Institution

# North Carolina State University

Education•Raleigh, North Carolina, United States•

About: North Carolina State University is a(n) education organization based out in Raleigh, North Carolina, United States. It is known for research contribution in the topic(s): Population & Thin film. The organization has 44161 authors who have published 101744 publication(s) receiving 3456774 citation(s). The organization is also known as: NCSU & North Carolina State University at Raleigh.

Topics: Population, Thin film, Silicon, Poison control, Gene

##### Papers published on a yearly basis

##### Papers

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TL;DR: In this article, the limit distributions of the estimator of p and of the regression t test are derived under the assumption that p = ± 1, where p is a fixed constant and t is a sequence of independent normal random variables.

Abstract: Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and {e t } t-1 n is a sequence of independent normal random variables with mean 0 and variance σ2. Properties of the regression estimator of p are obtained under the assumption that p = ±1. Representations for the limit distributions of the estimator of p and of the regression t test are derived. The estimator of p and the regression t test furnish methods of testing the hypothesis that p = 1.

21,509 citations

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TL;DR: The purpose of this discussion is to offer some unity to various estimation formulae and to point out that correlations of genes in structured populations, with which F-statistics are concerned, are expressed very conveniently with a set of parameters treated by Cockerham (1 969, 1973).

Abstract: This journal frequently contains papers that report values of F-statistics estimated from genetic data collected from several populations. These parameters, FST, FIT, and FIS, were introduced by Wright (1951), and offer a convenient means of summarizing population structure. While there is some disagreement about the interpretation of the quantities, there is considerably more disagreement on the method of evaluating them. Different authors make different assumptions about sample sizes or numbers of populations and handle the difficulties of multiple alleles and unequal sample sizes in different ways. Wright himself, for example, did not consider the effects of finite sample size. The purpose of this discussion is to offer some unity to various estimation formulae and to point out that correlations of genes in structured populations, with which F-statistics are concerned, are expressed very conveniently with a set of parameters treated by Cockerham (1 969, 1973). We start with the parameters and construct appropriate estimators for them, rather than beginning the discussion with various data functions. The extension of Cockerham's work to multiple alleles and loci will be made explicit, and the use of jackknife procedures for estimating variances will be advocated. All of this may be regarded as an extension of a recent treatment of estimating the coancestry coefficient to serve as a mea-

16,821 citations

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TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.

Abstract: A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...

16,171 citations

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National Institutes of Health

^{1}, University of Chicago^{2}, Duke University^{3}, Harvard University^{4}, University of Oxford^{5}, GlaxoSmithKline^{6}, Johns Hopkins University^{7}, Yale University^{8}, deCODE genetics^{9}, Howard Hughes Medical Institute^{10}, Princeton University^{11}, Washington University in St. Louis^{12}, University of California, Berkeley^{13}, Stanford University^{14}, University of Michigan^{15}, Cornell University^{16}, University of Washington^{17}, University of Queensland^{18}, Vanderbilt University^{19}, North Carolina State University^{20}, QIMR Berghofer Medical Research Institute^{21}TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

7,195 citations

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Baylor College of Medicine

^{1}, Chinese Academy of Sciences^{2}, Chinese National Human Genome Center^{3}, University of Hong Kong^{4}, The Chinese University of Hong Kong^{5}, Hong Kong University of Science and Technology^{6}, Illumina^{7}, McGill University^{8}, Washington University in St. Louis^{9}, University of California, San Francisco^{10}, Wellcome Trust Sanger Institute^{11}, Beijing Normal University^{12}, Health Sciences University of Hokkaido^{13}, Shinshu University^{14}, University of Tsukuba^{15}, Howard University^{16}, University of Ibadan^{17}, Case Western Reserve University^{18}, University of Utah^{19}, Cold Spring Harbor Laboratory^{20}, Johns Hopkins University^{21}, University of Oxford^{22}, North Carolina State University^{23}, National Institutes of Health^{24}, Massachusetts Institute of Technology^{25}, Chinese Academy of Social Sciences^{26}, Kyoto University^{27}, Nagasaki University^{28}, Wellcome Trust^{29}, Genome Canada^{30}, Foundation for the National Institutes of Health^{31}, University of Maryland, Baltimore^{32}, Vanderbilt University^{33}, Stanford University^{34}, New York University^{35}, University of California, Berkeley^{36}, University of Oklahoma^{37}, University of New Mexico^{38}, Université de Montréal^{39}, University of California, Los Angeles^{40}, University of Michigan^{41}, University of Wisconsin-Madison^{42}, London School of Economics and Political Science^{43}, Genetic Alliance^{44}, GlaxoSmithKline^{45}, University of Washington^{46}, Harvard University^{47}, University of Chicago^{48}, Fred Hutchinson Cancer Research Center^{49}, University of Tokyo^{50}TL;DR: The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance the ability to choose targets for therapeutic intervention.

Abstract: The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.

5,704 citations

##### Authors

Showing all 44161 results

Name | H-index | Papers | Citations |
---|---|---|---|

Yi Cui | 220 | 1015 | 199725 |

Jing Wang | 184 | 4046 | 202769 |

Rodney S. Ruoff | 164 | 666 | 194902 |

Carlos Bustamante | 161 | 770 | 106053 |

David W. Johnson | 160 | 2714 | 140778 |

Joseph Wang | 158 | 1282 | 98799 |

David Tilman | 158 | 340 | 149473 |

Jay Hauser | 155 | 2145 | 132683 |

James M. Tour | 143 | 859 | 91364 |

Joseph T. Hupp | 141 | 731 | 82647 |

Bin Liu | 138 | 2181 | 87085 |

Rudolph E. Tanzi | 135 | 638 | 85376 |

Richard C. Boucher | 129 | 490 | 54509 |

David B. Allison | 129 | 836 | 69697 |

Robert W. Heath | 128 | 1049 | 73171 |