Dataset Information

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

ABSTRACT: Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

SUBMITTER: Iotchkova V

PROVIDER: S-EPMC5279872 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

Iotchkova Valentina V Huang Jie J Morris John A JA Jain Deepti D Barbieri Caterina C Walter Klaudia K Min Josine L JL Chen Lu L Astle William W Cocca Massimilian M Deelen Patrick P Elding Heather H Farmaki Aliki-Eleni AE Franklin Christopher S CS Franberg Mattias M Gaunt Tom R TR Hofman Albert A Jiang Tao T Kleber Marcus E ME Lachance Genevieve G Luan Jian'an J Malerba Giovanni G Matchan Angela A Mead Daniel D Memari Yasin Y Ntalla Ioanna I Panoutsopoulou Kalliope K Pazoki Raha R Perry John R B JRB Rivadeneira Fernando F Sabater-Lleal Maria M Sennblad Bengt B Shin So-Youn SY Southam Lorraine L Traglia Michela M van Dijk Freerk F van Leeuwen Elisabeth M EM Zaza Gianluigi G Zhang Weihua W Amin Najaf N Butterworth Adam A Chambers John C JC Dedoussis George G Dehghan Abbas A Franco Oscar H OH Franke Lude L Frontini Mattia M Gambaro Giovanni G Gasparini Paolo P Hamsten Anders A Issacs Aaron A Kooner Jaspal S JS Kooperberg Charles C Langenberg Claudia C Marz Winfried W Scott Robert A RA Swertz Morris A MA Toniolo Daniela D Uitterlinden Andre G AG van Duijn Cornelia M CM Watkins Hugh H Zeggini Eleftheria E Maurano Mathew T MT Timpson Nicholas J NJ Reiner Alexander P AP Auer Paul L PL Soranzo Nicole N

Nature genetics 20160926 11

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5 ...[more]

PMID: 27668658

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Project description:BackgroundEpistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.ResultsWe identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.ConclusionsWe address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Dataset Information

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

Publications

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

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