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Association testing by haplotype-sharing methods applicable to whole-genome analysis
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| Zeitschriftentitel: | BMC Proceedings |
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| Personen und Körperschaften: | , , , , , , |
| In: | BMC Proceedings, 1, 2007, S1 |
| Format: | E-Article |
| Sprache: | Englisch |
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Springer Science and Business Media LLC
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| author_facet |
Nolte, Ilja M de Vries, André R Spijker, Geert T Jansen, Ritsert C Brinza, Dumitru Zelikovsky, Alexander te Meerman, Gerard J Nolte, Ilja M de Vries, André R Spijker, Geert T Jansen, Ritsert C Brinza, Dumitru Zelikovsky, Alexander te Meerman, Gerard J |
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| author |
Nolte, Ilja M de Vries, André R Spijker, Geert T Jansen, Ritsert C Brinza, Dumitru Zelikovsky, Alexander te Meerman, Gerard J |
| spellingShingle |
Nolte, Ilja M de Vries, André R Spijker, Geert T Jansen, Ritsert C Brinza, Dumitru Zelikovsky, Alexander te Meerman, Gerard J BMC Proceedings Association testing by haplotype-sharing methods applicable to whole-genome analysis General Biochemistry, Genetics and Molecular Biology General Medicine |
| author_sort |
nolte, ilja m |
| spelling |
Nolte, Ilja M de Vries, André R Spijker, Geert T Jansen, Ritsert C Brinza, Dumitru Zelikovsky, Alexander te Meerman, Gerard J 1753-6561 Springer Science and Business Media LLC General Biochemistry, Genetics and Molecular Biology General Medicine http://dx.doi.org/10.1186/1753-6561-1-s1-s129 <jats:title>Abstract</jats:title> <jats:p>We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.</jats:p> Association testing by haplotype-sharing methods applicable to whole-genome analysis BMC Proceedings |
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| title |
Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_unstemmed |
Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_full |
Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_fullStr |
Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_full_unstemmed |
Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_short |
Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_sort |
association testing by haplotype-sharing methods applicable to whole-genome analysis |
| topic |
General Biochemistry, Genetics and Molecular Biology General Medicine |
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http://dx.doi.org/10.1186/1753-6561-1-s1-s129 |
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2007 |
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<jats:title>Abstract</jats:title>
<jats:p>We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.</jats:p> |
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| author | Nolte, Ilja M, de Vries, André R, Spijker, Geert T, Jansen, Ritsert C, Brinza, Dumitru, Zelikovsky, Alexander, te Meerman, Gerard J |
| author_facet | Nolte, Ilja M, de Vries, André R, Spijker, Geert T, Jansen, Ritsert C, Brinza, Dumitru, Zelikovsky, Alexander, te Meerman, Gerard J, Nolte, Ilja M, de Vries, André R, Spijker, Geert T, Jansen, Ritsert C, Brinza, Dumitru, Zelikovsky, Alexander, te Meerman, Gerard J |
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| description | <jats:title>Abstract</jats:title> <jats:p>We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.</jats:p> |
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| spelling | Nolte, Ilja M de Vries, André R Spijker, Geert T Jansen, Ritsert C Brinza, Dumitru Zelikovsky, Alexander te Meerman, Gerard J 1753-6561 Springer Science and Business Media LLC General Biochemistry, Genetics and Molecular Biology General Medicine http://dx.doi.org/10.1186/1753-6561-1-s1-s129 <jats:title>Abstract</jats:title> <jats:p>We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.</jats:p> Association testing by haplotype-sharing methods applicable to whole-genome analysis BMC Proceedings |
| spellingShingle | Nolte, Ilja M, de Vries, André R, Spijker, Geert T, Jansen, Ritsert C, Brinza, Dumitru, Zelikovsky, Alexander, te Meerman, Gerard J, BMC Proceedings, Association testing by haplotype-sharing methods applicable to whole-genome analysis, General Biochemistry, Genetics and Molecular Biology, General Medicine |
| title | Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_full | Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_fullStr | Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_full_unstemmed | Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_short | Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_sort | association testing by haplotype-sharing methods applicable to whole-genome analysis |
| title_unstemmed | Association testing by haplotype-sharing methods applicable to whole-genome analysis |
| topic | General Biochemistry, Genetics and Molecular Biology, General Medicine |
| url | http://dx.doi.org/10.1186/1753-6561-1-s1-s129 |