Multiple hypothesis testing strategies for genetic case-control association studies
The genetic case–control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two‐stag...
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| Published in | Statistics in medicine Vol. 25; no. 18; pp. 3134 - 3149 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
30.09.2006
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0277-6715 1097-0258 |
| DOI | 10.1002/sim.2407 |
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| Abstract | The genetic case–control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two‐stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p‐value that controls a familywise error rate. In the second stage, summary p‐values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. Published in 2005 by John Wiley & Sons, Ltd. |
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| AbstractList | The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature.The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each genes association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. [PUBLICATION ABSTRACT] The genetic case–control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two‐stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p ‐value that controls a familywise error rate. In the second stage, summary p ‐values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. Published in 2005 by John Wiley & Sons, Ltd. The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. The genetic case–control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two‐stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p‐value that controls a familywise error rate. In the second stage, summary p‐values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. Published in 2005 by John Wiley & Sons, Ltd. |
| Author | Che, Anney Chen, Bingshu E. Rosenberg, Philip S. |
| Author_xml | – sequence: 1 givenname: Philip S. surname: Rosenberg fullname: Rosenberg, Philip S. email: rosenbep@mail.nih.gov organization: Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, U.S.A – sequence: 2 givenname: Anney surname: Che fullname: Che, Anney email: chea@mail.nih.gov organization: Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, U.S.A – sequence: 3 givenname: Bingshu E. surname: Chen fullname: Chen, Bingshu E. email: cheneric@mail.nih.gov organization: Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, U.S.A |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16252274$$D View this record in MEDLINE/PubMed |
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Human Heredity 2003; 55(4):179-190. 1986; 73 2002; 296 2002; 30 2000; 25 1995; 57 2002; 56 2003; 13 2002; 3 2001; 27 2000; 154 2003; 19 2001; 29 1999; 82 2003; 72 2003; 73 2003; 55 2004; 75 2001; 294 2000; 18 2003; 426 2001; 293 1997; 92 2000; 58 2002; 64 2002; 184 2002; 23 2000; 405 1996; 83 2002; 70 1999; 96 2001; 17 2001; 11 1990; 9 2003; 22 2003; 361 e_1_2_1_41_2 e_1_2_1_40_2 e_1_2_1_22_2 e_1_2_1_45_2 e_1_2_1_23_2 e_1_2_1_44_2 e_1_2_1_20_2 e_1_2_1_43_2 e_1_2_1_21_2 e_1_2_1_42_2 e_1_2_1_26_2 e_1_2_1_27_2 Zaykin DV (e_1_2_1_38_2) 2000; 154 e_1_2_1_24_2 e_1_2_1_47_2 e_1_2_1_25_2 e_1_2_1_46_2 e_1_2_1_28_2 e_1_2_1_29_2 Westfall PH (e_1_2_1_31_2) 2002; 184 e_1_2_1_6_2 e_1_2_1_30_2 e_1_2_1_7_2 e_1_2_1_4_2 e_1_2_1_5_2 e_1_2_1_2_2 e_1_2_1_11_2 e_1_2_1_34_2 e_1_2_1_3_2 e_1_2_1_12_2 e_1_2_1_33_2 e_1_2_1_32_2 e_1_2_1_10_2 e_1_2_1_15_2 e_1_2_1_16_2 e_1_2_1_37_2 e_1_2_1_13_2 e_1_2_1_36_2 e_1_2_1_14_2 e_1_2_1_35_2 e_1_2_1_19_2 e_1_2_1_8_2 e_1_2_1_17_2 e_1_2_1_9_2 e_1_2_1_18_2 e_1_2_1_39_2 |
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| SubjectTerms | Case-Control Studies Computer Simulation Data Interpretation, Statistical Disease epidemiology Genetic Predisposition to Disease Genetics Genotype & phenotype Haplotypes human genome Humans Hypothesis testing Logistic Models Polymorphism Polymorphism, Single Nucleotide research design single nucleotide statistics and numerical data |
| Title | Multiple hypothesis testing strategies for genetic case-control association studies |
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