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5 votes
Accepted

Does phasing improve imputation quality?

Yes. Phasing data leads to a better imputation accuracy. More specifically: Improves allele matching: Correct phased data ensures that alleles are matched correctly between the reference panel and ...
JRodrigoF's user avatar
  • 827
4 votes
Accepted

Imputing missing genotypes from separate genotyping panels

Given that you mention wanting to use 1000 Genomes as a reference panel for imputing genotypes into your two SNP chip panels, I am going to assume that you are working with human data. In that case ...
winni2k's user avatar
  • 2,286
3 votes

Topmed File Requirements

There are tools that allows you to sort your VCF file by genomic position like bcftools sort or vcf-sort. But if you don't want to install these software, you could always do with bash tools for one ...
user324810's user avatar
  • 1,125
3 votes

Imputing small region of the genome

I would take something like 1.5Mb either side of the SNP of interest (so a 3Mb chunk), subset that out from the target and reference panel and then perform imputation on that chunk alone. I think that ...
user438383's user avatar
  • 1,689
3 votes

Confusing result from Sanger Imputation Service (Eagle v2.4 for phasing, PBWT v3.1 for imputation)

Don't know. These could be retired or inconsistent SNPs (e.g. shifted locations, or otherwise found to be different from expected). It might be that the imputation suggests that the SNP should be ...
gringer's user avatar
  • 14.3k
3 votes
Accepted

Tutorials on phasing and imputing low-coverage sequencing data

This is all relevant for data 0.5x-1x coverage. Assuming you have genotype likelihoods data, if you want to phase low-coverage data, the most suitable options are GLIMPSE, Beagle4 and QUILT. Of the ...
user438383's user avatar
  • 1,689
3 votes

Filtering imputed GWAS SNPs based on a MAF difference of 10%

I cannot think of any principled rationale for choosing this filtering strategy. However, I am going to take a guess that this filtering strategy is supposed to filter out SNPs for which imputation ...
winni2k's user avatar
  • 2,286
3 votes

Order of batch effects removal, data imputation and library size normalization in scRNA-seq data

MAGIC assumes input data has been both library-size normalized, and either log or sqrt transformed prior to imputation (see also: MAGIC tutorial). Additionally, any graph-based methods (MAGIC, PHATE, ...
Scott Gigante's user avatar
2 votes

Recommendations for missing value imputation - DNA methylation data

I think this is still an active field of research. I have heard of Phenix, which might be appropriate.
winni2k's user avatar
  • 2,286
2 votes

Imputation of dog genotype

Impute2 is a great, if dated choice. Another great choice is beagle. The advantage of beagle in this case is that it is not as strict about requiring a recombination map. Without a map, results will ...
winni2k's user avatar
  • 2,286
2 votes
Accepted

Interpreting imputation result from GLIMPSE

It seems that you didn't use the following flag: ...
user438383's user avatar
  • 1,689
2 votes

How to filter info score post-imputation?

In simple GWAS setups, each SNP is analyzed independently. In those cases you can filter out SNPs with poor INFO scores at any point. For analyses that combine information across SNPs (for example ...
winni2k's user avatar
  • 2,286
2 votes

Impute phenotype under some constraints

Question 1: Is there a tool for imputing phenotypes while also using genetic data? Yes, try Andy Dahl's Phenix from the Marchini lab. Disclosure: I did my PhD in the Marchini lab, although I was ...
winni2k's user avatar
  • 2,286
2 votes

SNPs in LD in which populations?

There is a risk of a type 2 error. My advice would be to use Structure if it is still available given the situation. Its not great and it is old and its a GUI and I ...
M__'s user avatar
  • 12.5k
2 votes
Accepted

correlation between imputed genotype and true genotype

This is done by downsampling. Take the 1000 genomes, set some genotypes as missing ./., impute them using GLIMPSE, then measure correlation between the genotype ...
user438383's user avatar
  • 1,689
2 votes
Accepted

Imputing 23andMe 3v data to v4 and v5?

The different V numbers are just different number of SNPs as far as I'm aware. So there's not going to be a service which just imputes the variants to make e.g. V3 into V5. The Michigan Imputation ...
user438383's user avatar
  • 1,689
1 vote

Should genotype imputation be ancestry specific?

Basically, yes, you will get better results if you use a reference panel which contains samples with similar ancestry to your targets. Imputation works by matching haplotypes in your target samples to ...
user438383's user avatar
  • 1,689
1 vote
Accepted

imputed dosage values for vcf files

When i was still working in the field of genotype imputation, a dosage was the expectation (average) of the (posterior) genotype probabilities, coded as GP in the OP's VCF. I once contributed code to ...
winni2k's user avatar
  • 2,286
1 vote
Accepted

Chosing an imputation panel for SNP-Chip data?

If you're using an hmm-based method such as for example impute2 or later, then the imputation method will perform the haplotype matching for you. No need to cut down the reference panel. Try to get a ...
winni2k's user avatar
  • 2,286
1 vote

Chosing an imputation panel for SNP-Chip data?

If I were you, I would use ADMIXTURE in supervised mode --supervised and use some of the 1000 genomes populations as reference populations. This is a fast and ...
user438383's user avatar
  • 1,689
1 vote

How to filter info score post-imputation?

Depending on the size of your samples (1K, 10K, 100K?) or the size of imputation region (1 gene, 1 chromosome, whole genome?) imputation files even after zipping are usually huge. Instead of ...
zx8754's user avatar
  • 1,042
1 vote

Block wise protein imputation

This looks like a TMT proteomics experiment. What tools/pipeline did you use to arrive at your quantitative values? There are a variety of tools that are useful for analyzing this type of data and ...
Will Fondrie's user avatar
1 vote

Block wise protein imputation

40% missing data is huge. Missing data analysis is complicated on the underlying distribution. If the data set is periodic then missing data periodicity is needed. Non-periodic data can be solved ...
M__'s user avatar
  • 12.5k

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