The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform
Xia Tian - One of the best experts on this subject based on the ideXlab platform.
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dynamic evolution of hub spoke cluster s External Linkage
Economic Geography, 2013Co-Authors: Xia TianAbstract:External Linkage in fact has great influence on industrial cluster.Similar to the life cycle of industrial cluster,External Linkage will also experiences unique evolution process.Taking the typical hub-spoke industrial cluster,one of the cluster representatives,as object,we analyze the process and characteristics of External Linkage evolution.Finally the dynamic evolution of Sialkot Surgical Instrument Cluster’s External Linkage also proves our results.With above exploration,we eventually find that External Linkage can significantly promote the development of hub-spoke cluster,particularly the crucial role of export;besides,the effect of External Linkage indeed depends on the internal capacity of hub-spoke cluster.At last,we further provide reasonable and applicable suggestions for China’s hub-spoke industrial clusters.
Nilanjan Chatterjee - One of the best experts on this subject based on the ideXlab platform.
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estimation of complex effect size distributions using summary level statistics from genome wide association studies across 32 complex traits
Nature Genetics, 2018Co-Authors: Yan Zhang, Juhyun Park, Nilanjan ChatterjeeAbstract:We developed a likelihood-based approach for analyzing summary-level statistics and External Linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.
Daniel J Rader - One of the best experts on this subject based on the ideXlab platform.
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gene based interaction analysis by incorporating External Linkage disequilibrium information
European Journal of Human Genetics, 2011Co-Authors: Kai Wang, Andrew C Edmondson, Daniel J RaderAbstract:Gene–gene interactions have an important role in complex human diseases. Detection of gene–gene interactions has long been a challenge due to their complexity. The standard method aiming at detecting SNP–SNP interactions may be inadequate as it does not model Linkage disequilibrium (LD) among SNPs in each gene and may lose power due to a large number of comparisons. To improve power, we propose a principal component (PC)-based framework for gene-based interaction analysis. We analytically derive the optimal weight for both quantitative and binary traits based on pairwise LD information. We then use PCs to summarize the information in each gene and test for interactions between the PCs. We further extend this gene-based interaction analysis procedure to allow the use of imputation dosage scores obtained from a popular imputation software package, MACH, which incorporates multilocus LD information. To evaluate the performance of the gene-based interaction tests, we conducted extensive simulations under various settings. We demonstrate that gene-based interaction tests are more powerful than SNP-based tests when more than two variants interact with each other; moreover, tests that incorporate External LD information are generally more powerful than those that use genotyped markers only. We also apply the proposed gene-based interaction tests to a candidate gene study on high-density lipoprotein. As our method operates at the gene level, it can be applied to a genome-wide association setting and used as a screening tool to detect gene–gene interactions.
Minghui Jiang - One of the best experts on this subject based on the ideXlab platform.
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the effect of External Linkage on hub spoke industrial cluster
2013 Suzhou-Silicon Valley-Beijing International Innovation Conference, 2013Co-Authors: Tian Xia, Minghui JiangAbstract:Although industrial cluster attracts great attention, it still holds some research deficiencies. Rare exploration about cluster's External Linkage is the most significant one. To make up such deficiency, this paper takes hub-spoke industrial cluster as example to analyze the influence and effect path of External Linkage on cluster. Finally for more information about External Linkage's effects, this paper identifies the order of different External Linkage forms with ZhongGuanCun(ZGC) as research object and further analyzes the reason of the final quantitive result.
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
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estimation of complex effect size distributions using summary level statistics from genome wide association studies across 32 complex traits
Nature Genetics, 2018Co-Authors: Yan Zhang, Juhyun Park, Nilanjan ChatterjeeAbstract:We developed a likelihood-based approach for analyzing summary-level statistics and External Linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.
