Steven J. Mack

Balancing selection and heterogeneity across the classical human leukocyte antigen loci: a meta-analytic review of 497 population studies

This paper presents a meta-analysis of high-resolution human leukocyte antigen (HLA) allele frequency data describing 497 population samples. Most of the datasets were compiled from studies published in eight journals from 1990 to 2007; additional datasets came from the International Histocompatibility Workshops and from the AlleleFrequencies.net database. In all, these data represent approximately 66,800 individuals from throughout the world, providing an opportunity to observe trends that may not have been evident at the time the data were originally analyzed, especially with regard to the relative importance of balancing selection among the HLA loci. Population genetic measures of allele frequency distributions were summarized across populations by locus and geographic region. A role for balancing selection maintaining much of HLA variation was confirmed. Further, the breadth of this meta-analysis allowed the ranking of the HLA loci, with DQA1 and HLA-C showing the strongest balancing selection and DPB1 being compatible with neutrality. Comparisons of the allelic spectra reported by studies since 1990 indicate that most of the HLA alleles identified since 2000 are very-low-frequency alleles. The literature-based allele-count data, as well as maps summarizing the geographic distributions for each allele, are available online.

Signatures of demographic history and natural selection in the human major histocompatibility complex Loci.

Many lines of evidence show that several HLA loci have experienced balancing selection. However, distinguishing among demographic and selective explanations for patterns of variation observed with HLA genes remains a challenge. In this study we address this issue using data from a diverse set of human populations at six classical HLA loci and, employing a comparative genomics approach, contrast results for HLA loci to those for non-HLA markers. Using a variety of analytic methods, we confirm and extend evidence for selection acting on several HLA loci. We find that allele frequency distributions for four of the six HLA loci deviate from neutral expectations and show that this is unlikely to be explained solely by demographic factors. Other features of HLA variation are explained in part by demographic history, including decreased heterozygosity and increased LD for populations at greater distances from Africa and a similar apportionment of genetic variation for HLA loci compared to putatively neutral non-HLA loci. On the basis of contrasts among different HLA loci and between HLA and non-HLA loci, we conclude that HLA loci bear detectable signatures of both natural selection and demographic history.

Immunogenetics as a tool in anthropological studies

The genes coding for the main molecules involved in the human immune system – immunoglobulins, human leucocyte antigen (HLA) molecules and killer‐cell immunoglobulin‐like receptors (KIR) – exhibit a very high level of polymorphism that reveals remarkable frequency variation in human populations. ‘Genetic marker’ (GM) allotypes located in the constant domains of IgG antibodies have been studied for over 40 years through serological typing, leading to the identification of a variety of GM haplotypes whose frequencies vary sharply from one geographic region to another. An impressive diversity of HLA alleles, which results in amino acid substitutions located in the antigen‐binding region of HLA molecules, also varies greatly among populations. The KIR differ between individuals according to both gene content and allelic variation, and also display considerable population diversity. Whereas the molecular evolution of these polymorphisms has most likely been subject to natural selection, principally driven by host–pathogen interactions, their patterns of genetic variation worldwide show significant signals of human geographic expansion, demographic history and cultural diversification. As current developments in population genetic analysis and computer simulation improve our ability to discriminate among different – either stochastic or deterministic – forces acting on the genetic evolution of human populations, the study of these systems shows great promise for investigating both the peopling history of modern humans in the time since their common origin and human adaptation to past environmental (e.g. pathogenic) changes. Therefore, in addition to mitochondrial DNA, Y‐chromosome, microsatellites, single nucleotide polymorphisms and other markers, immunogenetic polymorphisms represent essential and complementary tools for anthropological studies.

Tracking human migrations by the analysis of the distribution of HLA alleles, lineages and haplotypes in closed and open populations

The human leucocyte antigen (HLA) system shows extensive variation in the number and function of loci and the number of alleles present at any one locus. Allele distribution has been analysed in many populations through the course of several decades, and the implementation of molecular typing has significantly increased the level of diversity revealing that many serotypes have multiple functional variants. While the degree of diversity in many populations is equivalent and may result from functional polymorphism(s) in peptide presentation, homogeneous and heterogeneous populations present contrasting numbers of alleles and lineages at the loci with high-density expression products. In spite of these differences, the homozygosity levels are comparable in almost all of them. The balanced distribution of HLA alleles is consistent with overdominant selection. The genetic distances between outbred populations correlate with their geographical locations; the formal genetic distance measurements are larger than expected between inbred populations in the same region. The latter present many unique alleles grouped in a few lineages consistent with limited founder polymorphism in which any novel allele may have been positively selected to enlarge the communal peptide-binding repertoire of a given population. On the other hand, it has been observed that some alleles are found in multiple populations with distinctive haplotypic associations suggesting that convergent evolution events may have taken place as well. It appears that the HLA system has been under strong selection, probably owing to its fundamental role in varying immune responses. Therefore, allelic diversity in HLA should be analysed in conjunction with other genetic markers to accurately track the migrations of modern humans.

A community standard for immunogenomic data reporting and analysis: proposal for a STrengthening the REporting of Immunogenomic Studies statement

Modern high-throughput HLA and KIR typing technologies are generating a wealth of immunogenomic data with the potential to revolutionize the fields of histocompatibility and immune-related disease association and population genetic research, much as SNP-based approaches have revolutionized association research. The STrengthening the REporting of Genetic Association studies (STREGA) statement provides community-based data reporting and analysis standards for genomic disease-association studies, identifying specific areas in which adoption of reporting guidelines can improve the consistent interpretation of genetic studies. While aspects of STREGA can be applied to immunogenomic studies, HLA and KIR research requires additional consideration, as the high levels of polymorphism associated with immunogenomic data pose unique methodological and computational challenges to the synthesis of information across datasets. Here, we outline the principle challenges to consistency in immunogenomic studies, and propose that an immunogenomic-specific analog to the STREGA statement, a STrengthening the REporting of Immunogenomic Studies (STREIS) statement, be developed as part of the 16th International HLA and Immunogenetics Workshop. We propose that STREIS extends at least four of the 22 elements of the STREGA statement to specifically address issues pertinent to immunogenomic data: HLA and KIR nomenclature, data-validation, ambiguity resolution, and the analysis of highly polymorphic genetic systems. As with the STREGA guidelines, the intent behind STREIS is not to dictate the design of immunogenomic studies, but to ensure consistent and transparent reporting of research, facilitating the synthesis of HLA and KIR data across studies.

Genotype List String: a grammar for describing HLA and KIR genotyping results in a text string

Knowledge of an individuals human leukocyte antigen (HLA) genotype is essential for modern medical genetics, and is crucial for hematopoietic stem cell and solid-organ transplantation. However, the high levels of polymorphism known for the HLA genes make it difficult to generate an HLA genotype that unambiguously identifies the alleles that are present at a given HLA locus in an individual. For the last 20 years, the histocompatibility and immunogenetics community has recorded this HLA genotyping ambiguity using allele codes developed by the National Marrow Donor Program (NMDP). While these allele codes may have been effective for recording an HLA genotyping result when initially developed, their use today results in increased ambiguity in an HLA genotype, and they are no longer suitable in the era of rapid allele discovery and ultra-high allele polymorphism. Here, we present a text string format capable of fully representing HLA genotyping results. This Genotype List (GL) String format is an extension of a proposed standard for reporting killer-cell immunoglobulin-like receptor (KIR) genotype data that can be applied to any genetic data that use a standard nomenclature for identifying variants. The GL String format uses a hierarchical set of operators to describe the relationships between alleles, lists of possible alleles, phased alleles, genotypes, lists of possible genotypes, and multilocus unphased genotypes, without losing typing information or increasing typing ambiguity. When used in concert with appropriate tools to create, exchange, and parse these strings, we anticipate that GL Strings will replace NMDP allele codes for reporting HLA genotypes.

Genotype List String

Knowledge of an individuals human leukocyte antigen (HLA) genotype is essential for modern medical genetics, and is crucial for hematopoietic stem cell and solid-organ transplantation. However, the high levels of polymorphism known for the HLA genes make it difficult to generate an HLA genotype that unambiguously identifies the alleles that are present at a given HLA locus in an individual. For the last 20 years, the histocompatibility and immunogenetics community has recorded this HLA genotyping ambiguity using allele codes developed by the National Marrow Donor Program (NMDP). While these allele codes may have been effective for recording an HLA genotyping result when initially developed, their use today results in increased ambiguity in an HLA genotype, and they are no longer suitable in the era of rapid allele discovery and ultra-high allele polymorphism. Here, we present a text string format capable of fully representing HLA genotyping results. This Genotype List (GL) String format is an extension of a proposed standard for reporting killer-cell immunoglobulin-like receptor (KIR) genotype data that can be applied to any genetic data that use a standard nomenclature for identifying variants. The GL String format uses a hierarchical set of operators to describe the relationships between alleles, lists of possible alleles, phased alleles, genotypes, lists of possible genotypes, and multilocus unphased genotypes, without losing typing information or increasing typing ambiguity. When used in concert with appropriate tools to create, exchange, and parse these strings, we anticipate that GL Strings will replace NMDP allele codes for reporting HLA genotypes.

Analytical Methods for Disease Association Studies with Immunogenetic Data

Disease association studies involving highly polymorphic immunogenetic data may involve analyses at one or many units of analysis, including amino acid, allele, genotype and haplotype levels, as well as consideration of gene-gene or gene-environment interactions. The selection of the appropriate statistical tests is critical and will be dependent on the nature of the dataset (e.g., case-control vs. family data) as well as the specific research hypotheses being tested. This paper describes the various study and analysis categories used for such analyses, including the advantages and limitations of such techniques.

Genetic origin of the Swedish Sami inferred from HLA class I and class II allele frequencies

Sami of northern Scandinavia are genetic outliers among European populations and their origin has been difficult to determine. In order to study the genetic origin of the Swedish Sami, we have performed high-resolution typing of the class I HLA-A and -B loci and the class II DRB1, DQB1 and DQA1 loci in the northern and southern Swedish Sami. Several of the common class I alleles in Sami (B*0702, B*1501, B*4002 and A*0301) are found at high frequency in other European populations. However, a number of class I and class II alleles (B*4001, A*2402, DRB1*0901 and DRB1*1101) in the Swedish Sami are characteristic of Asian populations. Admixture analyses indicate that 87% of the Sami gene pool is of European origin and that the Asian contribution is 13%. Our HLA analyses indicate a higher proportion of Asian ancestry in the Sami than shown by previous genetic studies.

Bridging ImmunoGenomic Data Analysis Workflow Gaps (BIGDAWG): An integrated case-control analysis pipeline

Bridging ImmunoGenomic Data-Analysis Workflow Gaps (BIGDAWG) is an integrated data-analysis pipeline designed for the standardized analysis of highly-polymorphic genetic data, specifically for the HLA and KIR genetic systems. Most modern genetic analysis programs are designed for the analysis of single nucleotide polymorphisms, but the highly polymorphic nature of HLA and KIR data require specialized methods of data analysis. BIGDAWG performs case-control data analyses of highly polymorphic genotype data characteristic of the HLA and KIR loci. BIGDAWG performs tests for Hardy-Weinberg equilibrium, calculates allele frequencies and bins low-frequency alleles for k×2 and 2×2 chi-squared tests, and calculates odds ratios, confidence intervals and p-values for each allele. When multi-locus genotype data are available, BIGDAWG estimates user-specified haplotypes and performs the same binning and statistical calculations for each haplotype. For the HLA loci, BIGDAWG performs the same analyses at the individual amino-acid level. Finally, BIGDAWG generates figures and tables for each of these comparisons. BIGDAWG obviates the error-prone reformatting needed to traffic data between multiple programs, and streamlines and standardizes the data-analysis process for case-control studies of highly polymorphic data. BIGDAWG has been implemented as the bigdawg R package and as a free web application at bigdawg.immunogenomics.org.