Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritabilit...
| Huvudupphovsmän: | , , , , |
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| Materialtyp: | Journal article |
| Språk: | English |
| Publicerad: |
2012
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| _version_ | 1826275695126380544 |
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| author | Mägi, R Asimit, J Day-Williams, A Zeggini, E Morris, A |
| author_facet | Mägi, R Asimit, J Day-Williams, A Zeggini, E Morris, A |
| author_sort | Mägi, R |
| collection | OXFORD |
| description | Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits. © 2012 Wiley Periodicals, Inc. |
| first_indexed | 2024-03-06T23:02:41Z |
| format | Journal article |
| id | oxford-uuid:62b2213c-afd3-4b9c-b78c-e1421f8d7f2c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:02:41Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:62b2213c-afd3-4b9c-b78c-e1421f8d7f2c2022-03-26T18:07:58ZGenome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex DiseasesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:62b2213c-afd3-4b9c-b78c-e1421f8d7f2cEnglishSymplectic Elements at Oxford2012Mägi, RAsimit, JDay-Williams, AZeggini, EMorris, AGenome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits. © 2012 Wiley Periodicals, Inc. |
| spellingShingle | Mägi, R Asimit, J Day-Williams, A Zeggini, E Morris, A Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases |
| title | Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases |
| title_full | Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases |
| title_fullStr | Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases |
| title_full_unstemmed | Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases |
| title_short | Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases |
| title_sort | genome wide association analysis of imputed rare variants application to seven common complex diseases |
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