Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data
<p>Abstract</p> <p>Background</p> <p>The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a class...
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doaj-8e986c2a61814a0ba5a824cd15236cab2020-11-24T21:21:30ZengBMCAlgorithms for Molecular Biology1748-71882010-02-01511710.1186/1748-7188-5-17Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP dataZwinderman Aeilko HWaaijenborg Sandra<p>Abstract</p> <p>Background</p> <p>The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured.</p> <p>Results</p> <p>We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results.</p> <p>Conclusions</p> <p>Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs.</p> http://www.almob.org/content/5/1/17 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zwinderman Aeilko H Waaijenborg Sandra |
spellingShingle |
Zwinderman Aeilko H Waaijenborg Sandra Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data Algorithms for Molecular Biology |
author_facet |
Zwinderman Aeilko H Waaijenborg Sandra |
author_sort |
Zwinderman Aeilko H |
title |
Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data |
title_short |
Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data |
title_full |
Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data |
title_fullStr |
Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data |
title_full_unstemmed |
Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data |
title_sort |
association of repeatedly measured intermediate risk factors for complex diseases with high dimensional snp data |
publisher |
BMC |
series |
Algorithms for Molecular Biology |
issn |
1748-7188 |
publishDate |
2010-02-01 |
description |
<p>Abstract</p> <p>Background</p> <p>The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured.</p> <p>Results</p> <p>We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results.</p> <p>Conclusions</p> <p>Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs.</p> |
url |
http://www.almob.org/content/5/1/17 |
work_keys_str_mv |
AT zwindermanaeilkoh associationofrepeatedlymeasuredintermediateriskfactorsforcomplexdiseaseswithhighdimensionalsnpdata AT waaijenborgsandra associationofrepeatedlymeasuredintermediateriskfactorsforcomplexdiseaseswithhighdimensionalsnpdata |
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