The Genome of the Netherlands: design, and project goals

Boomsma, Dorret I.; Wijmenga, Cisca; Slagboom, P. Eline; Swertz, Morris A.; Karssen, Lennart C.; Abdellaoui, Abdel; Yang, Kai; Guryev, Victor; Vermaat, Martijn; Dijk, Freerk van; Francioli, Laurent C.; Hottenga, Jouke Jan; Laros, Jeroen F. J.; Li, Qibin; Li, Yingrui; Cao, Hongzhi; Chen, Ruoyan; Du, Yuanping; Li, Ning; Cao, Sujie; Setten, Jessica van; Menelaou, Androniki; Pulit, Sara L.; Hehir-Kwa, Jayne Y.; Beekman, Marian; Elbers, Clara C.; Byelas, Heorhiy; Craen, Anton J. M. de; Deelen, Patrick; Dijkstra, Martijn; Dunnen, Johan T. den; Knijff, Peter de; Houwing-Duistermaat, Jeanine J.; Koval, Vyacheslav; Estrada, Karol; Hofman, Albert; Kanterakis, Alexandros; Enckevort, David van; Mai, Hailiang; Kattenberg, Mathijs; Leeuwen, Elisabeth M. van; Neerincx, Pieter; Oostra, Ben A.; Rivadeneira, F.; Suchiman, H. Eka D.; Uitterlinden, André G.; Willemsen, Gonneke; Wolffenbuttel, Bruce H. R.; Wang, Jun; Bakker, Paul I. W. de; Ommen, G J van; Duijn, Cornelia M. van

doi:10.1038/ejhg.2013.118

Cited by 245 publications

(231 citation statements)

References 27 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%

“…Of the three tools, only IMPUTE2 can combine two reference panels. This allows imputation with both the 1000 Genomes reference panel and the GoNL reference panel, which has been shown to improve imputation quality 3 . MaCH and minimac make their own recombination maps on the basis of input data; IMPUTE2 requires a recombination map.…”

Section: Tools For Imputationmentioning

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%

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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: Tools For Imputationmentioning

confidence: 99%

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Population-specific genotype imputations using minimac or IMPUTE2

et al. 2015

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“…12 We describe a similar custommade Axiom array for the Netherlands population, the Axiom-NL that allows good imputation and enhances association, and risk score analysis on DNA samples collected within Dutch Biobanks, such as the large number of Biobanks collaborating in BBMRI-NL. 13 Notwithstanding the application to this specific population, the SNP selection procedures and coverage testing can provide a general guideline for customizing the Axiom array in other populations for which valid reference sequence genomes are available. …”

mentioning

confidence: 99%

“…Here, SNPs were selected only if they were present in the Dutch population based on the GoNL reference sequence data. 13 We prioritized them based on the following criteria: R 2 o0.30, MAF40.07, and preferably high LD r 2 40.5-0.9 with other weakly imputed SNPs in the 1 MB region. Selected SNPs in high LD with each other were removed (PLINK 1.07 -indep 200 10 1.5).…”

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A method to customize population-specific arrays for genome-wide association testing

et al. 2016

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As an example of optimizing population-specific genotyping assays using a whole-genome sequence reference set, we detail the approach that followed to design the Axiom-NL array which is characterized by an improved imputation backbone based on the Genome of the Netherlands (GoNL) reference sequence and, compared with earlier arrays, a more comprehensive inclusion of SNPs on chromosomes X, Y, and the mitochondria. Common variants on the array were selected to be compatible with the Illumina Psych Array and the Affymetrix UK Biobank Axiom array. About 3.5% of the array (23 977 markers) represents SNPs from the GWAS catalog, including SNPs at FTO, APOE, Ion-channels, killer-cell immunoglobulin-like receptors, and HLA. Around 26 000 markers associated with common psychiatric disorders are included, as well as 6705 markers suggested to be associated with fertility and twinning. The platform can thus be used for risk profiling, detection of new variants, as well as ancestry determination. Results of coverage tests in 249 unrelated subjects with GoNL-based sequence data show that after imputation with 1000G as a reference, the median concordance between original and imputed genotypes is above 98%. The median imputation quality R 2 for MAF thresholds of 0.001, 0.01, 0.05, and 40.05 are 0.05, 0.28, 0.80, 0.99, respectively, for the 1000G imputed SNPs, with a similar quality for the autosomes and X chromosome, showing a good genome-wide coverage for association studies after imputation. 3,4 to hormones, 5 personality, 6 educational attainment, 7 and lifestyle characteristics. [8][9][10] The major technology behind these successes is the relatively cheap genotyping, in comparison with full genome sequencing, of DNA samples on genotyping arrays with 300 K-5 M single-nucleotide polymorphisms (SNPs), followed by imputation of the unmeasured SNPs. Initially, the contents of these arrays were determined by the manufacturers, but recently companies also allow researchers to select the variants on an array. Here, we focus on the Axiom array, a genotyping solution from Affymetrix, Inc., which provides a high throughput platform for high-density SNP genotyping on a diverse range of sample types. This array has been used for several large population-wide genome-screening projects including the UK biobank 11 and the GERA cohorts. 12 We describe a similar custommade Axiom array for the Netherlands population, the Axiom-NL that allows good imputation and enhances association, and risk score analysis on DNA samples collected within Dutch Biobanks, such as the large number of Biobanks collaborating in BBMRI-NL. 13 Notwithstanding the application to this specific population, the SNP selection procedures and coverage testing can provide a general guideline for customizing the Axiom array in other populations for which valid reference sequence genomes are available. MATERIALS AND METHODS SNP selection for the Axiom-NL arrayAn overview of the SNP selection is provided in Figure 1, a stepwise procedure of the selection is given in the Supple...

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Integrative multi‐omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention‐Deficit/Hyperactivity Disorder

Hubers

Hagenbeek

Pool

et al. 2023

American J of Med Genetics Pt B

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The evolving field of multi‐omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non‐transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi‐omics framework to identify biomarkers for Attention‐Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single‐ and next multi‐omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in‐sample prediction through cross‐validation. The multi‐omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out‐of‐sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non‐transmitted PGSs, CpGs, and amino acid levels and show that multi‐omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

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The Genome of the Netherlands: design, and project goals

Cited by 245 publications

References 27 publications

Population-specific genotype imputations using minimac or IMPUTE2

Population-specific genotype imputations using minimac or IMPUTE2

A method to customize population-specific arrays for genome-wide association testing

Integrative multi‐omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention‐Deficit/Hyperactivity Disorder

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