wiki:Impute2Pipeline

Version 7 (modified by a.kanterakis, 6 years ago) (diff)

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Impute2 pipeline


This page describes the Impute2 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

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

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.

Information on the Impute2 output format can be found here: http://mathgen.stats.ox.ac.uk/impute/output_file_options.html



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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