Hi there, I am trying to undertake a PRS using UK Biobank plink data. I am trying to generate a PRS using PRSice-2. However, the issue I am having is that I do not have a covariate file nor a phenotype file. I would like to know, how to generate them.
Thanks
You should at least have a phenotype of interest for you to work on. If not, then you need to better define what you are trying to do for us to help you.
Depends on your phenotype, you will usually include the PCs, Genotyping batch, Accessment centre, and maybe sex and age. All of those information should come with your UK biobank application. I am not sure how your UK biobank data were organized so it is rather difficult for me to give direct advice. A more general guide can be found here: https://choishingwan.gitlab.io/ukb-administration/
Hi Sam,
I have access to two different target data-sets. The first data-set has .bed .bim .fam files. The second has .bed .bim .bgen .bgen.bgi. The latter doesn't have .fam so I can't run the QC for the second dataset. There is no covariate file. The phenotype I am trying to look at is parkinson's disease.
Is there any difference between data-field 41202 and 41270?
There should be a .sample file for your bgen files, which act as thefam file for your bgen.
As for covariate, it should always come with your application if you have access to the genotype data. You just need to extract them from the phenotype file. For example, PCs has a field ID of 22009 (40 Arrays, one for each PC), genotype batch is field 22000, sex is 31 age is 21003 and assessment centre is 54. There are multiple ICD fields, and you might have to search for them yourselves (too lazy to type them all out)
To clarify, I would stratify the cohort according to age, gender, ethnicity, genotype batch, etc?
To reduce confounding, how would you use the data from the multiple field ID in a PRSice pipeline?
Thank you, I have the right target data and will also extract the phenotype and covariate data.
While running the first script for QCing the target data. I got the following "error".
7402791 variants loaded from .bim file.
487409 people (0 males, 0 females, 487409 ambiguous) loaded from .fam.
Ambiguous sex IDs written to /path/to/file
Am I missing something or why is the gender ambiguous for the UK biobank dataset? Has it something to do with the fact that each chromosome has it's own files?
The code i am running is as follows:
plink \
--bfile ~/path/to/file/ukb_imp_chr1 \
--maf 0.01 \
--hwe 1e-6 \
--geno 0.01 \
--mind 0.01 \
--write-snplist \
--make-just-fam \
--out ~/path/to/file/ukb_imp_chr1.QC
I have access to the phenotype dataset, and extracted field 31 (gender) and 21003 (age), I want to clarify the headers for the files e.g. my 31.csv has the following header eid and 31-0.0 and similar header for the age file, do I have to rename the headers and just pass them through as -cov 31.csv 21003.csv?
For example:
plink \
--bfile ~/path/to/file/ukb_imp_chr1 \
--maf 0.01 \
--hwe 1e-6 \
--geno 0.01 \
--mind 0.01 \
--write-snplist \
--make-just-fam \
--cov ~/31.csv ~/21003.csv \
--out ~/path/to/file/ukb_imp_chr1.QC
Thank You.
Okay, I have done so, and I generated my phenotype file. With the PC, your tutorial mentions using 6, in this instance would you still use 6 or all 40?
Additionally do you need to QC for missingness in the UK biobank .bed/.bim/.fam files, the files I have have been imputed, so there shouldn't be any missingness?
It all depends on your hypothesis. Sometime we adjust for 6, sometime 15, sometime 40. It is part of the analysis for you to determine what is the best PCs for you to use.
If it is imputed, then the data should be in .bgen format. Even then, you should still filter by info score, which indicate the quality of imputation.
I am now at the following step and I ran:
plink2 /path/to/file/ukb_imp_chr1 --fam /rds/general/user/js4120/home/fam_files/ukb_imp_chr.fam --extract /path/to/file/ukb_imp_chr1.QC.snplist --indep-pairwise 200 50 0.25 --out /path/to/file/ukb_imp_chr1.QC
I then ran:
plink2 /path/to/file/ukb_imp_chr1 --fam /rds/general/user/js4120/home/fam_files/ukb_imp_chr.fam --extract /path/to/file/ukb_imp_chr1.QC.snplist --set-all-var-ids @:# --new-id-max-allele-len 1000 --rm-dup retain-mismatch --indep-pairwise 200 50 0.25 --out /path/to/file/ukb_imp_chr1.QC
In both instances I am getting the following error:
Error: --indep-pairwise requires unique variant IDs. (--set-all-var-ids and/or
--rm-dup may help.)
To generate a covariate file, you can use your UK Biobank metadata files. These files contain information about the samples, including sex and age. To create the covariate file, extract the relevant columns from your metadata files: 'Sex' and 'Age'. Save these columns into a new file. For example, if you have a file named 'ukb_metadata.csv', you can use command-line tools like awk or grep to extract the 'Sex' and 'Age' columns and save them into a tab-separated text file named 'covariate_file.txt'.
To generate a phenotype file, you need to define your research question. A phenotype is any observable characteristic that varies among individuals. For instance, if you are interested in body mass index (BMI), BMI would be your phenotype. You can obtain phenotype data from UK Biobank or other sources relevant to your study. If you have BMI measurements for each individual in your dataset, this data can serve as your phenotype file. Ensure that the phenotype data is aligned with your genetic data samples.
In summary:
By preparing these files, you will be ready to use PRSice-2 for polygenic risk score analysis with your UK Biobank data.
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