# Optimal population-specific HLA imputation with dimension reduction

Venceslas Douillard, Nayane dos Santos Brito Silva, Sonia Bourguiba-hachemi, Michel S Naslavsky, Marilia O Scliar, Yeda a O Duarte, Mayana Zatz, Maria Rita Passos-bueno, Sophie Limou, Pierre-antoine Gourraud, et al.

► To cite this version:

Venceslas Douillard, Nayane dos Santos Brito Silva, Sonia Bourguiba-hachemi, Michel S Naslavsky, Marilia O Scliar, et al. Optimal population-specific HLA imputation with dimension reduction. HLA: Immune Response Genetics, 2023, 103 (1), 10.1111/tan.15282. hal-05115050

HAL Id: hal-05115050

https://nantes-universite.hal.science/hal-05115050v1

Submitted on 16 Jun 2025

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Distributed under a Creative Commons Attribution 4.0 International License
ORIGINAL ARTICLE

HLA
Immune Response Genetics

WILEY

# Optimal population-specific HLA imputation with dimension reduction

Venceslas Douillard<fnref n="1" /> ID | Nayane dos Santos Brito Silva<fnref n="1" />,<fnref n="2" /> ID |

Sonia Bourguiba-Hachemi<fnref n="1" /> ID | Michel S. Naslavsky<fnref n="3" />,<fnref n="4" />,<fnref n="5" /> ID | Marilia O. Scliar<fnref n="3" /> ID |

Yeda A. O. Duarte<fnref n="6" />,<fnref n="7" /> ID | Mayana Zatz<fnref n="3" />,<fnref n="4" /> ID | Maria Rita Passos-Bueno<fnref n="3" />,<fnref n="4" /> ID |

Sophie Limou<fnref n="1" /> ID | Pierre-Antoine Gourraud<fnref n="1" /> ID | Élise Launay<fnref n="1" />,<fnref n="8" /> ID |

Erick C. Castelli<fnref n="2" /> ID | Nicolas Vince<fnref n="1" /> ID | on behalf of the SNP-HLA Reference Consortium (SHLARC)

**Correspondence**

Nicolas Vince, Nantes Université, CR2TI
UMR1064 – ITUN, CHU Nantes Hôtel
Dieu, 30 bld Jean Monnet, 44093 Nantes
Cedex 01, France.

Email: nicolas.vince@univ-nantes.fr

**Funding information**

Conseil Régional des Pays de la Loire;
Conselho Nacional de Desenvolvimento
Científico e Tecnológico; European
Regional Development Fund; Fundação
de Amparo à Pesquisa do Estado de São
Paulo; H2020 Marie Skłodowska-Curie
Actions; Institut National de la Santé et de
la Recherche Médicale; Université de
Nantes

Human genomics has quickly evolved, powering genome-wide association studies (GWASs). SNP-based GWASs cannot capture the intense polymorphism of *HLA* genes, highly associated with disease susceptibility. There are methods to statistically impute *HLA* genotypes from SNP-genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1000 Genomes data as a reference panel for imputing *HLA* from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, and (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1-score of 0.66 for *HLA-B*. However, custom models outperformed the multiethnic or population models of similar size (F1-scores up to 0.53, against up to 0.42). We demonstrated the importance of using genetically specific models for imputing populations, which are currently underrepresented in public datasets, opening the door to HLA imputation for every genetic population.

**KEYWORDS**

HLA imputation, Admixed populations, Dimension reduction, Immunogenomics

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2023 The Authors. HLA: Immune Response Genetics published by John Wiley & Sons Ltd.

<footnote n="1">¹Nantes Université, INSERM, Ecole Centrale Nantes, Center for Research in Transplantation and Translational Immunology, Nantes, France</footnote>

<footnote n="2">²São Paulo State University, Molecular Genetics and Bioinformatics Laboratory, School of Medicine, Botucatu, Brazil</footnote>

<footnote n="3">³Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil</footnote>

<footnote n="4">⁴Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil</footnote>

<footnote n="5">⁵Hospital Israelita Albert Einstein, São Paulo, Brazil</footnote>

<footnote n="6">⁶Medical-Surgical Nursing Department, School of Nursing, University of São Paulo, São Paulo, Brazil</footnote>

<footnote n="7">⁷Epidemiology Department, Public Health School, University of São Paulo, São Paulo, Brazil</footnote>

<footnote n="8">⁸Department of Pediatrics and Pediatric Emergency, Hôpital Fémme Enfant Adolescent, CHU de Nantes, Nantes, France</footnote>
# 1 | INTRODUCTION

Genome-wide association studies (GWASs) have now become a strong ally in the understanding of the underlying mechanisms of diseases susceptibility and outcomes, with historical associations such as rs2395029 in HIV,¹,² or the identification of 233 genomic regions linked to multiple sclerosis susceptibility.³ GWASs have also been performed as first lines of research at the beginning of the SARS-CoV-2 outbreak to evaluate how host genetics can influence COVID-19 outcomes.⁴⁻⁷ Starting from the first GWAS with hundreds of individuals in the 2000s,⁸,⁹ multiple initiatives emerged in the last decade seeking to systematically gather clinical and genetic information, such as the UK Biobank,¹⁰ Japanese BioBank,¹¹ or TOPMed,¹² which count hundreds of thousands of samples. These studies greatly improved the comprehension of the genetic impact on phenotype variation.¹³⁻¹⁵ Along with the collective organization effort, continuous advances in the domain of single-nucleotide polymorphism (SNP) imputation,¹⁶ and the availability of computing power from imputation servers, globally helped the genomics community.¹⁷

A bystander effect of these GWASs has been confirming the central role of the major histocompatibility complex (MHC), especially the *HLA* genes, in immune-related diseases. The MHC was discovered in the 1950s,¹⁸ and was identified as crucial for transplantation success.¹⁹ Association studies expanded our understanding of the role of MHC since 2.5% of all significant associations in the GWAS catalog²⁰ coincide with the MHC region, and approximately 20% of all traits are associated with at least one SNP within the MHC.⁶ The associations go from autoimmune diseases such as type 1 diabetes²¹ or multiple sclerosis,³ neurological disorders such as Parkinson,²² to infectious diseases such as HIV,²,²³ Hepatitis B,²⁴ and C.²⁵

In the context of genetic association studies, a parallel effort focused on direct association with *HLA* polymorphisms to understand the mechanisms in which HLA molecules impact disease susceptibility and severity. These studies have identified protective and risk *HLA* alleles, such as *HLA-DRB1*\*15:01 in multiple sclerosis,²⁶ *HLA-DRB1*\*09:01 with tIGE levels in asthma,²⁷ specific HLA-DQB1 amino acids in hepatitis C virus infection,²⁸ or the HLA-DRB1 valine 11 in Parkinson's disease,²⁹ among many others. The five most polymorphic *HLA* genes (*HLA-A*, *HLA-B*, *HLA-C*, *HLA-DQB1*, and *HLA-DRB1*) are exceptionally diverse, with almost 30,000 alleles combined.³⁰ However, most of these alleles seem to have frequencies <1%.³¹ Therefore, because of the high number of alleles and their low frequency, the HLA typing of thousands of individuals is necessary to reach sufficient statistical power for detecting associations. The

cost-efficiency of directly typing *HLA* for such cohorts is limited. Thus, following the steps of the SNP association, the HLA community organized multiple typing initiatives and developed imputation tools.³²,³³ The literature on HLA imputation articulates a dual focus on algorithms and reference data.

Regarding algorithms, several *HLA* imputation tools allow to create reference panels for imputing *HLA* alleles from SNP data: HIBAG³⁴ and SNP2HLA³⁵ are the most common choices. Pappas et al. evaluated HIBAG to be the most accurate.³⁶ A new generation of software followed, with improvements to the existing algorithms such as *HLA*\*IMP:03³⁷ and CookHLA,³⁸ or using deep learning with DEEP-HLA,³⁹ all of which will probably gain traction over time. However, regarding reference datasets, the accuracy of *HLA* imputation results depends on the reference panel used to predict the target genotypes; if training and target data are not of the same ancestry, then it will provide inaccurate results due to different HLA alleles and linkage disequilibrium patterns between SNP and HLA in different populations. To circumvent this issue, researchers advocated for both: specific reference panels, such as in Japan,⁴⁰ Finland,⁴¹ or SweHLA,⁴² and large multi-ethnic reference panels.⁴³,⁴⁴ To pursue the different efforts, we created the SNP-HLA Reference Consortium, or SHLARC.⁴⁵ Our goal is to coordinate an international effort to gather HLA data and reference panels, make them available to the scientific community and improve the methodology of *HLA* association studies. Generally, *HLA* imputation is highly performant for European-origin populations as a large amount of data are available to build reference panels. Conversely, the challenge is higher when focusing on underrepresented populations as fewer data are available. It can also apply for admixed population, depending on how much of their genome proportion is contained in the reference panels. A clear HLA imputation strategy remain to be defined to improve accuracy in these populations: here, we want to increase our understanding about HLA imputation performance between larger reference panels or smaller but customized (ancestry-matched) reference panels. Indeed, our hypothesis is that oversampling individuals for the reference panel with close genetic ancestry to the target individuals would increase accuracy for their specific *HLA* alleles. It would help the community to create diverse reference panels without requiring as much data as they require now, and mitigating the decrease of accuracy when inferring the *HLA* alleles of populations absent from the reference panel. To explore this in our study, we focused on the results of *HLA* imputation on admixed populations using a multiethnic reference panel from the 1000 Genomes Project (1KG), and investigating dimension reduction as a method to mitigate *HLA* imputation errors on rare alleles.⁴⁶,⁴⁷
## 2 | RESULTS

### 2.1 | HLA imputation strategy

HLA imputation accuracy heavily depends on the data used as reference. Our study aims at finding the preferred *HLA* imputation combination of reference data selection and imputation method when dealing with a target population whose ancestry is absent or underrepresented in the available training data. The 1KG dataset presents a large diversity in populations as described in Table S1,46,48 which can be grouped in 5 populations: African (AFR), American (AMR), European (EUR), East Asian (EAS), and South Asian (SAS). We selected these data as a training dataset to create 395 reference panels to be tested (Figure 1), including: (a) the full dataset (full 1KG, N = 2504), (b) 10 replications from 6 populations (1KG, AFR, AMR, EUR, EAS, and SAS; N = 200 for each), (c) 18 conditions for the custom reference panels (further described in the next chapter; 200 < N < 485); each condition replicated 5 times for each *HLA* gene (*HLA-A*, *HLA-B*, *HLA-C*, *HLA-DQB1*, and *HLA-DRB1*).

The CAAPA cohort (Consortium on Asthma among African-ancestry Populations in the Americas) is constituted of 880 individuals with SNPs of the *MHC* region and HLA genotypes. These individuals are from admixed African and European ancestry in various proportions.27 Only a small fraction of these populations' ancestries are represented in the 1KG dataset, so we also wanted to evaluate the impact of admixture in the

<figure><img src="image_2.png" /><figcaption>**FIGURE 1** Selection strategy: description of the dataset selection for training and testing. Different subsets of the 1KG dataset are used as reference, selected by super-population or from genetic proximity with the CAAPA dataset. The HLA genotypes from CAAPA are either imputed from a single model or by multiple models specific to subsets of CAAPA.</figcaption></figure>

imputation process and accuracy. Thus, the CAAPA population was alternatively considered a unique dataset of 880 individuals, or as multiple subsets of it, depending on the representation with dimension reduction methods.

### 2.2 | Data selection for customized HLA imputation

We created models with individuals from 1KG genetically close to the CAAPA target data: the custom models. We decided to rely on dimension reduction, common in population genetics, to assess individuals' ancestry. The goal is to select 200 individuals from 1KG closest to the target data, regardless of their designated ancestry. Classically, ancestry is assessed with whole-genome SNPs by principal component analysis (PCA). However, since we focused our study on HLA, we decided to represent the populations using only SNPs within the MHC region (29–34 Mb from chr6). This representation strategy separated the African population and a portion of the American population in one part, and the rest of 1KG on the other (Figure 2A). The usual granularity of PCA on whole-genome genotypes (Figure S1A) is not obtained and does not allow grouping ancestries. However, we could identify well-separated groups with a two-dimensional UMAP (Uniform Manifold Approximation and Projection) of the MHC region (Figure 2B; UMAP on whole-genome genotypes in Figure S1B).

To investigate the effect of dimension reduction on HLA imputation, we tested three parameters for representation: the algorithm (PCA or UMAP), the number of dimensions used (2 or 10), and the genomic region covered by the genotypes dataset (“notMHC”: the chromosome 6 excluding the *MHC* region; or “MHC”: the *MHC* region only, see also Figure S1). The different conditions are named after the combination of these parameters. For instance, a selection of the training data based on a UMAP using the distance computed in 10 dimensions on every SNP available on chromosome 6, excluding the *MHC*, is named UMAPnotMHC_10D.

We performed a silhouette score analysis to the resulting projection of the CAAPA dataset. We identified that, in every UMAP condition and with the 10-dimensional PCA in the MHC region, we could cluster CAAPA in more than one group. In these cases, we decided to create one model per group. We computed the average coordinates of the CAAPA individuals in each group, then selected the 200 individuals from 1KG closest to this point (Figure 3). We also computed a supplementary model, named “whole,” wherein 200 individuals were sampled from the entire1KG dataset using the average coordinates in the full CAAPA
<figure><img src="image_3.png" /><figcaption>FIGURE 2 PCA (A) and UMAP (B) representation of 1KG and CAAPA dataset with merged genotypes of the MHC region. CAAPA is represented in black. Super-populations are colored in five main colors divided into different shades for each population (Table S1), including 5 super-populations: African (AFR) in blue, American (AMR) in purple, European (EUR) in orange, South Asian (SAS) in green, and East Asian (EAS) in red. PCA does not separate well the population when restrained to the MHC region, whereas UMAP creates different groups of ancestries.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>FIGURE 3 Creation of custom 1KG models for CAAPA imputation. (1) Dimension reduction allows the separation of individuals according to ancestry, using UMAP as an example. CAAPA is represented in black. Super-populations from 1KG are colored in five main colors: African (AFR) in blue, American (AMR) in purple, European (EUR) in orange, South Asian (SAS) in green, East Asian (EAS) in red. It is also possible to apply dimension reduction to one dataset and project another onto it. (2) Clustering of the target dataset: here CAAPA. The silhouette score allows to evaluate the preferred number of clusters, then k-means allows for subsetting. (3) Modeling. The barycenter of each cluster subset is computed, then 1KG individuals closest to this coordinate are selected (in light blue), allowing to create a custom model. Created with biorender.com.</figcaption></figure>

population for comparative analysis alongside the differ-
ent custom models. To avoid redundant models, we
checked the overlap of selected individuals between the
conditions. Surprisingly, they all yielded a unique list of
1KG individuals, with low overlap between conditions
(Figure S2). For the conditions where the CAAPA
dataset was separated into different subsets, we imputed

the individuals separately, thus relying on multiple
models, but merged the results into one table. For
example, with the two-dimension UMAP representa-
tion (genotypes from the MHC region), we computed
three different models of 200 1KG individuals
(Figure 3). We then imputed three CAAPA groups
independently (357, 344, and 179 individuals for a total
of 880) and combined results into a unique table of
imputation for the full dataset.

## 2.3 | CAAPA HLA imputation comparison between usual and custom reference panels from 1KG data

We have compared the different conditions by averaging
the F1-score of each allele. As explained by Cook et al.,³⁸
the F1-score has an advantage over other accuracy met-
rics for representing the rare alleles as it is the mean of
two metrics, taking into account both the potential
under- and over-prediction of an allele. As expected, the
full1KG model (N = 2504) displayed the highest F1-score
for all *HLA* genes, ranging from 0.64 for *HLA-DRB1* to
0.87 for *HLA-C* (Figure 4). For *HLA-B*, full1KG has a
score of 0.66. However, still for *HLA-B*, when considering
the smaller models, we found that the 1KG models
(F1-score of 0.42) and the populations with close ancestry
to CAAPA (AFR: 0.37, AMR: 0.42) had nominally lower
F1-score than some custom models (PCAnotMHC_10D:
0.52, UMAPnotMHC_10D: 0.53). This trend was also
observed for *HLA-A* and *HLA-DRB1*, while *HLA-C* and
*HLA-DQB1* show a higher mean F1-score for the small
<figure><img src="image_5.png" /><figcaption>FIGURE 4 Average F1-score of HLA allele predictions for HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1 based on imputation of the CAAPA dataset, with different training models from the 1000 Genomes dataset. We have removed alleles that are not represented in the training datasets. Nomenclature of the models can be found in Table S2. Full1KG, *N* = 2504. Small 1KG models (1KG, AFR, AMR, EUR, EAS, and SAS), *N* = 200. Custom models, 200 < *N* < 485.</figcaption></figure>

1KG models. Furthermore, the “whole” models consistently yielded inferior outcomes when compared to the custom models dividing the CAAPA dataset into subgroups. This finding underscores the interest of these specific models. F1-scores are not to be interpreted as regular accuracies. Indeed, when the same methodology is applied to compute the average accuracies of each allele, these accuracies obtained more than 98% (represented as error rates in Figure S3). Additionally, the individual and haplotype accuracies, which corresponds to the proportion of correct genotypes (individuals can be counted as 0 or 1; incorrect vs. correct imputation) and the proportion of correct allele (individuals can be counted as 0, 0.5, or 1; incorrect vs. 1 correct allele vs. 2 correct alleles imputation), respectively, also show values above 80% (Figure S4).

To investigate the impact of custom models on imputation and why they seemed to perform better for highly polymorphic genes, we stratified the mean F1-score metric by *HLA* allele frequency (Figure 5). As shown in Figure 4, the custom models yield homogeneous F1-scores on average. To avoid redundancy, we selected the two best-performing models: PCAMHC_10D, which exhibited the highest F1-scores for *HLA-C* and *HLA-DQB1*, and UMAPnotMHC_10D, which yielded the best results for *HLA-B* and *HLA-DRB1*. The full1KG model (*N* = 2504) yielded a higher F1-score through all allelic frequencies. Custom models performed equally or marginally better for the rarest alleles (frequency < = 0.1%) and the most common alleles (frequency > 10%). Still, they scored higher for every other category than population models. For *HLA-B*, UMAPnotMHC_10D (*N* = 485) presented an F1-score of 0.30, 0.70, 0.85, and 0.91 for the categories from 0.1 to 10% frequency, whereas the multi-ethnic model (1KG, *N* = 200) showed scores of 0.18, 0.45, 0.78, and 0.91. Notably, the reference panel based on the African population performed worse for *HLA-DQB1*. It can be explained by the allele *HLA-DQB1*\*06:01, which was represented only once and had an F1-score of 0.1.

The results showed that creating custom reference panels based on a genotypic distance between individuals can improve the outcome compared to multi-ethnic or declared ancestry panels. However, larger multi-ethnic reference panels are always more robust. We went further and looked directly at the imputation of *HLA* alleles individually.

When we analyzed results allele by allele, taking *HLA-A* (Figure 6) as an example, we observed that in most cases, custom models performed just as well, or a few points under the full dataset models (e.g., *HLA-A*\*01:01, *HLA-A*\*23:01). Several *HLA* alleles were better predicted with the custom models compared to the multi-
<figure><img src="image_6.png" /><figcaption>FIGURE 5 Mean F1-score of HLA alleles imputation, stratified by groups of frequency, for the full 1KG model, super-population models, and a selection of custom models. The custom models PCAMHC_10D and UMAPnotMHC_10D are displayed as they are the most accurate. #: HLA-C does not have the 10%–20% frequency category. Therefore, unlike other genes, the last two categories correspond to 5%–10% and > 20%.</figcaption></figure>

ethnic (1KG) and population models (e.g., *HLA-A*01:02, *HLA-A*80:01), highlighting the importance of creating specific reference panels. We found cases where the full1KG model (N = 2504) or population models (N = 200) were the only ones to predict the allele (e.g., *HLA-A*02:06, *HLA-A*03:02). However, we also found cases where custom models were the only ones to impute correctly the allele (e.g. *HLA-A*02:04). Zheng et al.³⁴ showed that at least

10 copies of an allele were needed in a model to be able to impute them. Nine *HLA-A* alleles were present in the training and target data but were not imputed by any of the models (e.g., *HLA-A*02:11, *HLA-A*24:03, *HLA-A*26:08). Often, the allele was present only in few individuals of the target data, causing the miscalled allele to weigh a lot in the score. We focused on *HLA-A* for visualization purposes, but the results applied to *HLA-B*,
<figure><img src="image_7.png" /><figcaption>FIGURE 6 Mean F1-score of each HLA-A alleles (N = 42) for the full 1KG training dataset, the African, American, European super-populations datasets, and the most accurate custom reference panels PCAMHC_10D and UMAPnotMHC_10D. Alleles are ordered by decreasing frequency in the 1KG dataset. Those absent from the training dataset have been removed to compute the means.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>FIGURE 7 Mean F1-score of SABE's imputed HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1 genotypes, using the full 1KG model, compared to super-populations from 1KG or individual custom models selected by dimension reduction. Alleles absent from the training datasets were removed to obtain these values.</figcaption></figure>

HLA-C, and *HLA-DRB1* (Figure S5). Interestingly for *HLA-DQB1*, the best custom models were never the best predictors. For most *HLA-DQB1* alleles, the best training dataset was the full1KG dataset. For *HLA-DQB1*\*03:01, however, the AMR and EUR super-populations yielded better results. The different examples presented here show how a custom reference panel could help in the imputation of certain *HLA* alleles. However, since bigger models produce better imputation results overall, we would need to know when to select the results from the custom reference panel.

HLA imputation with HIBAG yields post-probabilities for each genotype. We tried to harness the few cases where

custom models performed better (in terms of post-probabilities) to obtain hybrid imputation between the full models and the custom model. We chose UMAP-notMHC_10D as it performed the best on multiple *HLA* genes. Unfortunately, the small number of samples in the custom models led to lower post-probabilities than the full model. In the few cases where UMAPnotMHC_10D yielded better post-probabilities, the imputed genotype was not always correct, whereas the less likely genotype imputed by the other model was correct. In a real situation where the *HLA* alleles of the target data would not be known, there would be no way to choose between the imputed genotype of the two models (Figure S6).
## 2.4 | Replication with admixed Brazilian individuals from SABE

We replicated our methodology on another cohort of admixed individuals, the Longitudinal Health, Well-Being, and Aging cohort (SABE—*Saúde, Bem-estar e Envelhecimento*) from Brazil, to validate the impact of the models composition on *HLA* imputation (Figure 7). SABE is an independent dataset of 1322 individuals from Brazil, mostly with European and African admixed ancestry.⁴⁹ To validate our conclusions, we used the same models as with the CAAPA dataset; therefore, between 11.6% and 45.1% of the model SNPs were missing in the target data. Though it probably reduced the imputation score overall, the missing SNPs were homogeneous across conditions for each gene, with averages of 30,0% for *HLA-A*, 14,3% for *HLA-B*, 13,9% for *HLA-C*, 39,4% for *HLA-DQB1*, and 39,6% for *HLA-DRB1*. We also limited our study to the PCAMHC_10D and UMAPnotMHC_10D custom models, as these two models predicted *HLA-A*, *HLA-C*, *HLA-DQB1*, and *HLA-DRB1* better, out of all the custom models in the CAAPA dataset.

As with CAAPA, the custom models had nominally higher F1-score than the “whole” model with 200 individuals sampled from the full 1KG dataset, but only for the *HLA-B* (0.44, 0.50 for PCA and UMAP vs. 0.42 for 1KG) and *HLA-DRB1* (0.56 for UMAP vs. 0.48 for 1KG). Overall, the validation with the SABE population showed the same patterns as the CAAPA population, with a global preference for the full1KG model and multiple cases where the custom reference panels were to be preferred but presented low post-probabilities genotypes.

## 3 | DISCUSSION

The HLA and immunogenetic community, along with the SHLARC,⁴⁵ carries a broad dynamic to provide scientists with reliable tools and reference panels for *HLA* imputation, thus increasing the power of *HLA* association studies to that of existing GWASs. We believe our results contribute to this effort. Our work focused on improving existing methods of *HLA* imputation by finely accounting for ancestry in the choice of the training model. Our underlying hypothesis was that oversampling individuals to create reference panels with close genetic ancestry compared to the target individuals would increase HLA imputation accuracy for rare *HLA* alleles. In this context, we chose to evaluate the imputation of CAAPA, an admixed African-American cohort, using reference panels composed of different combinations of 1000 Genomes Project individuals: randomly selected from a population or selected for their estimated ancestry by

dimension reduction. We showed that, ultimately, the number of individuals was the crucial point of HLA imputation. The reference panel composed of 2504 individuals from 1KG systematically had a higher F1-score than other smaller models. Using fewer individuals for training by selecting individuals close to the ancestry of the target population was a good strategy and resulted in slightly better HLA imputation F1-scores, compared to multi-ethnic reference panels. The improvement did not concern the rarest or most common alleles, which are, respectively, badly and well imputed by all those models. At the allele level, we expected the full model to impute *HLA* alleles other models would not; we also saw the opposite with custom reference panels capturing a part of the information left out in the full model. Unfortunately, we could not conclude on its applicability since the custom reference panels had fewer individuals resulting in lower post-probabilities that rendered a hybrid imputation impossible. Research on SNP to SNP imputation also encounters the problem of lack of diversity for the imputation of rarer alleles, and are working with specific reference panels to enhance imputation accuracy.⁵⁰,⁵¹

Interestingly, we were also able to use UMAP for genomic ancestry representation, as can also be seen in recent research.⁵²⁻⁵⁴ It presented a good separation of ancestry groups in two dimensions when only using the MHC SNPs, concordant with the frequency difference of *HLA* alleles between populations.⁵⁵ In contrast, PCA would fail to separate them in only two dimensions, limiting the possibility to visualize. In terms of HLA imputation results, although similar, there are subtle differences between the custom models results. It seems PCA-based model shows better results when used only with MHC SNPs, whereas UMAP-based model yields better results when using the rest of chromosome 6. We assumed that the genetic distance using SNPs may be related to the *HLA* alleles born by individuals. PCA uses a linear combination of SNPs where each one has a weight. When computing the distance in two dimensions, for instance, it may be possible that a specific SNP has low weights on the two axes, resulting in minimal impact on the calculated distance. If this SNP has a strong statistical link to an *HLA* allele, the distance computed fails to group individuals sharing the similar *HLA* alleles. Therefore, a strategic focus on the MHC region seems cautious as it introduces more SNPs beneficial for the HLA inference. Differently, the UMAP relies on topology and preserves the distances calculated with every SNP while reducing the number of dimensions. In this scenario, every SNP counts to create the genetic distance, and the use of broader set of SNPs (the whole chromosome 6 in our study) supposedly will enhance the accuracy of inter-individuals distance which, in turn, can help to
select a better population for the HLA imputation. Besides, we observed a distance between individuals sometimes higher inside a labeled 1KG population than between populations, as described in Maróstica et al.⁵⁵,⁵⁶ This representation of this genomic diversity inside the MHC directly impacts how we should construct reference panels in the future and highlights the importance of gathering more data from different ancestry backgrounds.

Our work showed the potential interest of population-specific reference panels, as multiple studies have demonstrated.⁴⁰⁻⁴²,⁴⁴,⁵⁷⁻⁶⁰ However, we strayed further from the geographic definition of the population. We tried to find a local definition of ancestry to select training datasets. While doing so, we also omitted potential sides to the problem and created limits to our method. One important difference to *HLA* imputation compared to typing, inherent to the method, is the impossibility of predicting de novo alleles and the difficulty of imputing rare alleles. This issue is intrinsic to all training machine learning methods, and it is especially true for HLA, where each gene can have thousands of alleles. In HIBAG, for instance, an allele should be present at least 10 times in the training dataset to be predicted.³⁴ This study showed that this limit can be overcome to a certain extent but still hinders *HLA* imputation accuracy overall. We also chose to represent the accuracy of imputation along with the frequency of the alleles in the reference panel. It would be interesting to expand this idea and investigate the relationship between *HLA* imputation of an allele and its recentness in evolutionary time, since population-specific *HLA* alleles are often newer.⁵⁵ Two *HLA* allele could share a similar SNP haplotype background with each other, as showed by the Severe Covid-19 GWAS Group,⁶¹ as one HLA allele derives from the other. Additionally, the choice to limit the number of randomly selected individuals was directly linked to the maximum of samples in the smallest population (n<sub>AMR</sub> = 347). However, it has led to low imputation scores. Even though we performed replications, the difference between population models and the full dataset, or the custom models, may greatly vary if we increase this limit with another multi-ethnic dataset. It is one potential improvement to this work, which may validate or not our findings. We considered creating our custom models tailored to each individual in the target data rather than a population-level approach, but found this was not computationally tractable.

We chose to represent the *HLA* imputation with the F1-score, as seen in Cook et al.³⁸ This choice is convenient for the analysis of *HLA*, in which we encounter low and unbalanced frequencies between the different alleles. We set the F1-score at 0 when a specific allele was not

imputed at all (whereas F1-score should be null) to represent all alleles in common between the two datasets and weigh this absence of imputation negatively. It has increased the confidence interval of each averaged F1-score and limited the possibility to find statistical differences between them. It is important to note that the F1-score gives a harsher view on *HLA* imputation because rare alleles have low scores, however, HLA imputation performs very well for common alleles (Figure S3).³² This choice of F1-score, while convenient to focus on the rare alleles of the target population, also leaves unexplored the impact of the over-prediction of alleles exclusively present in the reference panel. As seen in the work by Dekeyser et al.,⁶² on SNP-SNP imputation, it may impact the overall results of imputation, particularly when sharing a similar SNP background with another allele in the target population.

Besides methodology, *HLA* imputation gains much accuracy from the number of samples and the diversity in the reference panels. This is why initiatives looking into expanding the *HLA* data and creating larger reference panels, such as Degenhardt et al., are essential to the field.⁴³,⁴⁴,⁶³ With the SHLARC,⁴⁵ we advocate for the coordination of such efforts to provide multi-ethnic panels of sufficient size, and help researchers do *HLA* imputation to investigate HLA risk and protection alleles, focusing on the coverage of the globe for data gathering. The evolution of imputation tools will also consequently improve *HLA* imputation. HLA-IMP*03³⁷ and CookHLA³⁸ showed improved results over the algorithms they are created upon, and DeepHLA³⁹ also showed high accuracy, with a specific focus on rare HLA alleles. While we did not investigate their results when confronted to our methodology of custom panels, the results could help to understand *HLA* imputation in general. Whether the use of a different algorithm does not change the results or if they each introduce their own variability depending on the reference panels is still an open question. Eventually, these efforts will reach a limit, and we think the main focus of research should be gathering data worldwide.

Our results demonstrated the interest of using genetically specific models for imputing admixed populations which are currently underrepresented, opening the door to *HLA* imputation for every genetic population, while also exemplifying some limitation. The SNP-HLA Reference Consortium (SHLARC) wants to contribute to the *HLA* association analysis community by providing a platform for *HLA* imputation with exhaustive and diverse reference panels. We hope this will help association studies to rapidly increase their statistical power and become a natural extension of GWASs pointing toward *HLA* association.
4 | METHODS

**4.1** | **Data description and processing**

SNPs data from the 1KG, CAAPA, and SABE cohorts
were obtained from whole-genome sequencing. Individ-
uals from these three cohorts gave an informed consent
to their primary promoter for research use of their data.
The 1KG dataset is one of the most diverse public dataset
with 2504 individuals from 26 populations.⁴⁶,⁴⁸ These
populations are grouped in 5 populations, as described in
Table S1: African (AFR), American (AMR), European
(EUR), East Asian (EAS), and South Asian (SAS). HLA
genotyping for the *HLA-A*, *HLA-B*, *HLA-C*, *HLA-DQB1*,
and *HLA-DRB1* genes was published and made accessible
using HLA calling algorithms for whole-genome sequenc-
ing data.⁶³ Moreover, the SNP data has been updated with
a new whole-genome sequencing of 30X coverage from the
New York Genome Center.⁴⁷ The CAAPA cohort
(Consortium on Asthma among African-ancestry Popula-
tions in the Americas) was created to study asthma in
African-ancestry populations. The aim of this study was to
catalog genetic diversity in these populations, especially the
African Diaspora in the Americas. From this, we had
access to 880 individuals with whole-genome sequencing
data of the *MHC* region and HLA genotypes.²⁷ The *HLA*
alleles were called with the Omixon software (Budapest,
Hungary) from whole-genome sequencing data.²⁷ The
SABE (*Saúde, Bem-estar e Envelhecimento*) data come from
the longitudinal, census-based follow-up, Health, Well-
Being, and Aging cohort of elderly people from São Paulo,
Brazil. SABE is an independent dataset of 1322 admixed
individuals from Brazil, mostly with European and African
admixed ancestry: details can be found in the whole-
genome sequencing flagship publication.⁴⁹ *HLA* genotypes
for SABE cohort were obtained after read alignment with
hla-mapper 4.1. This application was designed to optimize
the mapping of *HLA* sequences produced by massively
parallel sequencing procedures⁶⁴; the pipeline is available
at https://github.com/erickcastelli/HLA_genotyping/tree/
main/version_2.

SNPs data were handled with PLINK v1.90b6.21⁶⁵ and
went through the same quality control step: the removal of
A/T and G/C ambiguous SNPs, and SNPs with >2% missing
genotypes and < 1% minor allele frequency. *HLA* data com-
prises two-field alleles for *HLA-A*, *HLA-B*, *HLA-C*, *HLA-
DQB1*, and *HLA-DRB1*, stored in a CSV file. *HLA* imputa-
tion models were computed on R 3.5.3⁶⁶ with HIBAG
v1.19.3³⁴ and its complementary package HIBAG.gpu v0.9.1.
Training data were subsetted with PLINK to contain only
the SNPs present in the target data for CAAPA.

We limited the number of individuals within each ref-
erence panel to 200 to be able to compare the specific

reference panels to the population reference panels.
Indeed, this number is lower than the smallest popula-
tion, allowing to resample the population and repeat the
experiment.

4.2 | HLA imputation metrics

We have evaluated imputation accuracy using the F1-score.
The F1-score is a harmonic mean of sensitivity (for a spe-
cific allele, # of correctly predicted allele/# of said alleles in
the target dataset) and the positive predictive value (for a
specific allele, # of correctly predicted allele/# of predictions
of said allele). This score has the property to give important
weight to the coverage of a specific allele prediction. For
instance, if a rare allele is present once in a dataset of
100 alleles and not predicted by the model, then you would
have a 99% accuracy but a F1-score of 0.

HLA imputation models are limited by the pool of
HLA alleles in the training dataset and the SNPs avail-
able, contrary to HLA-typing software based on read
alignment, which relies on the complete database of
known HLA alleles and the assessment of all gene
regions. Therefore, we chose to average the results of all
alleles present in the training and target datasets. Addi-
tionally, if one of these alleles is not predicted by the
model, the positive predictive value, by definition, cannot
be computed; in this case, the F1-score is also null. Since
we wanted to focus our analysis on rare alleles, we
decided to set the F1 scores of such alleles to 0, to
visualize the impact of HLA alleles that are in the train-
ing dataset but do not manage to impute the ones in the
target data.

**4.3** | **Dimension reduction**

PCA is routinely used in population genomics and associ-
ation studies to study population ancestry. It relies on
SNPs which are attributed to different contributions,
maximizing the variance in their genotypes. It allows
separating populations along multiple orthogonal axes
with different contributions for each SNP. Uniform
Manifold Approximation Projection (UMAP) and t-SNE
are central in single-cell transcriptomics analyses.⁶⁷,⁶⁸
Recently, it has also appeared in population genomics
publications.⁵²,⁵³ UMAP is based on simplicial topology
to identify sets of neighbors for each individual and try to
preserve them while transforming coordinates into new
ones with less dimensions.

We performed dimension reduction after merging
1KG and CAAPA data. We ran PCA with PLINK, and
UMAP on the BiRD cluster from Nantes University,
using the umap R package. This package does not han-
dle missing data; therefore, we applied the PLINK geno-
filter with a 0 threshold beforehand to remove any SNP
with missing data. We followed the same process
with SABE but merged the dataset with both 1KG and
CAAPA.

We applied a silhouette score on the coordinates of
the CAAPA individuals to identify the preferred number
of clusters. We then performed k-means with the
number of clusters that had the highest silhouette score.
If the maximum score was inferior to 0.4, we chose not to
perform clustering because simulations showed different
groups would overlap greatly.

4.4 | Data access

1000 Genomes SNP genotypes were retrieved from
the International Genome Sample Resource (ISGR) and can
be accessed through (https://www.internationalgenome.org/
data-portal/data-collection/30x-grch38). 1000 Genomes HLA
genotypes of 2693 individuals were recovered from Abi-
Rached et al. (2018) at https://doi.org/10.1371/journal.pone.
0206512.s010.

CAAPA SNPs were retrieved from the WGS data
deposited in dbGAP with the accession code phs001123.
v2.p1, described in Mathias, R. A. et al. (2016). CAAPA
HLA genotypes were obtained with the Omixon software
as described in https://doi.org/10.1016/j.jaci.2020.01.011.

For SABE, individual-level sequence datasets (BAM
files) are available at the European Genome-phenome
Archive (EGA), under EGA Study accession number
EGAS00001005052. Further information about EGA can
be found at https://ega-archive.org.

AUTHOR CONTRIBUTIONS

Venceslas Douillard analyzed the data, produced the fig-
ures and wrote the article. Nicolas Vince supervised the
work, analyzed the data, wrote and edited the article.
Nayane dos Santos Brito Silva, Sophie Limou, Pierre-
Antoine Gourraud, Élise Launay, Michel S. Naslavsky,
Marilia O. Scliar, Yeda A. O. Duarte, Mayana Zatz, Maria
Rita Passos-Bueno and Erick C. Castelli wrote and edited
parts of the article. Yeda A. O. Duarte, Maria Rita Passos-
Bueno, and Mayana Zatz provided SABE cohort samples
and genomic data.

ACKNOWLEDGMENTS

We would like to thank the Centre de Calcul Intensif des
Pays de la Loire. This work was supported by de Nantes
Métropole, Région des Pays de la Loire and European
Union (FEDER) via the Programme d'investissements
d'Avenir (NExT, SHLARC Project, Nantes Université),

the ATIP-Avenir Inserm program, the Région Pays de
Loire ConnectTalent. Venceslas Douillard has received
funding from the Inserm and Région Pays de la Loire.
Nicolas Vince has received funding from the European
Union's Horizon 2020 research and innovation program
under the Marie Skłodowska-Curie grant agreement
No. 846520. Funding was provided by The São Paulo
Research Foundation (FAPESP) grants and fellow-
ships (CEPID 2013/08028-1, SABE 2014/50649-6, INCT
2014/50931-3, 2013/17084-2, 2017/19223-0, 2012/24731-1,
2018/15579-8, 2015/25020-0, 2020/02413-4) and Conselho
Nacional de Desenvolvimento Científico e Tecnológico
(CNPq INCT 465355/2014-5). The grant 2013/17084-2
supported creating and maintaining the hla-mapper
software and the pipeline from HLA calling from
NGS data.

CONFLICT OF INTEREST STATEMENT

The authors declare that there are no competing
interests.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are avail-
able on request from the corresponding author. The data
are not publicly available due to privacy or ethical
restrictions.

ETHICS STATEMENT

All individual data were obtained through third party
access either openly accessible (The 1000 Genomes Pro-
ject) or restricted access (see data availability). This
research obtained approval from the ethics committee for
non-interventional research. Nantes Université IRB
number: 14092022.

ORCID

Venceslas Douillard https://orcid.org/0000-0002-6762-4083

Nayane dos Santos Brito Silva https://orcid.org/0000-0001-5511-8426

Sonia Bourguiba-Hachemi https://orcid.org/0000-0002-2452-2861

Michel S. Naslavsky https://orcid.org/0000-0002-9068-1713

Yeda A. O. Duarte https://orcid.org/0000-0003-3933-2179

Mayana Zatz https://orcid.org/0000-0003-3970-8025

Maria Rita Passos-Bueno https://orcid.org/0000-0002-9248-3008

Sophie Limou https://orcid.org/0000-0002-7702-8234

Pierre-Antoine Gourraud https://orcid.org/0000-0003-1131-9554

Élise Launay https://orcid.org/0000-0002-4081-4653
Erick C. Castelli https://orcid.org/0000-0003-2142-7196

Nicolas Vince https://orcid.org/0000-0002-3767-6210

## REFERENCES

1. Limou S, Zagury J-F. Immunogenetics: genome-wide Association of non-Progressive HIV and viral load control: HLA genes and beyond. *Front Immunol.* 2013;4:1-13.

2. Fellay J, Shianna KV, Ge D, et al. A whole-genome association study of major determinants for host control of HIV-1. *Science.* 2007;317:944-947.

3. International Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. *Science.* 2019;365(6460):eaav7188.

4. Pairo-Castineira E et al. Genetic mechanisms of critical illness in COVID-19. *Nature.* 2021;591:92-98.

5. COVID-19 host genetics initiative. Mapping the human genetic architecture of COVID-19. *Nature.* 2021;600:472-477.

6. Douillard V, Castelli EC, Mack SJ, et al. Current HLA investigations on SARS-CoV-2 and perspectives. *Front Genet.* 2021;12:774922.

7. Castelli EC, de Castro MV, Naslavsky MS, et al. MUC22, HLA-A, and HLA-DOB variants and COVID-19 in resilient super-agers from Brazil. *Front Immunol.* 2022;13:975918.

8. Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. *Science.* 2005;308:385-389.

9. Duerr RH, Taylor KD, Brant SR, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. *Science.* 2006;314:1461-1463.

10. Bycroft C, Freeman C, Petkova D, et al. The UK biobank resource with deep phenotyping and genomic data. *Nature.* 2018;562:203-209.

11. Hirata M, Kamatani Y, Nagai A, et al. Cross-sectional analysis of BioBank Japan clinical data: a large cohort of 200,000 patients with 47 common diseases. *J Epidemiol.* 2017;27:S9-S21.

12. Taliun D, Harris DN, Kessler MD, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed program. *Nature.* 2021;590:290-299.

13. Visscher PM, Wray NR, Zhang Q, et al. 10 years of GWAS discovery: biology, function, and translation. *Am J Hum Genet.* 2017;101:5-22.

14. Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. *Nat Rev Genet.* 2019;20:467-484.

15. Claussnitzer M, Cho JH, Collins R, et al. A brief history of human disease genetics. *Nature.* 2020;577:179-189.

16. Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next-generation reference panels. *Am J Hum Genet.* 2018;103:338-348.

17. McCarthy S, das S, Kretzschmar W, et al. A reference panel of 64,976 haplotypes for genotype imputation. *Nat Genet.* 2016;48:1279-1283.

18. Dausset J. Iso-leuko-antibodies. *Acta Haematol.* 1958;20:156-166.

19. Dausset J. The major histocompatibility complex in man. *Science.* 1981;213:1469-1474.

20. MacArthur J, Bowler E, Cerezo M, et al. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS catalog). *Nucleic Acids Res.* 2017;45:D896-D901.

21. Concannon P, Chen WM, Julier C, et al. Genome-wide scan for linkage to type 1 diabetes in 2,496 multiplex families from the type 1 diabetes genetics consortium. *Diabetes.* 2009;58:1018-1022.

22. Nalls MA, Blauwendraat C, Vallerga CL, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies. *Lancet Neurol.* 2019;18:1091-1102.

23. Limou S, le Clerc S, Coulonges C, et al. Genomewide association study of an AIDS-nonprogression cohort emphasizes the role played by HLA genes (ANRS Genomewide association study 02). *J Infect Dis.* 2009;199:419-426.

24. Hu Z, Liu Y, Zhai X, et al. New loci associated with chronic hepatitis B virus infection in Han Chinese. *Nat Genet.* 2013;45:1499-1503.

25. Vergara C, Thio CL, Johnson E, et al. Multi-ancestry genome-wide association study of spontaneous clearance of hepatitis C virus. *Gastroenterology.* 2019;156:1496-1507.e7.

26. Moutsianas L, Jostins L, Beecham AH, et al. Class II HLA interactions modulate genetic risk for multiple sclerosis. *Nat Genet.* 2015;47:1107-1113.

27. Vince N, Limou S, Daya M, et al. Association of HLA-DRB1*09:01 with tIgE levels among African-ancestry individuals with asthma. *J Allergy Clin Immunol.* 2020;146:147-155.

28. Valencia A, Vergara C, Thio CL, et al. Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQβ1. *Am J Hum Genet.* 2022;109:299-310.

29. Domenighetti C, Douillard V, Sugier PE, et al. The interaction between HLA-DRB1 and smoking in Parkinson's disease revisited. *Mov Disord.* 2022;37:1929-1937. doi:10.1002/mds.29133

30. Robinson J, Barker DJ, Georgiou X, Cooper MA, Flicek P, Marsh SGE. IPD-IMGT/HLA Database. *Nucleic Acids Res.* 2020;48:D948-D955.

31. Maiers M, Gragert L, Klitz W. High-resolution HLA alleles and haplotypes in the United States population. *Hum Immunol.* 2007;68:779-788.

32. Meyer D, Nunes K. HLA imputation, what is it good for? *Hum Immunol.* 2017;78:239-241.

33. Douillard V, Castelli EC, Mack SJ, et al. Approaching genetics through the MHC lens: tools and methods for HLA research. *Front Genet.* 2021;12:774916.

34. Zheng X, Shen J, Cox C, et al. HIBAG-HLA genotype imputation with attribute bagging. *Pharmacogenomics J.* 2014;14:192-200.

35. Jia X, Han B, Onengut-Gumuscu S, et al. Imputing amino acid polymorphisms in human leukocyte antigens. *PloS One.* 2013;8:e64683.

36. Pappas DJ, Tomich A, Garnier F, Marry E, Gourraud P-A. Comparison of high-resolution human leukocyte antigen haplotype frequencies in different ethnic groups: consequences of sampling fluctuation and haplotype frequency distribution tail truncation. *Hum Immunol.* 2015;76:374-380.

37. Motyer A et al. Practical use of methods for imputation of HLA alleles from SNP genotype data. 2016. doi:10.1101/091009

38. Cook S, Choi W, Lim H, et al. Accurate imputation of human leukocyte antigens with CookHLA. *Nat Commun.* 2021;12:1264.

39. Naito T, Suzuki K, Hirata J, et al. A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes. *Nat Commun.* 2021;12:1639.
40. Okada Y, Momozawa Y, Ashikawa K, et al. Construction of a population-specific HLA imputation reference panel and its application to Graves' disease risk in Japanese. *Nat Genet.* 2015; 47:798-802.

41. Ritari J, Hyvärinen K, Clancy J, FinnGen, Partanen J, Koskela S. Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort. *NAR Genom Bioinform.* 2020;2:1qaa030.

42. Nordin J, Ameur A, Lindblad-Toh K, Gyllensten U, Meadows JRS. SweHLA: the high confidence HLA typing bio-resource drawn from 1000 Swedish genomes. *Eur J Hum Genet.* 2020;28:627-635.

43. Degenhardt F, Wendorff M, Wittig M, et al. Construction and benchmarking of a multi-ethnic reference panel for the imputation of HLA class I and II alleles. *Hum Mol Genet.* 2019;28: 2078-2092.

44. Luo Y, Kanai M, Choi W, et al. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response. *Nat Genet.* 2021; 53:1504-1516.

45. Vince N, Douillard V, Geffard E, et al. SNP-HLA reference consortium (SHLARC): HLA and SNP data sharing for promoting MHC-centric analyses in genomics. *Genet Epidemiol.* 2020;44: 733-740.

46. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. *Nature*. 2015;526(7571):68-74.

47. Byrska-Bishop M, Evani US, Zhao X, et al. High-coverage whole-genome sequencing of the expanded 1000 genomes project cohort including 602 trios. *Cell.* 2022;185:3426-3440.e19.

48. Clarke L, Fairley S, Zheng-Bradley X, et al. The international genome sample resource (IGSR): a worldwide collection of genome variation incorporating the 1000 genomes project data. *Nucleic Acids Res.* 2017;45:D854-D859.

49. Naslavsky MS, Scliar MO, Yamamoto GL, et al. Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo. *Brazil Nat Commun.* 2022;13:1004.

50. Kals M et al. Advantages of genotype imputation with ethnically matched reference panel for rare variant association analyses. doi:10.1101/579201

51. Herzig AF et al. Can imputation in a European country be improved by local reference panels? The example of France. doi:10.1101/2022.02.17.480829

52. Diaz-Papkovich A, Anderson-Trocmé L, Gravel S. A review of UMAP in population genetics. *J Hum Genet.* 2021;66:85-91.

53. Sakaue S, Hirata J, Kanai M, et al. Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction. *Nat Commun.* 2020;11:1569.

54. Dai CL, Vazifeh MM, Yeang CH, et al. Population histories of the United States revealed through fine-scale migration and haplotype analysis. *Am J Hum Genet.* 2020;106:371-388.

55. Maróstica AS, Nunes K, Castelli EC, et al. How HLA diversity is apportioned: influence of selection and relevance to transplantation. *Philos Trans R Soc Lond B Biol Sci.* 2022;377: 20200420.

56. Lewontin RC. The apportionment of human diversity. In: Dobzhansky T, Hecht MK, Steere WC, eds. *Evolutionary Biology*. Springer US; 1972:381-398. doi:10.1007/978-1-4684-9063-3_14

57. Mimori T, Yasuda J, Kuroki Y, et al. Construction of full-length Japanese reference panel of class I HLA genes with single-molecule, real-time sequencing. *Pharmacogenomics J.* 2019;19: 136-146.

58. Zhou F, Cao H, Zuo X, et al. Deep sequencing of the MHC region in the Chinese population contributes to studies of complex disease. *Nat Genet.* 2016;48:740-746.

59. Nunes K, Zheng X, Torres M, et al. HLA imputation in an admixed population: an assessment of the 1000 genomes data as a training set. *Hum Immunol.* 2016;77:307-312.

60. Huang Y-H, Khor SS, Zheng X, et al. A high-resolution HLA imputation system for the Taiwanese population: a study of the Taiwan biobank. *Pharmacogenomics J.* 2020;20:695-704. doi:10.1038/s41397-020-0156-3

61. Severe Covid-19 GWAS Group et al. Genomewide association study of severe Covid-19 with respiratory failure. *N Engl J Med.* 2020;383:1522-1534.

62. Dekeyser T, Génin E, Herzig AF. Opening the black box of imputation software to study the impact of reference panel composition on performance. *Genes (Basel).* 2023;14:410.

63. Abi-Rached L, Gouret P, Yeh JH, et al. Immune diversity sheds light on missing variation in worldwide genetic diversity panels. *PloS One.* 2018;13:e0206512.

64. 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.* 2018;79:678-684.

65. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. *Gigascience.* 2015;4:7.

66. R Core Team. *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing; 2022.

67. McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. 2018. doi:10.48550/ARXIV.1802.03426

68. Becht E, McInnes L, Healy J, et al. Dimensionality reduction for visualizing single-cell data using UMAP. *Nat Biotechnol.* 2018;37:38-44. doi:10.1038/nbt.4314

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Douillard V, dos Santos Brito Silva N, Bourguiba-Hachemi S, et al. Optimal population-specific HLA imputation with dimension reduction. *HLA*. 2024;103(1):e15282. doi:10.1111/tan.15282