yukt
commented
3 years ago Hi Alba,
Minimac4 imputes each individual haplotype independently, so the imputation result of one sample will not be affected by other samples. The two approaches you mentioned should give you exactly the same results except for R-squared itself.
The R-squared output by minimac4 is an estimate of the imputation accuracy and is calculated based on the imputation dosages of all input samples. R-squared = var(HDS)/(p(1-p)), where p=mean(HDS), HDS is the vector of the haplotype dosages of input samples at the marker. The only difference between the two approaches you mentioned is how they calculate the R-square: the first approach calculates the R-square over the vector of the haplotype dosages of all samples , and the second approach is equivalent to splitting the vector into pieces according to the ancestry of the samples and calculating the R-squared for each piece.
Therefore, the actual imputation accuracy will be the same no matter which approach you take, but the R-squared can be different.
Best,
Ketian
On Fri, Sep 17, 2021 at 7:05 AM albaicans @.***> wrote:
Hello,
we are trying to optimize our pipeline of phasing and imputation using Eagle and Minimac4 and the 1000 Genomes reference panel and we would like to know your suggestions regarding the best strategy for imputing heterogeneous datasets.
Our dataset contains individuals with different ancestries in different proportions: most of the individuals have a European ancestry but we also have a smaller group of admixed European-African and even smaller groups of East Asian and African, as well as several individuals with admixed American ancestry. We are interested in using the imputed data in an association analysis including all ancestries.
Our first approach was to phase and impute all samples together but we realized that the imputation accuracy (based on R squared distributions and alternate allele dosages) was not as good as with homogenous datasets. We did some tests imputing different ancestries separately (still using the whole reference panel) and we got better results for the populations with big sample size (European and admixed European-African) but for the populations with small sample size this is not clear. The overall accuracy of the variants that pass the quality filter (R squared > 0.3) is higher if we impute them alone compared to when imputing them together with all the other samples, but we lose about half of the variants, probably because of low MAF that translates to low R squared.
Based on your experience and your knowledge of the imputation algorithm and the calculation of the accuracy, what’s the best approach when phasing/imputing heterogeneous datasets? It looks like we are getting better results when imputing different populations separately but we are not sure how much a small sample size (let’s say 15 individuals) can affect the imputation result and the accuracy estimation.
Thank you in advance!
Best,
Alba
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albaicans
Shicheng-Guo
Hello,
we are trying to optimize our pipeline of phasing and imputation using Eagle and Minimac4 and the 1000 Genomes reference panel and we would like to know your suggestions regarding the best strategy for imputing heterogeneous datasets.
Our dataset contains individuals with different ancestries in different proportions: most of the individuals have a European ancestry but we also have a smaller group of admixed European-African and even smaller groups of East Asian and African, as well as several individuals with admixed American ancestry. We are interested in using the imputed data in an association analysis including all ancestries.
Our first approach was to phase and impute all samples together but we realized that the imputation accuracy (based on R squared distributions and alternate allele dosages) was not as good as with homogenous datasets. We did some tests imputing different ancestries separately (still using the whole reference panel) and we got better results for the populations with big sample size (European and admixed European-African) but for the populations with small sample size this is not clear. The overall accuracy of the variants that pass the quality filter (R squared > 0.3) is higher if we impute them alone compared to when imputing them together with all the other samples, but we lose about half of the variants, probably because of low MAF that translates to low R squared.
Based on your experience and your knowledge of the imputation algorithm and the calculation of the accuracy, what’s the best approach when phasing/imputing heterogeneous datasets? It looks like we are getting better results when imputing different populations separately but we are not sure how much a small sample size (let’s say 15 individuals) can affect the imputation result and the accuracy estimation.
Thank you in advance!
Best,
Alba