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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We acknowledge the Genetic Cluster Computer (http://www.geneticcluster.org), which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. We thank SURFsara Computing and Networking Services (http://www.surfsara.nl) for their support in using the Lisa Compute Cluster. This work was supported by the BioAssist Biobanking Task Force of the Netherlands Bioinformatics Centre, which is supported by the Netherlands Genomics Initiative. This work is part of the program of BiG Grid, the Dutch e-Science Grid, which is financially supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research, NWO). This work was financed as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL, RP-2), a Research Infrastructure financed by the Dutch government (NWO 184.021.007). The work of L.C.K. was partially funded by the European Union FP7 (2007–2013) program under grant agreement numbers 305280 (MIMOmics) and 602736 (PainOmics).
The authors declare no competing financial interests.
A full list of members and their affiliations is available in the Supplementary Note.
Integrated supplementary information
The walltimes per job for MaCH (a, c, e) and minimac (b, d, f) for various ways of splitting up the data set. The walltime is the time as measured by a clock on the wall (CPU time, disk writing etcetera) required to impute the target set. The walltime per job for running MaCH fits the linear regression models t=8.6 + 1.13n (Figure a), t=86.49 + 270.02n (Figure c) and t=1568.3 + 2.7n (Figure e). The walltime per job for running minimac fits the linear regression model t=33.8 + 0.13n (split before MaCH (blue circles)), t=50.2 + 0.10n (split after MaCH (green squares)) (Figure b), t=688.6 + 3.29n (Figure d) and t=687.7 + 0.02n (Figure f). t is the walltime in minutes and n the number of samples (a, b), the size of the chunks in Mb (c, d) and the percentage of overlap (e, f). The percentage overlap is 10% in Figure c and d and the chunk size is 5Mb in Figure e and f.
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van Leeuwen, E., Kanterakis, A., Deelen, P. et al. Population-specific genotype imputations using minimac or IMPUTE2. Nat Protoc 10, 1285–1296 (2015). https://doi.org/10.1038/nprot.2015.077
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