Literature DB >> 7607419

General purpose model and a computer program for combined segregation and path analysis (SEGPATH): automatically creating computer programs from symbolic language model specifications.

M A Province1, D C Rao.   

Abstract

A general purpose model and a flexible computer program, called SEGPATH, have been developed to assist in the creation and implementation of a variety of genetic epidemiological models. SEGPATH is a computer program which can be used to generate programs to implement linear models for pedigree data, based upon a flexible, model-specification syntax. SEGPATH models can perform segregation analysis, path analysis, or combined segregation and path analysis using any user-specified path model and can be structured to analyze any number of multivariate phenotypes, environmental indices, and/or measured covariate fixed effects (including measured genotypes). Population heterogeneity models, repeated-measures models, longitudinal models, auto-regressive models, developmental models, and gene-by-environment interaction models can all be created under SEGPATH. Pedigree structures can be defined to be arbitrarily complex, and the data analyzed with programs generated by SEGPATH can have any missing value structure, with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be done on a vector of phenotypes and/or other measures. Because the model specification syntax is general, SEGPATH can also be used in non-genetic applications where there is a hierarchical structure, such as longitudinal, repeated-measures, time series, or nested models. A variety of applications are demonstrated.

Mesh:

Year:  1995        PMID: 7607419     DOI: 10.1002/gepi.1370120208

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  7 in total

1.  Familial resemblance of adiposity-related parameters: results from a health check-up population in Taiwan.

Authors:  D M Wu; Y Hong; C A Sun; P K Sung; D C Rao; N F Chu
Journal:  Eur J Epidemiol       Date:  2003       Impact factor: 8.082

2.  Genome-wide linkage scans for prediabetes phenotypes in response to 20 weeks of endurance exercise training in non-diabetic whites and blacks: the HERITAGE Family Study.

Authors:  P An; M Teran-Garcia; T Rice; T Rankinen; S J Weisnagel; R N Bergman; R C Boston; S Mandel; D Stefanovski; A S Leon; J S Skinner; D C Rao; C Bouchard
Journal:  Diabetologia       Date:  2005-05-03       Impact factor: 10.122

3.  GenSalt: rationale, design, methods and baseline characteristics of study participants.

Authors: 
Journal:  J Hum Hypertens       Date:  2007-04-19       Impact factor: 3.012

Review 4.  Genetic determinants of C-reactive protein.

Authors:  Jacqueline Suk Danik; Paul M Ridker
Journal:  Curr Atheroscler Rep       Date:  2007-09       Impact factor: 5.113

Review 5.  Software for quantitative trait analysis.

Authors:  Laura Almasy; Diane M Warren
Journal:  Hum Genomics       Date:  2005-09       Impact factor: 4.639

6.  Use of a random coefficient regression (RCR) model to estimate growth parameters.

Authors:  Jonathan Corbett; Aldi Kraja; Ingrid B Borecki; Michael A Province
Journal:  BMC Genet       Date:  2003-12-31       Impact factor: 2.797

7.  An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study.

Authors:  Jun Wu; Michael A Province; Hilary Coon; Steven C Hunt; John H Eckfeldt; Donna K Arnett; Gerardo Heiss; Cora E Lewis; R Curtis Ellison; Dabeeru C Rao; Treva Rice; Aldi T Kraja
Journal:  BMC Genet       Date:  2007-09-10       Impact factor: 2.797

  7 in total

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