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A method to correct for population structure using a segregation model
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| Zeitschriftentitel: | BMC Proceedings |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | BMC Proceedings, 3, 2009, S7 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Springer Science and Business Media LLC
|
| Schlagwörter: |
| author_facet |
Feng, Qingfu Abraham, Joseph Feng, Tao Song, Yeunjoo Elston, Robert C Zhu, Xiaofeng Feng, Qingfu Abraham, Joseph Feng, Tao Song, Yeunjoo Elston, Robert C Zhu, Xiaofeng |
|---|---|
| author |
Feng, Qingfu Abraham, Joseph Feng, Tao Song, Yeunjoo Elston, Robert C Zhu, Xiaofeng |
| spellingShingle |
Feng, Qingfu Abraham, Joseph Feng, Tao Song, Yeunjoo Elston, Robert C Zhu, Xiaofeng BMC Proceedings A method to correct for population structure using a segregation model General Biochemistry, Genetics and Molecular Biology General Medicine |
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feng, qingfu |
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Feng, Qingfu Abraham, Joseph Feng, Tao Song, Yeunjoo Elston, Robert C Zhu, Xiaofeng 1753-6561 Springer Science and Business Media LLC General Biochemistry, Genetics and Molecular Biology General Medicine http://dx.doi.org/10.1186/1753-6561-3-s7-s104 <jats:title>Abstract</jats:title> <jats:p>To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples at the same time. More specifically, we adapt the multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal-components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation (i.e., the Offspring Cohort), in the Genetic Analysis Workshop 16 Framingham Heart Study 50 k data set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function as indicated by a flat likelihood, the distribution of the empirical <jats:italic>p</jats:italic>-values for the test statistic does show that the method has a correct type I error rate whenever the variance-covariance matrix of the estimates can be computed.</jats:p> A method to correct for population structure using a segregation model BMC Proceedings |
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| title |
A method to correct for population structure using a segregation model |
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A method to correct for population structure using a segregation model |
| title_full |
A method to correct for population structure using a segregation model |
| title_fullStr |
A method to correct for population structure using a segregation model |
| title_full_unstemmed |
A method to correct for population structure using a segregation model |
| title_short |
A method to correct for population structure using a segregation model |
| title_sort |
a method to correct for population structure using a segregation model |
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General Biochemistry, Genetics and Molecular Biology General Medicine |
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http://dx.doi.org/10.1186/1753-6561-3-s7-s104 |
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2009 |
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<jats:title>Abstract</jats:title>
<jats:p>To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples at the same time. More specifically, we adapt the multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal-components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation (i.e., the Offspring Cohort), in the Genetic Analysis Workshop 16 Framingham Heart Study 50 k data set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function as indicated by a flat likelihood, the distribution of the empirical <jats:italic>p</jats:italic>-values for the test statistic does show that the method has a correct type I error rate whenever the variance-covariance matrix of the estimates can be computed.</jats:p> |
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| description | <jats:title>Abstract</jats:title> <jats:p>To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples at the same time. More specifically, we adapt the multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal-components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation (i.e., the Offspring Cohort), in the Genetic Analysis Workshop 16 Framingham Heart Study 50 k data set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function as indicated by a flat likelihood, the distribution of the empirical <jats:italic>p</jats:italic>-values for the test statistic does show that the method has a correct type I error rate whenever the variance-covariance matrix of the estimates can be computed.</jats:p> |
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| spelling | Feng, Qingfu Abraham, Joseph Feng, Tao Song, Yeunjoo Elston, Robert C Zhu, Xiaofeng 1753-6561 Springer Science and Business Media LLC General Biochemistry, Genetics and Molecular Biology General Medicine http://dx.doi.org/10.1186/1753-6561-3-s7-s104 <jats:title>Abstract</jats:title> <jats:p>To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples at the same time. More specifically, we adapt the multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal-components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation (i.e., the Offspring Cohort), in the Genetic Analysis Workshop 16 Framingham Heart Study 50 k data set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function as indicated by a flat likelihood, the distribution of the empirical <jats:italic>p</jats:italic>-values for the test statistic does show that the method has a correct type I error rate whenever the variance-covariance matrix of the estimates can be computed.</jats:p> A method to correct for population structure using a segregation model BMC Proceedings |
| spellingShingle | Feng, Qingfu, Abraham, Joseph, Feng, Tao, Song, Yeunjoo, Elston, Robert C, Zhu, Xiaofeng, BMC Proceedings, A method to correct for population structure using a segregation model, General Biochemistry, Genetics and Molecular Biology, General Medicine |
| title | A method to correct for population structure using a segregation model |
| title_full | A method to correct for population structure using a segregation model |
| title_fullStr | A method to correct for population structure using a segregation model |
| title_full_unstemmed | A method to correct for population structure using a segregation model |
| title_short | A method to correct for population structure using a segregation model |
| title_sort | a method to correct for population structure using a segregation model |
| title_unstemmed | A method to correct for population structure using a segregation model |
| topic | General Biochemistry, Genetics and Molecular Biology, General Medicine |
| url | http://dx.doi.org/10.1186/1753-6561-3-s7-s104 |