Tag Archives: reference - Page 3

Dataset in Public

Posted by on August 31, 2011 3 comments

I get requests from time to time about sharing my Reference 3 dataset. I use a few datasets which I am not allowed to redistribute, but most of the others are actually public and the main issue is to convert them to plink format and merge them.

I have released code for the conversion already but to make the task even easier I am letting you guys know that I already released a subset of my dataset a long time ago. Razib wrote about it and added the detailed instructions on using that dataset.

So here's the link to the dataset which contains about 30,000 SNPs and almost 4,000 individuals from HapMap, HGDP, SGVP, Behar et al and Xing et al.

Admixture: Supervised Zombies Vs Unsupervised

Posted by on July 29, 2011 1 comment

I wanted to see how the supervised ADMIXTURE using zombies performed compared to regular unsupervised ADMIXTURE. Zombies here refers to genomes created using the --simulate option of plink from allele frequencies.

Therefore, I used the allele frequencies computed by Admixture for K=11 ancestral components for Reference 3 to generate 25 zombie individuals per ancestral component.

Using these 275 zombie samples as belonging 100% to one ancestral component, I ran Admixture in supervised mode on the Reference 3 dataset. You can see the population average results here (compare to unsupervised results).

Since I was interested in the difference between the supervised zombie admixture and the unsupervised results, here are the histograms for the difference between the two for all 3,886 samples and each ancestral component. The histogram bins are 0.5% wide.











Most of the results are within the usual error margins. Except for C7 West African component and C10 San/Pygmy component. Those two have larger differences between the unsupervised and supervised zombies approaches. Basically, individuals with West Africans or San/Pygmy ancestry get ~5-8% more West African component in the supervised zombie case with a corresponding decrease in the San/Pygmy component.

Ref4C Admixture

Posted by on May 24, 2011 15 comments

I removed the Gujarati-A samples from the previous set of runs and ran admixture on the resulting dataset.

Nothing new pops out except that the Siberian component splits into Turkic/Tungusic and Nganasan components at K=12.

The admixture results are in a spreadsheet as usual.

K=11 & 12 have the lowest errors.

At K=13, Chenchu split off as their own cluster.

More Reference Admixture Runs

Posted by on May 20, 2011 3 comments

In addition to the removals and changes in the previous set of runs, I removed the Onge, Great Andamanese and Kalash for this set.

The admixture results of this dataset are in a spreadsheet as usual and the bar chart is below.

K=10, 11, 12 are the ones with the lowest cross-validation error.

I wonder if anyone is going to mind my calling C2 at K=9 Pakistani instead of Balochistan/Caucasus? 😉

I like K=12 here and K=12 or 13 in the previous run. So the question is which one of all these K runs with two different datasets should I use to replace the old reference I K=12 admixture runs?

Reference 3 PCA Clustering for South Asians

Posted by on May 19, 2011 Comments Off on Reference 3 PCA Clustering for South Asians

Using the first 32 dimensions of the Reference 3 PCA, I tried to classify the 51 South Asian populations. I did not try a full clustering on all populations because that took too long and seemed like there were more than 150 clusters.

You can see the South Asians on 3-D PCA plots of the first four principal components.

The clustering results from Mclust are in a spreadsheet.

PS. I used 32 eigenvectors as that's what gave me the maximum number of clusters with a small number of outliers.

Another Reference Admixture Set

Posted by on May 17, 2011 2 comments

From my Reference 3 dataset, I excluded the following populations for this set of admixture runs:

  • Biaka Pygmy
  • Mbuti Pymy
  • San
  • Bantu South Africa
  • Hadza
  • Chukchis
  • Koryaks
  • Colombian
  • Dominican
  • Ecuadorian
  • Karitiana
  • Maya
  • Mexican
  • Pima
  • Puerto Rican
  • Surui
  • East Greenlanders
  • West Greenlanders
  • Australian aboriginals
  • Melanesian
  • Papuan

The San and Pygmy were removed since they are very distinct and take up clusters and the South African Bantu because they have significant admixture from the San. The Hadza seem to be a unique population too.

The Chukchis and Koryaks are Beringian populations from the Russian Far East which separate from the Siberian and Turco-Mongol groups at higher K's.

I also excluded all the American populations because our focus is on South Asia and environs. I have a few participants with Amerindian ancestry and I can always run their analyses with the full reference 3.

The Papuans and Melanesians take up 2 ancestral components in admixture at times and since admixture works well only for about K<12 or so, those are precious. Also, I originally thought that South Asians (specifically the ASI) might have some affinity with Papuans but that hasn't borne out. In addition to removing these populations, I reduced the number of samples of various groups (except South Asian ones) to 25 individuals so that admixture won't rely too heavily on any of those large groups (like the 161 Yoruba). In selecting individuals from these populations, I chose those closest to the median in terms of their admixture results. The admixture results of this dataset are in a spreadsheet as usual and the bar chart is below.

K=12 is the one with the lowest cross-validation error.

I am going to post another series of admixture runs tomorrow and then you guys can let me know which specific runs you like so we can switch to those for the project participants.

Reference 3 South Asians PCA

Posted by on May 16, 2011 Comments Off on Reference 3 South Asians PCA

Let's zoom into the PCA plots of Reference 3 (more here) and look at how the different South Asian populations line up.

First the 3-D plot of eigenvectors 1, 2 & 3 with principal component 1 being vertical (and axis of rotation).

And now principal components 2, 3 & 4 (with the vertical axis of rotation being 2):

Note that I performed PCA on the whole set of reference 3, so you are looking at the axes of variation of all populations, not just South Asians.

More Reference 3 PCA 3D Plots

Posted by on May 14, 2011 Comments Off on More Reference 3 PCA 3D Plots

As per Razib's request, here is the 3-D plot of principal components 1, 2 & 4 for reference 3.

And here are principal components 2, 3 & 4:

Reference 3 PCA

Posted by on May 13, 2011 12 comments

Here's the Principal Component Analysis (PCA) of Reference 3 data.

First the 3-D plot of the first three eigenvectors. The plot is rotating about the 1st eigenvector which is vertical. Also, I have stretched the principal components based on the corresponding eigenvalues.

And now the plots of the first 24 principal components. Please note that the eigenvectors are not scaled by the corresponding eigenvalues in these plots (unlike the 3D plot).

Here are the first 24 eigenvalues (expressed as percentage of the sum of all eigenvalues):

6.417%
4.045%
0.746%
0.624%
0.336%
0.330%
0.296%
0.250%
0.218%
0.166%
0.140%
0.131%
0.119%
0.112%
0.108%
0.105%
0.098%
0.087%
0.086%
0.080%
0.075%
0.073%
0.073%
0.071%

Together, the first 24 eigenvectors explain 14.79% of the variation in the data.

According to the Tracy-Widom statistics from eigensoft, the number of significant principle components is 118.

UPDATE: I thought the eigenvectors 2 & 4 looked interesting for South Asians so I plotted them together.

Reference 3 Admixture Error Estimation

Posted by on May 10, 2011 3 comments

Since no one paid any attention to the error estimation results for reference I admixture, I am back with the standard error and bias estimates for reference 3 admixture.

So I ran the default 200 bootstrap replicates to measure standard error in our Reference 3 K=11 admixture. Spreadsheet with population level admixture results is here and participant results are here.

Here are some statistics for the standard error estimates:

Min. 1st Qu. Median Mean 3rd Qu. Max.
C1 S Asian 0 0.127 0.9848 0.7505 1.2216 1.6833
C2 Onge 0 0.2074 0.56 0.5404 0.8268 1.6914
C3 E Asian 0 0.2013 0.6123 0.6751 1.136 1.9961
C4 SW Asian 0 0.0874 1.1462 0.9246 1.5347 2.1008
C5 Euro 0 0.042 1.3034 0.9684 1.6582 2.3861
C6 Siberian 0 0.2054 0.6566 0.6712 1.0969 2.0099
C7 W African 0 0 0.01905 0.38847 0.75713 2.1588
C8 Papuan 0 0.1936 0.375 0.3648 0.5308 1.9627
C9 American 0 0.1461 0.3958 0.4646 0.6342 2.0831
C10 San/Pygmy 0 0 0.0708 0.2514 0.4471 2.0991
C11 E African 0 0 0.1235 0.3969 0.7315 1.9318

You can see the mean value of the standard errors per population and realize how many are over 1% (marked in red).

As the average error for the Onge component among South Asian populations is a little higher than 1%, the standard error on the ASI (Ancestral South Indian) computation here is about 1.4-1.5% just from admixture. The regression error is in addition to that.

And statistics for bias estimates:

Min. 1st Qu. Median Mean 3rd Qu. Max.
C1 -0.9069 -0.28408 -0.0349 -0.12196 0.01158 0.5856
C2 -0.7701 0 0.04005 0.03847 0.153 0.5703
C3 -0.5778 -0.0888 0.01645 0.02105 0.13737 0.6127
C4 -0.7701 -0.1657 0 -0.06692 0.01298 0.745
C5 -1.2917 -0.247675 0 -0.113631 0.008975 0.6763
C6 -0.7921 -0.0856 0.0129 0.009492 0.1198 0.6464
C7 -0.5745 0 0 -0.02173 0.0016 0.3426
C8 -0.1842 0.05328 0.13175 0.1377 0.21247 0.4712
C9 -0.4202 0.0096 0.0811 0.0915 0.1682 0.5129
C10 -0.4596 0 0.0002 0.003271 0.023425 0.3447
C11 -0.5766 0 0.0018 0.02276 0.05758 0.6346

You can also see the average value of the bias in each ancestral component for each population.