Principle component analysis (PCA)
PCA aims to find the orthogonal directions of maximum variance and project the data onto a new subspace with equal or fewer dimensions than the original one.
Steps of PCA
A simple illustration of PCA
Source data:
cov = np.array([[6, -3], [-3, 3.5]])
pts = np.random.multivariate_normal([0, 0], cov, size=800)
The red arrow shows the first principal component axis (PC1) and the blue arrow shows the second principal component axis (PC2). The two axes are orthogonal.
Interpretation of PCs
The first principal component of a set of p variables, presumed to be jointly normally distributed, is the derived variable formed as a linear combination of the original variables that explains the most variance. The second principal component explains the most variance in what is left once the effect of the first component is removed, and we may proceed through p iterations until all the variance is explained.
Genotype PCA
Genotype PCs are often included in the association tests to correct for population stratification. Here, usually, the data we use is the genotype matrix from the SNP array, and the covariance matrix used in PCA calculation is called genetic relationship matrix (GRM). GRM is first estimated using independent common SNPs and then PCA calculation is applied to this matrix to generate eigenvectors and eigenvalues. Finally, the top \(k\) eigenvectors with the largest eigenvalues are used to project the original genotypes into a new feature subspace, which has much fewer dimensions than the original one (dimension reduction).
Genetic relationship matrix (GRM)
Citation: Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 88(1), 76-82.
PCA is by far the most commonly used dimension reduction approach used in population genetics which could identify the difference in ancestry among the sample individuals. The population outliers should be excluded from the samples used in GWAS to avoid bias caused by population stratification. For GWAS, we also need to include top PCs to adjust for the population stratification.
Please read the following paper on how we apply PCA to genetic data: Price, A., Patterson, N., Plenge, R. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904–909 (2006). https://doi.org/10.1038/ng1847 https://www.nature.com/articles/ng1847
So before association analysis, we will learn how to run PCA analysis first.
Genotype PCA workflow
Preparation
Exclude SNPs in high-LD or HLA regions
For PCA, we first exclude SNPs in high-LD or HLA regions from the genotype data.
The reason why we want to exclude such high-LD or HLA regions
- Price, A. L., Weale, M. E., Patterson, N., Myers, S. R., Need, A. C., Shianna, K. V., Ge, D., Rotter, J. I., Torres, E., Taylor, K. D., Goldstein, D. B., & Reich, D. (2008). Long-range LD can confound genome scans in admixed populations. American journal of human genetics, 83(1), 132–139. https://doi.org/10.1016/j.ajhg.2008.06.005
Download BED-like files for high-LD or HLA regions
You can simply copy the list of high-LD or HLA regions in genome build version(.bed format) to a text file high-ld.txt.
High LD regions were obtained from
https://genome.sph.umich.edu/wiki/Regions_of_high_linkage_disequilibrium_(LD)
High LD regions of hg19
1 48000000 52000000 highld
2 86000000 100500000 highld
2 134500000 138000000 highld
2 183000000 190000000 highld
3 47500000 50000000 highld
3 83500000 87000000 highld
3 89000000 97500000 highld
5 44500000 50500000 highld
5 98000000 100500000 highld
5 129000000 132000000 highld
5 135500000 138500000 highld
6 25000000 35000000 highld
6 57000000 64000000 highld
6 140000000 142500000 highld
7 55000000 66000000 highld
8 7000000 13000000 highld
8 43000000 50000000 highld
8 112000000 115000000 highld
10 37000000 43000000 highld
11 46000000 57000000 highld
11 87500000 90500000 highld
12 33000000 40000000 highld
12 109500000 112000000 highld
20 32000000 34500000 highld
Create a list of SNPs in high-LD or HLA regions
Next, use high-ld.txt to extract all SNPs that are located in the regions described in the file using the code as follows:
plink --file ${plinkFile} --make-set high-ld.txt --write-set --out hild
Create a list of SNPs in the regions specified in high-ld.txt
plinkFile="../04_Data_QC/sample_data.clean"
plink \
--bfile ${plinkFile} \
--make-set high-ld-hg19.txt \
--write-set \
--out hild
And all SNPs in the regions will be extracted to hild.set.
$head hild.set
highld
1:48000156:C:G
1:48002096:C:G
1:48003081:T:C
1:48004776:C:T
1:48006500:A:G
1:48006546:C:T
1:48008102:T:G
1:48009994:C:T
1:48009997:C:A
For downstream analysis, we can exclude these SNPs using --exclude hild.set.
PCA steps
Steps to perform a typical genomic PCA analysis
-
- LD-Pruning (https://www.cog-genomics.org/plink/2.0/ld#indep)
-
- Removing relatives from calculating PCs (usually 2-degree) (https://www.cog-genomics.org/plink/2.0/distance#king_cutoff)
-
- Running PCA using un-related samples and independent SNPs (https://www.cog-genomics.org/plink/2.0/strat#pca)
-
- Projecting to all samples (https://www.cog-genomics.org/plink/2.0/score#pca_project)
MAF filter for LD-pruning and PCA
For LD-pruning and PCA, we usually only use variants with MAF > 0.01 or MAF>0.05 ( --maf 0.01 or --maf 0.05) for robust estimation.
Sample codes
Sample codes for performing PCA
plinkFile="" #please set this to your own path
outPrefix="plink_results"
threadnum=2
hildset = hild.set
# LD-pruning, excluding high-LD and HLA regions
plink2 \
--bfile ${plinkFile} \
--maf 0.01 \
--threads ${threadnum} \
--exclude ${hildset} \
--indep-pairwise 500 50 0.2 \
--out ${outPrefix}
# Remove related samples using king-cuttoff
plink2 \
--bfile ${plinkFile} \
--extract ${outPrefix}.prune.in \
--king-cutoff 0.0884 \
--threads ${threadnum} \
--out ${outPrefix}
# PCA after pruning and removing related samples
plink2 \
--bfile ${plinkFile} \
--keep ${outPrefix}.king.cutoff.in.id \
--extract ${outPrefix}.prune.in \
--freq counts \
--threads ${threadnum} \
--pca approx allele-wts 10 \
--out ${outPrefix}
# Projection (related and unrelated samples)
plink2 \
--bfile ${plinkFile} \
--threads ${threadnum} \
--read-freq ${outPrefix}.acount \
--score ${outPrefix}.eigenvec.allele 2 6 header-read no-mean-imputation variance-standardize \
--score-col-nums 7-16 \
--out ${outPrefix}_projected
--pca and --pca approx
For step 3, please note that approx flag is only recommended for analysis of >5000 samples. (It was applied in the sample code anyway because in real analysis you usually have a much larger sample size, though the sample size of our data is just ~500)
After step 3, the allele-wts 10 modifier requests an additional one-line-per-allele .eigenvec.allele file with the first 10 PCs expressed as allele weights instead of sample weights.
We will get the plink_results.eigenvec.allele file, which will be used to project onto all samples along with an allele count plink_results.acount file.
In the projection, score ${outPrefix}.eigenvec.allele 2 5 sets the ID (2nd column) and A1 (5th column), score-col-nums 6-15 sets the first 10 PCs to be projected. Please check https://www.cog-genomics.org/plink/2.0/score#pca_project for more details on the projection.
Please check the content of your .eigenvec.allele file
Using recent plink2 versions, there are some minor changes in the output format.
A1 is the 6th column, and the score-col-nums should be 7-16
Please adjust the column number in your script accordingly.
Allele weight and count files
#CHROM ID REF ALT PROVISIONAL_REF? A1 PC1 PC2 PC3 PC4 PC5 PC6 PC7PC8 PC9 PC10
1 1:15774:G:A G A Y G 0.57834 -1.03002 0.744557 -0.161887 0.389223 -0.0514592 0.133195 -0.0336162 -0.846376 0.0542876
1 1:15774:G:A G A Y A -0.57834 1.03002 -0.744557 0.161887 -0.389223 0.0514592 -0.133195 0.0336162 0.846376 -0.0542876
1 1:15777:A:G A G Y A -0.585215 0.401872 -0.393071 -1.79583 0.89579 -0.700882 -0.103729 -0.694495 -0.007313 0.513223
1 1:15777:A:G A G Y G 0.585215 -0.401872 0.393071 1.79583 -0.89579 0.700882 0.103729 0.694495 0.007313 -0.513223
1 1:57292:C:T C T Y C -0.123768 0.912046 -0.353606 -0.220148 -0.893017 -0.374505 -0.141002 -0.249335 0.625097 0.206104
1 1:57292:C:T C T Y T 0.123768 -0.912046 0.353606 0.220148 0.893017 0.374505 0.141002 0.249335 -0.625097 -0.206104
1 1:77874:G:A G A Y G 1.49202 -1.12567 1.19915 0.0755314 0.401134 -0.015842 0.0452086 0.273072 -0.00716098 0.237545
1 1:77874:G:A G A Y A -1.49202 1.12567 -1.19915 -0.0755314 -0.401134 0.015842 -0.0452086 -0.273072 0.00716098 -0.237545
1 1:87360:C:T C T Y C -0.191803 0.600666 -0.513208 -0.0765155 -0.656552 0.0930399 -0.0238774 -0.330449 -0.192037 -0.727729
#CHROM ID REF ALT PROVISIONAL_REF? ALT_CTS OBS_CT
1 1:15774:G:A G A Y 28 994
1 1:15777:A:G A G Y 73 994
1 1:57292:C:T C T Y 104 988
1 1:77874:G:A G A Y 19 994
1 1:87360:C:T C T Y 23 998
1 1:125271:C:T C T Y 967 996
1 1:232449:G:A G A Y 185 996
1 1:533113:A:G A G Y 129 992
1 1:565697:A:G A G Y 334 996
Eventually, we will get the PCA results for all samples.
PCA results for all samples
#FID IID ALLELE_CT NAMED_ALLELE_DOSAGE_SUM PC1_AVG PC2_AVG PC3_AVG PC4_AVG PC5_AVG PC6_AVG PC7_AVG PC8_AVG PC9_AVG PC10_AVG
HG00403 HG00403 390256 390256 0.00290265 -0.0248649 0.0100408 0.00957591 0.00694349 -0.00222251 0.0082228 -0.00114937 0.00335249 0.00437471
HG00404 HG00404 390696 390696 -0.000141221 -0.027965 0.025389 -0.00582538 -0.00274707 0.00658501 0.0113803 0.0077766 0.0159976 0.0178927
HG00406 HG00406 388524 388524 0.00707397 -0.0315445 -0.00437011 -0.0012621 -0.0114932 -0.00539483 -0.00620153 0.00452379 -0.000870627 -0.00227979
HG00407 HG00407 388808 388808 0.00683977 -0.025073 -0.00652723 0.00679729 -0.0116 -0.0102328 0.0139572 0.00618677 0.0138063 0.00825269
HG00409 HG00409 391646 391646 0.000398695 -0.0290334 -0.0189352 -0.00135977 0.0290436 0.00942829 -0.0171194 -0.0129637 0.0253596 0.022907
HG00410 HG00410 391600 391600 0.00277094 -0.0280021 -0.0209991 -0.00799085 0.0318038 -0.00284209 -0.031517 -0.0010026 0.0132541 0.0357565
HG00419 HG00419 387118 387118 0.00684154 -0.0326244 0.00237159 0.0167284 -0.0119737 -0.0079637 -0.0144339 0.00712756 0.0114292 0.00404426
HG00421 HG00421 387720 387720 0.00157095 -0.0338115 -0.00690541 0.0121058 0.00111378 0.00530794 -0.0017545 -0.00121793 0.00393407 0.00414204
HG00422 HG00422 387466 387466 0.00439167 -0.0332386 0.000741526 0.0124843 -0.00362248 -0.00343393 -0.00735112 0.00944759 -0.0107516 0.00376537
Plotting the PCs
You can now create scatterplots of the PCs using R or Python.
For plotting using Python: plot_PCA.ipynb
Scatter plot of PC1 and PC2 using 1KG EAS individuals
Note : We only used a small proportion of all available variants. This figure only very roughly shows the population structure in East Asia.
Requirements: - python>3 - numpy,pandas,seaborn,matplotlib
PCA-UMAP
(optional) We can also apply another non-linear dimension reduction algorithm called UMAP to the PCs to further identify the local structures. (PCA-UMAP)
For more details, please check: - https://umap-learn.readthedocs.io/en/latest/index.html
An example of PCA and PCA-UMAP for population genetics: - Sakaue, S., Hirata, J., Kanai, M., Suzuki, K., Akiyama, M., Lai Too, C., ... & Okada, Y. (2020). Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction. Nature communications, 11(1), 1-11.
References
- (PCA) Price, A., Patterson, N., Plenge, R. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904–909 (2006). https://doi.org/10.1038/ng1847 https://www.nature.com/articles/ng1847
- (why removing high-LD regions) Price, A. L., Weale, M. E., Patterson, N., Myers, S. R., Need, A. C., Shianna, K. V., Ge, D., Rotter, J. I., Torres, E., Taylor, K. D., Goldstein, D. B., & Reich, D. (2008). Long-range LD can confound genome scans in admixed populations. American journal of human genetics, 83(1), 132–139. https://doi.org/10.1016/j.ajhg.2008.06.005
- (UMAP) McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426.
- (UMAP in population genetics) Diaz-Papkovich, A., Anderson-Trocmé, L. & Gravel, S. A review of UMAP in population genetics. J Hum Genet 66, 85–91 (2021). https://doi.org/10.1038/s10038-020-00851-4 https://www.nature.com/articles/s10038-020-00851-4
- (king-cutoff) Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W. M. (2010). Robust relationship inference in genome-wide association studies. Bioinformatics, 26(22), 2867-2873.