Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands’

Deelen, Patrick; Menelaou, Androniki; Leeuwen, Elisabeth M. van; Kanterakis, Alexandros; Dijk, Freerk van; Medina‐Gomez, Carolina; Francioli, Laurent C.; ouke, J; Hottenga, Jan; Karssen, Lennart C.; Estrada, Karol; Kreiner‐Møller, Eskil; Rivadeneira, Fernando; Setten, Jessica van; Gutiérrez-Achury, Javier; Franke, Lude; Enckevort, David van; Dijkstra, Martijn; Byelas, Heorhiy; Bakker, Paul I. W. de; Wijmenga, Cisca; Swertz, Morris A.

doi:10.1038/ejhg.2014.19

Cited by 94 publications

(108 citation statements)

References 24 publications

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“…To this end, 231 parentoffspring trios and 19 parent-offspring quartets of Dutch descent had their complete genome sequenced with at least 12× coverage [3][4][5] . The strength of this reference set comes from several factors.…”

Section: Gonl Reference Setmentioning

confidence: 99%

“…The second is the coverage, which is higher than that of the 1000 Genomes Project, and the third factor is the sequencing of samples from a homogeneous population. The quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower-frequency alleles 4 .…”

Section: Gonl Reference Setmentioning

confidence: 99%

“…The GoNL project [3][4][5] , the 1000 Genomes project 2 and the HapMap project 1 provide their data in VCF format 20 . The VCFtools 20 software package can convert these VCF files into phased haplotypes in IMPUTE2 reference-panel format.…”

Section: Tools For Imputationmentioning

confidence: 99%

“…When the true genotypes are available, the quality of the imputation can also be evaluated by calculating the false-positive and false-negative genotypes 4 . False-positive genotypes are those that have a high imputation R 2 , but that were in fact imputed incorrectly.…”

Section: Quality Metricsmentioning

confidence: 99%

“…The ped and map file can be converted into a more memory-and time-efficient binary file with the extensions .bed, .bim and .fam. [3][4][5] or the 1000 Genomes project 2 ) cannot be done on a commodity desktop computer, as that would take months of time and it requires more memory (RAM) than is usually available. As discussed earlier, the answer lies in splitting the imputation task into smaller pieces and running these subtasks on a computer cluster.…”

Section: Plink Format To Store Genotyped Datamentioning

confidence: 99%

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Population-specific genotype imputations using minimac or IMPUTE2

et al. 2015

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In order to meaningfully analyze common and rare genetic variants, results from genome-wide association studies (GWass) of multiple cohorts need to be combined in a meta-analysis in order to obtain enough power. this requires all cohorts to have the same single-nucleotide polymorphisms (snps) in their GWass. to this end, genotypes that have not been measured in a given cohort can be imputed on the basis of a set of reference haplotypes. this protocol provides guidelines for performing imputations with two widely used tools: minimac and IMpute2. these guidelines were developed and used by the Genome of the netherlands (Gonl) consortium, which has created a population-specific reference panel for genetic imputations and used this reference to impute various Dutch biobanks. We also describe several factors that might influence the final imputation quality. this protocol, which has been used by the largest Dutch biobanks, should take approximately several days, depending on the sample size of the biobank and the computer resources available.

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Section: Gonl Reference Setmentioning

confidence: 99%

Section: Gonl Reference Setmentioning

confidence: 99%

Section: Tools For Imputationmentioning

confidence: 99%

Section: Quality Metricsmentioning

confidence: 99%

Section: Plink Format To Store Genotyped Datamentioning

confidence: 99%

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Population-specific genotype imputations using minimac or IMPUTE2

et al. 2015

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Population Stratification in Genetic Association Studies

et al. 2017

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Population stratification (PS) is a primary consideration in studies of the genetic determinants of human traits. Failure to control for it may lead to confounding, causing a study to fail for lack of significant results or resources to be wasted following false positive signals. Here we review historical and current approaches for addressing PS when performing genetic association studies in human populations. We describe methods for detecting the presence of PS including global and local ancestry methods. We also describe approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, populations that may inherently feature PS.

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Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Zhou

Fritsche

et al. 2017

Genetic Epidemiology

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The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.

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Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands’

Cited by 94 publications

References 24 publications

Population-specific genotype imputations using minimac or IMPUTE2

Population-specific genotype imputations using minimac or IMPUTE2

Population Stratification in Genetic Association Studies

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

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