A quick update to SupportMix's Chromosome Painting

A thoughtful reader of our blog has noticed that some of chromosome_paintings (Chr 5 set 9, Chr 7 sets 4,5; Chr 9 set 7, Chr 11 set 4) were missing in the original tar.gz bundle  distributed via Google Data Drive. I've had to re-upload the archive (with missing files), the new location of  the archive is here.

Additional experiment.

In addition to that quick fix, i decided to test the accuracy of SupportMix's chromosome paintings by juxtaposing them over the MDLP-World22 chromosome graphs. Due to time limitations, i used only first 7 chromosomes of my own SNP data. At first, i ran the MDLP-World22 modification of DIYDodecad v2.1 in byseg mode on "windows" of 500 contiguous SNPs along a chromosome, slided  by increments of 50. After that i cut out chromosome paintings of each chromosome from SupportMix's graphic output and aligned them to the scale of corresponding DIYDodecad chromosome graphs:

 After the preliminary evaluation of results, i have mentioned an approximate correlation between the byseg-output MDLP World22-DIYcalculator and SupportMix for two major "components" in my genome (North-East-European and Atlantic-Mediterranean). Moreover, "Near-Eastern segments" (assigned by SupportMix"  partially overlaps with the peaks of "Near-East segments" in DIYDodecad output. However, the situation with the minor components is much less uncertain. The lack of correlation for the minor components could be explained by different factors:

1) DIYDodecad operates on the unphased raw data of genotypes

2) DIYDodecad program doesn't take into consideration genetic distance/recombination

3) last, but not least: small segments may appear more noisier than they are, because there may not be any informative SNPs in a particular region to distinguish between some of the minor ancestral components (Dienekes Pontikos' observation)

UPDATE: At first i thought that it would be a great idea to calculate the index of correlation between 'byseg' output and SupporMix' Tprobs output file. But it seems that the results are not directly comparable - the assignment of segments in 'byseg' is measured in frequencies, while the assignment of segments in SupportMix is expressed by probability of assignment. If someone has a solution to this problem, please let me know.

Geography of Ancestry: the SPA analysis of the MDLP participants

Geography of Ancestry:  the SPA analysis of the MDLP participants

A team of researchers (Wen-Yun Yang, John Novembre, Eleazar Eskin, Eran Halperin) from Tel Aviv University (TAU) and University of California, Los Angeles (UCLA) have created a method for more precisely pinpointing the geographic origin of a person's ancestry by developing an understanding of the spatial diversity of genes. The analysis of  diversity of genes within and between populations has broad applications in studies of human disease and human migrations. The afore-mentioned team of researchers proposed a new approach, spatial ancestry analysis, explicitly modeling the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space.
 

Although the authors were more concerned with detecting the signals of selective sweeps in human genome, the SPA software implements some interesting features that could be immediately applied to the analysis of genetic data collected in open genome projects.

The most important one is that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone.

From the original paper on model-based approach for analysis of spatial structure in genetic data:

If the geographic origins of the individuals are known, one can use this information to infer their allele frequency functions at each SNP. However, if locations are not known, our model can infer geographic origins for individuals using only their genetic data, in a manner similar in spirit to PCA-based approaches for spatial assignment.

 The experiment

Since the authors have made their software  publicly available, I have decided to give SPA software a try. A learning curve  was very smooth, because three of five supported formats are in Plink format (with which i am familiar). Actually, the hardest part of experiment with SPA analysis was deciding what to do with the unknown geographic origins of the MDLP participants. Following the hint found in another interesting paper (A Quantitative Comparison of the Similarity between Genes and Geography in Worldwide Human Populations), i divided the experiment into three parts:

1) first of all, i obtained the geographic coordinates (lats/longs) of  each population included in the run.

2) then i carried out  SPA analysis with 3 specified dimensions

3) after the SPA analysis was finished, i applied Procrustes analysis  to compare the individual-level coordinates of the first two components (1 and 2) in the SPA performed on the SNP data (1440447 snps) to the geographic coordinates

4) using Procrustes analysis, i identified an optimal alignment of the genetic coordinates to the (Gilbert-projected) geographic coordinates that involved a rotation of the longitudes and latitudes  by 16 counterclockwise.

5) finally, i projected the individual coordinates (which have been previously corrected for the optimal Procrustes alignment) onto the geographic map of Eurasia.

The MDLP participants can find their final geographic coordinates in the corresponding spreadsheet.

The allele frequency gradients and signals of recent positive selection

Another cool feature of SPA software is that it is able to identify loci showing extreme frequency gradients (i.e loci under selection), which does not require grouping individuals into populations. These are SNPs that show steep slopes of allele frequency change, with the consideration that some of these might show extreme gradients because of the impact of recent positive selection. 

The analysis of selective sweeps (as well as their possible implications) belongs to the domain of the molecular biology and  medical genetics,  and due to the project limitation i am not going to discuss them in all details. I'll limit my discussion by the following observations:   the direction gradients of allele frequencies resembles the presupposed genetic flow from East Eurasia to West Eurasia, and from South-Europe to North-Europe. The first two dimensions of SPA capture the main features of variation on the well-known East-West Eurasian cline, while the second and third dimension represent the gene flow from South-Europe to  North-Europe.

 

I've sorted SNPs according to the value of slope function and it appears that the most extreme individual value is detected in rs7568419 - a SNP, which is believed to have linked to a genetically inherited trait. Researchers at 23andMe have identified two genetic variants associated with the trait in people of European ancestry. The C version of rs10953183 is associated with more pronounced chin dimple and the C version of rs7568419 is associated with less of a chin dimple.

A couple of factoids about a cleft chin from Wikipedia:

"This is an inherited trait in humans, where the dominant gene causes the cleft chin while the recessive genotype presents without a cleft. However, it is also a classic example for variable penetrance^[5] with environmental factors or a modifier gene possibly affecting the phenotypical expression of the actual genotype. Although cleft chins are seen throughout the world, they are most predominate among people of Germanic and West Slavic (i.e., Polish) ethnicity. It is very common in that part of the world and among descendants of people originating in that part of Europe.^[6]It seems particularly prevalent among people living in the former Prussian areas of northern Poland bordering the Baltic Sea."

Those who are interested in more detailed analysis of loci under selection, could find SPA output file in the corresponding spreadsheet (note: a value of slope function in the last column). If you'll find an interesting SNP association with a particular trait, please report your finding to me.

 

 

Free SNP test offer from DNATribes

I'm encouraging all MDLP participants who haven't had yet an opportunity to receive  DNATribes Free SNP offer, send their Raw Data to Luke Martin for a free-of-charge analysis.

Free SNP Offer for Magnus Ducatus Lithuaniae Participants

"Good afternoon,

We have followed with interest your Magnus Ducatus Lituaniae Project. In case you or any of your participants are interested, we would like to offer a free DNA Tribes SNP analysis for unrelated participants with four grandparents from the same country. (Grandparent birth places of U.S. and Canada are not included in this offer.) This would help us include less sampled populations in our geographical analysis.

For free DNA Tribes SNP analysis, interested project participants can fill out the grandparent form (http://dnatribes.com/snpdiscount-project.html) and email their zipped genome file to snpsubmit@dnatribes.com between now and October 1, 2012.

If you have any questions, please let me know at dna@dnatribes.com.

Best regards,
Lucas Martin"

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.

 

Paint Me a Rainbow: Painting World 22 ancestral components

This update will be concerned with inter-related concepts of  "chromosome painting" and admixture. Modern genetics and personal genomics, particularly in the last 2-5 years, had paid very considerable attention to them, sometimes under the rubric of " determining ancestral origin of genomic segments".

In 2011 year, i performed a couple of experiments with LAMP software (cf. Experimental test III: (De)constructing ancestry with LAMP  and Experimental test: estimating ancestries at each locus in a population of admixed individuals (LAMP))).

Although the experiments met with moderate success, i wasn't satisfied with the results and decided to postpone the forthcoming experiments with chromosome painting to a future day. In so doing, i endorsed increasing  appreciation of the distinction between  population stratification's algorithms, implemented in LAMP and ADMIXTURE.

I have already discussed the differences between LAMP and Admixture, but in illustrating the idea of experiment, i need to turn to my previous explanation again: 

1) ADMIXTURE software  implements a model-based approach to estimate ancestry coefficients as the parameters of a statistical model. It is also important to add that the model-based approach in ADMIXTURE is based  on the global ancestry paradigm (i.e the goal of  ADMIXTURE/STRUCTURE analysis is to estimate the proportion of ancestry from each contributing population, considered as an average over the individual's entire genome).

2) LAMP software is built upon an efficient dynamic-programming algorithm WINPOP that infers locus-specific ancestries.Genome is partitioned into chromosome segments of definite ancestral origin (overlapping, contiguous windows of SNPs) and likelihood model optimized over each window. The goal then is to find the segment boundaries and assign each segment's origin.

I understand the problem in terms of the ancestry assignment. My experience shows that methods based on  the inference of locus-specific ancestries are usually very accurate for ancestral deconvolution of genotype data that has consistently been shown to do better than more popular statistical and PCA-based methods, while being able:
1) to handle more than two ancestral populations
2) to model the paths of recombination between ancestral segments.In June 2012, Jason Mezey Lab (Cornell University) released SupportMix - a machine learning algorithm for determining ancestral origin of genomic segments when analyzing individuals from a population with a recent or ancient history of admixture. As regards the accuracy of the software, the authors argued that SupportMix provides a robust tool for accurate and robust ancestral assignment by simultaneous analysis of a worldwide selection of ancestral populations. Such analyses will be critical for accurate assignment in the many world-wide admixed populations that are likely to have unexpected ancestry that reflects a richer history than known from anthropological or historical studies (from the provisional paper: Omberg et al.2012 "Inferring genome-wide patterns of admixture in Qataris using fifty-five ancestral populations"). The cited paper includes a number of other claims important for any analysis of genetic admixture: the accuracy of ancestry assignment was lower for more closely related populations but better than LAMP-ANC, a method that was been shown to consistently outperform other ancestry deconvolution methods.

This overly optimistic conclusion influenced my choice between LAMP-ANC and SupportMix in favor of latter. To be honest, i'm not the first genome blogger to use Supportmix - in July of 2012 Polako from Eurogenes carried out a loci-specific analysis of Finnish genomes using SupprotMix. I decided to repeat this experiment. There is, of course, a significant  difference between Polako's analaysis and my experiment. While Polako was using the modern populations as 'putative' donor populations, the final goal of my design was to imitate the results of the long-awaited update of 23andme's Ancestry Painting, which is is being updated to offer more detailed results based on approximately 20 world regions, drawn from both customer data and academic reference populations. In order to do so, i have used the dummy set of 22 simulated putative ancestral populations simulated from the allele frequencies of the World-22 calculator.

The experiment

SupportMix requires at least three input files. One file for each of the putative ancestral populations and one file containing the genetic information of the admixed individuals with additional requirement that he markers have to be phased. Each population should be represented by two files in Plink transposed format, a .tped file and a .tfam file.

The markers (80751 SNPs) were phased per each chromosome using default settings in BEAGLE software. While the original Plink format does not specify the order of the alleles in in the file, SupportMix works with phased data. Keeping that in mind, i have converted BEAGLE-phased dataset directly into Plink's tped format without pre-processing the dataset in Plink (hint: Kantele's beagle_to_tped script). Then i used UNIX text processing utilities to extract the genotypes from ancestral populations into the corresponding subsets ('references') and split the project dataset (93 individuals) into 9 subgroups.

Finally, i have interpolated the genetic map position of each SNP along chromosome using Rutgers genetic maps.
 SupportMix was run by specifying a configuration file with the default options: (window_size =400, generations_from_admixture_event=6).

Below are some screenshots of  SupportMiX output for the MDLP project participants:

Please note that each 'recipient' (i.e the project participant) is represented by two phased chromosomes, i.e V199_a and V199_b. The color legend of the components used in the analysis, has been attached to the right side of the plot.

The complete set (Chr.1-22 for all project participants) in tar.gz format (14.6 Mb) could be downloaded here.


SupportMix results: what to do next.

First of all, i encourage every participant of my project to compare their results to "chromosome painting" in MDLP World-22 calculator on John Olson's Gedmatch site. I have to contemplate the possibility that the painting on Gedmatch site might be different from that one produced by SupportMix. I would also suggest to compare SupportMix's paintings to other calculators' paintings and 23andme's Ancestry Finder, etc.

If you are familiar with basic techniques of image editing software, then it is a good idea to have your chromosomes cut&merged into the composite image (see example below):

Chromosome Painting (in 23andme's  style)

Chromosome Painting (an imitation of 23andme's Ancestry Finder)