On multi-marker tests for association in case-control studies
Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00252/full |
| _version_ | 1818550291998441472 |
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| author | Margaret A Taub Holger R Schwender Samuel G Younkin Thomas A Louis Ingo eRuczinski |
| author_facet | Margaret A Taub Holger R Schwender Samuel G Younkin Thomas A Louis Ingo eRuczinski |
| author_sort | Margaret A Taub |
| collection | DOAJ |
| description | Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we quantify the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007). |
| first_indexed | 2024-12-12T08:44:30Z |
| format | Article |
| id | doaj.art-c121f9ecb57c4069879c3fdbd3b8fa1e |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-12T08:44:30Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-c121f9ecb57c4069879c3fdbd3b8fa1e2022-12-22T00:30:37ZengFrontiers Media S.A.Frontiers in Genetics1664-80212013-12-01410.3389/fgene.2013.0025262988On multi-marker tests for association in case-control studiesMargaret A Taub0Holger R Schwender1Samuel G Younkin2Thomas A Louis3Ingo eRuczinski4Johns Hopkins UniversityUniversity of DuesseldorfJohns Hopkins UniversityJohns Hopkins UniversityJohns Hopkins UniversityGenome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we quantify the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007).http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00252/fullLinkage Disequilibriumgenome-wide association studiesMulti-marker testsMultiplicity adjustmentSingle nucleotide polymorhisms. |
| spellingShingle | Margaret A Taub Holger R Schwender Samuel G Younkin Thomas A Louis Ingo eRuczinski On multi-marker tests for association in case-control studies Frontiers in Genetics Linkage Disequilibrium genome-wide association studies Multi-marker tests Multiplicity adjustment Single nucleotide polymorhisms. |
| title | On multi-marker tests for association in case-control studies |
| title_full | On multi-marker tests for association in case-control studies |
| title_fullStr | On multi-marker tests for association in case-control studies |
| title_full_unstemmed | On multi-marker tests for association in case-control studies |
| title_short | On multi-marker tests for association in case-control studies |
| title_sort | on multi marker tests for association in case control studies |
| topic | Linkage Disequilibrium genome-wide association studies Multi-marker tests Multiplicity adjustment Single nucleotide polymorhisms. |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00252/full |
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