Abstract:
:The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates. Data-mining strategies are gaining popularity because they are extremely flexible and may have greater efficiency and potential in identifying the factors involved in complex disorders. While the methods grouped together here constitute a diverse collection, some papers asked similar questions with very different methods, while others used the same underlying methodology to ask very different questions. This paper briefly describes the data-mining methodologies applied to the Genetic Analysis Workshop 13 data sets and the results of those investigations.
journal_name
Genet Epidemioljournal_title
Genetic epidemiologyauthors
Costello TJ,Falk CT,Ye KQdoi
10.1002/gepi.10285subject
Has Abstractpub_date
2003-01-01 00:00:00pages
S57-63eissn
0741-0395issn
1098-2272journal_volume
25 Suppl 1pub_type
杂志文章abstract::The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic ...
journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.22245
更新日期:2019-10-01 00:00:00
abstract::Emerging evidence suggests that a genetic variant can affect multiple phenotypes, especially in complex human diseases. Therefore, joint analysis of multiple phenotypes may offer new insights into disease etiology. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes, includ...
journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.22263
更新日期:2020-01-01 00:00:00
abstract::Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The m...
journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.20221
更新日期:2007-07-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.20365
更新日期:2009-02-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.20514
更新日期:2010-09-01 00:00:00
abstract::Orientals consisting of Japanese, Chinese, Koreans, and Filipinos are clearly at higher risk for cleft lip with or without cleft palate [CL(P)] than whites, Puerto Ricans, and Hawaiians/part-Hawaiians in Hawaii. Using the model of diallele cross, CL(P) incidences in incrosses and outcrosses involving 564,002 live birt...
journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370040603
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journal_title:Genetic epidemiology
pub_type: 临床试验,杂志文章
doi:10.1002/(SICI)1098-2272(199911)17:4<274::AID-GEPI3
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abstract::A highly significant familial aggregation of eosinophil levels (X2(3) = 38.00) was detected in a sample from three Brazilian populations with a high incidence of helminthic parasitism. The data were unable to resolve genetic or common environment causation due to the lack of environmental concomitant variables. Result...
journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370090305
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journal_title:Genetic epidemiology
pub_type: 杂志文章,评审
doi:10.1002/gepi.1370060111
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370070306
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370060303
更新日期:1989-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.22083
更新日期:2017-12-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1040
更新日期:2002-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.20558
更新日期:2011-02-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370060140
更新日期:1989-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370170768
更新日期:1999-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.21628
更新日期:2012-05-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370100608
更新日期:1993-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370050409
更新日期:1988-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.20223
更新日期:2007-07-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
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pub_type: 杂志文章
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.21939
更新日期:2016-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/1098-2272(200012)19:4<422::AID-GEPI12>3.0.
更新日期:2000-12-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
doi:10.1002/gepi.1370170728
更新日期:1999-01-01 00:00:00
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journal_title:Genetic epidemiology
pub_type: 杂志文章
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更新日期:2018-06-01 00:00:00
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pub_type: 杂志文章
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更新日期:2019-04-01 00:00:00
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pub_type: 杂志文章,meta分析
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更新日期:2001-01-01 00:00:00
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