Title |
Significance testing in ridge regression for genetic data
|
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Published in |
BMC Bioinformatics, September 2011
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DOI | 10.1186/1471-2105-12-372 |
Pubmed ID | |
Authors |
Erika Cule, Paolo Vineis, Maria De Iorio |
Abstract |
Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. |
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Geographical breakdown
Country | Count | As % |
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Japan | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 4 | 2% |
United Kingdom | 3 | 2% |
France | 1 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Turkey | 1 | <1% |
Belgium | 1 | <1% |
Indonesia | 1 | <1% |
Japan | 1 | <1% |
Other | 1 | <1% |
Unknown | 182 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 53 | 27% |
Researcher | 38 | 19% |
Student > Master | 18 | 9% |
Student > Bachelor | 14 | 7% |
Student > Doctoral Student | 11 | 6% |
Other | 33 | 17% |
Unknown | 30 | 15% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 21 | 11% |
Mathematics | 21 | 11% |
Engineering | 13 | 7% |
Computer Science | 7 | 4% |
Other | 37 | 19% |
Unknown | 39 | 20% |