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This article mainly explains "how to use IMPUTE2 for genotype filling". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to use IMPUTE2 for genotype filling".

Provides the following two major functions

Haplotype phasing, haplotype analysis

Genotype imputation, genotype filling

The basic model of genotype filling is as follows

Two basic elements are needed, the first is the typing result of the test sample, that is, the study genotypes shown in the figure, and the second element is called reference panel, which corresponds to the reference haplotypes in the diagram. The high-density reference panel is used to fill the SNP sites covered by the test samples, or the missing typing results, corresponding to the sites represented by the question mark in the map.

The installation of the software is relatively simple. The compiled executable file is provided on the official website, which can be downloaded and decompressed.

The corresponding code is as follows

Wget https://mathgen.stats.ox.ac.uk/impute/impute_v2.3.2_x86_64_static.tgz

Tar xzvf impute_v2.3.2_x86_64_static.tgz

In addition to the software, reference panel is also required, and the corresponding download files are also available on the official website, including two commonly used reference panel, hapmap and 1000G, with the following links

Https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#download

Impute2 officially recommended a set of best practices for genotype filling, as follows

For the quality control of the original typing results of the test samples, the quality control conditions of GWAS analysis can be used.

The corrected genome version, hapmap and 1000G are based on the hg19 version. It must be consistent with the genome version of reference panel before it can be filled accurately. If inconsistent, you can use UCSC's liftOver tool for conversion.

The direction of the correction chain, the results of hapmap and 1000G are based on the positive chain of the reference genome. In order to match the reference panel, the typing results of the chip must also be statistically corrected to the positive chain.

The number and density of snp loci of reference panel,1000G is higher than that of hapmap, so it is the most commonly used reference panel at present, and it is divided into different populations. For some studies, you can choose people that are more suitable for you, such as Asian people for analysis.

Genotypic filling

Quality control after filling, filtering the results of typing after filling, also based on the quality control conditions of GWAS

Association analysis shows that the number of snp loci after filling is more, which is helpful to detect positive signals.

The significant correlation regions are re-filled, and the positive areas screened by GWAS can be re-filled with more stringent parameters, and then correlation analysis is carried out to ensure the reliability of the analysis.

Impute2 provides the following two uses

1. One step

The method with the highest filling accuracy is as follows

Impute2\

-m. / Example/example.chr22.map\

-h. / Example/example.chr22.1kG.haps\

-l. / Example/example.chr22.1kG.legend\

-g. / Example/example.chr22.study.gens\

-strand_g. / Example/example.chr22.study.strand\

-int 20.4e6 20.5e6\

-Ne 20000\

-o. / Example/example.chr22.one.phased.impute2

The calculation of genotype filling is very large, so it is necessary to split the chromosomes and fill each chromosome. The above is an official example. Chromosome 22 is filled, the-m parameter specifies the linkage map, the-h and-l parameters specify the haplotype result of reference panel, the corresponding suffix specifies the study sample typing result for the haps and legend,-g parameters, the format is GEN, and the-strand_g parameter specifies the positive and negative chain information of the snp locus, which is used to correct the direction of the chain, and the-int parameter specifies the chromosome region to be filled. It contains the values of the start and end positions, and the corresponding long beard is recommended to be less than 5Mb. The official recommended value of the-Ne parameter is the filling result of the output specified by the 20000Morello parameter.

2. Two step

Added pre-phasing for study samples to run faster. The basic usage of pre-phasing is as follows

Impute2\

-prephase_g\

-m. / Example/example.chr22.map\

-g. / Example/example.chr22.study.gens\

-int 20.4e6 20.5e6\

-Ne 20000\

-o. / Example/example.chr22.prephasing.impute2

The-prephase_g parameter indicates that the study sample is pre-phasing, and the-m parameter can improve the accuracy of haplotype analysis.

The basic usage of padding based on the results of pre-phasing is as follows

. / impute2\

-use_prephased_g\

-m. / Example/example.chr22.map\

-h. / Example/example.chr22.1kG.haps\

-l. / Example/example.chr22.1kG.legend\

-known_haps_g. / Example/example.chr22.prephasing.impute2_haps\

-strand_g. / Example/example.chr22.study.strand\

-int 20.4e6 20.5e6\

-Ne 20000\

-o. / Example/example.chr22.one.phased.impute2

-phase

Even if the two-step method is adopted, genotype filling is still a long-running step. In practice, we can combine chromosome resolution and chromosome division window at the same time, plus parallel to improve the running efficiency.

Officials have provided more examples of usage, with the following link

Https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#examples

Thank you for reading, the above is the content of "how to use IMPUTE2 for genotype filling". After the study of this article, I believe you have a deeper understanding of how to use IMPUTE2 for genotype filling, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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