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Thursday, December 29, 2011

Last posting in the year 2011

Earlier this month I experimented with Chromopainter and fineSTUCTURE software. For the sake of comparision with ADMIXTURE/LAMP results, i used PLINK linkage file with previously extracted SNPs on Chr.6. The PLINK file included thinned set of LD-pruned SNPs (9155 SNPs in total). I also limited the dataset to include the projectäs participants only. I streamlined the phasing of this dataset by using Gusev's phasing_pipeline (which is indispensable for conversion between PLINK and BEAGLE format, and phasing genotypes with BEAGLE).

After that i deployed, a conversion script for going from PLINK linkage-style PED and MAP files to ChromoPainters PHASE and MAP files. The converted MAP file was merged with HapMap's pre-compiled recombination file for Chr.6 (in order to accomplish this task i used a simple buggy but efficient AWK script). This trick allowed me to avoid/skip the painstaking exercise in UNLINKED model of Chromopainter .

As a next step, i changed default -k parameter to 200 (to give approximately 200 random samples to estimate the local variance). It is important to mention, that Chromopainter could be run in two modes: "donor mode" (which assumes the prior existence of "donor" and "recipient" haplotypic populations) and "all against all" mode. I we used the latter approach in which a single haplotype within an individual is reconstructed using the haplotypes from all other individuals in the sample as potential donors. This process is repeated for every haplotype in turn, so every individual is ultimately reconstructed in terms of all the other individuals, i.e every individual in my sample is conditioned on all the other individual.

It took me approximately 1,45 hours to finish Chromopainter's job. After that in combined Chromopainter output files, creating the file "MDLP.Chr6.chunkcounts.out", which is the coancestry matrix that fineSTRUCTURE requires as input.

After that i fed fineSTRUCTURE with required input (the coancestry matrix) and let fineSTRUCTURE to find a correct number of sample's "splits" (clusters). Surprisingly, even in such a limited (LD-prunned and tiny) dataset of SNPs, fineSTRUCTURE was able to detect four population-like (2 East-European, 1 West-European and outliers) clusters. In comparision, ADMIXTURE had some troubles with fleshing out sample's K-clusters, when i ran it on both phased and unphased version of the same Chr6. dataset (you can see them in this Excel spreadsheet).
From what i gather, author's claim holds water: fineSTRUCTURE indeed outperform ADMIXTURE/STRUCTURE approaches: "We next turn to our fineSTRUCTURE model-based analysis, again considering the unlinked coancestry matrix even though strong and variable LD exists in the dataset. We first compared performance of our unlinked model to the popular ADMIXTURE [15] software (Figure 3B and D, details in Section S8). Encouragingly, as the number of 5Mb regions increased from 5 to 200 we saw a monotonic performance increase for the no-linkage model, separating all groups with 200 markers. Further, our approach outperformed ADMIXTURE, with the ADMIXTURE performance levelling at around 60% correlation with the truth. In practice, we observed ADMIXTURE successfully splitting groups A, B and C and mostly splitting C1 and C2, but not B1 and B2, as detailed in Figures S6-11. ADMIXTURE performs inference under a model where markers are treated as unlinked, and where individuals may have genomes made up of mixtures of inferred source populations, while our simulation incorporated drift between populations, but not admixture. To examine whether violations of both these modelling assumptions explain the different results, we simulated a new dataset with the same underlying population structure of 5 populations as before, but no linkage (i.e. independence) between markers within each population. We analysed these data with STRUCTURE, which uses a similar underlying model to that of ADMIXTURE, but includes a no-admixture model (Section S7). For small datasets, STRUCTURE slightly improved performance relative to our unlinked fineSTRUCTURE model, but for larger SNP numbers, fineSTRUCTURE was able to identify all population splits (K = 5) while again, STRUCTURE was able to split only populations A, B and C (K = 3). Thus, even when LD information is not used (or even present), fineSTRUCTURE can offer advantages in some settings over these existing approaches."

With this gist of fineSTRUCTURE methodology, is is now safe to conclude that the combined set of all 22 chromosome, the linkage model can do even better. 

The painting process gives three square matrices of size NxN (N=number of individuals): 1) the frequency individuals copy chunks from each other, 2) the average length of those chunks, and 3) the mutation rate. These contain different aspects of the ancestry history, and almost completely summarize population ancestry.

The origin of some individual haplotypes could be ascertained using more comprehensive GERMLINe matching algorithm. Indeed, after applying GERMLINE algorithm to BEAGLE-phased chromosomes (1..22), i can see that the pairwise matches in haplotypes located far away from the chromosomal centromere and telomeres, tend to cluster into "ethnic" groups. However, haplotypes which are observed near centromere, are rather omnipresent - as would be expected if these regions had low rates of recombination because of proximity to centromeres.  The IBD segments detected by GERMLINE are listed (per chromosome) below:

With the scope on xMHC (see our previous posting),Of special interest here are, perhaps, haplotype matches on Chromosome 6, which match the pattern of massive extensive sharing of linked SNPs in the region of xMHC  - Chrom6-cM

Tuesday, December 27, 2011

Experimental test: inferring HLA (haplo)types from DNA nucleotide sequences using HLA*IMP framework

Molecular differences between HLA alleles vary up to 57 nucleotides within the peptide binding coding region of human Major Histocompatibility Complex (MHC) genes, but it is still unclear whether this variation results from a stochastic process or from selective constraints related to functional differences among HLA molecules. Although HLA alleles are generally treated as equidistant molecular units in population genetic studies, DNA sequence diversity among populations is also crucial to interpret the observed HLA polymorphism (Buhler S, Sanchez-Mazas A, 2011 HLA DNA Sequence Variation among Human Populations: Molecular Signatures of Demographic and Selective Events. PLoS ONE 6(2): e14643. doi:10.1371/journal.pone.0014643).

Traditionally, serology has been used for HLA typing for many decades; however, serological typing of histocompatibility class II molecules depends on the adequate expression of these molecules on the surface of B lymphocytes, the availability of viable cells and a complete set of antisera. However, the application of molecular methods (RFLP, PCR, SSO etc.) to HLA typing has led to the situation whereby nearly every laboratory performs some DNA typing for the detection of HLA alleles.

Why HLA loci are so important?

 HLA loci present the highest degree of polymorphism of all human genetic systems. Prior knowledge of the extent of diversity is essential in the development and selection of molecular typing methods. Reliable allele frequencies are also important in allogeneic unrelated hematopoietic stem cell transplantation to determine the likelihood of finding closely HLA matched donors for each patient.The genetic diversity of the HLA loci is responsible for the efficiency of the immune system in eliminating cells carrying foreign antigens. There is a need to develop a measure of this genetic diversity in order to assess how different populations are equipped to respond to foreign-antigen exposure, and to evaluate the contribution of each HLA locus.

The HLA system has been extensively studied from an evolutionary perspective. The region contains a number of closely linked genes whose products control a variety of functions concerned with the regulation of immune responses. In addition, the genetic predisposition to over 40 diseases maps to this region. A number of observations indicate that strong selection is acting on the HLA region, including its extensive polymorphism with very even allele frequencies, the preferential occurrence of high levels of variability at positions critical to antigen recognition, the great age of alleles and the patterns of linkage disequilibrium among loci. The form of the selection is unknown. Although balancing selection is a strong candidate, it seems unlikely that only one selective mechanism is operating in this complex multigene family region. Mutation, recombination and gene conversion all contribute to the generation of HLA variability. The apparent great age of many HLA alleles revealed by phylogenetic analysis suggests that the absolute rate of production of new variants is not high. Detailed studies of population and evolutionary features of the HLA region are necessary for an informed discussion of the evolution of disease predisposing genes and epitopes, and of complex multigene families (Thomson G.HLA population genetics.1991 Jun;5(2):247-60.)
Nomenclature of HLA Alleles

Each HLA allele name has a unique number corresponding to up to four sets of digits separated by colons. The length of the allele designation is dependant on the sequence of the allele and that of its nearest relative. All alleles receive at least a four digit name, which corresponds to the first two sets of digits, longer names are only assigned when necessary.The digits before the first colon describe the type, which often corresponds to the serological antigen carried by an allotype. The next set of digits are used to list the subtypes, numbers being assigned in the order in which DNA sequences have been determined. Alleles whose numbers differ in the two sets of digits must differ in one or more nucleotide substitutions that change the amino acid sequence of the encoded protein. Alleles that differ only by synonymous nucleotide substitutions (also called silent or non-coding substitutions) within the coding sequence are distinguished by the use of the third set of digits. Alleles that only differ by sequence polymorphisms in the introns or in the 5' or 3' untranslated regions that flank the exons and introns are distinguished by the use of the fourth set of digits (further information:


HLA-A    identifies HLA A locus
HLA-A1 serologically defined antigen
HLA-A* asterisk denotes HLA alleles defined by molecular methods
HLA-A*01 2 digit resolution denotes a group of alleles corresponds usually to serological group – low resolution
HLA-A*0101 4 digit resolution – sequence variation between alleles results in amino acid substitutions
HLA-A010101 6 digit resolution – non coding variation: sequence changes synonymous no amino acid substitution

HLA types and linked SNPs
 Still, classical HLA alleles (e.g. HLA-, HLA-B, etc.) are tricky to analyze with chip technology employed in popular commercial personal genomic services (23andme, FTDNA's Family Finder and deCODEme) , because analysis is a complex process, requiring a large number of multiplex PCR reactions to obtain a patient's full genotype. That is why classical HLA typing methods are often imparctical for large-scale studies.

Fortunately for us , Wellcome Trust Centre for Human Genetics described a method for imputing classical alleles from linked SNP genotype data, implemented in the imputation framework (HLA*IMP) (Alexander Dilthey, Loukas Moutsianas, Stephen Leslie, and Gil McVean. HLA*IMP – an integrated framework for imputing classical HLA alleles from SNP genotypes Bioinformatics btr061 first published online February 7, 2011 doi:10.1093/bioinformatics/btr061).

HLA*IMP  imputes HLA type information based on SNP genotype, using methods of iterative model building to select a set of informative SNPs  for particular supported genotyping chips (AffyMetrix 500K, AffyMetrix 900K, Illumina 300K, Illumina 550K,Illumina 650K, Illumina 1M). Thus, HLA*IMP enables researchers to perform classical HLA allele imputation from genotype data collected from several available genome-wide SNP sets through reference to a reference data set of over 2,500 samples of European ancestry with dense SNP data and classical HLA allele types. This reference set includes:
  • The 1958 Birth Cohort  typed both on the Illumina 1.2M and Affymetrix Genome-Wide Human SNP Array 6.0 chips (TheWellcome Trust Case Control Consortium, 2007) - 2420 genotype samples x 7733 SNP  in the extended HLA region.
  • The HapMap CEU samples (The International HapMap Consortium, 2007) and CEPH CEU+ additional samples (de Bakker et al., 2006)    - 92 samples x 7733 BC58-overlapping SNPs)

SNP haplotypes for the 1958BC and CEU+ samples were phased using IMPUTE v2  using the trio-phased HapMap samples as a reference dataset. Classically typed HLA genotypes were then phased into SNP haplotypes by using PHASE (Stephens and Scheet, 2005) applying standard settings for multiallelic loci. The combined reference dataset consists of 5024 haplotypes with data on 7733 SNPs in the HLA region. This splits up into 2474 (HLA-A), 3090 (HLA-B), 2022 (HLA-C), 175 (HLA-DQA1), 2629 (HLA-DQB1), 2665 (HLA-DRB1) locus-specific haplotypes which are used for inference.

The experiment with MDLP dataset


As i previously mentioned in my blog (re: Chromosome 6), 23andme's RelativeFinder and AncestryFinder revealed (among my results) a cluster of a prominent number of HIR-matches (315), positioned in the exactly the same subregion of HLA-MHC domain on Chr.6 (21Mb-38Mb). This remarkable group of matches constitutes almost a half of my total AF/RF matches (315/720 or 43,75%).

Earlier i suggested that such a preponderance of HIR-matches to HLA region is indicative of case of sharing the same extended HLA haplotype. Until recently, my suggestion rested solely on my intiution.  Then, i found solution to HLA*IMP challenges and so far, i've managed to make HLA*IMP work with 23andme genotyper (Illumina Omnio Express) call raw data.

For my test, i needed to make sure that my data meets the following requirements:

  • SNPs must be from the xMHC region (chromosome 6) 
  • European ancestry of the typed individuals 
  • sufficient quality and typed SNP density in the HLA region to enable reliable phasing
  • since HLA*IMP doesn't provide a direct support for 23andme's chips and i was using the combined set of genotypes from 23andme's chipset v2 and chipset v3, i've choosed "a downgraded" version of Illumina genotyping platform (Illumina 300K)
In order to my initial assumption of HLA haplotype sharing i selected 7 participants from my project (including 4 individuals without  HIR-sharing on Chromosome 6 (not detected by 23andme's AF)  me, my mother and an individual, who has a HIR-match with me and my mother in xMHC region). I  converted 23andme's raw data of the project's participants into Plink format,  merged the files into one dataset and extracted SNPs on Chromosome 6 using Plink command --chr 6. After that, i converted the genotype data from Plink format to HLA*IMP input data format. As a next step, i carried out quality control  on the genotype data, removing SNPs and individuals with too much missing data and aligning complementary SNPs to the HapMap reference strand. Finally, i phased  included genotypes to obtain haplotype data. Note: each individual was anonymized by substituting project's ID with prefix N.

The haplotype data was uploaded to HLA*IMP back-end web service    for imputing HLA types.

The imputed HLA types look as follows (each individual is represented by 2 HLA haplotypes in HLA-A:HLA-B:HLA-C:HLA-DQA:HLA-DQB:HLA-DRB format)
N1 1 101 801 701 501 201 301
N1 2 2601 2705 102 101 501 101
N6 1 3101 801 701 501 201 301
N6 2 201 1501 304 501 201 301
N3 1 6801 1501 102 101 501 101
N3 2 2301 5201 501 101 501 101
N2 1 101 801 701 501 201 301
N2 2 2601 3801 1203 102 602 1501
N5 1 301 1501 304 501 302 401
N5 2 205 5001 602 501 202 701
N7 1 101 801 701 501 301 1101
N7 2 101 1501 303 103 604 1301
N4 1 301 702 702 401 402 801
N4 2 2402 4002 202 501 301 1101

The haplotypes should be read as  follows (for example, in case of N1): HLA A*0101 : Cw*0701 : B*0801 : DRB1*0301 : DQA1*0501 : DQB1*0201. 

From the table posted above, one can easily mention a matching pattern between  N1, N2 and N7, as they share the similiar haplotype.  As a matter of fact N1 (ny mother), N2 (me) and N7 share HIR-segment (matching DNA segment equal to or greater than seven Centimorgans (abbreviated as cMs) on one segment with 700 or more SNPs), found by RelativeFinder service at 23andme.

Thus, it is safe to conclude that the imputation was accurate and  my initial suggestion seems to be backed up by HLA imputation

The practical implications of test

Aside from the medical usefulness, there are also some benefits of knowing HLA type for genetic genealogists:

1) First of all,  it is possible to determine the nature of the sharing the segments in xMHC region on Chromosome 6. Consider the aforementioned extended haplotype HLA A*0101 : Cw*0701 : B*0801 : DRB1*0301 : DQA1*0501 : DQB1*0201 (in shorthand: A1::DQ2).A1::DQ2 haplotype creates a conundrum for the evolutionary study of recombination.A1::DQ2 does not follow the expected dynamics. Other haplotypes exist in the region of Europe where this haplotype formed and expanded, some of these haplotypes also are ancestral and also are quite large. At 4.7 million nucleotides in length and ~300 genes the locus had resisted the effects of recombination, either as a consequence of recombination-obstruction within the DNA, as a consequence of repeated selection for the entire haplotype, or both. The length of the haplotype is remarkable because of the rapid rate of evolution at the HLA locus should degrade such long haplotypes. A1::DQ2 is the most frequent haplotype of its length found in US Caucasians, ~15% carry this common haplotype. The SNP analysis of the haplotype suggests a potential founding affect of 20,000 years within Europe, though conflicts in interpreting this information are now apparent. The last possible point of a constriction forcing climate was the Younger Dryas before 11,500 calendar years ago, and so the haplotype has taken on various forms of the name, Ancestral European Haplotype, lately called Ancestral Haplotype A1-B8 (AH8.1). It is one of 4 that appear common to western Europeans and other Asians. Assuming that the haplotype frequency was 50% at the Younger Dryas and declined by 50% every 500 years the haplotypes should only be present below 0.1% in any European population. Therefore it exceeds the expected frequency for a founding haplotype by almost 100 fold. For genetic genealogist, the said would mean that the hotspot of  DNA matching in xMHC region could be indicative of very distant common ancestor, probably as recent as Neolithic era.

2)It is also possible to infer the geographic ditribution of  a particular HLA haplotype. Using the same A 1::DQ2 haplotype, we could see that it is found in Iceland, Pomors of Northern Russia, the Serbians of Northern Slavic descent, Basque, and areas of Mexico where Basque settled in larger numbers. The haplotypes great abundance in the most isolated geographic region of Western Europe, Ireland, in Scandinavians and Swiss suggests that low abundance in France and Latinized Iberia are the result of displacements that took place after the Neolithic onset. This implies a founding presence in Europe that exceeds 8000 years.  

The database of allele frequencies is a very convenient tool for analyzing HLA haplotype frequency in a worldwide scale.

1 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01 Ireland South
2 A*01:01-B*08:01-C*07:01-DRB1*03:01:01-DQB1*02:01 England North West
3 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*04:01 Ireland South
4 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01 Poland
5 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01 USA Hispanic pop 2
6 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*01:01 Ireland South
7 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01 USA African American pop 4
8 A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01 USA Asian pop 2