogenesis of tuberculosis. From linkage analyses to genome-wide association studies, it has been discovered that human leucocyte antigen (HLA) genes as well as non-HLA genes (such as SLC11A1, VDR, ASAP1 as well as genes encoding cytokines and pattern recognition receptors) are associated with tuberculosis susceptibility. To provide ideas for subsequent studies about risk prediction of MTB infection and the diagnosis and treatment of tuberculosis, we review the research progress on tuberculosis susceptibility related genes in recent years, focusing on the correlation of HLA genes and non-HLA genes with the pathogenesis of tuberculosis. We also report the results of an enrichment analysis of the genes mentioned in the article. Most of these genes appear to be involved in the regulation of immune system and inflammation， and are also closely related to autoimmune diseases.
Humans ; Genome-Wide Association Study ; Tuberculosis/genetics* ; Gene Expression Regulation ; Cytokines/genetics* ; Autoimmune Diseases ; Mycobacterium tuberculosis/genetics* ; Genetic Predisposition to Disease