Disagreement between symptom-reflux association analysis parameters in pediatric gastroesophageal reflux disease investigation

AIM: To assess the agreement within 3 commonly used symptom-reflux association analysis (SAA) parameters investigating gastroesophageal reflux disease (GERD) in infants. METHODS: Twenty three infants with suspected GERD were included in this study. Symptom index (SI), Symptom sensitivity index (SSI) and symptom association probability (SAP) related to cough and irritability were calculated after 24 h combined pH/multiple intraluminal impedance (MII) monitoring. Through defined cutoff values, SI, SSI and SAP values are differentiated in normal and abnormal, whereas abnormal values point towards gastroesophageal reflux (GER) as the origin of symptoms. We analyzed the correlation and the concordance of the diagnostic classification of these 3 SAA parameters.RESULTS: Evaluating the GER-irritability association, SI, SSI and SAP showed non-identical classification of normal and abnormal cases in 39.2% of the infants. When irritability was taken as a symptom, there was only a poor inter-parameter association between SI and SSI, and between SI and SAP (Kendall’s tau b = 0.37, P < 0.05; Kendall’s tau b = 0.36, P < 0.05, respectively). Evaluating the GER-cough association, SI, SSI and SAP showed non-identical classification of normal and abnormal cases in 52.2% of the patients. When cough was taken as a symptom, only SI and SSI showed a poor inter-parameter association (Kendall’s tau b = 0.33, P < 0.05). CONCLUSION: In infants investigated for suspected GERD with pH/MII-monitoring, SI, SSI and SAP showed a poor inter-parameter association and important dis-agreements in diagnostic classification. These limitations must be taken into consideration when interpreting the results of SAA in infants.

Causal association between 731 immunocyte phenotypes and liver cirrhosis: A bidirectional two-sample mendelian randomization analysis

BACKGROUND Liver cirrhosis is a progressive hepatic disease whose immunological basis has attracted increasing attention.However,it remains unclear whether a concrete causal association exists between immunocyte phenotypes and liver cirrhosis.AIM To explore the concrete causal relationships between immunocyte phenotypes and liver cirrhosis through a mendelian randomization(MR)study.METHODS Data on 731 immunocyte phenotypes were obtained from genome-wide assoc-iation studies.Liver cirrhosis data were derived from the Finn Gen dataset,which included 214403 individuals of European ancestry.We used inverse variable weighting as the primary analysis method to assess the causal relationship.Sensitivity analyses were conducted to evaluate heterogeneity and horizontal pleiotropy.RESULTS The MR analysis demonstrated that 11 immune cell phenotypes have a positive association with liver cirrhosis[P<0.05,odds ratio(OR)>1]and that 9 immu-nocyte phenotypes were negatively correlated with liver cirrhosis(P<0.05,OR<1).Liver cirrhosis was positively linked to 9 immune cell phenotypes(P<0.05,OR>1)and negatively linked to 10 immune cell phenotypes(P<0.05;OR<1).None of these associations showed heterogeneity or horizontally pleiotropy(P>0.05).CONCLUSION This bidirectional two-sample MR study demonstrated a concrete causal association between immunocyte phenotypes and liver cirrhosis.These findings offer new directions for the treatment of liver cirrhosis.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Population genomic analysis reveals key genetic variations and the driving force for embryonic callus induction capability in maize

Genetic transformation has been an effective technology for improving the agronomic traits of maize.However,it is highly reliant on the use of embryonic callus(EC)and shows a serious genotype dependence.In this study,we performed genomic sequencing for 80 core maize germplasms and constructed a high-density genomic variation map using our newly developed pipeline(MQ2Gpipe).Based on the induction rate of EC(REC),these inbred lines were categorized into three subpopulations.The low-REC germplasms displayed more abundant genetic diversity than the high-REC germplasms.By integrating a genome-wide selective signature screen and region-based association analysis,we revealed 95.23 Mb of selective regions and 43 REC-associated variants.These variants had phenotypic variance explained values ranging between 21.46 and 49.46%.In total,103 candidate genes were identified within the linkage disequilibrium regions of these REC-associated loci.These genes mainly participate in regulation of the cell cycle,regulation of cytokinesis,and other functions,among which MYB15 and EMB2745 were located within the previously reported QTL for EC induction.Numerous leaf area-associated variants with large effects were closely linked to several REC-related loci,implying a potential synergistic selection of REC and leaf size during modern maize breeding.

Genetic analysis and candidate gene identification of salt tolerancerelated traits in maize

Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maize is of great importance.In the current study,a teosinte-maize BC2F7 population was used to investigate the genetic basis of 21 salt tolerance-related traits.In total,125 QTLs were detected using a high-density genetic bin map,with one to five QTLs explaining 6.05–32.02%of the phenotypic variation for each trait.The total phenotypic variation explained(PVE)by all detected QTLs ranged from 6.84 to 63.88%for each trait.Of all 125 QTLs,only three were major QTLs distributed in two genomic regions on chromosome 6,which were involved in three salt tolerance-related traits.In addition,10 pairs of epistatic QTLs with additive effects were detected for eight traits,explaining 0.9 to 4.44%of the phenotypic variation.Furthermore,18 QTL hotspots affecting 3–7 traits were identified.In one hotspot(L5),a gene cluster consisting of four genes(ZmNSA1,SAG6,ZmCLCg,and ZmHKT1;2)was found,suggesting the involvement of multiple pleiotropic genes.Finally,two important candidate genes,Zm00001d002090 and Zm00001d002391,were found to be associated with salt tolerance-related traits by a combination of linkage and marker-trait association analyses.Zm00001d002090 encodes a calcium-dependent lipid-binding(CaLB domain)family protein,which may function as a Ca^(2+)sensor for transmitting the salt stress signal downstream,while Zm00001d002391 encodes a ubiquitin-specific protease belonging to the C19-related subfamily.Our findings provide valuable insights into the genetic basis of salt tolerance-related traits in maize and a theoretical foundation for breeders to develop enhanced salt-tolerant maize varieties.

Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality.Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait.Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection.We performed a genome-wide association study(GWAS)and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpe?o and non-Tuxpe?o heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms(SNPs).Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses.More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines.Incorporating genotype-by-environment(G×E)interaction increased genomic prediction accuracy.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

Integrated genomic and transcriptomic analysis reveals genes associated with plant height of foxtail millet

Foxtail millet(Setaria italica)is an important C4 model crop;however,due to its high-density planting and high stature,lodging at the filling stage resulted in a serious reduction in yield and quality.Therefore,it is imperative to identify and deploy the genes controlling foxtail millet plant height.In this study,we used a semi-dwarf line 263A and an elite high-stalk breeding variety,Chuang 29 to construct an F2 population to identify dwarf genes.We performed transcriptome analysis(RNA-seq)using internode tissues sampled at three jointing stages of 263A and Chuang 29,as well as bulk segregant analysis(BSA)on their F2 population.A total of 8918 differentially expressed genes(DEGs)were obtained from RNA-seq analysis,and GO analysis showed that DEGs were enriched in functions such as‘‘gibberellin metabolic process”and‘‘oxidoreductase activity”,which have previously been shown to be associated with plant height.A total 593 mutated genes were screened by BSA-seq method.One hundred and seventy-six out of the 593 mutated genes showed differential expression levels between the two parental lines,and seven genes not only showed differential expression in two or three internode tissues but also showed high genomic variation in coding regions,which indicated they play a crucial role in plant height determination.Among them,we found a gibberellin biosynthesis related GA20 oxidase gene(Seita.5G404900),which had a single-base at the third exon,leading to the frameshift mutation at 263A.Cleaved amplified polymorphic sequence assay and association analysis proved the single-base in Seita.5G404900 co-segregated with dwarf phenotype in two independent F2 populations planted in entirely different environments.Taken together,the candidate genes identified in this study will help to elucidate the genetic basis of foxtail millet plant height,and the molecular marker will be useful for marker-assisted dwarf breeding.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

The association analysis polymorphism of CDKAL1 and diabetic retinopathy in Chinese Han population

AIM： To identify the contribution of CDKAL1 to the development of diabetic retinopathy（DR） in Chinese population.·METHODS： A case-control study was performed to investigate the genetic association between DR and polymorphic variants of CDKAL1 in Chinese Han population with type 2 diabetes mellitus（T2DM）. A welldefined population with T2 DM, consisting of 475 controls and 105 DR patients, was recruited. All subjects were genotyped for the genetic variant（rs10946398） of CDKAL1. Genotyping was performed by i PLEX technology. The association between rs10946398 and T2 DM was assessed by univariate and multivariate logistic regression（MLR） analysis.· RESULTS： There were significant differences in C allele frequencies of rs10946398（CDKAL1） between control and DR groups（45.06% versus 55.00%, P 〈0.05）.The rs10946398 of CDKAL1 was found to be associated with the increased risk of DR among patients with diabetes.·CONCLUSION： Our findings suggest that rs10946398 of CDKAL1 is independently associated with DR in a Chinese Han population.

Molecular Diversity and Association Analysis of Drought and Salt Tolerance in Gossypium hirsutum L. Germplasm

Association mapping is a useful tool for the detection of genes selected during plant domestication based on their linkage disequilibrium（LD）. This study was carried out to estimate genetic diversity, population structure and the extent of LD to develop an association framework in order to identify genetic variations associated with drought and salt tolerance traits. 106 microsatellite marker primer pairs were used in 323 Gossypium hirsutum germplasms which were grown in the drought shed and salt pond for evaluation. Polymorphism（PIC=0.53） was found, and three groups were detected（K=3） with the second likelihood ΔK using STRUCTURE software. LD decay rates were estimated to be 13-15 cM at r2 0.20. Significant associations between polymorphic markers and drought and salt tolerance traits were observed using the general linear model（GLM） and mixed linear model（MLM）（P 0.01）. The results also demonstrated that association mapping within the population structure as well as stratification existing in cotton germplasm resources could complement and enhance quantitative trait loci（QTLs） information for marker-assisted selection.

An approach to incorporate linkage disequilibrium structure into genomic association analysis

In this study, we propose to use the principal component analysis （PCA） and regression model to incorporate linkage disequilibrium （LD） in genomic association data analysis. To accommodate LD in genomic data and reduce multiple testing, we suggest performing PCA and extracting the PCA score to capture the variation of genomic data, after which regression analysis is used to assess the association of the disease with the principal component score. An empirical analysis result shows that both genotype-based correlation matrix and haplotype-based LD matrix can produce similar results for PCA. Principal component score seems to be more powerful in detecting genetic association because the principal component score is quantitatively measured and may be able to capture the effect of multiple loci.

Association analysis of grain traits with SSR markers between Aegilops tauschii and hexaploid wheat(Triticum aestivum L.)

Seven important grain traits, including grain length（GL）, grain width（GW）, grain perimeter（GP）, grain area（GA）, grain length/width ratio（GLW）, roundness（GR）, and thousand-grain weight（TGW）, were analyzed using a set of 139 simple sequence repeat（SSR） markers in 130 hexaploid wheat varieties and 193 Aegilops tauschii accessions worldwide. In total, 1 612 alleles in Ae. tauschii and 1 360 alleles in hexaploid wheat（Triticum aestivum L.） were detected throughout the D genome. 197 marker-trait associations in Ae. tauschii were identified with 58 different SSR loci in 3 environments, and the average phenotypic variation value（R2） ranged from 0.68 to 15.12%. In contrast, 208 marker-trait associations were identified in wheat with 66 different SSR markers in 4 environments and the average phenotypic R2 ranged from 0.90 to 19.92%. Further analysis indicated that there are 6 common SSR loci present in both Ae. tauschii and hexaploid wheat, which are significantly associated with the 5 investigated grain traits（i.e., GA, GP, GR, GL, and TGW） and in total, 16 alleles derived from the 6 aforementioned SSR loci were shared by Ae. tauschii and hexaploid wheat. These preliminary data suggest the existence of common alleles may explain the evolutionary process and the selection between Ae. tauschii and hexaploid wheat. Furthermore, the genetic differentiation of grain shape and thousand-grain weight were observed in the evolutionary developmental process from Ae. tauschii to hexaploid wheat.

Single-nucleotide polymorphisms,mapping and association analysis of 1-FFT-A1 gene in wheat

Fructans are major nonstructural carbohydrates in wheat （Triticum aestivum L.）. Fructan 1-fructosyltransferase （1-FFT） is the key enzyme in fructan biosynthesis. In the present study, 96 sequence variants were detected in the 1-FFT-A 1 gene among 26 wheat accessions including UR208, and 15 of them result in amino acid substitutions, forming four haplotypes. Two markers M39 and M2164 were developed based on the InDe121-39 and SNP-2164 polymorphisms to distinguish the three haplotypes in the 1-FFT-AI. 1-FFT-A1 was located on chromosome 4A using marker M2164 and was flanked by markers Xcwm27 and 6-SFT-A 1. By association analysis using a natural wheat population consisted of 154 accessions, the results showed that the two markers were significantly associated with water-soluble carbohydrate （WSC） content in the lower internode stem and total stem at the early and middle grain filling stages, 1 000-grain weight （TGW） at different grain filling stages and peduncle length （PLE）. Comparison of the effects of three haplotypes on agronomic traits indicated that TGW, PLE and total number of spikelets per spike （TNSS）were significantly influenced by haplotypes. Haplll showed a significant positive effect on TGW, PLE and TNSS.

A Developed Algorithm of Apriori Based on Association Analysis

A method for mining frequent itemsets by evaluating their probability of supports based on association analysis is presented. This paper obtains the probability of every 1\|itemset by scanning the database, then evaluates the probability of every 2\|itemset, every 3\|itemset, every k \|itemset from the frequent 1\|itemsets and gains all the candidate frequent itemsets. This paper also scans the database for verifying the support of the candidate frequent itemsets. Last, the frequent itemsets are mined. The method reduces a lot of time of scanning database and shortens the computation time of the algorithm.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Genetic dissection of the grain-filling rate and related traits through linkage analysis and genome-wide association study in bread wheat

Wheat grain yield is generally sink-limited during grain filling.The grain-filling rate(GFR)plays a vital role but is poorly studied due to the difficulty of phenotype surveys.This study explored the grain-filling traits in a recombinant inbred population and wheat collection using two highly saturated genetic maps for linkage analysis and genome-wide association study(GWAS).Seventeen stable additive quantitative trait loci(QTLs)were identified on chromosomes 1B,4B,and 5A.The linkage interval between IWB19555 and IWB56078 showed pleiotropic effects on GFR_(1),GFR_(max),kernel length(KL),kernel width(KW),kernel thickness(KT),and thousand kernel weight(TKW),with the phenotypic variation explained(PVE)ranging from 13.38%(KW)to 33.69%(TKW).198 significant marker-trait associations(MTAs)were distributed across most chromosomes except for 3D and 4D.The major associated sites for GFR included IWB44469(11.27%),IWB8156(12.56%)and IWB24812(14.46%).Linkage analysis suggested that IWB35850,identified through GWAS,was located in approximately the same region as QGFR_(max)2B.3-11,where two high-confidence candidate genes were present.Two important grain weight(GW)-related QTLs colocalized with grain-filling QTLs.The findings contribute to understanding the genetic architecture of the GFR and provide a basic approach to predict candidate genes for grain yield trait QTLs.

Regression Analysis of the Number of Association Rules

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

Polymorphism and association analysis of a drought-resistant gene TaLTP-s in wheat

Lipid transfer protein （LTP） is a kind of small molecular protein, which is named for its ability to transfer lipid between cell membranes. It has been proved that the protein is involved in the responding to abiotic stresses. In this study, TaLTP-s, a genomic sequence of TaLTP was isolated from A genome of wheat （Triticum aestivum L）. Sequencing analysis exhibited that there was no diversity in the coding region of TaLTP-s, but seven single nucleotide polymorphisms （SNPs） and 1 bp insertion/deletion （InOel） were detected in the promoter regions of different wheat accessions. Nucleotide diversity （T1） in the region was 0.00033, and linkage disequilibrium （LD） extended over almost the entire TaLTP-s region in wheat. The dCAPS markers based on sequence variations in the promoter regions （SNP-207 and SNP-1696） were developed, and three haplotypes were identified based on those markers. Association analysis between the haplotypes and agronomic traits of natural population consisted of 262 accessions showed that three haplotypes of TaLTP-s were significantly associated with plant height （PH）. Among the three haplotypes, Haplll is considered as the superior haplotype for increasing plant height in the drought stress environments. The G variance at the position of 207 bp could be a superior allele that significantly increased number of spikes per plant （NSP）. The functional marker of TaLTP-s provide a tool for marker-assisted selection regarding to plant height and number of spikelet per plant in wheat.

Pathway-based analysis of genome-wide association study of circadian phenotypes

Sleepiness affects normal social life, which attracts more and more attention. Circadian phenotypes contribute to obvious individual differences in susceptibility to sleepiness. We aimed to identify candidate single nucleotide polymorphisms（SNPs） which may cause circadian phenotypes, elucidate the potential mechanisms, and generate corresponding SNP-gene-pathways. A genome-wide association studies（GWAS） dataset of circadian phenotypes was utilized in the study. Then, the Identify Candidate Causal SNPs and Pathways analysis was employed to the GWAS dataset after quality control filters. Furthermore, genotype-phenotype association analysis was performed with HapMap database. Four SNPs in three different genes were determined to correlate with usual weekday bedtime,totally providing seven hypothetical mechanisms. Eleven SNPs in six genes were identified to correlate with usual weekday sleep duration, which provided six hypothetical pathways. Our results demonstrated that fifteen candidate SNPs in eight genes played vital roles in six hypothetical pathways implicated in usual weekday bedtime and six potential pathways involved in usual weekday sleep duration.