N. Montazeri-Najafabady, M. Dabbaghmanesh, Rajeeh Mohammadian Amiri, Zahra Mirzai
Background: Bone mass acquisition in childhood is directly linked to adult bone mineral density (BMD) and fracture risk. BMD is a heritable trait, more than 70% of its variability among a population is affected by genetic factors. Objectives: In the present study, we wanted to investigate the association between estrogen receptor alpha (ESR1) polymorphisms, PvuII (rs2234693) and XbaI (rs9340799), and bone area, bone mineral content (BMC), and BMD of the lumbar spine, femoral neck, and also of the total body less the head in Iranian children. Methods: The ESR1 gene PvuII and XbaI genotypes were determined by polymerase chain reaction-restriction fragment length polymorphism. Bone area, BMC, BMD, and bone mineral apparent density (BMAD) were assessed by dual-energy X-ray absorptiometry (DEXA). Linear regression was carried out to examine the effects of the ESR1 (PvuII and XbaI) polymorphisms on DEXA outputs when adjusted for confounding factors (i.e., age, sex, BMI, and pubertal stage) in 3 models. Results: ESR1 (PvuII) gene polymorphisms (CT vs. CC) showed significant effects on the BMC of the total body less the head in all 3 models. For ESR1 (XbaI), individuals with the AG genotype had higher lumbar spine BMD and lumbar spine BMAD compared to other genotypes. Conclusions: It seems that the PvuII and XbaI polymorphisms of ESR1 could be associated with BMC and BMD variation in Iranian children and adolescents.
背景:儿童期骨量获取与成年期骨密度(BMD)和骨折风险直接相关。骨密度是一种可遗传的性状，其在人群中的变异有70%以上受遗传因素的影响。目的:在本研究中，我们希望研究伊朗儿童雌激素受体α (ESR1)多态性、PvuII (rs2234693)和XbaI (rs9340799)与腰椎、股骨颈以及全身除头部外的骨面积、骨矿物质含量(BMC)和骨密度之间的关系。方法:采用聚合酶链反应-限制性片段长度多态性检测ESR1基因PvuII和XbaI基因型。采用双能x线吸收仪(DEXA)评估骨面积、BMC、BMD和骨矿物质表观密度(BMAD)。在3个模型中，采用线性回归检验ESR1 (PvuII和XbaI)多态性在校正混杂因素(即年龄、性别、BMI和青春期)后对DEXA输出的影响。结果:ESR1 (PvuII)基因多态性(CT vs. CC)对3种模型的全身除头部BMC均有显著影响。对于ESR1 (XbaI)，与其他基因型相比，AG基因型个体具有更高的腰椎BMD和腰椎BMAD。结论:ESR1的PvuII和XbaI多态性可能与伊朗儿童和青少年的BMC和BMD变异有关。
{"title":"Influence of Estrogen Receptor Alpha Polymorphism on Bone Mineral Density in Iranian Children","authors":"N. Montazeri-Najafabady, M. Dabbaghmanesh, Rajeeh Mohammadian Amiri, Zahra Mirzai","doi":"10.1159/000502230","DOIUrl":"https://doi.org/10.1159/000502230","url":null,"abstract":"Background: Bone mass acquisition in childhood is directly linked to adult bone mineral density (BMD) and fracture risk. BMD is a heritable trait, more than 70% of its variability among a population is affected by genetic factors. Objectives: In the present study, we wanted to investigate the association between estrogen receptor alpha (ESR1) polymorphisms, PvuII (rs2234693) and XbaI (rs9340799), and bone area, bone mineral content (BMC), and BMD of the lumbar spine, femoral neck, and also of the total body less the head in Iranian children. Methods: The ESR1 gene PvuII and XbaI genotypes were determined by polymerase chain reaction-restriction fragment length polymorphism. Bone area, BMC, BMD, and bone mineral apparent density (BMAD) were assessed by dual-energy X-ray absorptiometry (DEXA). Linear regression was carried out to examine the effects of the ESR1 (PvuII and XbaI) polymorphisms on DEXA outputs when adjusted for confounding factors (i.e., age, sex, BMI, and pubertal stage) in 3 models. Results: ESR1 (PvuII) gene polymorphisms (CT vs. CC) showed significant effects on the BMC of the total body less the head in all 3 models. For ESR1 (XbaI), individuals with the AG genotype had higher lumbar spine BMD and lumbar spine BMAD compared to other genotypes. Conclusions: It seems that the PvuII and XbaI polymorphisms of ESR1 could be associated with BMC and BMD variation in Iranian children and adolescents.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"82 - 89"},"PeriodicalIF":1.8,"publicationDate":"2019-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000502230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47102739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Wiersinga, G. Kahaly, V. Blanchette, L. Brandão, V. Breakey, S. Revel-Vilk
{"title":"Front & Back Matter","authors":"W. Wiersinga, G. Kahaly, V. Blanchette, L. Brandão, V. Breakey, S. Revel-Vilk","doi":"10.1159/000503430","DOIUrl":"https://doi.org/10.1159/000503430","url":null,"abstract":"","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46315632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ming-Juan Wu, Ying-Lian Gao, Jin-Xing Liu, Rong Zhu, Juan Wang
Principal component analysis (PCA) is a widely used method for evaluating low-dimensional data. Some variants of PCA have been proposed to improve the interpretation of the principal components (PCs). One of the most common methods is sparse PCA which aims at finding a sparse basis to improve the interpretability over the dense basis of PCA. However, the performances of these improved methods are still far from satisfactory because the data still contain redundant PCs. In this paper, a novel method called PCA based on graph Laplacian and double sparse constraints (GDSPCA) is proposed to improve the interpretation of the PCs and consider the internal geometry of the data. In detail, GDSPCA utilizes L2,1-norm and L1-norm regularization terms simultaneously to enforce the matrix to be sparse by filtering redundant and irrelative PCs, where the L2,1-norm regularization term can produce row sparsity, while the L1-norm regularization term can enforce element sparsity. This way, we can make a better interpretation of the new PCs in low-dimensional subspace. Meanwhile, the method of GDSPCA integrates graph Laplacian into PCA to explore the geometric structure hidden in the data. A simple and effective optimization solution is provided. Extensive experiments on multi-view biological data demonstrate the feasibility and effectiveness of the proposed approach.
{"title":"Principal Component Analysis Based on Graph Laplacian and Double Sparse Constraints for Feature Selection and Sample Clustering on Multi-View Data","authors":"Ming-Juan Wu, Ying-Lian Gao, Jin-Xing Liu, Rong Zhu, Juan Wang","doi":"10.1159/000501653","DOIUrl":"https://doi.org/10.1159/000501653","url":null,"abstract":"Principal component analysis (PCA) is a widely used method for evaluating low-dimensional data. Some variants of PCA have been proposed to improve the interpretation of the principal components (PCs). One of the most common methods is sparse PCA which aims at finding a sparse basis to improve the interpretability over the dense basis of PCA. However, the performances of these improved methods are still far from satisfactory because the data still contain redundant PCs. In this paper, a novel method called PCA based on graph Laplacian and double sparse constraints (GDSPCA) is proposed to improve the interpretation of the PCs and consider the internal geometry of the data. In detail, GDSPCA utilizes L2,1-norm and L1-norm regularization terms simultaneously to enforce the matrix to be sparse by filtering redundant and irrelative PCs, where the L2,1-norm regularization term can produce row sparsity, while the L1-norm regularization term can enforce element sparsity. This way, we can make a better interpretation of the new PCs in low-dimensional subspace. Meanwhile, the method of GDSPCA integrates graph Laplacian into PCA to explore the geometric structure hidden in the data. A simple and effective optimization solution is provided. Extensive experiments on multi-view biological data demonstrate the feasibility and effectiveness of the proposed approach.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"47 - 58"},"PeriodicalIF":1.8,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43266794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiu-Xiu Xu, Lingyun Dai, Xiangzhen Kong, Jin-Xing Liu
Differentially expressed genes selection becomes a hotspot and difficulty in recent molecular biology. Low-rank representation (LRR) uniting graph Laplacian regularization has gained good achievement in the above field. However, the co-expression information of data cannot be captured well by graph regularization. Therefore, a novel low-rank representation method regularized by dual-hypergraph Laplacian is proposed to reveal the intrinsic geometrical structures hidden in the samples and genes direction simultaneously, which is called dual-hypergraph Laplacian regularized LRR (DHLRR). Finally, a low-rank matrix and a sparse perturbation matrix can be recovered from genomic data by DHLRR. Based on the sparsity of differentially expressed genes, the sparse disturbance matrix can be applied to extracting differentially expressed genes. In our experiments, two gene analysis tools are used to discuss the experimental results. The results on two real genomic data and an integrated dataset prove that DHLRR is efficient and effective in finding differentially expressed genes.
{"title":"A Low-Rank Representation Method Regularized by Dual-Hypergraph Laplacian for Selecting Differentially Expressed Genes","authors":"Xiu-Xiu Xu, Lingyun Dai, Xiangzhen Kong, Jin-Xing Liu","doi":"10.1159/000501482","DOIUrl":"https://doi.org/10.1159/000501482","url":null,"abstract":"Differentially expressed genes selection becomes a hotspot and difficulty in recent molecular biology. Low-rank representation (LRR) uniting graph Laplacian regularization has gained good achievement in the above field. However, the co-expression information of data cannot be captured well by graph regularization. Therefore, a novel low-rank representation method regularized by dual-hypergraph Laplacian is proposed to reveal the intrinsic geometrical structures hidden in the samples and genes direction simultaneously, which is called dual-hypergraph Laplacian regularized LRR (DHLRR). Finally, a low-rank matrix and a sparse perturbation matrix can be recovered from genomic data by DHLRR. Based on the sparsity of differentially expressed genes, the sparse disturbance matrix can be applied to extracting differentially expressed genes. In our experiments, two gene analysis tools are used to discuss the experimental results. The results on two real genomic data and an integrated dataset prove that DHLRR is efficient and effective in finding differentially expressed genes.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"127 8","pages":"21 - 33"},"PeriodicalIF":1.8,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41331149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chaokun Yan, Jingjing Ma, Huimin Luo, Ge Zhang, Junwei Luo
In the biomedical field, large amounts of biological and clinical data have been accumulated rapidly, which can be analyzed to emphasize the assessment of at-risk patients and improve diagnosis. However, a major challenge encountered associated with biomedical data analysis is the so-called “curse of dimensionality.” For this issue, a novel feature selection method based on an improved binary clonal flower pollination algorithm is proposed to eliminate unnecessary features and ensure a highly accurate classification of disease. The absolute balance group strategy and adaptive Gaussian mutation are adopted, which can increase the diversity of the population and improve the search performance. The KNN classifier is used to evaluate the classification accuracy. Extensive experimental results in six, publicly available, high-dimensional, biomedical datasets show that the proposed method can obtain high classification accuracy and outperforms other state-of-the-art methods.
{"title":"A Novel Feature Selection Method for High-Dimensional Biomedical Data Based on an Improved Binary Clonal Flower Pollination Algorithm","authors":"Chaokun Yan, Jingjing Ma, Huimin Luo, Ge Zhang, Junwei Luo","doi":"10.1159/000501652","DOIUrl":"https://doi.org/10.1159/000501652","url":null,"abstract":"In the biomedical field, large amounts of biological and clinical data have been accumulated rapidly, which can be analyzed to emphasize the assessment of at-risk patients and improve diagnosis. However, a major challenge encountered associated with biomedical data analysis is the so-called “curse of dimensionality.” For this issue, a novel feature selection method based on an improved binary clonal flower pollination algorithm is proposed to eliminate unnecessary features and ensure a highly accurate classification of disease. The absolute balance group strategy and adaptive Gaussian mutation are adopted, which can increase the diversity of the population and improve the search performance. The KNN classifier is used to evaluate the classification accuracy. Extensive experimental results in six, publicly available, high-dimensional, biomedical datasets show that the proposed method can obtain high classification accuracy and outperforms other state-of-the-art methods.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"34 - 46"},"PeriodicalIF":1.8,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41393812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sha Shi, Xin-Li Zhang, Xian-Li Zhao, Le Yang, Wei Du, Yun-Jiang Wang
Quantum-inspired genetic algorithms (QGAs) were recently introduced for the prediction of RNA secondary structures, and they showed some superiority over the existing popular strategies. In this paper, for RNA secondary structure prediction, we introduce a new QGA named multi-population assisted quantum genetic algorithm (MAQGA). In contrast to the existing QGAs, our strategy involves multi-populations which evolve together in a cooperative way in each iteration, and the genetic exchange between various populations is performed by an operator transfer operation. The numerical results show that the performances of existing genetic algorithms (evolutionary algorithms [EAs]), including traditional EAs and QGAs, can be significantly improved by using our approach. Moreover, for RNA sequences with middle-short length, the MAQGA improves even this state-of-the-art software in terms of both prediction accuracy and sensitivity.
{"title":"Prediction of the RNA Secondary Structure Using a Multi-Population Assisted Quantum Genetic Algorithm","authors":"Sha Shi, Xin-Li Zhang, Xian-Li Zhao, Le Yang, Wei Du, Yun-Jiang Wang","doi":"10.1159/000501480","DOIUrl":"https://doi.org/10.1159/000501480","url":null,"abstract":"Quantum-inspired genetic algorithms (QGAs) were recently introduced for the prediction of RNA secondary structures, and they showed some superiority over the existing popular strategies. In this paper, for RNA secondary structure prediction, we introduce a new QGA named multi-population assisted quantum genetic algorithm (MAQGA). In contrast to the existing QGAs, our strategy involves multi-populations which evolve together in a cooperative way in each iteration, and the genetic exchange between various populations is performed by an operator transfer operation. The numerical results show that the performances of existing genetic algorithms (evolutionary algorithms [EAs]), including traditional EAs and QGAs, can be significantly improved by using our approach. Moreover, for RNA sequences with middle-short length, the MAQGA improves even this state-of-the-art software in terms of both prediction accuracy and sensitivity.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"1 - 8"},"PeriodicalIF":1.8,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46369170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuhui Zhu, J. Shang, Y. Sun, Feng Li, Jin-Xing Liu, Shasha Yuan
Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark datasets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.
{"title":"PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping","authors":"Xuhui Zhu, J. Shang, Y. Sun, Feng Li, Jin-Xing Liu, Shasha Yuan","doi":"10.1159/000501481","DOIUrl":"https://doi.org/10.1159/000501481","url":null,"abstract":"Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark datasets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"9 - 20"},"PeriodicalIF":1.8,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45683405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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