How natural selection shapes genetic differentiation in the MHC region: A case study with Native Americans

The most frequent HLA alleles around the world: A fundamental synopsis

A comprehensive knowledge of human leukocyte antigen (HLA) molecular variation worldwide is essential in human population genetics research and disease association studies and is also indispensable for clinical applications such as allogeneic hematopoietic cell transplantation, where ensuring HLA compatibility between donors and recipients is paramount. Enormous progress has been made in this field thanks to several decades of HLA population studies allowing the development of helpful databases and bioinformatics tools. However, it is still difficult to appraise the global HLA population diversity in a synthetic way. We thus introduce here a novel approach, based on approximately 2000 data sets, to assess this complexity by providing a fundamental synopsis of the most frequent HLA alleles observed in different regions of the world. This new knowledge will be useful not only as a fundamental reference for basic research, but also as an efficient guide for clinicians working in the field of transplantation.

HLA allelic diversity in the Waorani population of Ecuador: Its significance to their ancestry and migration

The Waorani, an isolated indigenous tribe in Ecuador, have long been characterized by limited genetic diversity, with few studies delving into their genetic background. Human Leukocyte Antigen (HLA) genes which are located in the human major histocompatibility complex (MHC) provides valuable insights into population evolution due to its highly polymorphic nature. However, little is known about the HLA diversity and ancestry of the Waorani population. In this study, we sequenced eight HLA genes using Next Generation Sequencing (NGS) from 134 Waorani individuals and obtained up to four-field HLA allele resolution. Cluster and phylogenetic analysis show that the Waorani are genetically distant from other Ecuador populations, but instead show genetic affinities with the Puyanawa and Terena tribes from Brazil, as well as the Mixe tribe from Mexico. The identification of alleles common within the Waorani population, previously linked to specific health conditions, notably paves the way for future association analyses. This extensive study, employing Next-Generation Sequencing (NGS) technology, significantly enriches the sparse and segmented understanding of HLA diversity in the South American region. Our findings enhance the global comprehension of human genetic diversity and underscore the value of studying indigenous populations. Such research is vital for deepening our insights into human migration patterns and evolutionary processes.

PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

Human genetic determinants of COVID-19 in Brazil: challenges and future plans

COVID-19 pandemic represented a worldwide major challenge in different areas, and efforts undertaken by the scientific community led to the understanding of some of the genetic determinants that influence the different COVID-19 outcomes. In this paper, we review the studies about the role of human genetics in COVID-19 severity and how Brazilian studies also contributed to those findings. Rare variants in genes related to Inborn Errors of Immunity (IEI) in the type I interferons pathway, and its phenocopies, have been described as being causative of severe outcomes. IEI and its phenocopies are present in Brazil, not only in COVID-19 patients, but also in autoimmune conditions and severe reactions to yellow fever vaccine. In addition, studies focusing on common variants and GWAS studies encompassing worldwide patients have found several loci associated with COVID-19 severity. A GWAS study including only Brazilian COVID-19 patients identified a new locus 1q32.1 associated with COVID-19 severity. Thus, more comprehensive studies considering the Brazilian genomic diversity should be performed, since they can help to reveal not only what are the genetic determinants that contribute to the different outcomes for COVID-19 in the Brazilian population, but in the understanding of human genetics in different health conditions.

Understanding the evolution of immune genes in jawed vertebrates

Driven by co-evolution with pathogens, host immunity continuously adapts to optimize defence against pathogens within a given environment. Recent advances in genetics, genomics and transcriptomics have enabled a more detailed investigation into how immunogenetic variation shapes the diversity of immune responses seen across domestic and wild animal species. However, a deeper understanding of the diverse molecular mechanisms that shape immunity within and among species is still needed to gain insight into-and generate evolutionary hypotheses on-the ultimate drivers of immunological differences. Here, we discuss current advances in our understanding of molecular evolution underpinning jawed vertebrate immunity. First, we introduce the immunome concept, a framework for characterizing genes involved in immune defence from a comparative perspective, then we outline how immune genes of interest can be identified. Second, we focus on how different selection modes are observed acting across groups of immune genes and propose hypotheses to explain these differences. We then provide an overview of the approaches used so far to study the evolutionary heterogeneity of immune genes on macro and microevolutionary scales. Finally, we discuss some of the current evidence as to how specific pathogens affect the evolution of different groups of immune genes. This review results from the collective discussion on the current key challenges in evolutionary immunology conducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the vertebrate immune system, from the lab to natural populations.

An HLA map of the world: A comparison of HLA frequencies in 200 worldwide populations reveals diverse patterns for class I and class II

HLA frequencies show widespread variation across human populations. Demographic factors as well as selection are thought to have shaped HLA variation across continents. In this study, a worldwide comparison of HLA class I and class II diversity was carried out. Multidimensional scaling techniques were applied to 50 HLA-A and HLA-B (class I) as well as 13 HLA-DRB1 (class II) first-field frequencies in 200 populations from all continents. Our results confirm a strong effect of geography on the distribution of HLA class I allele groups, with principal coordinates analysis closely resembling geographical location of populations, especially those of Africa-Eurasia. Conversely, class II frequencies stratify populations along a continuum of differentiation less clearly correlated to actual geographic location. Double clustering analysis revealed finer intra-continental sub-clusters (e.g., Northern and Western Europe vs. South East Europe, North Africa and Southwest Asia; South and East Africa vs. West Africa), and HLA allele group patterns characteristic of these clusters. Ancient (Austronesian expansion) and more recent (Romani people in Europe) migrations, as well as extreme differentiation (Taiwan indigenous peoples, Native Americans), and interregional gene flow (Sámi, Egyptians) are also reflected by the results. Barrier analysis comparing D_ST and geographic location identified genetic discontinuities caused by natural barriers or human behavior explaining inter and intra-continental HLA borders for class I and class II. Overall, a progressive reduction in HLA diversity from African to Oceanian and Native American populations is noted. This analysis of HLA frequencies in a unique set of worldwide populations confirms previous findings on the remarkable similarity of class I frequencies to geography, but also shows a more complex development for class II, with implications for both human evolutionary studies and biomedical research.

How HLA diversity is apportioned: influence of selection and relevance to transplantation

In his 1972 paper ‘The apportionment of human diversity’, Lewontin showed that, when averaged over loci, genetic diversity is predominantly attributable to differences among individuals within populations. However, selection can alter the apportionment of diversity of specific genes or genomic regions. We examine genetic diversity at the human leucocyte antigen (HLA) loci, located within the major histocompatibility complex (MHC) region. HLA genes code for proteins that are critical to adaptive immunity and are well-documented targets of balancing selection. The single-nucleotide polymorphisms (SNPs) within HLA genes show strong signatures of balancing selection on large timescales and are broadly shared among populations, displaying low F_ST values. However, when we analyse haplotypes defined by these SNPs (which define ‘HLA alleles’), we find marked differences in frequencies between geographic regions. These differences are not reflected in the F_ST values because of the extreme polymorphism at HLA loci, illustrating challenges in interpreting F_ST. Differences in the frequency of HLA alleles among geographic regions are relevant to bone-marrow transplantation, which requires genetic identity at HLA loci between patient and donor. We discuss the case of Brazil's bone marrow registry, where a deficit of enrolled volunteers with African ancestry reduces the chance of finding donors for individuals with an MHC region of African ancestry.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Allele imputation for the killer cell immunoglobulin-like receptor KIR3DL1/S1

Highly polymorphic interaction of KIR3DL1 and KIR3DS1 with HLA class I ligands modulates the effector functions of natural killer (NK) cells and some T cells. This genetically determined diversity affects severity of infections, immune-mediated diseases, and some cancers, and impacts the course of immunotherapies, including transplantation. KIR3DL1 is an inhibitory receptor, and KIR3DS1 is an activating receptor encoded by the KIR3DL1/S1 gene that has more than 200 diverse and divergent alleles. Determination of KIR3DL1/S1 genotypes for medical application is hampered by complex sequence and structural variation, requiring targeted approaches to generate and analyze high-resolution allele data. To overcome these obstacles, we developed and optimized a model for imputing KIR3DL1/S1 alleles at high-resolution from whole-genome SNP data. We designed the model to represent a substantial component of human genetic diversity. Our Global imputation model is effective at genotyping KIR3DL1/S1 alleles with an accuracy ranging from 88% in Africans to 97% in East Asians, with mean specificity of 99% and sensitivity of 95% for alleles >1% frequency. We used the established algorithm of the HIBAG program, in a modification named Pulling Out Natural killer cell Genomics (PONG). Because HIBAG was designed to impute HLA alleles also from whole-genome SNP data, PONG allows combinatorial diversity of KIR3DL1/S1 with HLA-A and -B to be analyzed using complementary techniques on a single data source. The use of PONG thus negates the need for targeted sequencing data in very large-scale association studies where such methods might not be tractable.