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In this study, rapid evaporative ionization mass spectrometry(REIMS) fingerprints of 388 samples of roots of Pulsatilla chinensis(PC) and its common counterfeits, roots of P. cernua and roots of Anemone tomentosa were analyzed based on REIMS combined with machine learning. The samples were determined by REIMS through dry burning, and the REIMS data underwent cluster analysis, similarity analysis(SA), and principal component analysis(PCA). After dimensionality reduction by PCA, the data were analyzed by similarity analysis and self-organizating map(SOM), followed by modeling. The results indicated that the REIMS fingerprints of the samples showed the characteristics of variety differences and the SOM model could accurately distinguish PC, P. cernua, and A. tomentosa. REIMS combined with machine learning algorithm has a broad application prospect in the field of traditional Chinese medicine.
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Medicine, Chinese Traditional , Algorithms , Anemone , Machine LearningABSTRACT
The aging society has led to a substantial increase in the number of clinical comorbidities. To meet the needs of comorbidity treatment, polypharmacy is widely used in clinical practice. However, polypharmacy has drawbacks such as treatment conflict. Same treatment of different diseases refers to treating different diseases with same treatment. Therefore, the principle of same treatment of different diseases can alleviate the problems caused by polypharmacy. Under the research background of precision medicine, it becomes possible to explore the mechanism of same treatment of different diseases and achieve its clinical application. However, drugs successfully developed in the past have revealed shortcomings in clinical use. To better interpret the mechanism of precision medicine for same treatment of different diseases, under the multi-dimensional attributes including dynamic space and time, omics was performed, and a new strategy of tensor decomposition was proposed. With the characteristics of complete data, tensor decomposition is advantageous in data mining and can fully grasp the connotation of precision treatment of different diseases with same treatment under dynamic spatiotemporal changes. This method is used for drug repositioning in some biocomputations. By taking advantage of the dimensionality reduction of tensor decomposition and integrating the dual influences of time and space, this study achieved accurate target prediction of same treatment of different diseases at each stage, and discovered the mechanism of precision medicine of same treatment for different diseases, providing scientific support for precision prescription and treatment of different diseases with same treatment in clinical practice. This study thus conducted preliminary exploration of the pharmacological mechanism of precision Chinese medicine treatment.
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Humans , Data Mining , Medicine, East Asian Traditional , Precision MedicineABSTRACT
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.
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Biomechanical Phenomena , Brain-Computer Interfaces , Motor Cortex/physiology , Neurons/physiologyABSTRACT
Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based on Support Vector Machine (SVM) was constructed by using radiomics method. Firstly, the definition and processing flow of radiomics were introduced. The experimental samples were selected from 816 lung cancer patients on LIDC. Firstly, ROI was extracted by central pooling convolution neural network segmentation method. Then, Pyradiomics and FSelector feature selection models were used to extract features and reduce dimension. Finally, SVM was used to construct the classification and prediction model of lung tumors. The predictive accuracy of the model is 80.4% for the classification of benign and malignant pulmonary nodules larger than 5 mm, and the value of the area under the curve (AUC) is 0.792. This indicates that the SVM classifier model can accurately distinguish benign and malignant pulmonary nodules larger than 5 mm.
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Humans , Algorithms , Lung Neoplasms/diagnostic imaging , Neural Networks, Computer , Radiometry , Support Vector Machine , Tomography, X-Ray ComputedABSTRACT
Objective: To provide clinical criteria with regional basis of the reference values of carbohydrateantigen 19-9 (CA19-9) in healthy Chinese male adults. Methods: First, the reference values of CA19-9 in 9,382 healthy male adults distributed in 64 cities and counties in China were collected to construct the database. Then geographical factors received dimensionality reduction by comprehensive application of the principal component analysis, variation coefficient method, and correlation analysis; then the classification regression tree model was constructed. Third, the hotspot and spatial distribution of the reference values of CA19-9 in healthy male adults was drawn based on 2,322 observation data points. Results: The reference value of CA19-9 showed spatial variation, which was higher in the southern region than in the northern region, and higher in Jilin and Liaoning provinces than in the eastern central region. Hotspots were mostly distributed in the southern region, and cold spots in the northern region. Conclusion: There exist spatial variation of the reference values of CA19-9 at spatial level. Therefore, when it comes to clinical diagnosis, it is necessary to consider regional variation.
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Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP (c++ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.
Subject(s)
Genotype , Methods , Multifactor Dimensionality ReductionABSTRACT
Objective To verify the interaction between single nucleotide polymorphism of trypto-phan hydroxylase 2 (TPH2) (rs4570625,rs11178997,rs120074175) and negative life events and the asso-ciation with major depressive disorder (MDD) in a Chinese population.Methods Totally 300 cases of pa-tients with major depressive disorder and 300 healthy controls in northern China were enrolled and the ge-nomic DNA were extracted. PCR was used to detect the polymorphisms of rs4570625, rs11178997, rs120074175.Questionnaire survey was conducted on the case group and the control group.Chi-square test was used to compare the differences in the frequency distribution of alleles and genotype between two groups. The generalized multifactor dimensionality reduction ( GMDR) method was used to analyze the interaction between gene and environment.Binary logistic regression was used to verify the optimal model.Results After adjusting the factors of sex and age,the GMDR analysis showed rs4570625,rs11178997,rs120074175 and negative life events were the optimal model.In this model, the testing balanced accuracy was 0.7838 and cross-validation consistency value was 10/10.There was statistically significant effect on the risk of major de-pressive disorder ( P ＝ 0.001 ). Binary logistic regression analysis showed that individuals, who had rs11178997 A+ genotype (AA,AT),rs120074175 A+genotype (AA,AG) and negative life events,had sig-nificant OR values of 24.307(95%CI＝13.007-45.427) and 38.2502(95%CI＝1.148-69.181),showing a higher risk of depression.Conclusion The interaction between TPH2 gene (rs11178997,rs120074175) and negative life events plays an important role in depression.
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Objective To investigate gene-gene interactions of suicidal behavior with single-nucleotide-polymorphism (SNP) in MAOA ,GAD1 and 5-HTR2C by multifactor dimensionality reduction .Methods For this case-control study ,six SNPs were captured in related genes and detected in blood samples obtained from 21 patients with suicidal behavior and 50 healthy individuals .The genotype frequency and allele frequency as well as the Hardy-Weinberg equilibrium (HWE) ,tests were performed and compared by plink software .The gene-gene interactions models were built by the MDR software .Results The HWE test for case group showed that rs3813928 rs518147 of 5-HTR2C gene was not in line with HWE ( P< 0 .05) .However ,the additive model analysis after adjustment by gender indicated that the polymorphism had a positive correlation with suicidal behavior in case group .The case and control groups differed significantly only in genotype frequencies of 5-HTR2C gene (χ2 =6 .18 , P=0 .04) .There was no significant difference in allele and genotype frequencies of the other genes ( P>0 .05) .The best combination model of MDR was rs5953210-rs769391 OR=20 .19 ,95% CI 4 .19-97 .38 , P<0 .01 ,with significant interaction . Conclusion The 5-HTR2C gene rs3813928 and rs518147 polymorphisms may play an important role in the susceptibility to suicidal behavior .The combination of MAOA with GAD1 has a significant interaction which may increase the risk of suicidal behavior .
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Objective To investigate the association between ten single nucleotide polymorphisms (SNPs) in the peroxisome proliferator-activated receptors and pulse pressure (PP) as well as the relationships between gene-gene interaction between PPARα/δ/γ genes and PP.Methods A total of 820 subjects,with 550 females and 270 males,were recruited from a cohort study of “Prevention of Metabolic Syndrome and Multi-metabolic Disorders in Jiangsu Province of China Study (PMMJS)”.Ten SNPs of PPARα/δ/γ genes were selected.GMDR software (version 1.0.1) was used to evaluate the gene-gene interactions among PPARs SNPs associated with PP.Results The mean levels of PP in people with mutant genotype of rs1805192 in PPARγ genes (PA+AA) showed a significant increase by 1.341 mmHg (95％CI:0.431-2.252 mmHg) when compared to the persons with wild genotype (PP).In the subgroup of subjects with more than 30 mmHg levels of PP,a six-locus model comprised rs135539 of PPARα,rs2016520 of PPARδ,rs10865710,rs1805192,rs709158 and rs3856806 of PPARγshowed a highest level of prediction accuracy (0.577) and displayed a better cross-validation consistency (10/10).In the subgroup of subjects with less than 40 mmHg levels of PP,a two-locus model was statistically associated with PP with 0.628 of prediction accuracy and 10/10 of cross-validation consistency.Conclusion PPARγrs1805192 was associated with the occurrence of PP.Gene-gene interactions among rs135539 of PPARα,rs2016520 of PPARδ,rs10865710,rs1805192,rs709158 and rs3856806 of PPARγ were all significantly related to PP.
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Objective To investigate possible effect of 6 obesity-associated SNPs in contribution to central obesity and examine whether there is an interaction in the 6 SNPs in the cause of central obesity in school-aged children in China.Methods A total of 3502 school-aged children who were included in Beijing Child and Adolescent Metabolic Syndrome (BCAMS) Study were selected,and based on the age and sex specific waist circumference (WC) standards in the BCAMS study,1196 central obese cases and 2306 controls were identified.Genomic DNA was extracted from peripheral blood white cells using the salt fractionation method.A total of 6 single nucleotide polymorphisms (FTO rs9939609,MC4R rs17782313,BDNF rs6265,PCSK1 rs6235,SH2B1 rs4788102,and CSK rs1378942) were genotyped by TaqMan allelic discrimination assays with the GeneAmp 7900 sequence detection system (Applied Biosystems,Foster City,CA,USA).Logistic regression model was used to investigate the association between 6 SNPs and central obesity.Gene-gene interactions among 6 polymorphic loci were analyzed by using the Generalized Multifactor Dimensionality Reduction (GMDR) method,and then logistic regression model was constructed to confirm the best combination of loci identified in the GMDR.Results After adjusting gender,age,Tanner stage,physical activity and family history of obesity,the FTO rs9939609-A,MC4Rrs 17782313-C and BDNF rs6265-G alleles were associated with central obesity under additive genetic model (OR=1.24,95％CI:1.06-1.45,P=0.008;OR=1.26,95％CI:1.11-1.43,P=2.98 × 10-4;OR=1.18,95％ CI:1.06-1.32,P=0.003).GMDR analysis showed a significant gene-gene interaction between MC4R rs17782313 and BDNF rs6265 (P=0.001).The best two-locus combination showed the cross-validation consistency of 10/10 and testing accuracy of 0.539.This interaction showed the maximum consistency and minimum prediction error among all gene-gene interaction models evaluated.Moreover,the combination of MC4R rs17782313-C and BDNF rs6265-G was associated with an increased risk of central obesity after adjustment for gender,age,Tanner stage,physical activity and family history of obesity.Conclusions Our study showed that FTO rs9939609-A,MC4R rs17782313-C and BDNF rs6265-G alleles were associated with central obesity,and statistical interaction between MC4R rs17782313-C and BDNF rs6265-G increased risk of central obesity in school-aged children in China.
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Objective To investigate the interactions between cAMP-response element-binding protein 1 (CREB 1) gene polymorphisms (rs889895,rs3770704,rs2551645,rs4675690) and brain derived neurotrophic factor (BDNF) gene polymorphisms (rs7124442,rs 10835210) and the association with recurrent major depressive disorder.Methods The blood samples were taken from 768 recurrent major depressive disorder patients and 511 healthy controls.The DNA was isolated from blood samples and was detected by SNP Sequenom Mass Array analysis.Chi-square test was used to compare differences in the frequency distribution of alleles and genotype between depression and controls.The generalized multifactor dimensionality reduction (GMDR) method was used to analyze the gene-gene interaction.Binary logistic regression was used to verify the optimal model.Results After adjusting the factors of sex and age,the GMDR analysis showed rs10835210 was the optimal model.In this model,the testing balanced accuracy was 0.5319 and cross-validation consistency value was 10/10.And rs10835210 had a statistically significant effect on the risk of recurrent major depressive disorder(P=0.0107).There was no significant gene-gene interaction of five tag SNPs on recurrent major depressive disorder(P＞0.05).Binary logistic regression analysis showed the AC contributed to a significantly lower risk of recurrent major depressive disorder than did the CC (OR =0.772,95％ CI=0.608-0.980,P=0.033).It was failed to find the genetic polymorphism of CREB1 rs889895.Conclusion BDNF rs10835210 may be one of the biological markers of recurrent major depressive disorder.
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Objective To investigate the association between ten single nucleotide polymorphisms (SNPs) in the peroxisome proliferator-activated receptors and pulse pressure (PP) as well as the relationships between gene-gene interaction between PPARα/δ/γ genes and PP.Methods A total of 820 subjects,with 550 females and 270 males,were recruited from a cohort study of “Prevention of Metabolic Syndrome and Multi-metabolic Disorders in Jiangsu Province of China Study (PMMJS)”.Ten SNPs of PPARα/δ/γ genes were selected.GMDR software (version 1.0.1) was used to evaluate the gene-gene interactions among PPARs SNPs associated with PP.Results The mean levels of PP in people with mutant genotype of rs1805192 in PPARγ genes (PA+AA) showed a significant increase by 1.341 mmHg (95％CI:0.431-2.252 mmHg) when compared to the persons with wild genotype (PP).In the subgroup of subjects with more than 30 mmHg levels of PP,a six-locus model comprised rs135539 of PPARα,rs2016520 of PPARδ,rs10865710,rs1805192,rs709158 and rs3856806 of PPARγshowed a highest level of prediction accuracy (0.577) and displayed a better cross-validation consistency (10/10).In the subgroup of subjects with less than 40 mmHg levels of PP,a two-locus model was statistically associated with PP with 0.628 of prediction accuracy and 10/10 of cross-validation consistency.Conclusion PPARγrs1805192 was associated with the occurrence of PP.Gene-gene interactions among rs135539 of PPARα,rs2016520 of PPARδ,rs10865710,rs1805192,rs709158 and rs3856806 of PPARγ were all significantly related to PP.
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Objective To investigate possible effect of 6 obesity-associated SNPs in contribution to central obesity and examine whether there is an interaction in the 6 SNPs in the cause of central obesity in school-aged children in China.Methods A total of 3502 school-aged children who were included in Beijing Child and Adolescent Metabolic Syndrome (BCAMS) Study were selected,and based on the age and sex specific waist circumference (WC) standards in the BCAMS study,1196 central obese cases and 2306 controls were identified.Genomic DNA was extracted from peripheral blood white cells using the salt fractionation method.A total of 6 single nucleotide polymorphisms (FTO rs9939609,MC4R rs17782313,BDNF rs6265,PCSK1 rs6235,SH2B1 rs4788102,and CSK rs1378942) were genotyped by TaqMan allelic discrimination assays with the GeneAmp 7900 sequence detection system (Applied Biosystems,Foster City,CA,USA).Logistic regression model was used to investigate the association between 6 SNPs and central obesity.Gene-gene interactions among 6 polymorphic loci were analyzed by using the Generalized Multifactor Dimensionality Reduction (GMDR) method,and then logistic regression model was constructed to confirm the best combination of loci identified in the GMDR.Results After adjusting gender,age,Tanner stage,physical activity and family history of obesity,the FTO rs9939609-A,MC4Rrs 17782313-C and BDNF rs6265-G alleles were associated with central obesity under additive genetic model (OR=1.24,95％CI:1.06-1.45,P=0.008;OR=1.26,95％CI:1.11-1.43,P=2.98 × 10-4;OR=1.18,95％ CI:1.06-1.32,P=0.003).GMDR analysis showed a significant gene-gene interaction between MC4R rs17782313 and BDNF rs6265 (P=0.001).The best two-locus combination showed the cross-validation consistency of 10/10 and testing accuracy of 0.539.This interaction showed the maximum consistency and minimum prediction error among all gene-gene interaction models evaluated.Moreover,the combination of MC4R rs17782313-C and BDNF rs6265-G was associated with an increased risk of central obesity after adjustment for gender,age,Tanner stage,physical activity and family history of obesity.Conclusions Our study showed that FTO rs9939609-A,MC4R rs17782313-C and BDNF rs6265-G alleles were associated with central obesity,and statistical interaction between MC4R rs17782313-C and BDNF rs6265-G increased risk of central obesity in school-aged children in China.
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RESUMO: Introdução: O cenário epidemiológico mundial revela um crescimento das doenças cardiovasculares, no qual se destaca o infarto agudo do miocárdio (IAM), devido à sua grande magnitude e severidade. No Brasil, doenças coronarianas representam já cerca de 5% dos gastos com internação hospitalar. Objetivo: Caracterizar as internações dos pacientes do Sistema Único de Saúde (SUS) por IAM por meio da identificação de agrupamentos sugeridos por uma análise de agrupamentos tradicional e por uma análise de correspondência múltipla (ACM). Métodos: Registros do Sistema de Internações Hospitalares (SIH/SUS) com diagnóstico principal de IAM, no Estado do Rio de Janeiro, 2002, foram selecionados e posteriormente relacionados aos do Sistema de Informações sobre Mortalidade (SIM/SUS). A seguir, uma ACM e uma métrica chamada distância de tolerância foram utilizadas para a identificação de clusters , sendo a variável de interesse "gastos com internação" classificada em duas categorias (acima e abaixo de R$ 905,00). Resultados: Foi possível associar "maiores gastos" com "utilização de Centro de Tratamento Intensivo (CTI)" e com "gravidade moderada do caso", e "menores gastos" com "gravidade leve" e "não utilização de CTI". Por outro lado, casos de alta gravidade apresentaram-se isolados, sem associação com CTI ou outras variáveis. Também foi detectada associação entre a categoria "menores gastos" e as categorias: "não deslocamento do paciente", "sexo feminino", "idade entre 56 e 75 anos", "óbito até 30 dias" e "óbito até 1 ano". Conclusão: o aspecto isolado dos casos de maior gravidade e a associação entre "óbitos" e "menores gastos" sugere que os recursos tecnológicos disponíveis durante a internação por IAM não estão sendo adequadamente empregados.
ABSTRACT: Introduction: The global epidemiologic scenario indicates an increase in cardiovascular disease rates, with special emphasis on acute myocardial infarction (AMI), owing to its large magnitude and severity. In Brazil, coronary diseases now account for about 5% of hospital admission expenditures. Objective: To characterize the admissions in the Brazilian Unified Health System of patients with AMI, by identifying clusters suggested by a traditional cluster analysis and by a multiple correspondence analysis (MCA). Methods: The records of the Hospital Information System/Brazilian Unified Health System with a primary diagnosis of AMI in the State of Rio de Janeiro, Brazil, 2002, were selected and subsequently related to the records of the Mortality Information System. Next, an MCA and a metric called the tolerance distance were used for cluster identification. The variable of interest was "hospital expenditures", classified into two categories (above and below BRL 905). Results: "Higher costs" were associated with "use of the Intensive Care Unit (ICU)" and "moderate severity of the case" and "lower costs" with "low severity" and "nonuse of the ICU". On the other hand, high severity cases, with no apparent association with "use of ICU" or other categories. Other associations identified were "lower costs" and "no displacement of the patient," "female," "age between 56 and 75 years," "death within 30 days," and "death within 1 year". Conclusions: The nonclustered characteristic of the most serious cases and the association between "deaths" and "lower costs" suggests that the technological resources available during hospitalization for AMI are not being properly used.
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Humans , Male , Female , Adolescent , Adult , Middle Aged , Aged , Young Adult , Delivery of Health Care/economics , Delivery of Health Care/statistics & numerical data , Health Care Costs , Myocardial Infarction/economics , Myocardial Infarction/therapy , Patient Admission/economics , Brazil , Cross-Sectional Studies , Hospital Mortality , Myocardial Infarction/mortalityABSTRACT
Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.
Subject(s)
Classification , Genome-Wide Association Study , Genotype , Methods , Multifactor Dimensionality Reduction , PhenotypeABSTRACT
A comparison was made for the correlation and application scope of the statistical methods commonly used by hospitals for their efficiency measurement.Hospital data processed with PCA (principal component analysis)for dimension reduction were used in a correlation analysis for the results of ratio analysis (RA),stochastic frontier analysis(SFA)and data envelopment analysis(DEA).The authors hold that the RA can expediently display the order of hospital efficiency,the SFA demands a stricter premise yet presents more stable results,while the DEA boasts greater relative advantages and thus suitable for processing hospital efficiency measurement tasks of multi-input and multi-output indexes.
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Objective To investigate the interrelations of ALOX5AP SG13S114A/T , COX-2 765G/C , COX-1-50C/T polymorphisms and cerebral infarction. Methods The ALOX5AP SG13S114A/T, COX-2 765G/C and COX-1 50C/T polymorphisms in 411 cases with cerebral infarction and 411 controls were measured by Polymerase Chain Reaction-Restriction Fragment Length Polymorphism method. The generalized multifactor dimensionality reduction (GMDR) method was employed to detect gene-gene interactions. Results Single-gene analysis showed that there were no significant differences in the genotype and allele frequency distributions of ALOX5AP SG13S114A/T, COX-2 765G/C and COX-1 50C/T between two groups. However, in those cases carrying ALOX5AP SG13S114AA as well as COX-2 765CC , the risk of cerebral infarction increased significantly by 2.842 times. Conclusions The combinational analysis among genes used in this study may be helpful in the elucidation of genetic risk factors for common and complex diseases.
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A principal contribuição da genética molecular é a utilização direta das informações de DNA no processo de identificação de indivíduos geneticamente superiores. Sob esse enfoque, idealizou-se a seleção genômica ampla (Genome Wide Selection - GWS), a qual consiste na análise de marcadores SNPs (Single Nucleotide Polymorphisms) amplamente distribuídos no genoma. Devido a esse grande número de SNPs, geralmente maior que o número de indivíduos genotipados, e à alta colinearidade entre eles, métodos de redução de dimensionalidade são requeridos. Dentre estes, destaca-se o método de regressão via Quadrados Mínimos Parciais (Partial Least Squares - PLS), que além de solucionar tais problemas, permite uma abordagem multivariada, considerando múltiplos fenótipos. Diante do exposto, objetivou-se aplicar e comparar a regressão PLS univariada (UPLS) e multivariada (MPLS) na GWS para características de carcaça em uma população F2 de suínos Piau×Comercial. Os resultados evidenciaram a superioridade do método MPLS, uma vez que este proporcionou maiores valores de acurácia em relação à abordagem univariada.
The main contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. Under this approach, genome-wide selection (GWS) can be used with this purpose. GWS consists in analyzing of a large number of SNP markers widely distributed in the genome, and due to the fact that the number of markers is much larger than the number of genotyped individuals and also to the fact that such markers are highly correlated special statistical methods, like Partial Least Squares (PLS), are widely required. Thus, the aim of this paper was to propose an application of Uni (UPLS) and Multivariate (MPLS) Partial Least Squares regression to GWS of carcass traits in an F2 (Piau × commercial) pig population. The results showed that MPLS method provided most accurate genomic breeding values estimates than univariate method.
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Objective To explore the impact of the gene-gene interaction among the single nucleotide polymorphisms (SNPs) of peroxisome proliferator-activated receptor α/δ/γ on essential hypertension (EH).Methods Participants were recruited based on the previous work of the PMMJS (Prevention of Multiple Metabolic Disorders and Metabolic Syndrome in Jiangsu Province) cohort study in Jiangsu province of China.A total number of 820 subjects were randomly selected from the cohort and received gene polymorphism detection covered ten SNPs:PPARα/δ/γ (PPARα:rs 135539,rs 1800206 and rs4253778 ; PPARδ:rs2016520 and rs9794; PPARγ:rs 10865710,rs 1805192,rs4684847,rs709158 and rs3856806).Generalized Multifactor Dimensionality Reduction (GMDR)model was used to evaluate the association between gene-gene interaction among the ten SNPs and EH.Results After adjusting factors as gender,age,BMI,FPG,TG,HDL-C,high fat diet,low fiber diet and physical activity,results from the GMDR analysis showed that the best qualitative trait models were 7/9-dimensional model (EH:cross-validation consistency were 9/10 and 10/10,prediction accuracy were 0.5862 and 0.5885),5/9-dimensional model (SBP:cross-validation consistency were 10/10 and 8/10,prediction accuracy were 0.6055 and 0.6011),and 8/9-dimensional model (DBP:cross-validation consistency both were 10/10,prediction accuracy were 0.5926 and 0.5972),while the best quantitative trait models were 4/5-dimensional model (SBP:cross-validation consistency were 10/10 and 8/10,prediction accuracy were 0.6111 and 0.6072),and 5-dimensional model (DBP:cross-validation consistency were 9/10,prediction accuracy were 0.5753).Conclusion Interactions among ten SNPs of PPARs seemed to have existed and with significant impact on the levels of blood pressure.
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Objective To investigate 4 variants single nucleotide polymorphisms (SNPs) of 5-lipoxygenase-activating protein(ALOX5AP) in lipoxygenase pathway and in cytochrome P450 pathway as susceptibility genes for stroke in a southeastern Chinese population,and evaluate the associations between susceptibility genes and cerebral infarction,to find whether gene-gene interactions increase the risk of cerebral infarction.Methods By case-control study,two hundred and ninety-two patients with cerebral infarction and 259 healthy control subjects were included.Eight variants in 5 candidate genes were examined for stroke risk,including the SG13S32 (rs9551963),SG13S42 (rs4769060),SG13S89 (rs4769874),and SG13Sl14 (rs10507391) variants of the ALOX5AP gene,the G860A (rs751141) variant of the soluble epoxide hydrolase (EPHX2) gene,the A1075C (rs1057910) variant of the CYP2C9 *2 gene,the C430T (rs1799853) variant of the CYP2C9* 3 gene,and the A6986G (rs776746) variant of the CYP3A5 gene.Gene-gene interactions were explored using generalized multifactor dimensionality reduction (GMDR)methods.Results There were no statistically significant differences in the frequencies of the genotypes of the 8 candidate genes.The GMDR analysis showed a significant gene-gene interaction between SG13S114 and A6986G,with scores of 10 for cross-validation consistency and 9 for the sign test (P =0.011).These genegene interactions predicted a significantly higher risk of cerebral infarction (adjusted for age,hypertension,and diabetes mellitus;OR =1.804,95％ CI 1.180-2.759,P =0.006).Conclusions A two-loci gene interaction confers significantly higher risk for cerebral infarction.The combinational analysis used in this study may be helpful in the elucidation of genetic risk factors for common and complex diseases.