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deep [2021/01/21 16:38] sylvia [Data availability] |
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| ====== DEEP/DAG1 ====== | ====== DEEP/DAG1 ====== | ||
| - | Lifelines-DEEP, also known as DAG1, is one of [[start|Lifelines]]' [[additional assessments]] performed in collaboration with the UMCG [[https://www.systemsgenetics.nl/|department of genetics]] (see also: [[DAG2]] and [[DAG3]]). DAG is the abbreviation of DArmGezondheid, or "Gastrointestinal health" in Dutch, a research project in which the microbiome is analysed in [[faecal analyses|faecal samples]].\\ | + | Lifelines-DEEP, also known as DAG1, is one of [[start|Lifelines]]' [[additional assessments]] performed in collaboration with the UMCG [[https://www.systemsgenetics.nl/|department of genetics]] (see also: [[DAG2]] and [[DAG3]]). DAG is the abbreviation of DArmGezondheid, or "Gastrointestinal health" in Dutch, a research project in which the microbiome is analysed in faecal samples.\\ |
| The primary goal of the Lifelines-DEEP project is to get insight in the relations between [[https://en.wikipedia.org/wiki/Genome|genome]], [[https://en.wikipedia.org/wiki/epigenome|epigenome]], [[https://en.wikipedia.org/wiki/Transcriptome|transcriptome]], [[https://en.wikipedia.org/wiki/microbiome|microbiome]], [[https://en.wikipedia.org/wiki/metabolome|metabolome]], and other biological and phenotypic parameters. Lifelines-DEEP is an example of a ‘next- generation’ population cohort study—in which multiple molecular data levels are combined with observational research methods ((Tigchelaar EF et al. (2015) Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5(8): e006772)) | The primary goal of the Lifelines-DEEP project is to get insight in the relations between [[https://en.wikipedia.org/wiki/Genome|genome]], [[https://en.wikipedia.org/wiki/epigenome|epigenome]], [[https://en.wikipedia.org/wiki/Transcriptome|transcriptome]], [[https://en.wikipedia.org/wiki/microbiome|microbiome]], [[https://en.wikipedia.org/wiki/metabolome|metabolome]], and other biological and phenotypic parameters. Lifelines-DEEP is an example of a ‘next- generation’ population cohort study—in which multiple molecular data levels are combined with observational research methods ((Tigchelaar EF et al. (2015) Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5(8): e006772)) | ||
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| ===== Data availability ===== | ===== Data availability ===== | ||
| - | ^ Type ^ N ^ Available? ^ | + | ^ Type ^ N ^ Available? ^ |
| - | | Genomics | +/- 1400 | Yes | | + | | Genomics (cytosnp) | +/- 1400 | Yes | |
| - | | Methylation | +/- 800 | Yes | | + | | Methylation | +/- 800 | Yes | |
| - | | RNAseq | +/- 1300 | No | | + | | RNAseq | +/- 1300 | Yes | |
| - | | MGS | +/- 1200 | Yes* | | + | | MGS | +/- 1200 | Yes* | |
| - | | 16S | +/- 1000 | Yes* | | + | | 16S | +/- 1000 | Yes* | |
| - | | Metabolomics | - | No | | + | | Metabolomics | +/- 1400 | Yes | |
| - | | Proteomics | - | No | | + | | Cytokines | +/- 1100 | Yes | |
| + | | Proteomics | +/- 1100 | Yes | | ||
| + | | WES | +/- 1000 | Yes | | ||
| *Raw data is available; the processed data is not available yet | *Raw data is available; the processed data is not available yet | ||
| ===== Genomics ===== | ===== Genomics ===== | ||
| - | Genotyping of genomic DNA was performed using both the Hum | + | Genotyping of genomic DNA was performed using both the HumanCytoSNP-12 BeadChip15 and the ImmunoChip, a customised Illumina Infinium array.16 Genotyping was successful for 1385 samples (CytoSNP) and 1374 samples (IChip), respectively. First, SNP quality control was applied independently for both platforms. SNPs were filtered on MAF above 0.001, a HWE p value >1e−4 and call rate of 0.98 using Plink.17 The genotypes from both platforms were merged into one data set. For genotypes present on both platforms, the genotypes were put on missing in the case of non-concordant calls. After merging, SNPs were filtered again on MAF 0.05 and call rate of 0.98, resulting in a total of 379 885 genotyped SNPs. Next, these data were imputed based on the Genome of the Netherlands (GoNL) reference panel.18–20 The merged genotypes were prephased using SHAPEIT221 and aligned to the GoNL reference panel using Genotype Harmonizer22 in order to resolve strand issues. The imputation was performed using IMPUTE223 V.2.3.0 against the GoNL reference panel. We used a MOLGENIS compute24 imputation pipeline to generate our scripts and monitor the imputation. Imputation yielded 8 606 371 variants with Info score ≥0.8. In addition, HLA type was established via the Broad SNP2HLA imputation pipeline.25 |
| - | anCytoSNP-12 BeadChip15 and the ImmunoChip, a customised Illumina Infinium array.16 Genotyping was successful for 1385 samples (CytoSNP) and 1374 samples (IChip), respectively. First, SNP quality control was applied independently for both platforms. SNPs were filtered on MAF above 0.001, a HWE p value >1e−4 and call rate of 0.98 using Plink.17 The genotypes from both platforms were merged into one data set. For genotypes present on both platforms, the genotypes were put on missing in the case of non-concordant calls. After merging, SNPs were filtered again on MAF 0.05 and call rate of 0.98, resulting in a total of 379 885 genotyped SNPs. Next, these data were imputed based on the Genome of the Netherlands (GoNL) reference panel.18–20 The merged genotypes were prephased using SHAPEIT221 and aligned to the GoNL reference panel using Genotype Harmonizer22 in order to resolve strand issues. The imputation was performed using IMPUTE223 V.2.3.0 against the GoNL reference panel. We used a MOLGENIS compute24 imputation pipeline to generate our scripts and monitor the imputation. Imputation yielded 8 606 371 variants with Info score ≥0.8. In addition, HLA type was established via the Broad SNP2HLA imputation pipeline.25 | + | |
| * immunochip | * immunochip | ||
deep.1611243510.txt.gz · Last modified: 2021/01/21 16:38 by sylvia
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