Statistical analysis of rare sequence variants: an overview of collapsing methods
With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane‐Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subj...
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| Published in | Genetic epidemiology Vol. 35; no. S1; pp. S12 - S17 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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2011
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| Abstract | With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane‐Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data‐adaptive methods. Genet. Epidemiol. 35:S12–S17, 2011. © 2011 Wiley Periodicals, Inc. |
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| AbstractList | With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods. With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods. Genet. Epidemiol. 35:S12-S17, 2011. copyright 2011 Wiley Periodicals, Inc. With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane‐Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data‐adaptive methods. Genet. Epidemiol . 35:S12–S17, 2011. © 2011 Wiley Periodicals, Inc. With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods.With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods. With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane‐Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data‐adaptive methods. Genet. Epidemiol. 35:S12–S17, 2011. © 2011 Wiley Periodicals, Inc. |
| Author | Dering, Carmen Ziegler, Andreas Hemmelmann, Claudia Pugh, Elizabeth |
| AuthorAffiliation | 2 Center for Inherited Disease Research, School of Medicine, Johns Hopkins University, Baltimore, MD 1 Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany |
| AuthorAffiliation_xml | – name: 1 Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany – name: 2 Center for Inherited Disease Research, School of Medicine, Johns Hopkins University, Baltimore, MD |
| Author_xml | – sequence: 1 givenname: Carmen surname: Dering fullname: Dering, Carmen organization: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany – sequence: 2 givenname: Claudia surname: Hemmelmann fullname: Hemmelmann, Claudia organization: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany – sequence: 3 givenname: Elizabeth surname: Pugh fullname: Pugh, Elizabeth organization: Center for Inherited Disease Research, School of Medicine, Johns Hopkins University, Baltimore, MD – sequence: 4 givenname: Andreas surname: Ziegler fullname: Ziegler, Andreas email: ziegler@imbs.uni-luebeck.de organization: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany |
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| Cites_doi | 10.1016/j.ajhg.2010.10.014 10.1146/annurev-genet-102209-163421 10.1093/bioinformatics/btn435 10.1056/NEJMra0905980 10.1016/j.mrfmmm.2006.09.003 10.1186/1471-2105-7-166 10.1038/nrg2626 10.1371/journal.pgen.1001156 10.1002/9783527633654 10.1002/gepi.20450 10.1038/456018a 10.1038/nrg2809 10.1093/nar/gkf493 10.1093/nar/gkg509 10.1371/journal.pone.0013584 10.1093/bioinformatics/btp211 10.1371/journal.pgen.1000384 10.1016/j.ajhg.2008.06.024 10.1038/nature08494 10.1038/nrg2579 10.1007/978-1-4757-2346-5 10.1038/nrg2841 10.1038/nrg2867 10.1016/S0140-6736(02)08218-1 10.1016/j.ajhg.2010.04.005 10.1159/000288704 10.1038/nature09230 10.1086/521032 10.1002/gepi.20005 |
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| References | Morgenthaler S, Thilly WG. 2007. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res 615:28-56. Yue P, Melamud E, Moult J. 2006. SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinform 7:166. Metzker ML. 2009. Sequencing technologies: the next generation. Nat Rev Genet 11:31-46. Li Y, Byrnes AE, Li M. 2010. To identify associations with rare variants, just WHaIT: weighted haplotype and imputation-based tests. Am J Hum Genet 87:728-735. Han F, Pan W. 2010. A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered 70:42-54. Madsen BE, Browning SR. 2009. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5:e1000384. Petronis A. 2010. Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature 465:721-727. Ziegler A, König IR. 2010. A Statistical Approach to Genetic Epidemiology: Concepts and Applications, 2nd ed. Weinheim, Germany: Wiley-VCH. Easton DF, Deffenbaugh AM, Pruss D, Frye C, Wenstrup RJ, Allen-Brady K, Tavtigian SV, Monteiro AN, Iversen ES, Couch FJ, et al. 2007. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am J Hum Genet 81:873-883. Li B, Leal SM. 2008. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83:311-321. Liu DJ, Leal SM. 2010. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 6:e1001156. Cordell HJ. 2009. Genome-wide association studies: detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392-404. Maher B. 2008. Personal genomes: the case of the missing heritability. Nature 456:18-21. Qian D. 2004. Haplotype sharing correlation analysis using family data: a comparison with family-based association test in the presence of allelic heterogeneity. Genet Epidemiol 27:43-52. Hoffmann TJ, Marini NJ, Witte JS. 2010. Comprehensive approach to analyzing rare genetic variants. PLoS One 5:e13584. Asimit J, Zeggini E. 2010. Rare variant association analysis methods for complex traits. Annu Rev Genet 44:293-308. Meyerson M, Gabriel S, Getz G. 2010. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11:685-696. Tintle N, Aschard H, Hu I, Nock N, Wang H, Pugh E. 2011. Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17. Genet Epidemiol X:X-X. Bansal V, Libiger O, Torkamani A, Schork NJ. 2010. Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11:773-785. Morris AP, Zeggini E. 2010. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34:188-193. Bromberg Y, Yachdav G, Rost B. 2008. SNAP predicts effect of mutations on protein function. Bioinformatics 24:2397-2398. Ramensky V, Bork P, Sunyaev S. 2002. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30:3894-3900. Ng PC, Henikoff S. 2003. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812-3814. Fitze G, Cramer J, Ziegler A, Schierz M, Schreiber M, Kuhlisch E, Roesner D, Schackert HK. 2002. Association between c135G/A genotype and RET proto-oncogene germline mutations and phenotype of Hirschsprung's disease. Lancet 359:1200-1205. Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ, Sunyaev SR. 2010. Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 86:832-838. Good P. 1994. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. New York: Springer. Sun YV, Sung YJ, Tintle N, Ziegler A. 2011. Identification of genetic association of multiple rare variants using collapsing methods. Genet Epidemiol X:X-X. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH. 2010. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11:446-450. Manolio TA. 2010. Genomewide association studies and assessment of the risk of disease. New Engl J Med 363:166-176. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747-753. Knijnenburg TA, Wessels LF, Reinders MJ, Shmulevich I. 2009. Fewer permutations, more accurate P-values. Bioinformatics 25:161-168. 2010; 34 2010; 11 2009; 25 2002; 30 2004; 27 2010 2010; 465 2002; 359 2010; 363 2006; 7 1994 2003; 31 2010; 44 2009; 11 2010; 87 2010; 86 2009; 10 2008; 24 2007; 81 2008; 456 2009; 5 2009; 461 2010; 70 2008; 83 2010; 5 2007; 615 2011; X 2010; 6 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 e_1_2_7_28_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_23_1 e_1_2_7_22_1 e_1_2_7_21_1 e_1_2_7_20_1 Sun YV (e_1_2_7_29_1) 2011 Tintle N (e_1_2_7_30_1) 2011 |
| References_xml | – reference: Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH. 2010. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11:446-450. – reference: Morris AP, Zeggini E. 2010. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34:188-193. – reference: Maher B. 2008. Personal genomes: the case of the missing heritability. Nature 456:18-21. – reference: Ramensky V, Bork P, Sunyaev S. 2002. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30:3894-3900. – reference: Bansal V, Libiger O, Torkamani A, Schork NJ. 2010. Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11:773-785. – reference: Meyerson M, Gabriel S, Getz G. 2010. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11:685-696. – reference: Knijnenburg TA, Wessels LF, Reinders MJ, Shmulevich I. 2009. Fewer permutations, more accurate P-values. Bioinformatics 25:161-168. – reference: Yue P, Melamud E, Moult J. 2006. SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinform 7:166. – reference: Li B, Leal SM. 2008. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83:311-321. – reference: Bromberg Y, Yachdav G, Rost B. 2008. SNAP predicts effect of mutations on protein function. Bioinformatics 24:2397-2398. – reference: Han F, Pan W. 2010. A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered 70:42-54. – reference: Sun YV, Sung YJ, Tintle N, Ziegler A. 2011. Identification of genetic association of multiple rare variants using collapsing methods. Genet Epidemiol X:X-X. – reference: Easton DF, Deffenbaugh AM, Pruss D, Frye C, Wenstrup RJ, Allen-Brady K, Tavtigian SV, Monteiro AN, Iversen ES, Couch FJ, et al. 2007. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am J Hum Genet 81:873-883. – reference: Hoffmann TJ, Marini NJ, Witte JS. 2010. Comprehensive approach to analyzing rare genetic variants. PLoS One 5:e13584. – reference: Tintle N, Aschard H, Hu I, Nock N, Wang H, Pugh E. 2011. Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17. Genet Epidemiol X:X-X. – reference: Liu DJ, Leal SM. 2010. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 6:e1001156. – reference: Manolio TA. 2010. Genomewide association studies and assessment of the risk of disease. New Engl J Med 363:166-176. – reference: Ziegler A, König IR. 2010. A Statistical Approach to Genetic Epidemiology: Concepts and Applications, 2nd ed. Weinheim, Germany: Wiley-VCH. – reference: Morgenthaler S, Thilly WG. 2007. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res 615:28-56. – reference: Qian D. 2004. Haplotype sharing correlation analysis using family data: a comparison with family-based association test in the presence of allelic heterogeneity. Genet Epidemiol 27:43-52. – reference: Cordell HJ. 2009. Genome-wide association studies: detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392-404. – reference: Madsen BE, Browning SR. 2009. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5:e1000384. – reference: Good P. 1994. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. New York: Springer. – reference: Petronis A. 2010. Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature 465:721-727. – reference: Ng PC, Henikoff S. 2003. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812-3814. – reference: Asimit J, Zeggini E. 2010. Rare variant association analysis methods for complex traits. Annu Rev Genet 44:293-308. – reference: Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747-753. – reference: Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ, Sunyaev SR. 2010. Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 86:832-838. – reference: Fitze G, Cramer J, Ziegler A, Schierz M, Schreiber M, Kuhlisch E, Roesner D, Schackert HK. 2002. Association between c135G/A genotype and RET proto-oncogene germline mutations and phenotype of Hirschsprung's disease. Lancet 359:1200-1205. – reference: Li Y, Byrnes AE, Li M. 2010. To identify associations with rare variants, just WHaIT: weighted haplotype and imputation-based tests. Am J Hum Genet 87:728-735. – reference: Metzker ML. 2009. Sequencing technologies: the next generation. Nat Rev Genet 11:31-46. – volume: 87 start-page: 728 year: 2010 end-page: 735 article-title: To identify associations with rare variants, just WHaIT: weighted haplotype and imputation‐based tests publication-title: Am J Hum Genet – volume: 24 start-page: 2397 year: 2008 end-page: 2398 article-title: SNAP predicts effect of mutations on protein function publication-title: Bioinformatics – volume: 11 start-page: 31 year: 2009 end-page: 46 article-title: Sequencing technologies: the next generation publication-title: Nat Rev Genet – volume: 44 start-page: 293 year: 2010 end-page: 308 article-title: Rare variant association analysis methods for complex traits publication-title: Annu Rev Genet – volume: 615 start-page: 28 year: 2007 end-page: 56 article-title: A strategy to discover genes that carry multi‐allelic or mono‐allelic risk for common diseases: a cohort allelic sums test (CAST) publication-title: Mutat Res – volume: 70 start-page: 42 year: 2010 end-page: 54 article-title: A data‐adaptive sum test for disease association with multiple common or rare variants publication-title: Hum Hered – volume: 31 start-page: 3812 year: 2003 end-page: 3814 article-title: SIFT: Predicting amino acid changes that affect protein function publication-title: Nucleic Acids Res – volume: 7 start-page: 166 year: 2006 article-title: SNPs3D: candidate gene and SNP selection for association studies publication-title: BMC Bioinform – volume: X start-page: X year: 2011 end-page: X article-title: Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17 publication-title: Genet Epidemiol – volume: 11 start-page: 685 year: 2010 end-page: 696 article-title: Advances in understanding cancer genomes through second‐generation sequencing publication-title: Nat Rev Genet – volume: 11 start-page: 773 year: 2010 end-page: 785 article-title: Statistical analysis strategies for association studies involving rare variants publication-title: Nat Rev Genet – volume: 30 start-page: 3894 year: 2002 end-page: 3900 article-title: Human non‐synonymous SNPs: server and survey publication-title: Nucleic Acids Res – year: 1994 – volume: 34 start-page: 188 year: 2010 end-page: 193 article-title: An evaluation of statistical approaches to rare variant analysis in genetic association studies publication-title: Genet Epidemiol – year: 2010 – volume: 83 start-page: 311 year: 2008 end-page: 321 article-title: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data publication-title: Am J Hum Genet – volume: 27 start-page: 43 year: 2004 end-page: 52 article-title: Haplotype sharing correlation analysis using family data: a comparison with family‐based association test in the presence of allelic heterogeneity publication-title: Genet Epidemiol – volume: 6 start-page: e1001156 year: 2010 article-title: A novel adaptive method for the analysis of next‐generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions publication-title: PLoS Genet – volume: 11 start-page: 446 year: 2010 end-page: 450 article-title: Missing heritability and strategies for finding the underlying causes of complex disease publication-title: Nat Rev Genet – volume: 465 start-page: 721 year: 2010 end-page: 727 article-title: Epigenetics as a unifying principle in the aetiology of complex traits and diseases publication-title: Nature – volume: 363 start-page: 166 year: 2010 end-page: 176 article-title: Genomewide association studies and assessment of the risk of disease publication-title: New Engl J Med – volume: 5 start-page: e1000384 year: 2009 article-title: A groupwise association test for rare mutations using a weighted sum statistic publication-title: PLoS Genet – volume: 5 start-page: e13584 year: 2010 article-title: Comprehensive approach to analyzing rare genetic variants publication-title: PLoS One – volume: 359 start-page: 1200 year: 2002 end-page: 1205 article-title: Association between c135G/A genotype and RET proto‐oncogene germline mutations and phenotype of Hirschsprung's disease publication-title: Lancet – volume: X start-page: X year: 2011 end-page: X article-title: Identification of genetic association of multiple rare variants using collapsing methods publication-title: Genet Epidemiol – volume: 456 start-page: 18 year: 2008 end-page: 21 article-title: Personal genomes: the case of the missing heritability publication-title: Nature – volume: 86 start-page: 832 year: 2010 end-page: 838 article-title: Pooled association tests for rare variants in exon‐resequencing studies publication-title: Am J Hum Genet – volume: 461 start-page: 747 year: 2009 end-page: 753 article-title: Finding the missing heritability of complex diseases publication-title: Nature – volume: 25 start-page: 161 year: 2009 end-page: 168 article-title: Fewer permutations, more accurate ‐values publication-title: Bioinformatics – volume: 81 start-page: 873 year: 2007 end-page: 883 article-title: A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the and breast cancer‐predisposition genes publication-title: Am J Hum Genet – volume: 10 start-page: 392 year: 2009 end-page: 404 article-title: Genome‐wide association studies: detecting gene‐gene interactions that underlie human diseases publication-title: Nat Rev Genet – ident: e_1_2_7_14_1 doi: 10.1016/j.ajhg.2010.10.014 – ident: e_1_2_7_2_1 doi: 10.1146/annurev-genet-102209-163421 – ident: e_1_2_7_4_1 doi: 10.1093/bioinformatics/btn435 – ident: e_1_2_7_18_1 doi: 10.1056/NEJMra0905980 – ident: e_1_2_7_22_1 doi: 10.1016/j.mrfmmm.2006.09.003 – ident: e_1_2_7_31_1 doi: 10.1186/1471-2105-7-166 – start-page: X year: 2011 ident: e_1_2_7_29_1 article-title: Identification of genetic association of multiple rare variants using collapsing methods publication-title: Genet Epidemiol – ident: e_1_2_7_20_1 doi: 10.1038/nrg2626 – ident: e_1_2_7_15_1 doi: 10.1371/journal.pgen.1001156 – ident: e_1_2_7_32_1 doi: 10.1002/9783527633654 – ident: e_1_2_7_23_1 doi: 10.1002/gepi.20450 – ident: e_1_2_7_17_1 doi: 10.1038/456018a – ident: e_1_2_7_7_1 doi: 10.1038/nrg2809 – ident: e_1_2_7_28_1 doi: 10.1093/nar/gkf493 – ident: e_1_2_7_24_1 doi: 10.1093/nar/gkg509 – ident: e_1_2_7_11_1 doi: 10.1371/journal.pone.0013584 – ident: e_1_2_7_12_1 doi: 10.1093/bioinformatics/btp211 – ident: e_1_2_7_16_1 doi: 10.1371/journal.pgen.1000384 – ident: e_1_2_7_13_1 doi: 10.1016/j.ajhg.2008.06.024 – ident: e_1_2_7_19_1 doi: 10.1038/nature08494 – ident: e_1_2_7_5_1 doi: 10.1038/nrg2579 – ident: e_1_2_7_9_1 doi: 10.1007/978-1-4757-2346-5 – ident: e_1_2_7_21_1 doi: 10.1038/nrg2841 – start-page: X year: 2011 ident: e_1_2_7_30_1 article-title: Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17 publication-title: Genet Epidemiol – ident: e_1_2_7_3_1 doi: 10.1038/nrg2867 – ident: e_1_2_7_8_1 doi: 10.1016/S0140-6736(02)08218-1 – ident: e_1_2_7_26_1 doi: 10.1016/j.ajhg.2010.04.005 – ident: e_1_2_7_10_1 doi: 10.1159/000288704 – ident: e_1_2_7_25_1 doi: 10.1038/nature09230 – ident: e_1_2_7_6_1 doi: 10.1086/521032 – ident: e_1_2_7_27_1 doi: 10.1002/gepi.20005 |
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| Title | Statistical analysis of rare sequence variants: an overview of collapsing methods |
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