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Showing posts with label World22. Show all posts
Showing posts with label World22. Show all posts

Thursday, November 15, 2012

New add-on tool to MDLP World-22 calculator

Alex_Axe from Molgen forum announced the release of a new tool Oracle_AdMix4.
Basically, you can think of this tool as an useful add-on that extends the Oracle two-population results into four-population format.

Currently the tool support only DOS/Windows platform, and its usage is quite straightforward. The current distribution of  Oracle_AdMix4 includes the required file (data.txt) with the frequencies of 22 components in each of reference populations. The second file - input.txt - is the one you need to modify in order to use the tool on your own World-22 calculator results:

30     {Quantity of output results}
1,5    {Threshold of components to ignore noise} 
0,5    {Threshold of method to ignore noise}
2      {Power parameter for weighted method or zero for least-squares method}   
0      Pygmy
15     West-Asian
0      North-European-Mesolithic
0      Indo-Tibetan
0      Mesoamerican
0      Arctic-Amerind
0      South-America_Amerind
5      Indian
0      North-Siberean
25     Atlantic_Mediterranean_Neolithic
5      Samoedic
0      Indo-Iranian
0      East-Siberean
40     North-East-European
0      South-African
0      North-Amerind
0      Sub-Saharian
0      East-South-Asian
10     Near_East
0      Melanesian
0      Paleo-Siberian
0      Austronesian

All you need to do is to change the default values of components with your own results of World-22 calculator.

The output should appear in the following format:

1 Mordovian+Russian_South+Swedish+Swedish @ 20,749639
2 Estonian+Mordovian+Swedish+Swedish @ 23,267935
3 Finnish-North+Mordovian+Polish+Swedish @ 23,656645
4 Finnish-North+Mordovian+Russian_South+Swedish @ 23,83736
5 Estonian+Finnish-South+Mordovian+Swedish @ 24,192354
6 Mordovian+Polish+Swedish+Swedish @ 24,846391
7 Estonian+German-North+Mordovian+Swedish @ 25,176036
8 Lithuanian+Mordovian+Swedish+Swedish @ 25,511204
9 Finnish-North+Lithuanian+Mordovian+Swedish @ 25,523722
10 Finnish-South+Lithuanian_V+Mordovian+Swedish @ 25,539837

Saturday, October 13, 2012

World-22 dataset: fastIBD analysis

 Various methods for detecting IBD, including those implemented in the software programs fastIBD, GERMLINE, Chromopainter have been developed in the past several years using population genotype data from microarray platforms. Now, next-generation DNA sequencing data is becoming increasingly available, enabling the comprehensive analysis of genomes, including identifying rare variants. These sequencing data may provide an opportunity to detect IBD with higher resolution than previously possible, potentially enabling the detection of disease causing loci that were previously undetectable with sparser genetic data. 

fastIBD is a fast and computationally efficient method for detecting the identity by descent.The fastIBD algorithm starts by sampling a fixed number of haplotype pairs (four pairs by default) for each individual from the posterior haplotype distribution. Each sampled haplotype corresponds to a sequence of hidden Markov model (HMM) states. The fastIBD algorithm searches for pairs of sampled haplotypes sharing the same sequence of HMM states for a set of consecutive markers. If the pair of sampled haplotypes belongs to two distinct individuals, the shared haplotype tract is recorded. For each pair of individuals, overlapping shared haplotype tracts are merged, and the merged shared haplotype tract is a mosaic of pairs of sampled haplotype. The method has been implemented in BEAGLE, a very popular genetic analysis software.

The similar IBD analyses  have been already carried out by other genome bloggers.
Dienkes Pontikos has performed different fastIBD analyses  for detecting the degree of sharing between various Euroasian and African groups. Davidski of Eurogenes project has also used fastIBD method in his Intra-European chromosome paintings.

Inspired by those analyses i decided to run IBD sharing analysis of  World 22 calculator dataset using robust and powerful fastIBD software (with increased IBD detection threshold) and ibd2segment script.

I have created ad-hoc subset of various East-European populations by including samples from the following populations:
 
-->
Mordovian
Sorb
Hungarian
Belarusian
Tatar
Lithuanian
Polish
Bosnian
Ukrainian
Slovakian
Nogai
Serbian
Estonian
German
Swedish
Macedonian
Latvian
Moldavian
Montenegrin
Bulgarian
NorthOssetian
Kazakh
Slovenian
Uzbek
Adygei
Armenian
British
Czech
Orcadian
Russian
Turk

 I have calculated  the sum of IBD shared segments (measured in cM - centimorgans). The obtained matrix of pairwise sharing has been visualized in the following heat maps. The populations in the first heat map are clustered according to z-score values: high values indicate a high degree of IBD sharing, while low values indicate a low degree of IBD sharing.  

 

 In the second heat map, a tree-like hierarchic grouping of population is tied to the total value of cM in segments shared by two populations in pairwise-sharing.

I've also made some visualizations of IBD sharing for selected East-European populations




 UPDATE I:  I've uploaded a spreadsheet with IBD pairwise-sharing values to GoogleDrive.

Thursday, September 20, 2012

The component maps of MDLP-World22 calculator


I would like to express my gratitude to the fellow members of ABF - Loxias and Wojewoda- for creating amazing maps of components and plots showing interrelationships between 22 components.

Since the readers of my blog might be interested in visual inspecting of components , i have decided to upload them on-line. 

The first batch of maps  includes "the component portraits", created by Loxias:

West-Asian


Atlantic-Mediterranean
East-Siberian
Amerind
Indian
Indo-Iranian
Indo-Tibetan
Paleo-Siberian
Pygmy
Sub-Saharan
Near-East
North-East-European
North-Siberian
North-Mesolithic-European
 The second batch includes the PCA scatter plot of components, created by Wojewoda. PCA scatter plots one PCA component vs. another PCA component of data obtained from ancestry coefficients  collected for each of MDLP World22 components: the spots are connected over time to produce a trajectory for each component.






Saturday, September 15, 2012

The reliability of 'ancestral components' in MDLP World-22 calculator: trying TreeMix on 22 ancestral components

In the recent paper of Pickrell and Pritchard outlined the most challenging problems in  the analysis of relationships between populations: 

Many aspects of historical relationships between populations  are reflected in genetic data. Inferring these relationships from dense genetic data remains a challenging and very difficult task. 

One such aspect is the (re)construction of ancestral population from precomputed allele frequencies.In the recent supervised Admixture analysis of the MDLP analysis (see previous post), i have  carried the analysis of dataset which consists of both extant populations and 'simulated un-admixed populations'. However, one can always question the accuracy of  the results of  such an experiment by posing a very simple question: is it possible to carry out the  simultaneous analysis of 22 putative ancestral populations while being independent of prior demographic information ( involving population splits, gene flow, and changes in population size). Accounting for demographic information is especially important in cases when we have only limited genetic data for the few ancestral populations that are known with greater certainty.

To the ultimate relief of genome bloggers, Joe Pickrell and Jonathan Pritchard have released a companion program to Structure/Admixture programs called TreeMix.   TreeMix uses large SNP data sets to estimate the relationships among populations  including both population splits and admixture events.  According to Pickrell and Pritchard, the new method provides a better representation of population histories than do standard tree-building methods when they  are applied to the worldwide populations.  In my opinion, the real advantage of TreeMix over traditional admixture programs is that it accounts for unknown ancestral allele frequencies. Indeed, in most cases  we do not know the ancestral values of allele frequencies, but instead only the values in sampled descendant populations.

I decided to give TreeMix a try on 22 putative ancestral ADMIXTURE components from the latest run. For this purpose, I used a conversion script, which was kindly provided by Dienekes Pontikos. This script takes ADMIXTURE P file and converts it into  a plink.treemix.gz file, which is ready for input into TreeMix.

 I ran TreeMix using following settings > treemix -i MDLPworld22.treemix.gz -root South-African -k 500   -o MDLP22world (consult TreeMix manual for explanation of program parameters).


After careful examination of the output tree, i was really surprised by how  remarkably similar the tree is to that one in the original paper of Pickrell & Pritchard:





In the next of experiment i have introduced to the tree a rudimentary model of migrations/gene flows by including -m 5 parameter. The model is, however, very rudimentary, because Pritchard and Pickrell have  modeled "migration between populations as occurring at single, instantaneous time points. This is, of course, a dramatic simplification of the migration process. This model will work best when gene flow between populations is restricted to a relatively short time period. Situations of continuous migration violate this assumption and lead to unclear results" (see the cited paper for the further discussion).


 Nevertheless, we may, though with all due caution, speculate about the hypothetical gene flow directions:

a) from East-South-Asian ancestral component -> to Tibetan ancestral component
b) from the split_point between Austronesian and Melanesian ancestral components -> to East-South-Asian ancestral component
c) from the split_point between East-Siberian and East-South-Asian -> to the split_point between  Indian and ((Austronesian;Melanesian) Tibetian)* ancestral component
d) and finally, from North-European-Mesolithic component to the split_point between West-Asian and (North-East-European; Atlantic-Mediterranean-Neolithic)* component.

The last point d) calls for a further explanation. As one can see from the attached population tree,North-European-Mesolithic component is unexpectedly shifted towards East-Asian populations.  Despite of all simplicity of test, i think that the result in consideration could probably support  a recent statement made by David Reich in the recent Reich et al. (2012) paper:
we took advantage of the fact that east/central Asian admixture  has affected northern Europeans to a greater extent than Sardinians (in our separate  manuscript in submission, we show that this is a result of the different amounts of central/east  Asian-related gene flow into these groups).
Cf. also Dienekes' comments 

If our finding is congruent Reich's hypothesis, then one could assume that he Mesolithic Europeans were Asian-shifted themselves, i.e the earliest episode of admixture could have occurred in the Mesolithic period.

UPDATE: 
The same scenario seems to be supported in (Patterson et al. 2012).


Another important question that could be raised in regard of the reliability of the inferred components is the question of 'component purity' (the notion of 'purity' is used here in rather speculative sense of Kant's Reinheit, and has nothing to do with 'racial purity'). Indeed,  a population tree (such as one presented above) could represent with the same degree of reliability both pure nested morphology of components  variable components of nested and non-nested morphology.

Fortunately for genome bloggers, we can try to tackle this problem by using three- and four- population tests for treeness from Reich et al. 2009. The 3-population test (Reich et al. 2009) allows one to detect the presence of admixture in a population X from two other populations A and B. The value
f3(X; A, B)

is negative when X does not appear to form a simple tree with A and B but appears to be a mixture of A and B (in Dienekes' laconic phrasing).  In order to calculate f3 statistics  i used the implementation of three-pop  TreeMix's  program threepop:


X                      A,B
Samoedic    North-Siberean,Atlantic_Mediterranean_Neolithic    0.00206352    0.000146163    14.118
North-Amerind    South-America_Amerind,South-African    0.00564354    0.00034763    16.2344
Samoedic    North-Siberean,North-East-European    0.00230317    0.000134738    17.0936
North-Amerind    South-America_Amerind,Melanesian    0.0063216    0.000345733    18.2846
Sub-Saharian    South-America_Amerind,South-African    0.0058646    0.000302046    19.4162
Sub-Saharian    Pygmy,South-America_Amerind    0.00420139    0.000216014    19.4496
Sub-Saharian    Pygmy,Paleo-Siberean    0.00426537    0.000217229    19.6354
Sub-Saharian    Pygmy,North-Siberean    0.00438367    0.000221767    19.767
North-Amerind    Pygmy,South-America_Amerind    0.0058802    0.000295087    19.927
Sub-Saharian    Pygmy,Mesomerican    0.00439921    0.000219885    20.0068
Sub-Saharian    Pygmy,Arctic-Amerind    0.00427798    0.000212131    20.1667
Samoedic    South-America_Amerind,South-African    0.00679588    0.000332084    20.4643
North-Amerind    South-America_Amerind,Austronesian    0.00658824    0.000314661    20.9375
Samoedic    North-Siberean,South-African    0.00504898    0.000240205    21.0195
North-Amerind    South-America_Amerind,Paleo-Siberean    0.00580508    0.000275451    21.0748
 



The full f3statistics file could be downloaded here.


After the careful examination of  f3statistics for 22 putative ancestral components, i haven't found negative values (negative value is a strong unambiguous signal of admixture). Thus, we could safely reject the hypothesis of mixture in ancestral components, and assume that each X components forms a simple tree with A and B components.