Impute2 pipeline

This page describes the Imputate2 pipeline developed by the GoNL - Impute team. The pipeline performs the following steps:

• Pre-processing of study data
  1. Liftover PED/MAP files from build 36 to build 37
  2. Quality control of study data
• Analysis
  1. Phasing the study data
  2. Imputing the study data

A list of used software and versions can be found on the bottom of this page.

All analysis jobs are generated using ​MOLGENIS Compute, more information about MOLGENIS Compute and the other analysis pipelines can be found here: ​https://github.com/molgenis/molgenis-pipelines

Reference data

• ALL_1000G_phase1integrated_v3_impute.tgz from the impute website, this dataset contains 1,092 individuals
• GoNL release 4, 499 unrelated individuals, 998 haplotypes
  
  

Pre processing of study data

Liftover PED/MAP files from build 36 to build 37

• Location: ​https://github.com/molgenis/molgenis-pipelines/blob/master/compute4/Liftover_genome_build_PEDMAP/protocols/liftoverPEDMAP.ftl
  This protocol applies the following steps:
• Create a bed file based on MAP file.
• Map the bed file to build 37 using ​UCSVC liftover tool.
• Create list of unmapped SNPs.
• Create plink mappings file.
• Create new plink data without unmapped SNPs using ​Plink.
  

Quality control of study data

• Location: ​https://github.com/molgenis/molgenis-pipelines/blob/master/compute4/Imputation_imputationtool_studyQC/protocols/studyQC.ftl
  This protocol applies QC to the study data using ​imputationTool. This tool employs a binary format, called ​TriTyper for rapid loading of big genotypic data. ImputationTool performs the following checks:
  1. Assesses strand alignment of alleles and swap SNPs if needed. For example, if a SNP which is in LD with multiple SNPs has a negative score the alleles are swapped and LD is calculated again. If the score of the SNP is still negative the SNP is removed from the study data.
  2. Regular Quality Checks done during routine processing of GWAS data. These checks include: Hardy-Weinberq equilibrium should be higher that 10^{-4, minor allele frequency should be higher than 0.01 and call rate should be higher than 0.95. If any of these criteria fails the SNP is removed from the study panel. These SNPs are removed because of the high likelihood that they contain erroneous genotypes.}
  3. Simple sanity checks like check if the SNP is present in the reference panel or if it has null alleles. In both cases the SNP is removed from the study panel.
  4. Check if there are significant differences between the allele frequencies in the two panels. Difference higher than 25% indicates that there is an important qualitative difference that guides this contrast. A possible imputation of this SNP is prone to introduce invalid information. For this reason these SNPs are removed from the study panel.
  5. Assesses if the haplotype structure is comparable between reference and GWAS data. This is performed by pairwise comparison of r-squared between SNPs in both reference and GWAS. For SNPs in LD (r-squared > 0.1), the allele frequencies are compared. SNPs are removed from the GWAS data when the major allele differs more often than it is identical.

Analysis

Phasing the study data

• Location: ​https://github.com/molgenis/molgenis-pipelines/blob/master/compute4/Imputation_shapeit_phasing/protocols/shapeitPhasing.ftl
  This protocol phases the study data using ​shapeit, study data can be in the following formats: Ped/Map?, Bed/Bim?, Gen/Haps?.
  This protocol applies the following steps:
• Gather the map files as provided in the ALL_1000G_phase1integrated_v3_impute.tgz from the impute website.
• Phase the study data using the map files.
  

Imputing study data

• Location: ​https://github.com/molgenis/molgenis-pipelines/blob/master/compute4/Imputation_impute2/protocols/impute2Imputation.ftl
  This protocol imputes the study data in chromosome bins of 5million bases using ​Impute2. Afterwards the results per chromosome bin are merged into full chromosomes.
  This protocol applies the following steps:
• Imputation using Impute2 the phased study data, 1000G map files and the following two parameters over 5million base bins per chromosome:
  • k_hap: 1500, this parameter sets the number of reference haplotypes to use (default: 500).
  • Ne: 20000, this parameter controls the effective population size in the population genetic-model (default: 20000).
• Concatenating all chromosome bins into a full chromosome.
  
  

Used software

Below is a list of used software and versions.

Software	Version
​UCSC liftOver tool	20120905
​Plink	v1.07
​ImputationTool	20120912
​Shapeit	v2.r644
​Impute	v2.3.0

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