RESSALVA
Atendendo solicitação do(a)
autor(a), o texto completo desta
tese será disponibilizado
somente a partir de 12/12/2025.
UNIVERSIDADE ESTADUAL PAULISTA
“JÚLIO DE MESQUITA FILHO”
INSTITUTO DE BIOCIÊNCIAS DE BOTUCATU – IBB
Programa de Pós-Graduação em Ciências Biológicas (GENÉTICA)
Development and implementation of new reference panels for
HLA imputation from different populations to accelerate
immunogenetic association studies
NAYANE DOS SANTOS BRITO SILVA
Botucatu, 2024
UNIVERSIDADE ESTADUAL PAULISTA
“JÚLIO DE MESQUITA FILHO”
INSTITUTO DE BIOCIÊNCIAS DE BOTUCATU – IBB
Programa de Pós-Graduação em Ciências Biológicas (GENÉTICA)
Nayane dos Santos Brito Silva
Development and implementation of new reference panels for
HLA imputation from different populations to accelerate
immunogenetic association studies
Orientadores: Dr. Erick C. Castelli & Dr. Nicolas Vince
Tese apresentada ao Instituto de Biociências,
Universidade Estadual Paulista “Júlio de
Mesquita Filho”, Campus de Botucatu, para
obtenção do título de Doutora pelo Programa
de Pós-Graduação em Ciências Biológicas
(Genética).
FICHA CATALOGRÁFICA ELABORADA PELA SEÇÃO TÉC. AQUIS. TRATAMENTO DA INFORM.
DIVISÃO TÉCNICA DE BIBLIOTECA E DOCUMENTAÇÃO - CÂMPUS DE BOTUCATU - UNESP
BIBLIOTECÁRIA RESPONSÁVEL: MARIA CAROLINA A. CRUZ E SANTOS-CRB 8/10188
Silva, Nayane dos Santos Brito.
Development and implementation of new reference panels
for HLA imputation from different populations to accelerate
immunogenetic association studies / Nayane dos Santos Brito
Silva. – Botucatu; Nantes, 2024
Tese (doutorado) - Universidade Estadual Paulista
(UNESP), Instituto de Biociências, Botucatu. Nantes
Université.
Orientador: Erick da Cruz Castelli
Coorientação: Nicolas Vince
Capes: 20200005
1. HLA Antigens. 2. Immunogenetics. 3. HLA
histocompatibility antigens.
Palavras-chave: HLA; Immunogenetics; Imputation HLA;
SHLARC.
3
“And once the storm is over, you won’t remember how you
made it through. But one thing is certain. When you come out
of the storm, you won’t be the same person who walked in.”
Haruki Murakami
4
ACKNOWLEDGMENTS
Since the beginning of this PhD journey, every moment of learning, overcoming
challenges, and discovery has been illuminated by the support of special people. This work
represents more than results; it is the result of countless contributions, inspirations, and the
generous support of those who walked with me.
To my Brazilian supervisor, Dr. Erick Castelli, thank you for your excellent guidance, for
your dedication in teaching and sharing your knowledge, and for all the opportunities you
provided throughout my PhD. I am deeply grateful for your attention and patience.
I would also like to express my gratitude to the entire GemBio team: Viviane Ciriaco,
Heloisa Andrade, Joyce Machado, Ingrid Miranda, Gabi Sato, Rapha Pereira Neto, Isa Mira, Prof.
Camila, Icaro Scalisse, João Toloi, Marcel Ferreira, Lívia Ramos, and Luciana Borali. More than just
colleagues, you have been true friends during every step of this journey. Thank you for all the
shared moments—I will carry you all in my heart always.
À tous ceux que j'ai rencontrés en France et qui, d'une manière ou d'une autre, ont fait
partie de ce magnifique séjour, m'apportant de nombreux apprentissages, tant personnels que
professionnels. Ce fut une période incroyable que j'ai eu la chance de vivre aux côtés de
personnes formidables, et qui m’a transformée pour le mieux.
To my French supervisor, Dr. Nicolas Vince, thank you for your trust and patience. I am
deeply grateful for all the opportunities you provided throughout my PhD. Your guidance and
inspiration have been invaluable in shaping my academic journey and personal growth.
To the entire 3 iTHINK team, especially the Bioinformatics group: Sonia Bourguiba-
Hachemi, Venceslas Douillard, Olivia Rousseau, Julien Paris, Mitra Barzine, Morgane Gélin, Prof.
Pierre-Antoine Gourraud, Afaf Tissafi, Nathalie Bouatlaoui, Mélissa Menga, Ketsia Mortant,
5
Mohamed Benzahi, Agathe Bugnon, Martin Morin, Vincent Mauduit, Simon Brocard, Stanislas
Demuth, Killian Maudet, Adrian Lapoutge, Lisa Thiébaut, Axelle Durand, Irène Charles, Léo
Boussamet, Mame Fatou, Rafaela Miranda, Assane Sylla, Alice Clément, and Romain Gautier, who
were part of this journey and brought me so much learning. Thank you all so much!
A special thanks to Venceslas Douillard for all the support you gave me throughout my
PhD and for teaching me everything about imputation. Thank you for keeping in touch and for
your friendship.
I am also deeply grateful to the group leader, Prof. Sophie Limou, for always being
available, for your valuable advice, and for making me feel welcome in the team from day one.
A heartfelt thank you to Sonia Bourguiba-Hachemi, who welcomed me and helped me
with everything I needed. Your presence made all the difference, not only professionally but also
personally. I love you, and I will carry you in my heart with great affection. I am grateful for all the
moments we shared, and the lessons I learned from you.
To my dear friends, Igor Faddeenkov and Marielle Oloude, who brightened my days inside
and outside the lab. Thank you for your support, kindness, and friendship. You made my days so
much happier—thank you for the lunches, the conversations, and for simply being there. Your
companionship has meant more to me than words can express. Thank you very much!
Thanks to the CR2TI and the Graduate Program in Genetics at the Institute of Biosciences
of Botucatu (IBB-UNESP) for their support, and collaboration, and for providing the academic
foundation and opportunities that made this work possible.
I would like to thank the São Paulo Research Foundation (FAPESP) – Process number
2021/02815-8, as well as the Pays de la Loire region and ANR PIA-Investment NExT for their
financial support, which made this research possible.
6
I would also like to thank the members of my French thesis advisory committee — Dr.
Diogo Meyer and Dr. Sigrid Leclerc — for following the progress of my thesis over these three
years. Additionally, I extend my gratitude to Dr. Robson Carvalho, Dr. Kelly Nunes, and Dr. Celso
Teixeira Mendes Junior for their valuable contributions during my Brazilian qualifying exam.
My sincere gratitude to the members of my thesis jury — Dr. Anne-Louise Leutenegger,
Prof. Luis Cristóvão Porto , Dr. Kelly Nunes, Prof. François Cornelis, Prof. Sophie Limou, and Prof.
Eduardo Donadi — for accepting the invitation and making themselves available to contribute to
my education and to the improvement of this work. A special thank you to Anne-Louise and Luis
Cristóvão for being the reviewers of this thesis.
To the Maison des Chercheurs Étrangers a Nantes, especially Nathalie Heinry and Soline
Puente Rodriguez, thank you for your warm welcome and support. I am grateful for all the joyful
moments you gave us. With you, I experienced some of the best times of my stay in Nantes and
made lifelong friends I could not have hoped for.
To my dear friends Alejandra Valasquez, Juan Gonzalez, Bruno Storti, Florência Ferrero,
Mosbah Kiwan, Mansoor Chaaban, Adilson Roberto, Laura, Aravind Vel, Laura Lenglin, and Gabriel
Ladera, thank you for all the laughter and the incredible moments we shared together. You
certainly made my time in Nantes much lighter. Thank you for everything, my friends. Your
friendship is something we will cherish for a lifetime.
À minha família, meu sincero agradecimento por todo o amor, apoio e incentivo
incondicional. A presença constante de vocês, mesmo à distância, me deu forças para seguir em
frente. Cada conquista que celebro hoje é, também, de vocês.
To the most important person in the world to me—my love, husband, and best friend,
Leonardo Menezes—who has always been by my side, supporting and encouraging me. Thank
7
you for being with me throughout this entire journey, for your unconditional support, for giving
me strength, for encouraging me, and for believing in me. With you, everything became easier. I
love you forever and one more day.
In each challenge faced, I found strength in the words of encouragement and supportive
gestures from those around me. To everyone who was present at some point, directly or
indirectly, I express my sincere gratitude. May this achievement also be a tribute to the
collaboration and trust I received along this journey.
8
LIST OF ABBREVIATIONS AND ACRONYMS
1KG 1,000 Genomes
AFR African
AMR American
APCs Professional antigen-presenting cells
AS Ankylosing spondylitis
B2M β2-microglobulin
BAM Binary alignment map
BiP Binding immunoglobulin protein
BWA Burrows-Wheeler Aligner
CDS Coding sequence
CIWD Common, Intermediate, and Well-Documented
CLIP Class II-association Invariant chain Peptide
CNV Copy number variation
CPU Central Processing Units
ddNTPs Dideoxynucleotides
DNA Deoxyribonucleic acid
EAS East Asian
EM Expectation-Maximization
ER Endoplasmic reticulum
ERp57 Endoplasmic reticulum resident protein 57
EUR European
GATK Genome Analysis Toolkit
GPU Graphics Processing Units
GWAS Genome-wide association study
hg19 Homo sapiens (human) genome assembly GRCh19
hg38 Homo sapiens (human) genome assembly GRCh38
9
HGDP-CEPH Human genome diversity project
HIGAB HLA Imputation using Attribute Bagging
HIV Human Immunodeficiency Virus
HLA Human Leukocyte Antigen
Ig Immunoglobulin
IHWS International HLA & Immunogenetics Workshop
ILT2 Immunoglobulin-like transcript 2
ILT4 Immunoglobulin-like transcript 4
IMGT/HLA IMmunoGeneTics/HLA
Indels insertions and/or deletions
IPD Immune Polymorphism Database
KIR Killer-cell Immunoglobulin-like Receptors
LD linkage disequilibrium
li Invariant chain
MHC Major Histocompatibility Complex
MICA/B MHC class I polypeptide related sequence A/B
MIIC MHC class II compartments
NAM Native Americans
NGS Next-Generation Sequencing
NK Natural Killer
PCR Polymerase chain reaction
PCR-SSO PCR with sequence-specific oligonucleotide
PCR-SSP PCR with sequence-specific primers
PRGs Population reference graphs
SABE Saúde, Bem Estar e Envelhecimento – Health, Well-Being, and Aging
SAM Sequence alignment map
SAS South Asian
10
SBT Sanger sequencing-based typing
SHLARC SNP-HLA Reference Consortium
SNP Single nucleotide polymorphisms
TAP1/2 Transporter associated with antigen processing ½
UTR Untranslated region
VCF Variant calling format
VQSR Variant Quality Score Recalibration
WES Whole-exome sequencing
WGS Whole-genome sequencing
WTCCC Wellcome Trust Case Control Consortium
11
ABSTRACT
The Human Leukocyte Antigen (HLA) genes within the Major Histocompatibility Complex (MHC)
region encode essential molecules for immune system activation. These molecules are involved
in the antigen presentation pathway, presenting both self and non-self antigens. HLA gene
polymorphisms are associated with many diseases and influence transplant outcomes. The
analysis of HLA genes from second-generation sequencing data (also known as NGS) requires
specialized tools to avoid alignment and genotyping errors. In this study, we applied the hla-
mapper pipeline to call SNPs and haplotypes within HLA genes in different worldwide populations,
including over 1,000 samples from Brazil. This methodology effectively analyzed HLA genetic
diversity at multiple levels, encompassing SNPs, InDels, haplotypes, and alleles with 2- to 4-field
resolution, as we demonstrated for the HLA-B gene (the most polymorphic HLA gene). After,
within the SNP-HLA Reference Consortium (SHLARC) framework, we used the data obtained in
the previous step to create and test HLA imputation models. These models were validated using
an independent Brazilian sample and cross-validated within the reference panel by performing
repetitive subsampling. The best results for imputation were obtained using the full reference
panel, i.e., when we pooled together all samples (including Brazilians) in a single reference panel,
highlighting the importance of including admixed samples in multiethnic panels. Moreover, our
imputation accuracy outperformed those obtained with the Michigan Imputation Server, a widely
used imputation platform for HLA genes, validating the effectiveness of our method. The findings
of this work represent a significant contribution to understanding the genetic diversity of HLA
genes and improving imputation techniques.
Keywords: HLA, Imputation HLA, SHLARC, Immunogenetics
12
RESUMO
Os genes HLA (Human Leukocyte Antigen) na região do Complexo Principal de
Histocompatibilidade (MHC) codificam moléculas essenciais para a ativação do sistema
imunológico. Essas moléculas estão envolvidas na via de apresentação de antígenos,
apresentando tanto antígenos próprios quanto não próprios aos linfócitos T. Os polimorfismos
dos genes HLA estão associados a diversas doenças e influenciam os resultados de transplantes.
A análise dos genes HLA a partir de dados de sequenciamento de segunda geração (também
conhecido como NGS) requer ferramentas especializadas para evitar erros de alinhamento e
genotipagem. Neste estudo, aplicamos o programa hla-mapper para identificar SNPs e haplótipos
dentro dos genes HLA em diferentes populações mundiais, incluindo mais de 1.000 amostras do
Brasil. Essa metodologia analisou de forma eficaz a diversidade genética dos genes HLA em vários
níveis, abrangendo SNPs, InDels, haplótipos e alelos com resolução de 2 a 4 campos, como
demonstramos para o gene HLA-B (o gene HLA mais polimórfico). Posteriormente, no âmbito do
SNP-HLA Reference Consortium (SHLARC), utilizamos os dados obtidos na etapa anterior para
criar e testar modelos de imputação de HLA. Esses modelos foram validados usando uma amostra
brasileira independente e validados de forma cruzada dentro do painel de referência, por meio
de subamostragens repetidas. Os melhores resultados de imputação foram obtidos utilizando o
painel de referência completo, ou seja, quando agrupamos todas as amostras (incluindo as
brasileiras) em um único painel de referência, destacando a importância de incluir amostras
miscigenadas em painéis multiétnicos. Além disso, nossa precisão de imputação superou os
resultados obtidos com o Michigan Imputation Server, uma plataforma amplamente utilizada
para imputação de genes HLA, validando a eficácia do nosso método. As descobertas deste
trabalho representam uma contribuição significativa para a compreensão da diversidade genética
dos genes HLA e para o aprimoramento de técnicas de imputação.
Palavras-chave: HLA, Imputação de HLA, SHLARC, Imunogenética
13
Table of contents
1. INTRODUCTION TO THE MAJOR HISTOCOMPATIBILITY COMPLEX ------------------------------- 15
2. THE HUMAN LEUKOCYTE ANTIGEN SYSTEM ----------------------------------------------------------- 18
2.1 Structure and function of HLA molecules --------------------------------------------------------- 19
2.1.1 HLA class I molecules -------------------------------------------------------------------------------- 19
2.1.1.1 HLA class I antigen presentation pathway -------------------------------------------------- 20
2.1.2 HLA class II molecules ------------------------------------------------------------------------------- 22
2.1.2.1 HLA class II antigen presentation pathway ----------------------------------------------- 23
2.2 Immunological function of HLA molecules ------------------------------------------------------- 24
2.3 HLA polymorphism ------------------------------------------------------------------------------------- 26
2.4 Naming HLA alleles ------------------------------------------------------------------------------------- 30
2.5 Functional and regulatory polymorphisms in HLA genes ------------------------------------- 32
2.6 HLA diversity across different populations ------------------------------------------------------- 34
2.7 Diseases associations ---------------------------------------------------------------------------------- 36
2.7.1 Genome-wide association studies ---------------------------------------------------------------- 38
3. EVOLUTION OF HLA TYPING METHODOLOGIES ------------------------------------------------------- 42
3.1 Complement-dependent microlymphocytotoxicity (serology) HLA typing ------------------ 42
3.2 Polymerase chain reaction (PCR)-based HLA typing methods ---------------------------------- 43
3.2.1 PCR-SSO -------------------------------------------------------------------------------------------------- 43
3.2.2 PCR-SSP --------------------------------------------------------------------------------------------------- 44
3.3 Sequencing-based methods ------------------------------------------------------------------------------ 44
3.3.1 Sanger sequencing-based typing (SBT)------------------------------------------------------------ 45
3.2.2 NGS-based HLA typing -------------------------------------------------------------------------------- 46
3.4 NGS in HLA research --------------------------------------------------------------------------------------- 47
3.4.1 HLA Typing Tools for NGS data from whole-genomes and exomes ------------------------ 49
3.5 Current challenges and perspectives in HLA typing ------------------------------------------------ 52
4. HLA IMPUTATION ----------------------------------------------------------------------------------------------- 53
4.1. Methods and advances in HLA imputation ---------------------------------------------------------- 53
4.2 Challenges in HLA imputation --------------------------------------------------------------------------- 56
4.3. SNP-HLA Reference Consortium (SHLARC) ---------------------------------------------------------- 57
4.3.1 Creating SHLARC imputation models -------------------------------------------------------------- 58
4.3.2. SNP-HLA Reference Consortium component in the International HLA and
Immunogenetics Workshop -------------------------------------------------------------------------------- 60
5. GOALS ----------------------------------------------------------------------------------------------------------- 62
6. DEVELOPMENT OF REFERENCE PANELS FOR IMPROVING HLA IMPUTATION ----------------- 63
14
6.1 Generation of MHC data through the hla-mapper methodology ------------------------------ 63
6.1.1 Description of study samples for genetic diversity evaluation ------------------------------ 64
6.1.2 Alignment optimization ------------------------------------------------------------------------------ 65
6.1.3 Variant calling, HLA haplotypes, and alleles ----------------------------------------------------- 67
6.1.4 Creating a HLA data resource from worldwide populations --------------------------------- 67
6.1.5 A focus on the HLA-B gene --------------------------------------------------------------------------- 68
6.1.5.1 Article - Immunogenetics of HLA-B: SNP, allele, and haplotype diversity in
populations from different continents and ancestry backgrounds ----------------------------- 69
6.2 Development of multiethnic reference panels for HLA imputation --------------------------- 93
6.2.1 Article - A multi-ethnic reference panel to impute HLA classical and non-classical class
I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil
--------------------------------------------------------------------------------------------------------------------- 94
6.2.2 A multi-ethnic reference panel to impute HLA classical and non-classical class II alleles
------------------------------------------------------------------------------------------------------------------- 118
6.2.2.1 HLA imputation for HLA class II alleles in worldwide populations ------------------- 118
6.2.2.2 Testing imputation for HLA class II alleles in an independent Brazilian sample -- 128
6.3 SHLARC: Sharing a multiethnic reference panel through a free online platform for HLA
imputation ------------------------------------------------------------------------------------------------------- 131
7. DISCUSSION -------------------------------------------------------------------------------------------------- 134
8. CONCLUSION ------------------------------------------------------------------------------------------------- 139
9. REFERENCES -------------------------------------------------------------------------------------------------- 141
10. LIST OF COMMUNICATIONS ------------------------------------------------------------------------------- 163
Appendix 1 --------------------------------------------------------------------------------------------------------- 167
Appendix 2 --------------------------------------------------------------------------------------------------------- 175
15
1. INTRODUCTION TO THE MAJOR HISTOCOMPATIBILITY COMPLEX
The discovery of the Major Histocompatibility Complex (MHC) and the research that
followed has significantly shaped our understanding of immunology, disease susceptibility, and
transplantation biology. The first studies date back to the early 20th century, with initial research
carried out through transplantation experiments in animals. Clarence Little was one of the
pioneers, demonstrating that susceptibility and resistance to transplants were influenced by
multiple genes with Mendelian inheritance(1). In the 1930s, Peter Gorer identified antigen II (later
known as H-2) in mice and discovered that these antigens were associated with the rejection of
tumor grafts. He introduced the concept that specific antigens determined the immune response
to transplanted tissues(2). Later, Snell selectively bred two mouse strains, achieving a new strain
nearly identical to one of the progenitor strains but differing in histocompatibility, which allowed
for the genetic mapping of loci associated with graft rejection(3). His collaboration with Peter
Gorer confirmed that the H-2 locus identified by both was the same, demonstrating the dominant
influence of H-2 on graft survival(4). This H-2 locus was later proved to be very complex through
many genes specifying many antigens, and it has since become established as the principal
histocompatibility locus in mice—that is, central to understanding immune response and
rejection of transplants(4,5).
The term MHC was adopted in the early 70s after researchers discovered that systems
genetically homologous to H-2 were found in many other vertebrates(5). Since then, there have
been significant advances in in understanding the structure and function of MHC molecules. MHC
genes, present in all mammals, are crucial in the immune response, especially adaptive immunity.
They influence the ability to distinguish between self and non-self, as well as the susceptibility to
autoimmune, infectious, neoplastic diseases(6), and their subsequent progression.
In humans, the MHC is located on the short arm of chromosome 6 at position 6p21.3,
covering about 4Mb and containing around 250 genes, most of which regulate immune
responses(7). The MHC region is considered the most gene-dense region of the human genome,
with an average of 1 gene every 16Kb(8), containing 1% of the human coding genes within just
0.1% of the genome's length(9). It consists of highly polymorphic blocks originated through
duplications, deletions, and various genomic recombination events during the evolution of
16
mammals and primates(10). The MHC is the most variable region in the human genome, mainly
because of the high diversity of a specific group of genes known as Human Leukocyte Antigens
(HLA) genes(11).
The MHC is divided into three regions or classes: classes I, II, and III(7) (Figure 1). Focusing
on the immune-related genes, the class I and II regions comprise the HLA system, with genes
encoding membrane glycoproteins responsible for presenting protein antigens(12). The class I
region also harbors the MIC (MHC class I chain-related genes) genes, whose products modulate
the Natural Killer (NK) cell activity by interacting with NKG2D receptors in cells upon stress(13).
In the class II region, TAP1 and TAP2 form heterodimers that are associated with the transport
and processing of peptides from class I HLA molecules(14). The class III region, located between
class I and II, does not encode molecules involved in antigen presentation but has other essential
components of the immune system, such as proteins of the complement system (e.g., C2 and C4),
hormones, and cytokines (e.g., Tumor Necrosis Factor, Lymphotoxin alpha and beta)(7,12).
The extensive variability and gene density of the MHC highlights its evolutionary
importance and essential role in both innate and adaptive immunity(12,14). This variability has
clinical significance in transplantation and autoimmune diseases(6). Advances in molecular
biology have helped researchers better understand the complex interactions between MHC
molecules and immune cells, which are essential for distinguishing between self and non-self, a
key in the development of many diseases(9). HLA typing is a standard procedure for organ and
bone marrow transplants, significantly impacting the transplant success rates(15). Therefore, the
intricate genetic organization and polymorphism of the MHC, especially the HLA genes,
underscore its vital role in modulating immune responses.
17
Figure 1: Location and organization of the MHC complex on chromosome 6. The MHC complex is divided
into three regions: I, II, and III. White, gray, striped, and black boxes represent expressed genes, gene
candidates, non-coding genes, and pseudogenes, respectively. Figure adapted from Shiina et al(12).
141
9. REFERENCES
1. Little CC. A Possible Mendelian Explanation for a Type of Inheritance Apparently Non-
Mendelian in Nature. Science (80- ) [Internet]. 1914 Dec 18;40(1042):904–6. Available
from: https://www.science.org/doi/10.1126/science.40.1042.904
2. Gorer PA. The genetic and antigenic basis of tumour transplantation. J Pathol Bacteriol
[Internet]. 1937 May 9;44(3):691–7. Available from:
https://onlinelibrary.wiley.com/doi/10.1002/path.1700440313
3. Snell GD. Methods for the study of histocompatibility genes. J Genet [Internet]. 1948
Oct;49(2):87–108. Available from: https://link.springer.com/10.1007/BF02986826
4. SNELL GD. Studies in Histocompatibility. Scand J Immunol [Internet]. 1992 Oct
29;36(4):514–25. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1365-
3083.1992.tb03218.x
5. Klein J. George Snell’s First Foray Into the Unexplored Territory of the Major
Histocompatibility Complex. Genetics [Internet]. 2001 Oct 1;159(2):435–9. Available from:
https://academic.oup.com/genetics/article/159/2/435/6052459
6. Trowsdale J, Knight JC. Major Histocompatibility Complex Genomics and Human Disease.
Annu Rev Genomics Hum Genet [Internet]. 2013 Aug 31;14(1):301–23. Available from:
https://www.annualreviews.org/doi/10.1146/annurev-genom-091212-153455
7. Klein, J.; Sato A. The HLA System — First of Two Parts. N Engl J Med. 2000;343:702–9.
8. Meyer D, C. Aguiar VR, Bitarello BD, C. Brandt DY, Nunes K. A genomic perspective on HLA
evolution. Immunogenetics [Internet]. 2018 Jan 7;70(1):5–27. Available from:
http://link.springer.com/10.1007/s00251-017-1017-3
9. Douillard V, Castelli EC, Mack SJ, Hollenbach JA, Gourraud P-A, Vince N, et al. Approaching
Genetics Through the MHC Lens: Tools and Methods for HLA Research. Front Genet
[Internet]. 2021 Dec 2;12:774916. Available from:
https://www.frontiersin.org/articles/10.3389/fgene.2021.774916/full
10. Hughes AL, Yeager M. Natural selection and the evolutionary history of major
histocompatibility complex loci. Front Biosci [Internet]. 1998 May 26;3(4):d509-16.
Available from: https://imrpress.com/journal/FBL/3/4/10.2741/A298
142
11. Robinson J, Barker DJ, Georgiou X, Cooper MA, Flicek P, Marsh SGE. IPD-IMGT/HLA
Database. Nucleic Acids Res [Internet]. 2019 Oct 31;48(D1):D948–55. Available from:
https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkz950/5610347
12. Shiina T, Hosomichi K, Inoko H, Kulski JK. The HLA genomic loci map: expression,
interaction, diversity and disease. J Hum Genet [Internet]. 2009 Jan 9;54(1):15–39.
Available from: http://www.nature.com/articles/jhg20085
13. Collins RWM. Human MHC class I chain related (MIC) genes: their biological function and
relevance to disease and transplantation. Eur J Immunogenet [Internet]. 2004 Jun
7;31(3):105–14. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1365-
2370.2004.00457.x
14. Rock KL, Reits E, Neefjes J. Present Yourself! By MHC Class I and MHC Class II Molecules.
Trends Immunol [Internet]. 2016 Nov;37(11):724–37. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S1471490616301004
15. Montgomery RA, Tatapudi VS, Leffell MS, Zachary AA. HLA in transplantation. Nat Rev
Nephrol [Internet]. 2018 Sep 9;14(9):558–70. Available from:
https://www.nature.com/articles/s41581-018-0039-x
16. Thorsby E. A short history of HLA. Tissue Antigens [Internet]. 2009 Aug 10;74(2):101–16.
Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1399-0039.2009.01291.x
17. Dausset J. Iso-leuco-anticorps. Acta Haematol [Internet]. 1958;20(1–4):156–66. Available
from: https://karger.com/AHA/article/doi/10.1159/000205478
18. VAN ROOD JJ, EERNISSE JG, VAN LEEUWEN A. Leucocyte Antibodies in Sera from Pregnant
Women. Nature [Internet]. 1958 Jun;181(4625):1735–6. Available from:
https://www.nature.com/articles/1811735a0
19. Payne R, Rolfs MR. Fetomaternal Leukocyte Incompatibility. J Clin Invest [Internet]. 1958
Dec 1;37(12):1756–63. Available from: http://www.jci.org/articles/view/103768
20. Rood JJ Van, Leeuwen A Van. LEUKOCYTE GROUPING. A METHOD AND ITS APPLICATION. J
Clin Invest [Internet]. 1963 Sep 1;42(9):1382–90. Available from:
http://www.jci.org/articles/view/104822
21. Payne R, Tripp M, Weigle J, Bodmer W, Bodmer J. A New Leukocyte Isoantigen System in
143
Man. Cold Spring Harb Symp Quant Biol [Internet]. 1964 Jan 1;29:285–95. Available from:
http://symposium.cshlp.org/cgi/doi/10.1101/SQB.1964.029.01.031
22. O’Callaghan CA, Bell JI. Structure and function of the human MHC class Ib molecules HLA‐
E, HLA‐F and HLA‐G. Immunol Rev [Internet]. 1998 Jun 28;163(1):129–38. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/j.1600-065X.1998.tb01192.x
23. Singh RK, Singh D, Yadava A, Srivastava AK. Molecular fossils “pseudogenes” as functional
signature in biological system. Genes Genomics [Internet]. 2020 Jun 10;42(6):619–30.
Available from: https://link.springer.com/10.1007/s13258-020-00935-7
24. Kaur G, Gras S, Mobbs JI, Vivian JP, Cortes A, Barber T, et al. Structural and regulatory
diversity shape HLA-C protein expression levels. Nat Commun [Internet]. 2017 Jun
26;8(1):15924. Available from: https://www.nature.com/articles/ncomms15924
25. Hackmon R, Pinnaduwage L, Zhang J, Lye SJ, Geraghty DE, Dunk CE. Definitive class I human
leukocyte antigen expression in gestational placentation: HLA‐F, HLA‐E, HLA‐C, and HLA-G
in extravillous trophoblast invasion on placentation, pregnancy, and parturition. Am J
Reprod Immunol [Internet]. 2017 Jun 10;77(6):e12643. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/aji.12643
26. Donadi EA, Castelli EC, Arnaiz-Villena A, Roger M, Rey D, Moreau P. Implications of the
polymorphism of HLA-G on its function, regulation, evolution and disease association. Cell
Mol Life Sci [Internet]. 2011 Feb 24;68(3):369–95. Available from:
http://link.springer.com/10.1007/s00018-010-0580-7
27. Yang Y, Sempé P, Peterson PA. Molecular mechanisms of class I major histocompatibility
complex antigen processing and presentation. Immunol Res [Internet]. 1996
Sep;15(3):208–33. Available from: http://link.springer.com/10.1007/BF02918250
28. ABBAS AK, LICHTMAN AH, PILLAI S. Cellular and molecular immunology. 6th ed.
Philadelphia: Saunders Elsevier; 2007. 566 p.
29. Lehnert E, Tampé R. Structure and Dynamics of Antigenic Peptides in Complex with TAP.
Front Immunol [Internet]. 2017 Jan 30;8. Available from:
http://journal.frontiersin.org/article/10.3389/fimmu.2017.00010/full
30. Goldberg AC, Rizzo LV. MHC structure and function – antigen presentation. Part 1. Einstein
144
(São Paulo) [Internet]. 2015 Mar 24;13(1):153–6. Available from:
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-
45082015000100027&lng=en&tlng=en
31. Blum JS, Wearsch PA, Cresswell P. Pathways of Antigen Processing. Annu Rev Immunol
[Internet]. 2013 Mar 21;31(1):443–73. Available from:
https://www.annualreviews.org/doi/10.1146/annurev-immunol-032712-095910
32. Cresswell P. Invariant Chain Structure and MHC Class II Function. Cell [Internet]. 1996
Feb;84(4):505–7. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0092867400810259
33. Goldberg AC arl., Rizzo LV icent. MHC structure and function − antigen presentation. Part
2. Einstein (São Paulo) [Internet]. 2015 Mar 24;13(1):157–62. Available from:
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-
45082015000100028&lng=en&tlng=en
34. Ferrante A. HLA-DM: arbiter conformationis. Immunology. 2013 Feb;138(2):85–92.
35. van Ham M, van Lith M, Griekspoor A, Neefjes J. What to do with HLA-DO? Immunogenetics
[Internet]. 2000 Aug 3;51(10):765–70. Available from:
http://link.springer.com/10.1007/s002510000208
36. Chen X, Jensen PE. Biological Function of HLA-DO (H2-O). Crit Rev Immunol [Internet].
2014;34(3):215–25. Available from:
http://www.dl.begellhouse.com/journals/2ff21abf44b19838,7f24896522e1c5c0,1185be9
d54e82690.html
37. Parham P, Moffett A. Variable NK cell receptors and their MHC class I ligands in immunity,
reproduction and human evolution. Nat Rev Immunol [Internet]. 2013 Feb 21;13(2):133–
44. Available from: https://www.nature.com/articles/nri3370
38. Augusto DG, Petzl-Erler ML. KIR and HLA under pressure: evidences of coevolution across
worldwide populations. Hum Genet [Internet]. 2015 Sep 23;134(9):929–40. Available from:
http://link.springer.com/10.1007/s00439-015-1579-9
39. Béziat V, Hilton HG, Norman PJ, Traherne JA. Deciphering the killer‐cell immunoglobulin‐
like receptor system at super‐resolution for natural killer and T‐cell biology. Immunology
145
[Internet]. 2017 Mar 14;150(3):248–64. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/imm.12684
40. Braud VM, Allan DS, McMichael AJ. Functions of nonclassical MHC and non-MHC-encoded
class I molecules. Curr Opin Immunol [Internet]. 1999 Feb;11(1):100–8. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0952791599800181
41. Carosella ED, Rouas-Freiss N, Tronik-Le Roux D, Moreau P, LeMaoult J. HLA-G: An Immune
Checkpoint Molecule. Adv Immunol [Internet]. 2015;127:33–144. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/26073983
42. Pietra G, Romagnani C, Manzini C, Moretta L, Mingari MC. The Emerging Role of HLA-E-
Restricted CD8 + T Lymphocytes in the Adaptive Immune Response to Pathogens and
Tumors. J Biomed Biotechnol [Internet]. 2010;2010:1–8. Available from:
http://www.hindawi.com/journals/bmri/2010/907092/
43. Buttura R V., Ramalho J, Lima THA, Donadi EA, Veiga-Castelli LC, Mendes-Junior CT, et al.
HLA-F displays highly divergent and frequent haplotype lineages associated with different
mRNA expression levels. Hum Immunol [Internet]. 2019 Feb;80(2):112–9. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885918309741
44. Morris P, Shaman J, Attaya M, Amaya M, Goodman S, Bergman C, et al. An essential role
for HLA–DM in antigen presentation by class II major histocompatibility molecules. Nature
[Internet]. 1994 Apr;368(6471):551–4. Available from:
https://www.nature.com/articles/368551a0
45. Luiza M, Erler P, Luz R, Sotomaior VS. The HLA polymorphsm of two distinctive South‐
American Indian tribes: The Kaingang and the Guarani. Tissue Antigens [Internet]. 1993
May 11;41(5):227–37. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/j.1399-0039.1993.tb02011.x
46. Kourilsky P, Claverie J-M. MHC restriction, alloreactivity, and thymic education: A common
link? Cell [Internet]. 1989 Feb;56(3):327–9. Available from:
https://linkinghub.elsevier.com/retrieve/pii/009286748990233X
47. Ogahara S, Noda R, Tanaka T, Hasegawa Y, Murata T, Matsumae T, et al. Effect of
mismatched combinations of HLA-A antigens on graft survival in the transplanted kidney.
146
Transplant Proc [Internet]. 1998 Nov;30(7):3500–1. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0041134598011130
48. MEYER D, THOMSON G. How selection shapes variation of the human major
histocompatibility complex: a review. Ann Hum Genet [Internet]. 2001 Jan 18;65(1):1–26.
Available from: https://onlinelibrary.wiley.com/doi/10.1046/j.1469-1809.2001.6510001.x
49. Trowsdale J. Genetic and Functional Relationships between MHC and NK Receptor Genes.
Immunity [Internet]. 2001 Sep;15(3):363–74. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S1074761301001972
50. Degenhardt F, Wendorff M, Wittig M, Ellinghaus E, Datta LW, Schembri J, et al.
Construction and benchmarking of a multi-ethnic reference panel for the imputation of
HLA class I and II alleles. Hum Mol Genet [Internet]. 2019 Jun 15;28(12):2078–92. Available
from: https://academic.oup.com/hmg/article/28/12/2078/5261434
51. Andersson G. Evolution of the human HLA-DR region. Front Biosci [Internet].
1998;3(4):A317. Available from: https://imrpress.com/journal/FBL/3/4/10.2741/A317
52. Robinson J, Guethlein LA, Cereb N, Yang SY, Norman PJ, Marsh SGE, et al. Distinguishing
functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B
and -C alleles. Keating BJ, editor. PLOS Genet [Internet]. 2017 Jun 26;13(6):e1006862.
Available from: https://dx.plos.org/10.1371/journal.pgen.1006862
53. Silva N dos SB, Souza A da S, Andrade H de S, Pereira RN, Castro CFB, Vince N, et al.
Immunogenetics of HLA‐B: SNP, allele, and haplotype diversity in populations from
different continents and ancestry backgrounds. HLA [Internet]. 2023 Jun 2;101(6):634–46.
Available from: https://onlinelibrary.wiley.com/doi/10.1111/tan.15043
54. Castelli EC, de Almeida BS, Muniz YCN, Silva NSB, Passos MRS, Souza AS, et al. HLA-G genetic
diversity and evolutive aspects in worldwide populations. Sci Rep [Internet]. 2021 Nov
29;11(1):23070. Available from: https://www.nature.com/articles/s41598-021-02106-4
55. Hurley CK, Kempenich J, Wadsworth K, Sauter J, Hofmann JA, Schefzyk D, et al. Common,
intermediate and well‐documented HLA alleles in world populations: CIWD version 3.0.0.
HLA [Internet]. 2020 Jun;95(6):516–31. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.13811
147
56. Hurley CK. Naming HLA diversity: A review of HLA nomenclature. Hum Immunol [Internet].
2021 Jul;82(7):457–65. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885920300653
57. Marsh SGE, Albert ED, Bodmer WF, Bontrop RE, Dupont B, Erlich HA, et al. Nomenclature
for factors of the HLA system, 2010. Tissue Antigens [Internet]. 2010 Apr;75(4):291–455.
Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1399-0039.2010.01466.x
58. Carey BS, Poulton KV, Poles A. Factors affecting HLA expression: A review. Int J
Immunogenet [Internet]. 2019 Oct 10;46(5):307–20. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/iji.12443
59. Handunnetthi L, Ramagopalan S V., Ebers GC, Knight JC. Regulation of major
histocompatibility complex class II gene expression, genetic variation and disease. Genes
Immun [Internet]. 2010 Mar 5;11(2):99–112. Available from:
https://www.nature.com/articles/gene200983
60. Vince N, Li H, Ramsuran V, Naranbhai V, Duh F-M, Fairfax BP, et al. HLA-C Level Is Regulated
by a Polymorphic Oct1 Binding Site in the HLA-C Promoter Region. Am J Hum Genet
[Internet]. 2016 Dec;99(6):1353–8. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0002929716304360
61. Apps R, Qi Y, Carlson JM, Chen H, Gao X, Thomas R, et al. Influence of HLA-C expression
level on HIV control. Science [Internet]. 2013 Apr 5;340(6128):87–91. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/23559252
62. Thomas R, Thio CL, Apps R, Qi Y, Gao X, Marti D, et al. A Novel Variant Marking HLA-DP
Expression Levels Predicts Recovery from Hepatitis B Virus Infection. J Virol [Internet]. 2012
Jun 15;86(12):6979–85. Available from: https://journals.asm.org/doi/10.1128/JVI.00406-
12
63. Shieh M, Chitnis N, Clark P, Johnson FB, Kamoun M, Monos D. Computational assessment
of miRNA binding to low and high expression HLA-DPB1 allelic sequences. Hum Immunol
[Internet]. 2019 Jan;80(1):53–61. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885918308425
64. Petersdorf EW, Malkki M, O’hUigin C, Carrington M, Gooley T, Haagenson MD, et al. High
148
HLA-DP Expression and Graft-versus-Host Disease. N Engl J Med [Internet]. 2015 Aug
13;373(7):599–609. Available from: http://www.nejm.org/doi/10.1056/NEJMoa1500140
65. Grifoni A, Montesano C, Palma P, Salerno A, Colizzi V, Amicosante M. Role of HLA-B α-3
domain amino acid position 194 in HIV disease progression. Mol Immunol [Internet]. 2013
Apr;53(4):410–3. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0161589012004105
66. Ashiru O, López-Cobo S, Fernández-Messina L, Pontes-Quero S, Pandolfi R, Reyburn HT, et
al. A GPI anchor explains the unique biological features of the common NKG2D-ligand allele
MICA*008. Biochem J [Internet]. 2013 Sep 1;454(2):295–302. Available from:
https://portlandpress.com/biochemj/article/454/2/295/46498/A-GPI-anchor-explains-
the-unique-biological
67. Castelli EC, Veiga-Castelli LC, Yaghi L, Moreau P, Donadi EA. Transcriptional and
Posttranscriptional Regulations of the HLA-G Gene. J Immunol Res [Internet]. 2014;2014:1–
15. Available from: http://www.hindawi.com/journals/jir/2014/734068/
68. Probst CM, Bompeixe EP, Pereira NF, de O Dalalio MM, Visentainer JE, Tsuneto LT, et al.
HLA polymorphism and evaluation of European, African, and Amerindian contribution to
the white and mulatto populations from Paraná, Brazil. Hum Biol [Internet]. 2000
Aug;72(4):597–617. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11048789
69. Sanchez-Mazas A, Nunes JM, Di D, Dominguez EA, Gerbault P, Faye NK, et al. The most
frequent HLA alleles around the world: A fundamental synopsis. Best Pract Res Clin
Haematol [Internet]. 2024 Jun;37(2):101559. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S1521692624000252
70. Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR. Allele frequency net: a database
and online repository for immune gene frequencies in worldwide populations. Nucleic
Acids Res [Internet]. 2011 Jan 1;39(Database):D913–9. Available from:
https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkq1128
71. O’CALLAGHAN CA. Molecular basis of human natural killer cell recognition of HLA-E (human
leucocyte antigen-E) and its relevance to clearance of pathogen-infected and tumour cells.
Clin Sci [Internet]. 2000 Jul 1;99(1):9–17. Available from:
149
https://portlandpress.com/clinsci/article/99/1/9/77977/Molecular-basis-of-human-
natural-killer-cell
72. Solberg OD, Mack SJ, Lancaster AK, Single RM, Tsai Y, Sanchez-Mazas A, et al. Balancing
selection and heterogeneity across the classical human leukocyte antigen loci: A meta-
analytic review of 497 population studies. Hum Immunol [Internet]. 2008 Jul;69(7):443–64.
Available from: https://linkinghub.elsevier.com/retrieve/pii/S0198885908000803
73. Sanchez‐Mazas A, Černý V, Di D, Buhler S, Podgorná E, Chevallier E, et al. The HLA‐B
landscape of Africa: Signatures of pathogen‐driven selection and molecular identification
of candidate alleles to malaria protection. Mol Ecol [Internet]. 2017 Nov 16;26(22):6238–
52. Available from: https://onlinelibrary.wiley.com/doi/10.1111/mec.14366
74. Maróstica AS, Nunes K, Castelli EC, Silva NSB, Weir BS, Goudet J, et al. How HLA diversity is
apportioned: influence of selection and relevance to transplantation. Philos Trans R Soc B
Biol Sci [Internet]. 2022 Jun 6;377(1852). Available from:
https://royalsocietypublishing.org/doi/10.1098/rstb.2020.0420
75. Arrieta‐Bolaños E, Madrigal‐Sánchez JJ, Stein JE, Órlich‐Pérez P, Moreira‐Espinoza MJ,
Paredes‐Carias E, et al. High‐resolution HLA allele and haplotype frequencies in majority
and minority populations of Costa Rica and Nicaragua: Differential admixture proportions
in neighboring countries. HLA [Internet]. 2018 Jun 25;91(6):514–29. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.13280
76. Boquett JA, Bisso‐Machado R, Zagonel‐Oliveira M, Schüler‐Faccini L, Fagundes NJR. HLA
diversity in Brazil. HLA [Internet]. 2020 Jan 13;95(1):3–14. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.13723
77. Ayo CM, da Silveira Camargo A V., Xavier DH, Batista MF, Carneiro OA, Brandão de Mattos
CC, et al. Frequencies of allele groups HLA‐A, HLA‐B and HLA-DRB1 in a population from
the northwestern region of São Paulo State, Brazil. Int J Immunogenet [Internet]. 2015 Feb
22;42(1):19–25. Available from: https://onlinelibrary.wiley.com/doi/10.1111/iji.12159
78. Carvalho MG, Tsuneto LT, Moita Neto JM, Sousa LCDM, Sales Filho HLA, Macêdo MB, et al.
HLA-A, HLA-B and HLA-DRB1 haplotype frequencies in Piauí’s volunteer bone marrow
donors enrolled at the Brazilian registry. Hum Immunol [Internet]. 2013 Dec;74(12):1598–
150
602. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0198885913005089
79. Reis PG, Ambrosio-Albuquerque EP, Fabreti-Oliveira RA, Moliterno RA, de Souza VH, Sell
AM, et al. HLA-A, -B, -DRB1, -DQA1, and -DQB1 profile in a population from southern Brazil.
HLA [Internet]. 2018 Nov;92(5):298–303. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.13368
80. Naslavsky MS, Scliar MO, Yamamoto GL, Wang JYT, Zverinova S, Karp T, et al. Whole-
genome sequencing of 1,171 elderly admixed individuals from Brazil. Nat Commun
[Internet]. 2022 Mar 4;13(1):1004. Available from:
https://www.nature.com/articles/s41467-022-28648-3
81. Dendrou CA, Petersen J, Rossjohn J, Fugger L. HLA variation and disease. Nat Rev Immunol
[Internet]. 2018 May 2;18(5):325–39. Available from:
https://www.nature.com/articles/nri.2017.143
82. Howell WM. HLA and disease: guilt by association. Int J Immunogenet [Internet]. 2014 Feb
4;41(1):1–12. Available from: https://onlinelibrary.wiley.com/doi/10.1111/iji.12088
83. Matzaraki V, Kumar V, Wijmenga C, Zhernakova A. The MHC locus and genetic susceptibility
to autoimmune and infectious diseases. Genome Biol [Internet]. 2017 Dec 27;18(1):76.
Available from: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-
1207-1
84. Bodis G, Toth V, Schwarting A. Role of Human Leukocyte Antigens (HLA) in Autoimmune
Diseases. Rheumatol Ther [Internet]. 2018 Jun 7;5(1):5–20. Available from:
http://link.springer.com/10.1007/s40744-018-0100-z
85. Sticht J, Álvaro-Benito M, Konigorski S. Type 1 Diabetes and the HLA Region: Genetic
Association Besides Classical HLA Class II Genes. Front Genet [Internet]. 2021 Jun 17;12.
Available from: https://www.frontiersin.org/articles/10.3389/fgene.2021.683946/full
86. Caillat-Zucman S. Molecular mechanisms of HLA association with autoimmune diseases.
Tissue Antigens [Internet]. 2009 Jan;73(1):1–8. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/j.1399-0039.2008.01167.x
87. Melief CJM, Holwerda AM, Miedema F, van der Burg SH, Drijfhout JW, Hovenkamp E, et al.
Characterization of HLA-B57-restricted human immunodeficiency virus type 1 Gag- and RT-
151
specific cytotoxic T lymphocyte responses. J Gen Virol [Internet]. 1998 Sep 1;79(9):2191–
201. Available from:
https://www.microbiologyresearch.org/content/journal/jgv/10.1099/0022-1317-79-9-
2191
88. Goulder PJR, Watkins DI. Impact of MHC class I diversity on immune control of
immunodeficiency virus replication. Nat Rev Immunol [Internet]. 2008 Aug;8(8):619–30.
Available from: https://www.nature.com/articles/nri2357
89. Dean L. Abacavir Therapy and HLA-B*57:01 Genotype [Internet]. Vol. 91, Medical Genetics
Summaries. 2012. 1–7 p. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25934581
90. Hviid TVF. HLA-G in human reproduction: aspects of genetics, function and pregnancy
complications. Hum Reprod Update [Internet]. 2006 Jun 1;12(3):209–32. Available from:
http://academic.oup.com/humupd/article/12/3/209/553364/HLAG-in-human-
reproduction-aspects-of-genetics
91. Trowsdale J, Moffett A. NK receptor interactions with MHC class I molecules in pregnancy.
Semin Immunol [Internet]. 2008 Dec;20(6):317–20. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S1044532308000511
92. Ayala García MA, González Yebra B, López Flores AL, Guaní Guerra E. The Major
Histocompatibility Complex in Transplantation. J Transplant [Internet]. 2012;2012:1–7.
Available from: http://www.hindawi.com/journals/jtrans/2012/842141/
93. Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y, Martin AR, et al. Genome-wide
association studies. Nat Rev Methods Prim [Internet]. 2021 Aug 26;1(1):59. Available from:
https://www.nature.com/articles/s43586-021-00056-9
94. Nguyen DT, Tran TTH, Tran MH, Tran K, Pham D, Duong NT, et al. A comprehensive
evaluation of polygenic score and genotype imputation performances of human SNP arrays
in diverse populations. Sci Rep [Internet]. 2022 Oct 20;12(1):17556. Available from:
https://www.nature.com/articles/s41598-022-22215-y
95. Kennedy AE, Ozbek U, Dorak MT. What has GWAS done for HLA and disease associations?
Int J Immunogenet [Internet]. 2017 Oct;44(5):195–211. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/iji.12332
152
96. Limou S, Zagury J-F. Immunogenetics: Genome-Wide Association of Non-Progressive HIV
and Viral Load Control: HLA Genes and Beyond. Front Immunol [Internet]. 2013;4. Available
from: http://journal.frontiersin.org/article/10.3389/fimmu.2013.00118/abstract
97. Fellay J, Shianna K V., Ge D, Colombo S, Ledergerber B, Weale M, et al. A Whole-Genome
Association Study of Major Determinants for Host Control of HIV-1. Science (80- )
[Internet]. 2007 Aug 17;317(5840):944–7. Available from:
https://www.science.org/doi/10.1126/science.1143767
98. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog
of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res
[Internet]. 2017 Jan 4;45(D1):D896–901. Available from:
https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw1133
99. Douillard V, Castelli EC, Mack SJ, Hollenbach JA, Gourraud P-A, Vince N, et al. Current HLA
Investigations on SARS-CoV-2 and Perspectives. Front Genet [Internet]. 2021 Nov 29;12.
Available from: https://www.frontiersin.org/articles/10.3389/fgene.2021.774922/full
100. Vince N, Douillard V, Geffard E, Meyer D, Castelli EC, Mack SJ, et al. SNP‐HLA Reference
Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in
genomics. Genet Epidemiol [Internet]. 2020 Oct 18;44(7):733–40. Available from:
https://onlinelibrary.wiley.com/doi/10.1002/gepi.22334
101. De Santis D, Dinauer D, Duke J, Erlich HA, Holcomb CL, Lind C, et al. 16 th IHIW : Review of
HLA typing by NGS. Int J Immunogenet [Internet]. 2013 Feb 9;40(1):72–6. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/iji.12024
102. Erlich H. HLA DNA typing: past, present, and future. Tissue Antigens [Internet]. 2012
Jul;80(1):1–11. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22651253
103. Baxter-Lowe LA. The changing landscape of HLA typing: Understanding how and when HLA
typing data can be used with confidence from bench to bedside. Hum Immunol [Internet].
2021 Jul;82(7):466–77. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885921001154
104. Spellman S, Setterholm M, Maiers M, Noreen H, Oudshoorn M, Fernandez-Viña M, et al.
Advances in the Selection of HLA-Compatible Donors: Refinements in HLA Typing and
153
Matching over the First 20 Years of the National Marrow Donor Program Registry. Biol
Blood Marrow Transplant [Internet]. 2008 Sep;14(9):37–44. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S1083879108001833
105. Agrawal P, Tote S, Sapkale B. Diagnosis and Treatment of Ankylosing Spondylitis. Cureus
[Internet]. 2024 Jan 19; Available from: https://www.cureus.com/articles/183949-
diagnosis-and-treatment-of-ankylosing-spondylitis
106. Hosomichi K, Shiina T, Tajima A, Inoue I. The impact of next-generation sequencing
technologies on HLA research. J Hum Genet [Internet]. 2015 Nov 27;60(11):665–73.
Available from: https://www.nature.com/articles/jhg2015102
107. Choo SY. The HLA System: Genetics, Immunology, Clinical Testing, and Clinical Implications.
Yonsei Med J [Internet]. 2007;48(1):11. Available from:
https://eymj.org/DOIx.php?id=10.3349/ymj.2007.48.1.11
108. Althaf MM, El Kossi M, Jin JK, Sharma A, Halawa AM. Human leukocyte antigen typing and
crossmatch: A comprehensive review. World J Transplant [Internet]. 2017 Dec 24;7(6):339–
48. Available from: http://www.wjgnet.com/2220-3230/full/v7/i6/339.htm
109. Park I, Terasaki P. Origins of the first HLA specificities. Hum Immunol [Internet]. 2000
Mar;61(3):185–9. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885999001548
110. Cao K, Chopek M, Fernández-Viña MA. High and intermediate resolution DNA typing
systems for class I HLA-A, B, C genes by hybridization with sequence-specific
oligonucleotide probes (SSOP). Rev Immunogenet [Internet]. 1999;1(2):177–208. Available
from: http://www.ncbi.nlm.nih.gov/pubmed/11253946
111. Cornaby C, Weimer ET. HLA Typing by Next-Generation Sequencing: Lessons Learned and
Future Applications. Clin Lab Med [Internet]. 2022 Dec;42(4):603–12. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/36368785
112. Santamaria P, Lindstrom AL, Boyce-Jacino MT, Myster SH, Barbosa JJ, Faras AJ, et al. HLA
class I sequence-based typing. Hum Immunol [Internet]. 1993 May;37(1):39–50. Available
from: http://www.ncbi.nlm.nih.gov/pubmed/8376187
113. Voorter CEM, Palusci F, Tilanus MGJ. Sequence-based typing of HLA: an improved group-
154
specific full-length gene sequencing approach. Methods Mol Biol [Internet].
2014;1109:101–14. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24473781
114. Kishore A, Petrek M. Next-Generation Sequencing Based HLA Typing: Deciphering
Immunogenetic Aspects of Sarcoidosis. Front Genet [Internet]. 2018 Oct 25;9. Available
from: https://www.frontiersin.org/article/10.3389/fgene.2018.00503/full
115. Ananeva A, Leksina Y, Andryushkina A, Shagimardanova E. The novel HLA‐A*02:941 allele
was identified during high‐resolution HLA typing. HLA [Internet]. 2021 Feb 13;97(2):136–8.
Available from: https://onlinelibrary.wiley.com/doi/10.1111/tan.14088
116. Hu T, Chitnis N, Monos D, Dinh A. Next-generation sequencing technologies: An overview.
Hum Immunol [Internet]. 2021 Nov;82(11):801–11. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885921000628
117. Bauer DC, Zadoorian A, Wilson LOW, Thorne NP. Evaluation of computational programs to
predict HLA genotypes from genomic sequencing data. Brief Bioinform [Internet]. 2016 Nov
1;bbw097. Available from: https://academic.oup.com/bib/article-
lookup/doi/10.1093/bib/bbw097
118. Brandt DYC, Aguiar VRC, Bitarello BD, Nunes K, Goudet J, Meyer D. Mapping Bias
Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project
Phase I Data. G3 Genes|Genomes|Genetics [Internet]. 2015 May 1;5(5):931–41. Available
from: https://academic.oup.com/g3journal/article/5/5/931/6025555
119. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform.
Bioinformatics [Internet]. 2009 Jul 15;25(14):1754–60. Available from:
https://academic.oup.com/bioinformatics/article/25/14/1754/225615
120. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods
[Internet]. 2012 Apr 4;9(4):357–9. Available from:
https://www.nature.com/articles/nmeth.1923
121. Castelli EC, Paz MA, Souza AS, Ramalho J, Mendes-Junior CT. Hla-mapper: An application
to optimize the mapping of HLA sequences produced by massively parallel sequencing
procedures. Hum Immunol [Internet]. 2018 Sep;79(9):678–84. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885918302350
155
122. Byrska-Bishop M, Evani US, Zhao X, Basile AO, Abel HJ, Regier AA, et al. High-coverage
whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602
trios. Cell [Internet]. 2022 Sep;185(18):3426-3440.e19. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0092867422009916
123. Gourraud P-A, Khankhanian P, Cereb N, Yang SY, Feolo M, Maiers M, et al. HLA Diversity in
the 1000 Genomes Dataset. Colombo GI, editor. PLoS One [Internet]. 2014 Jul
2;9(7):e97282. Available from: https://dx.plos.org/10.1371/journal.pone.0097282
124. Klasberg S, Surendranath V, Lange V, Schöfl G. Bioinformatics Strategies, Challenges, and
Opportunities for Next Generation Sequencing-Based HLA Genotyping. Transfus Med
Hemotherapy [Internet]. 2019;46(5):312–25. Available from:
https://karger.com/TMH/article/doi/10.1159/000502487
125. Warren RL, Choe G, Freeman DJ, Castellarin M, Munro S, Moore R, et al. Derivation of HLA
types from shotgun sequence datasets. Genome Med [Internet]. 2012;4(12):95. Available
from: http://genomemedicine.biomedcentral.com/articles/10.1186/gm396
126. Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O. OptiType: precision
HLA typing from next-generation sequencing data. Bioinformatics [Internet]. 2014 Dec
1;30(23):3310–6. Available from:
https://academic.oup.com/bioinformatics/article/30/23/3310/206910
127. Liu C, Yang X, Duffy B, Mohanakumar T, Mitra RD, Zody MC, et al. ATHLATES: accurate
typing of human leukocyte antigen through exome sequencing. Nucleic Acids Res
[Internet]. 2013 Aug 1;41(14):e142–e142. Available from:
https://academic.oup.com/nar/article/41/14/e142/1750166
128. Nariai N, Kojima K, Saito S, Mimori T, Sato Y, Kawai Y, et al. HLA-VBSeq: accurate HLA typing
at full resolution from whole-genome sequencing data. BMC Genomics [Internet]. 2015
Dec 21;16(S2):S7. Available from:
https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-16-S2-S7
129. Shukla SA, Rooney MS, Rajasagi M, Tiao G, Dixon PM, Lawrence MS, et al. Comprehensive
analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol
[Internet]. 2015 Nov 15;33(11):1152–8. Available from:
156
https://www.nature.com/articles/nbt.3344
130. Huang Y, Yang J, Ying D, Zhang Y, Shotelersuk V, Hirankarn N, et al. HLAreporter: a tool for
HLA typing from next generation sequencing data. Genome Med [Internet]. 2015 Dec
16;7(1):25. Available from: http://genomemedicine.com/content/7/1/25
131. Dilthey AT, Gourraud P-A, Mentzer AJ, Cereb N, Iqbal Z, McVean G. High-Accuracy HLA Type
Inference from Whole-Genome Sequencing Data Using Population Reference Graphs.
Franke A, editor. PLOS Comput Biol [Internet]. 2016 Oct 28;12(10):e1005151. Available
from: https://dx.plos.org/10.1371/journal.pcbi.1005151
132. Kawaguchi S, Higasa K, Shimizu M, Yamada R, Matsuda F. HLA‐HD: An accurate HLA typing
algorithm for next‐generation sequencing data. Hum Mutat [Internet]. 2017 Jul
12;38(7):788–97. Available from:
https://onlinelibrary.wiley.com/doi/10.1002/humu.23230
133. Xie C, Yeo ZX, Wong M, Piper J, Long T, Kirkness EF, et al. Fast and accurate HLA typing from
short-read next-generation sequence data with xHLA. Proc Natl Acad Sci [Internet]. 2017
Jul 25;114(30):8059–64. Available from:
https://pnas.org/doi/full/10.1073/pnas.1707945114
134. Lee H, Kingsford C. Kourami: graph-guided assembly for novel human leukocyte antigen
allele discovery. Genome Biol [Internet]. 2018 Dec 7;19(1):16. Available from:
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1388-2
135. Dilthey AT, Mentzer AJ, Carapito R, Cutland C, Cereb N, Madhi SA, et al. HLA*LA—HLA
typing from linearly projected graph alignments. Berger B, editor. Bioinformatics [Internet].
2019 Nov 1;35(21):4394–6. Available from:
https://academic.oup.com/bioinformatics/article/35/21/4394/5426702
136. Abi-Rached L, Gouret P, Yeh J-H, Di Cristofaro J, Pontarotti P, Picard C, et al. Immune
diversity sheds light on missing variation in worldwide genetic diversity panels. DeAngelis
MM, editor. PLoS One [Internet]. 2018 Oct 26;13(10):e0206512. Available from:
https://dx.plos.org/10.1371/journal.pone.0206512
137. Wang S, Wang M, Chen L, Pan G, Wang Y, Li SC. SpecHLA enables full-resolution HLA typing
from sequencing data. Cell Reports Methods [Internet]. 2023 Sep;3(9):100589. Available
157
from: https://linkinghub.elsevier.com/retrieve/pii/S2667237523002400
138. Song L, Bai G, Liu XS, Li B, Li H. Efficient and accurate KIR and HLA genotyping with massively
parallel sequencing data. Genome Res [Internet]. 2023 Jun;33(6):923–31. Available from:
http://genome.cshlp.org/lookup/doi/10.1101/gr.277585.122
139. Liu C. A long road/read to rapid high-resolution HLA typing: The nanopore perspective.
Hum Immunol [Internet]. 2021 Jul;82(7):488–95. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885920301890
140. Naito T, Okada Y. HLA imputation and its application to genetic and molecular fine-mapping
of the MHC region in autoimmune diseases. Semin Immunopathol [Internet]. 2022 Jan
16;44(1):15–28. Available from: https://link.springer.com/10.1007/s00281-021-00901-9
141. Meyer D, Nunes K. HLA imputation, what is it good for? Hum Immunol [Internet]. 2017
Mar;78(3):239–41. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885917300277
142. Howie BN, Donnelly P, Marchini J. A Flexible and Accurate Genotype Imputation Method
for the Next Generation of Genome-Wide Association Studies. Schork NJ, editor. PLoS
Genet [Internet]. 2009 Jun 19;5(6):e1000529. Available from:
https://dx.plos.org/10.1371/journal.pgen.1000529
143. Browning SR, Browning BL. Rapid and Accurate Haplotype Phasing and Missing-Data
Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering.
Am J Hum Genet [Internet]. 2007 Nov;81(5):1084–97. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0002929707638828
144. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype
imputation in genome-wide association studies through pre-phasing. Nat Genet [Internet].
2012 Aug 22;44(8):955–9. Available from: https://www.nature.com/articles/ng.2354
145. Li Y, Willer C, Sanna S, Abecasis G. Genotype Imputation. Annu Rev Genomics Hum Genet
[Internet]. 2009 Sep 1;10(1):387–406. Available from:
https://www.annualreviews.org/doi/10.1146/annurev.genom.9.081307.164242
146. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev
Genet [Internet]. 2010 Jul 2;11(7):499–511. Available from:
158
https://www.nature.com/articles/nrg2796
147. Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, et al. Genome-
wide association study of 14,000 cases of seven common diseases and 3,000 shared
controls. Nature [Internet]. 2007 Jun 7;447(7145):661–78. Available from:
https://www.nature.com/articles/nature05911
148. Consortium TIH. A haplotype map of the human genome. Nature [Internet]. 2005
Oct;437(7063):1299–320. Available from: https://www.nature.com/articles/nature04226
149. Dilthey AT, Moutsianas L, Leslie S, McVean G. HLA*IMP—an integrated framework for
imputing classical HLA alleles from SNP genotypes. Bioinformatics [Internet]. 2011 Apr
1;27(7):968–72. Available from:
https://academic.oup.com/bioinformatics/article/27/7/968/232401
150. Dilthey A, Leslie S, Moutsianas L, Shen J, Cox C, Nelson MR, et al. Multi-Population Classical
HLA Type Imputation. Browning S, editor. PLoS Comput Biol [Internet]. 2013 Feb
14;9(2):e1002877. Available from: https://dx.plos.org/10.1371/journal.pcbi.1002877
151. Jia X, Han B, Onengut-Gumuscu S, Chen W-M, Concannon PJ, Rich SS, et al. Imputing Amino
Acid Polymorphisms in Human Leukocyte Antigens. Tang J, editor. PLoS One [Internet].
2013 Jun 6;8(6):e64683. Available from:
https://dx.plos.org/10.1371/journal.pone.0064683
152. Motyer A, Vukcevic D, Dilthey A, Donnelly P, McVean G, Leslie S. Practical Use of Methods
for Imputation of HLA Alleles from SNP Genotype Data [Internet]. bioRxiv. 2016. Available
from: http://biorxiv.org/lookup/doi/10.1101/091009
153. Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, et al. HIBAG—HLA genotype
imputation with attribute bagging. Pharmacogenomics J [Internet]. 2014 Apr 28;14(2):192–
200. Available from: https://www.nature.com/articles/tpj201318
154. Cook S, Choi W, Lim H, Luo Y, Kim K, Jia X, et al. Accurate imputation of human leukocyte
antigens with CookHLA. Nat Commun [Internet]. 2021 Feb 24;12(1):1264. Available from:
https://www.nature.com/articles/s41467-021-21541-5
155. Naito T, Suzuki K, Hirata J, Kamatani Y, Matsuda K, Toda T, et al. A deep learning method
for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes. Nat Commun
159
[Internet]. 2021 Mar 12;12(1):1639. Available from:
https://www.nature.com/articles/s41467-021-21975-x
156. Tanaka K, Kato K, Nonaka N, Seita J. Efficient HLA imputation from sequential SNPs data by
transformer. J Hum Genet [Internet]. 2024 Oct 2;69(10):533–40. Available from:
https://www.nature.com/articles/s10038-024-01278-x
157. Karnes JH, Shaffer CM, Bastarache L, Gaudieri S, Glazer AM, Steiner HE, et al. Comparison
of HLA allelic imputation programs. Tang J, editor. PLoS One [Internet]. 2017 Feb
16;12(2):e0172444. Available from: https://dx.plos.org/10.1371/journal.pone.0172444
158. Nunes K, Zheng X, Torres M, Moraes ME, Piovezan BZ, Pontes GN, et al. HLA imputation in
an admixed population: An assessment of the 1000 Genomes data as a training set. Hum
Immunol [Internet]. 2016 Mar;77(3):307–12. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0198885915005571
159. Ritari J, Hyvärinen K, Clancy J, Partanen J, Koskela S. Increasing accuracy of HLA imputation
by a population-specific reference panel in a FinnGen biobank cohort. NAR Genomics
Bioinforma [Internet]. 2020 Jun 1;2(2). Available from:
https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqaa030/5831010
160. Kim K, Bang S-Y, Lee H-S, Bae S-C. Construction and Application of a Korean Reference Panel
for Imputing Classical Alleles and Amino Acids of Human Leukocyte Antigen Genes. De Re
V, editor. PLoS One [Internet]. 2014 Nov 14;9(11):e112546. Available from:
https://dx.plos.org/10.1371/journal.pone.0112546
161. Luo Y, Kanai M, Choi W, Li X, Sakaue S, Yamamoto K, et al. A high-resolution HLA reference
panel capturing global population diversity enables multi-ancestry fine-mapping in HIV
host response. Nat Genet [Internet]. 2021 Oct 5;53(10):1504–16. Available from:
https://www.nature.com/articles/s41588-021-00935-7
162. Silva NSB, Bourguiba‐Hachemi S, Douillard V, Koskela S, Degenhardt F, Clancy J, et al. 18th
International HLA and Immunogenetics Workshop: Report on the SNP‐HLA Reference
Consortium (SHLARC) component. HLA [Internet]. 2023 Nov 10; Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.15293
163. Fairley S, Lowy-Gallego E, Perry E, Flicek P. The International Genome Sample Resource
160
(IGSR) collection of open human genomic variation resources. Nucleic Acids Res [Internet].
2020 Jan 8;48(D1):D941–7. Available from:
https://academic.oup.com/nar/article/48/D1/D941/5580898
164. Cann HM, de Toma C, Cazes L, Legrand M-F, Morel V, Piouffre L, et al. A Human Genome
Diversity Cell Line Panel. Science (80- ) [Internet]. 2002 Apr 12;296(5566):261–2. Available
from: https://www.science.org/doi/10.1126/science.296.5566.261b
165. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome
Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing
data. Genome Res [Internet]. 2010 Sep;20(9):1297–303. Available from:
http://genome.cshlp.org/lookup/doi/10.1101/gr.107524.110
166. Patterson M, Marschall T, Pisanti N, van Iersel L, Stougie L, Klau GW, et al. WhatsHap:
Weighted Haplotype Assembly for Future-Generation Sequencing Reads. J Comput Biol
[Internet]. 2015 Jun;22(6):498–509. Available from:
http://www.liebertpub.com/doi/10.1089/cmb.2014.0157
167. Delaneau O, Marchini J, Zagury J-F. A linear complexity phasing method for thousands of
genomes. Nat Methods [Internet]. 2012 Feb 4;9(2):179–81. Available from:
https://www.nature.com/articles/nmeth.1785
168. Degenhardt F, Ellinghaus D, Juzenas S, Lerga-Jaso J, Wendorff M, Maya-Miles D, et al.
Detailed stratified GWAS analysis for severe COVID-19 in four European populations. Hum
Mol Genet [Internet]. 2022 Nov 28;31(23):3945–66. Available from:
https://academic.oup.com/hmg/article/31/23/3945/6644888
169. Souza AS, Sonon P, Paz MA, Tokplonou L, Lima THA, Porto IOP, et al. HLA‐C
genetic diversity and evolutionary insights in two samples from Brazil and Benin. HLA
[Internet]. 2020 Oct 24;96(4):468–86. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.13996
170. Lima THA, Souza AS, Porto IOP, Paz MA, Veiga‐Castelli LC, Oliveira MLG, et al. HLA‐A
promoter, coding, and 3′UTR sequences in a Brazilian cohort, and their evolutionary
aspects. HLA [Internet]. 2019 Feb 22;93(2–3):65–79. Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.13474
161
171. Castelli EC, de Castro M V., Naslavsky MS, Scliar MO, Silva NSB, Andrade HS, et al. MHC
Variants Associated With Symptomatic Versus Asymptomatic SARS-CoV-2 Infection in
Highly Exposed Individuals. Front Immunol [Internet]. 2021 Sep 28;12. Available from:
https://www.frontiersin.org/articles/10.3389/fimmu.2021.742881/full
172. Ramos PM, Garbers LEFM, Silva NSB, Castro CFB, Andrade HS, Souza AS, et al. A large
familial cluster and sporadic cases of frontal fibrosing alopecia in Brazil reinforce known
human leucocyte antigen (HLA) associations and indicate new HLA susceptibility
haplotypes. J Eur Acad Dermatology Venereol [Internet]. 2020 Oct 26;34(10):2409–13.
Available from: https://onlinelibrary.wiley.com/doi/10.1111/jdv.16629
173. Castelli EC, de Castro M V., Naslavsky MS, Scliar MO, Silva NSB, Pereira RN, et al. MUC22,
HLA-A, and HLA-DOB variants and COVID-19 in resilient super-agers from Brazil. Front
Immunol [Internet]. 2022 Oct 25;13. Available from:
https://www.frontiersin.org/articles/10.3389/fimmu.2022.975918/full
174. Douillard V, dos Santos Brito Silva N, Bourguiba‐Hachemi S, Naslavsky MS, Scliar MO,
Duarte YAO, et al. Optimal population‐specific HLA imputation with dimension
reduction. HLA [Internet]. 2023 Nov 11; Available from:
https://onlinelibrary.wiley.com/doi/10.1111/tan.15282
175. Sonon P, Sadissou I, Tokplonou L, M’po KKG, Glitho SSC, Agniwo P, et al. HLA-G, -E and -F
regulatory and coding region variability and haplotypes in the Beninese Toffin population
sample. Mol Immunol [Internet]. 2018 Dec;104:108–27. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0161589018305133
176. Ritari J, Hyvärinen K, Partanen J, Koskela S. KIR gene content imputation from single-
nucleotide polymorphisms in the Finnish population. PeerJ [Internet]. 2022 Jan
7;10:e12692. Available from: https://peerj.com/articles/12692
177. Vukcevic D, Traherne JA, Næss S, Ellinghaus E, Kamatani Y, Dilthey A, et al. Imputation of
KIR Types from SNP Variation Data. Am J Hum Genet [Internet]. 2015 Oct;97(4):593–607.
Available from: https://linkinghub.elsevier.com/retrieve/pii/S0002929715003699
178. Harrison GF, Leaton LA, Harrison EA, Kichula KM, Viken MK, Shortt J, et al. Allele imputation
for the killer cell immunoglobulin-like receptor KIR3DL1/S1. Kouyos RD, editor. PLOS
162
Comput Biol [Internet]. 2022 Feb 22;18(2):e1009059. Available from:
https://dx.plos.org/10.1371/journal.pcbi.1009059
Immunogenetics of HLA-B: SNP, allele, and haplotype diversity in populations from different continents and ancestry backgrounds
1 INTRODUCTION
2 METHODS
2.1 Population samples
2.2 MHC alignment optimization to correct alignment errors
2.3 HLA-B target region
2.4 Genotype and haplotype calling
2.5 HLA allele calling
2.6 Complementary analysis
3 RESULTS
3.1 HLA-B resources
3.2 Genotyping and haplotyping accuracy
3.3 Overview of worldwide HLA-B genetic diversity and linkage disequilibrium
3.4 Nucleotide diversity, Tajima's D, and gene diversity across HLA-B
3.5 HLA-B coding sequences
3.6 HLA-B promoter sequences
4 DISCUSSION
4.1 HLA-B population genetics
4.2 Functional and evolutionary insights from HLA-B diversity
AUTHOR CONTRIBUTIONS
ACKNOWLEDGMENTS
CONFLICT OF INTEREST STATEMENT
DATA AVAILABILITY STATEMENT
REFERENCES
A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputa...
1 INTRODUCTION
2 METHODS
2.1 Samples used to build reference panels
2.2 Genotyping HLA genes by NGS: alignment optimization, SNP calls and allele calls
2.3 HLA imputation and cross-validation
2.4 Validation of the reference panels
2.5 Statistical analyses
3 RESULTS
3.1 HLA class I diversity
3.2 HLA imputation in worldwide populations
3.3 Testing HLA imputation in an independent Brazilian population
4 DISCUSSION
ACKNOWLEDGEMENTS
CONFLICT OF INTEREST STATEMENT
DATA AVAILABILITY STATEMENT
REFERENCES
bd3ba6c48480d4a01a612ce79a0677f622a9777ceeeff4f88ba138b112f3ab28.pdf
bd3ba6c48480d4a01a612ce79a0677f622a9777ceeeff4f88ba138b112f3ab28.pdf