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Computational Methods for Single‐Point and Multipoint Analysis of Genetic Variants Associated with a Simulated Complex Disorder in a General Population
Author(s) -
Shoemaker Christopher A.,
Pungliya Manish,
Sao Pedro Michael A.,
Ruiz Carolina,
Alvarez Sergio A.,
Ward Matthew,
Ryder Elizabeth F.,
Krushkal Julia
Publication year - 2001
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.2001.21.s1.s738
Subject(s) - population , genetic data , computer science , point (geometry) , single point , genetic association , machine learning , data mining , statistics , computational biology , artificial intelligence , biology , genetics , mathematics , genotype , single nucleotide polymorphism , demography , gene , geometry , sociology , triz
Several techniques for association analysis have been applied to simulated genetic data for a general population. We describe and compare the performance of three single‐point methods and two multipoint approaches rooted in machine learning and data mining. © 2001 Wiley‐Liss, Inc.
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