- title = "The challenge for genetic epidemiologists: how to analyze large numbers of {SNP}s in relation to complex diseases",
- author = "A Geert Heidema and Jolanda M A Boer and Nico Nagelkerke and Edwin C M Mariman and Daphne L {van der A} and Edith J M Feskens",
- year = "2006",
- month = apr # "~21",
- journal = "BMC Genetics",
- volume = "7",
- number = "23",
- publisher = "BioMed Central Ltd.",
- bibsource = "OAI-PMH server at www.biomedcentral.com",
- language = "en",
- oai = "oai:biomedcentral.com:1471-2156-7-23",
- rights = "Copyright 2006 Heidema et al; licensee BioMed Central Ltd.",
- type = "Commentary",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1471-2156",
-
URL = "
http://www.biomedcentral.com/content/pdf/1471-2156-7-23.pdf",
-
URL = "
http://www.biomedcentral.com/1471-2156/7/23",
-
DOI = "
doi:10.1186/1471-2156-7-23",
- size = "15 pages",
-
abstract = "Genetic epidemiologists have taken the challenge to
identify genetic polymorphisms involved in the
development of diseases. Many have collected data on
large numbers of genetic markers but are not familiar
with available methods to assess their association with
complex diseases. Statistical methods have been
developed for analysing the relation between large
numbers of genetic and environmental predictors to
disease or disease-related variables in genetic
association studies.
In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimised neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted.
Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.",
- notes = "Open Access",
