Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation
Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived f...
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| Format: | Online |
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Public Library of Science
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655956/ |
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pubmed-36559562013-05-21 Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation Wood, Andrew R. Perry, John R. B. Tanaka, Toshiko Hernandez, Dena G. Zheng, Hou-Feng Melzer, David Gibbs, J. Raphael Nalls, Michael A. Weedon, Michael N. Spector, Tim D. Richards, J. Brent Bandinelli, Stefania Ferrucci, Luigi Singleton, Andrew B. Frayling, Timothy M. Research Article Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ≤ MAF <5%) and rare variants (<1%)) can enhance previously identified associations and identify novel loci, we selected 93 quantitative circulating factors where data was available from the InCHIANTI population study. These phenotypes included cytokines, binding proteins, hormones, vitamins and ions. We selected these phenotypes because many have known strong genetic associations and are potentially important to help understand disease processes. We performed a genome-wide scan for these 93 phenotypes in InCHIANTI. We identified 21 signals and 33 signals that reached P<5×10−8 based on HapMap and 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P<5×10−11 respectively. Imputation of 1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P<5×10−8 in both analyses (17 of which represent well replicated signals in the NHGRI catalogue), six were captured by the same index SNP, five were nominally more strongly associated in 1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF = 0.007) and alpha1-antitrypsin that predisposes to emphysema (P = 2.5×10−12). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations. Public Library of Science 2013-05-16 /pmc/articles/PMC3655956/ /pubmed/23696881 http://dx.doi.org/10.1371/journal.pone.0064343 Text en © 2013 Wood et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Wood, Andrew R. Perry, John R. B. Tanaka, Toshiko Hernandez, Dena G. Zheng, Hou-Feng Melzer, David Gibbs, J. Raphael Nalls, Michael A. Weedon, Michael N. Spector, Tim D. Richards, J. Brent Bandinelli, Stefania Ferrucci, Luigi Singleton, Andrew B. Frayling, Timothy M. |
| spellingShingle |
Wood, Andrew R. Perry, John R. B. Tanaka, Toshiko Hernandez, Dena G. Zheng, Hou-Feng Melzer, David Gibbs, J. Raphael Nalls, Michael A. Weedon, Michael N. Spector, Tim D. Richards, J. Brent Bandinelli, Stefania Ferrucci, Luigi Singleton, Andrew B. Frayling, Timothy M. Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation |
| author_facet |
Wood, Andrew R. Perry, John R. B. Tanaka, Toshiko Hernandez, Dena G. Zheng, Hou-Feng Melzer, David Gibbs, J. Raphael Nalls, Michael A. Weedon, Michael N. Spector, Tim D. Richards, J. Brent Bandinelli, Stefania Ferrucci, Luigi Singleton, Andrew B. Frayling, Timothy M. |
| author_sort |
Wood, Andrew R. |
| title |
Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation |
| title_short |
Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation |
| title_full |
Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation |
| title_fullStr |
Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation |
| title_full_unstemmed |
Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation |
| title_sort |
imputation of variants from the 1000 genomes project modestly improves known associations and can identify low-frequency variant - phenotype associations undetected by hapmap based imputation |
| description |
Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ≤ MAF <5%) and rare variants (<1%)) can enhance previously identified associations and identify novel loci, we selected 93 quantitative circulating factors where data was available from the InCHIANTI population study. These phenotypes included cytokines, binding proteins, hormones, vitamins and ions. We selected these phenotypes because many have known strong genetic associations and are potentially important to help understand disease processes. We performed a genome-wide scan for these 93 phenotypes in InCHIANTI. We identified 21 signals and 33 signals that reached P<5×10−8 based on HapMap and 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P<5×10−11 respectively. Imputation of 1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P<5×10−8 in both analyses (17 of which represent well replicated signals in the NHGRI catalogue), six were captured by the same index SNP, five were nominally more strongly associated in 1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF = 0.007) and alpha1-antitrypsin that predisposes to emphysema (P = 2.5×10−12). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations. |
| publisher |
Public Library of Science |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655956/ |
| _version_ |
1611978425966264320 |