Tag Archives: reference - Page 7

Harappa Gene Similarity

Posted by on April 8, 2011 21 comments

I was looking at Simranjit's DNA Tribes results and I thought I could provide you guys a list of how similar (part of) your genome is to different reference populations in a somewhat similar way to DNA Tribes results.

Basically, I computed an IBS (identity by state) matrix for all Harappa participants from HRP0001 to HRP0080 and my Reference II samples (info). These are the same as the Genome-wide comparison feature at 23andme.

Then I took the median similarity percentage between you and a reference population group. I found that median worked better here than mean as the mean was affected a lot by some outlier samples in the reference data.

Of course, since I am giving you a big discount compared to DNA Tribes, I am not doing a nice individual report. Instead, all you get is one spreadsheet including everyone. Click on your ID in the column headers to sort by your similarity to the different reference populations.

We see four outliers among the project participants who don't match any reference populations very well. One is HRP0074, a Brazilian, which is expected since I don't have any Native American populations. Then there are me (HRP0001) and my sister (HRP0035) which was well-known already. Finally, HRP0044, a Kashmiri.

Do note that this analysis was done using about 20,000 SNPs.

Rasmussen Likely Relatives

Posted by on April 7, 2011 Comments Off on Rasmussen Likely Relatives

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to the Rasmussen et al dataset, which you can download from here.

While there are no duplicates, 9 pairs of samples have high IBS values (85% similar or more) and seem to be related (Plink PI_HAT > 0.5). You can see the IBD results in a spreadsheet, along with the 8 samples I removed.

Henn Duplicates

Posted by on April 6, 2011 2 comments

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to the Henn et al dataset, which you can download from their website.

There are 107 samples common from the HapMap (IDs start with NA) and 131 from HGDP (IDs start with HGDP).

Henn et al has two PED files. One for the Khoisan data and one for all Africa 55k SNP set. Unfortunately they have 31 San duplicated in both these PED files with same individual IDs but different family IDs (SAN and SAN_SA). So they do not get automatically merged per Plink procedures. Just remove all the ones with SAN_SA FID since they have fewer SNPs. All the IBD info etc is in this spreadsheet.

Xing Redo

Posted by on April 3, 2011 Comments Off on Xing Redo

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to the Xing et al dataset, which you can download from their website.

I found no duplicates within the Xing et al data but there are 259 samples common from the HapMap. Since they are not assigned any family IDs they will pass through the ped files without being merged into HapMap samples. So you need to remove any samples with IDs starting with "NA".

Xing et al also contains 6 duplicates from HGDP with completely different IDs and two Xing samples look to be related to HGDP samples.

There are also three pairs with very high identity-by-descent values, which I calculated using Plink. You can see the samples with PI_HAT greater than 0.5 in this spreadsheet. PI_HAT is the proportion IBD estimated by plink. Notice also that all these pairs also have high IBS similarity (the DSC column), more than 85% similar.

The samples I have removed as a result of this (other than HapMap) are listed in this spreadsheet.

Behar Redo

Posted by on April 2, 2011 1 comment

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to the Behar et al dataset, which you can download from the GEO Accession website.

I found three set of duplicates and two pairs with very high identity-by-descent values, which I calculated using Plink. You can see the samples with PI_HAT greater than 0.5 in this spreadsheet. PI_HAT is the proportion IBD estimated by plink. Notice also that all these pairs also have high IBS similarity (the DSC column), more than 83% similar.

The five samples I have removed as a result of this are listed in this spreadsheet.

HapMap Redo

Posted by on April 2, 2011 Comments Off on HapMap Redo

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to HapMap, which you can download from their website. I am using HapMap 3 public release #3 from May 28, 2010.

I found one set of duplicates, NA21344 is identical to NA21737. And a whole bunch of pairs with high identity-by-descent values, which I calculated using Plink. You can see the samples with PI_HAT greater than 0.5 in this spreadsheet. PI_HAT is the proportion IBD estimated by plink. Notice also that all these pairs also have high IBS similarity (the DSC column), more than 85% similar in fact.

All the 41 samples I have removed as a result of this are listed in this spreadsheet.

Reference II PCA

Posted by on March 26, 2011 2 comments

I ran PCA on the Reference II dataset which includes 3.161 samples from various populations but with only 23,000 SNPs in common.

Here are the top ten eigenvalues:

  • 219.225396
  • 146.835968
  • 20.719760
  • 9.721733
  • 7.552482
  • 6.216977
  • 3.991663
  • 3.484690
  • 3.106919
  • 2.805874

While the first two eigenvalues are much bigger than the rest, the first explains 7.12% of the variation and the second 4.77%, the Tracy-Widom stats show that about 54 eigenvectors are significant.

Here are the plots for the first 10 principal components. Remember that the 1st eigenvector is 1.5 times the 2nd.

Here is a 3-D PCA plot (hat tip: Doug McDonald) showing 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.

I also ran MClust on the PCA data and got 17 clusters. The results are in a spreadsheet. I am sure with more principal components than the 10 I used, I would be able to deduce finer population structure.

Do take a look at the clusters assigned to the South Asian populations from Xing et al.

Reference I PCA

Posted by on March 24, 2011 5 comments

I ran PCA on the Reference I dataset which includes 2,654 samples from various populations.

Here are the top ten eigenvalues:

  • 178.727040
  • 118.884690
  • 15.014072
  • 9.346602
  • 5.983225
  • 5.140090
  • 3.322723
  • 2.739313
  • 2.559640
  • 2.475389

While the first two eigenvalues are much bigger than the rest, the first explains 6.82% of the variation and the second 4.54%, the Tracy-Widom stats show that about 70-something eeigenvectors are significant.

Here are the plots for the first 10 principal components. Remember that the 1st eigenvector is 1.5 times the 2nd.

Here is a 3-D PCA plot (hat tip: Doug McDonald) showing 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.

I also ran MClust on the PCA data and got 16 clusters. The results are in a spreadsheet. I am sure with more principal components than the 10 I used, I would be able to deduce finer population structure.

Note that African Americans cluster with East Africans in CL1. That's because African Americans have some European ancestry (20% on average) and that pulls them away from West Africans and towards Europeans. East Africans also lie in that direction, so they cluster together in a PCA. However, that doesn't mean that African Americans have East African ancestry. If you look at the Admixture results for African Americans, you see that their East African ancestry is negligible.

Iranians

Posted by on March 24, 2011 21 comments

Since we have 7 Iranians in the project, it's time to look at them as a group. We also have 19 Iranians from the Behar et al dataset.

Let's look at their admixture results at K=12.

The big difference between Harappa Project Iranians and Behar et al Iranians is African admixture. Only one Harappa Iranian (HRP0046) has 1% African admixture while three Behar Iranians have more than 10%.

Let's do hierarchical clustering with complete linkage using the Euclidean distance between admixture components. First a caveat or two. This is not a phylogeny. Also, the Euclidean distance measure is not a good one for measuring differences in admixture but I am not sure what would be better.

HRP0010 who is an Assyrian actually clusters better with Caucasian, Iranian and Iraqi Jews than with Iranians.

I'll run an MDS or PCA of the whole region from Punjab/Kashmir to the Levant and Caucasus soon which should be more interesting for clustering.

UPDATE: Since Palisto wondered, I checked and found out that he, an Iraqi Kurd, is very like the Iranians in his admixture result. So I have included him (HRP0059).

Reference I Admixture Analysis K=17

Posted by on March 22, 2011 27 comments

Continuing with Reference I admixture analysis, here is the results spreadsheet.

You can click on the legend to the right of the bar chart to sort by different ancestral components.

If you can't see the interactive chart above, here's a static image.

C1 South Asian C2 Balochistan/Caucasus
C3 Gujarati C4 Kalash
C5 Southeast Asian C6 European
C7 Mediterranean C8 Japanese
C9 Southwest Asian C10 Melanesian
C11 Siberian C12 Papuan
C13 Chinese C14 Eastern Bantu
C15 Northwest African C16 West African
C17 East African

The new ancestral component is the tightly clustered Gujarati. This consists of almost two-thirds of the Gujaratis sampled by HapMap in Houston, TX. So my question is does anyone have any idea which Gujarati communities are the biggest in Houston? I know that Patel is a very common name, probably the most common South Asian last name in the US. Most Patels I know have been from Gujarat. Are Patels a tightly knit community who are endogamous but likely don't marry close cousins? Are there different Patel subcommunities?

Fst divergences between estimated populations for K=17:

Here are the Fst numbers:

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12
C2 0.072
C3 0.032 0.044
C4 0.076 0.061 0.062
C5 0.085 0.120 0.085 0.129
C6 0.076 0.045 0.059 0.072 0.123
C7 0.085 0.062 0.073 0.088 0.138 0.050
C8 0.084 0.119 0.084 0.128 0.035 0.122 0.138
C9 0.091 0.059 0.076 0.095 0.139 0.062 0.058 0.139
C10 0.168 0.203 0.168 0.215 0.171 0.206 0.220 0.172 0.221
C11 0.090 0.116 0.088 0.127 0.064 0.117 0.135 0.039 0.138 0.188
C12 0.188 0.225 0.189 0.237 0.209 0.228 0.242 0.207 0.243 0.145 0.220
C13 0.086 0.122 0.087 0.130 0.030 0.125 0.140 0.014 0.142 0.173 0.044 0.210
C14 0.151 0.155 0.146 0.177 0.186 0.163 0.164 0.186 0.152 0.257 0.190 0.275
C15 0.089 0.066 0.076 0.096 0.133 0.060 0.054 0.132 0.063 0.211 0.131 0.232
C16 0.160 0.164 0.155 0.186 0.194 0.173 0.173 0.195 0.162 0.265 0.199 0.283
C17 0.114 0.111 0.107 0.136 0.150 0.119 0.114 0.151 0.106 0.223 0.154 0.242
C13 C14 C15 C16
C14 0.188
C15 0.135 0.115
C16 0.197 0.013 0.122
C17 0.153 0.034 0.079 0.041

PS. This was run using Admixture version 1.04 so I can make an apples-to-apples comparison with the previous runs.