IN PROGRESS…

GWAS is the name of an additional assessment. GWAS is the abbreviation for Genome Wide Association Study. GWAS aims at facilitating and accelerating genetic data generation and data analysis and thereby scientific output through using the LifeLines GWAS data. The name GWAS might be confusing, it is in fact not a genome wide association study on itself, it is a database of SNP data that can be used to perform genome wide association studies.

Genome-wide genotype data based on the Illumina CytoSNP-12v2 array are currently available for 15.400 participants. All GWAS participants are independent (no biological family relations), Caucasian-ancestry samples of adults which were collected at Baseline assessment second visit.

Table 1: General information on the GWAS subcohort. *Age at Baseline 2nd visit.

SNP genotyping is the measurement of genetic variations of single nucleotide polymorphisms (SNPs) between members of a species.
NEEDS WORK!! Maybe better to put this on a seperate (general information) page? So in UGLI you can link to this
The Illumina human CytoSNP12 Beadchip has been chosen to study genetic variations in the LifeLines cohort study.

…EXPLAIN WHAT a SNP array is…
The 12-sample HumanCytoSNP-12 BeadChip is a powerful, whole-genome scanning panel designed for efficient, high-throughput analysis of genetic and structural variations that are the most relevant to human disease. Many types and sizes of structural variation in the human genome that affect phenotypes can be detected with the HumanCytoSNP-12 BeadChip, including duplications, deletions, amplifications, copy-neutral LOH, and mosaicism. This BeadChip includes a complete panel of genome-wide tag SNPs and markers targeting all regions of known cytogenetic importance. It incorporates 200,000 SNPs which cover around 250 genomic regions commonly screened in cytogenetics laboratories, including subtelomeric regions, pericentromeric regions, sex chromosomes, and targeted coverage in around 400 additional disease-related genes.

Quality controls of the data are based on SNP filtering on minor allele frequency (MAF) above 0.001, Hardy-Weinberg equilibrium (HWE) P-value >1e-4, call rate of 0.95 using Plink ¹⁾, and principal component analysis (PCA) to check for population outliers ²⁾. Description and criteria for quality checks are listed in the Table 2.

Table 2: Description and criteria for quality checks

Maybe better to put this first part of imputation on a seperate (general information) page? So in UGLI you can link to this

To get more information about the genome of the participants and therefore to scale up the number of SNPs, the genotype data generated using the arrays can be imputed against reference genomes obtained by means of whole genome sequencing. IMPUTE2 (ref) is a program that can predict what the missing SNPs will be based on known SNPs or haplotypes (a combination of SNPs) by mapping the known genotypes against reference genomes. See Figure 1 for illustration.

Figure 1: Most common scenario in which imputation is used: unobserved genotypes (red question marks) in a set of study individuals are imputed (or predicted) using a set of reference haplotypes and genotypes from a SNP chip. Figure taken from ³⁾

Before imputation, the genotypes were pre-phased using SHAPEIT2 (ref) and aligned to the reference panels using Genotype Harmonizer (www.molgenis.org/systemsgenetics) in order to resolve strand issues. Generation of cleaned pedigree files and in- and output files for different imputation algorithms: Pedigree files are created in PLINK binary format. Genotypes of SNPs not directly genotyped on the Cyto12-SNP chip, but genotyped in another study, can be inferred by imputation. Imputation analysis is performed through Beagle 3.1.0 and data in these formats will be made available.

The samples were imputed using Minimac ⁴⁾ version 2012.10.3 against human reference genomes, i.e. the Genome of The Netherlands (GoNL) release 5 ⁵⁾ and the 1000 Genomes phase1 v3 ⁶⁾ reference panels. 1000G is a human reference panel which contains world-wide collected genomes. Since only a small population of Dutch people (from the North) are part of the 1000G panel, the genotyped SNP dataset from the array was also imputed against GoNL. This is a reference panel that contains genomes from individuals with Dutch ancestry (with parents born in the Netherlands). 165 genomes in this panel were collected from Lifelines participants, meaning that genome data from Northern-Dutch people are available. The GoNL panel showed more accurate genotypes when imputing European samples compared with the 1000 Genomes Phase1 v3 reference panel. The MOLGENIS compute ⁷⁾ imputation pipeline was used to generate and monitor our job scripts on the distributed file system.

In summary:

-1000G imputed: missing genotypes of participants are imputed (or predicted) based on 1000G reference panel (taken from worldwide population) and the available generated genotypes of the SNP array

-GoNL imputed: missing genotypes of participants are imputed (or predicted) based on the GoNL reference panel (taken from the Dutch population) and the available generated genotypes of the SNP array

-Unimputed: Genotypes determined on the basis of the SNP array, not imputed against genomic reference panels.

To prevent false-positive association, non-Caucasian samples are excluded. Samples are determined by:

• The LifeLines phenotype database (self-report)

• Outlier (IBS) analysis

• Population stratification (using Eigenstrat)

Samples are selected using self reported family relations. After cleaning of the data, samples are compared with each other to determine the relationship by genetic similarity. If a pair of samples are indicated as first degree relatives, the sample with the best genotyping quality will be included.

The following files are available through the Lifelines workspace or HPC:

• files with phenotype data

• files with genotyped and imputed data

• quality control files:

1. list of samples excluded
2. list of SNPs excluded
3. PCA component file

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