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Sci Rep
Scientific Reports
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Nature Publishing Group UK London

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10.1038/s41598-024-71803-7
Article
Europeans and Americans of European origin show differences between their biological pathways related to the major histocompatibility complex
Vaulin Andrey 1
Karpulevich Evgeny 2
Kasianov Artem artem.kasianov@cibio.up.pt

45
Morozova Irina irina.morozova@igdore.org

3
1 https://ror.org/02e7b5302 grid.59025.3b 0000 0001 2224 0361 Nanyang Technological University, Singapore, Singapore
2 https://ror.org/017ef8252 grid.454315.2 0000 0004 0619 3712 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), Moscow, Russia
3 Institute for Globally Distributed Open Research and Education (IGDORE), Moscow, Russia
4 grid.5808.5 0000 0001 1503 7226 Centro de Investigação em Biodiversidade e Recursos Genéticos, CIBIO, InBIO Laboratório Associado, Universidade do Porto, Vairão, Portugal
5 grid.5808.5 0000 0001 1503 7226 BIOPOLIS, Program in Genomics, Biodiversity and Land Planning, CIBIO, Vairão, Portugal
18 9 2024
18 9 2024
2024
14 2181616 12 2023
30 8 2024
© The Author(s) 2024
2024
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In this study, we analysed biological pathway diversity among Europeans and Northern Americans of European origin, the groups of people that share a common genetic ancestry but live in different geographic regions. We used a novel complex approach for analysing genomic data: we studied the total effects of multiple weak selection signals, accumulated from independent SNPs within a pathway. We found significant differences between immunity-related biological pathways from the two groups. All identified pathways included genes belonging to the major histocompatibility complex (MHC) system, which plays an important role in adaptive immune responses. We suggest that the ways of evolution were different for the MHC-I and MHC-II gene groups at least in Europeans and Americans of European origin. We hypothesise that the observed variability between the two populations was triggered by selection pressures due to the different pathogen landscapes and pathogen loads on the two continents. Our findings can be important for epidemic prevention and control, as well as for analysing processes related to allergies, organ transplantation, and autoimmune diseases.

Subject terms

Genomics
Genome evolution
http://dx.doi.org/10.13039/501100012190 Ministry of Science and Higher Education of the Russian Federation 075-15-2022-294 Karpulevich Evgeny issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Throughout the history of Homo sapiens sapiens, migration has been the main trigger for not only sociocultural but also evolutionary processes. The high variability among modern humans resulted from the extensive migration from the African continent to the rest of the world. The reasons for migrating could have been very different, but migrations, since the beginning of civilization, with their positive and cruel sides, have shaped human population gene pools1,2.

European colonization of the Americas was one of the most remarkable events in modern human history; it contributed to the conformation of the genetic map of the contemporary world. From the end of the fifteenth century, millions of Europeans moved to the Americas. People from different ethnic, social, and religious backgrounds strived to colonise new lands. In 1776, European immigrants founded a new state, the United States of America3,4.

Modern USA residents of European origin represent a population that has been living in the American continent for several centuries now. Despite their European ancestry, they have experienced different cultural practices, lifestyles, diets, and pathogen environments. Currently, Europeans and Americans of European origin show several dissimilarities, including differences in the prevalence of certain diseases, mortality rates, and longevity5–9. Though it is difficult to evaluate the contribution of environmental (e.g., different living conditions, diets, and healthcare specifics) and genetic factors to such multifactorial traits, their possible influence have been studied intensively. One can suggest that the differences in selection pressure on these groups may have also played a role.

Different selection pressures on populations with the same genetic ancestry but living under different environmental conditions have been demonstrated in various previous studies10–12. Most of these works were based on the analysis of single nucleotide polymorphisms (SNPs), and therefore detected strong selective signals from individual mutations. In a previous study13, we demonstrated the perspectivity of using a complex approach for analysing biological pathways that include many different genes aggregated into groups that are responsible for functional processes. We analysed the total effects of multiple weak selection signals, accumulated from independent SNPs within a biological pathway. Therefore, we studied the evolution of an entire biological process (pathway) instead of studying the evolution of separate SNPs. This approach has proven to be effective in the study of genetic differences between ancient and modern Europeans13. Here, the similar method was used to understand the differences between two groups of people that were separated not only by time but also by geographical space. Our goal was to analyse the variability between the biological pathways of Europeans and North Americans of European origin.

Results

Compatibility of the data

We compared whole-genome data from 404 European individuals (Iberians, Tuscans, British, and Finns, hereafter referred to as Europeans) and 92 North Americans of European origin (Utah residents of Northern and Western European ancestry, hereafter referred to as Americans) obtained from the 1000 Genomes Project14. In total, 14,258 synonymous and 14,471 non-synonymous SNPs were compared. The pipeline used for the analysis is presented in Fig. 1.Fig. 1 General pipeline of the study.

To ensure that the Americans possessed a European origin, we performed a principal component analysis of whole-genome data using the studied groups, as well as samples from other geographic regions. As shown in Fig. 2, the American samples are located within the European cluster (closer to British samples) and the European cluster differed from the African and Asian clusters.Fig. 2 Principal component analysis of the groups from different regions of the world. PCA was conducted on whole-genome data consisting of 8,137,497 SNPs from groups representing different regions of the world (A), and on whole-genome data comprising 6,996,820 SNPs from European ancestor populations and the Utah population (B).

Therefore, the European and American groups studied here share a common genetic origin. Further analysis showed the changes that occurred after the split of these two groups.

Comparison of the European and American groups

We compared the distribution of synonymous and non-synonymous SNPs in biological pathways between the European and American groups. As in our previous study13, we made the following assumptions: First, under neutral evolution, the same biological pathways in both groups should accumulate mutations at the same rate, whereas under conditions of different selection pressures, the rates of accumulation of mutations in the same pathways should differ between the groups. Second, due to the neutral character of synonymous SNP evolution, they should not lead to differences between the same pathways in the European and American groups in the case of selection; thus, synonymous SNP differences between the two groups would indicate microevolutionary processes that are not connected with any selection pressure. At the same time, the differences between non-synonymous SNPs in the two groups, in the absence of differences between synonymous SNPs, would indicate that European and American populations underwent different selection pressures after they split.

Our analysis revealed no differences in the synonymous SNPs between the European and American groups (Supplementary Table 1). In addition, a comparison of non-synonymous SNPs revealed several pathways that were significantly different between the European and American groups (Table 1; Supplementary Table 2). Therefore, we suggest that these differences are the result of selection pressures acting differently on European and American populations, rather than the result of general interpopulation differences between these two groups.Table 1 Biological pathways showing significant genetic differences between European and American groups.

Pathway	Number of SNPs	Number of genes	Statistic for population differentiation	p-value	Bonferroni adjusted p-value	Benjamini–Hochberg test	
Caffeine metabolism	25	3	1.3622	0.0000	0.0000	True	
Hematopoietic cell lineage	216	48	0.1801	0.0000	0.0000	True	
Rheumatoid arthritis	170	30	0.2059	4 × 10−6	0.0014	True	
Intestinal immune network for IgA production	147	14	0.2018	8 × 10−6	0.0027	True	
Tuberculosis	245	63	0.1605	8 × 10−6	0.0027	True	
Staphylococcus aureus infection	251	55	0.1631	12 × 10−6	0.0041	True	
Systemic lupus erythematosus	174	27	0.2018	12 × 10−6	0.0041	True	
Asthma	144	16	0.2016	4 × 10−5	0.0137	True	
Type I diabetes mellitus	318	23	0.1296	0.0001	0.0220	True	
Leishmaniasis	167	25	0.1739	0.0001	0.0274	True	
Th17 cell differentiation	183	32	0.1607	0.0001	0.0392	True	
Allograft rejection	308	17	0.1293	0.0001	0.0394	True	
Th1 and Th2 cell differentiation	176	31	0.1636	0.0001	0.0410	True	
Graft-versus-host disease	323	21	0.1277	0.0001	0.0424	True	
Autoimmune thyroid disease	337	25	0.1256	0.0001	0.0442	True	
Inflammatory bowel disease	171	23	0.1611	0.0003	0.0858	True	
Epstein-Barr virus infection	413	75	0.1073	0.0003	0.1070	True	
Human T-cell leukemia virus 1 infection	390	74	0.1073	0.0006	0.1907	True	
Antigen processing and presentation	343	36	0.1081	0.0008	0.2804	True	
Phagosome	408	72	0.1021	0.0011	0.3673	True	
Cell adhesion molecules	416	65	0.1000	0.0011	0.3857	True	
Viral myocarditis	333	28	0.1073	0.0012	0.4248	True	
Herpes simplex virus 1 infection	793	248	0.0859	0.0015	0.5070	True	
Influenza A	250	67	0.1191	0.0015	0.5197	True	
Pantothenate and CoA biosynthesis	20	11	0.5917	0.0021	0.7165	True	
Kaposi sarcoma-associated herpesvirus infection	268	66	0.1074	0.0029	1.0048	True	
Natural killer cell mediated cytotoxicity	272	52	0.1090	0.0030	1.0138	True	
Toxoplasmosis	223	43	0.1201	0.0030	1.0173	True	
The pathways that passed the Bonferroni threshold are in bold (p < 0.05). The statistic for population differentiation was calculated as described in Methods (Analysis of selective pressure).

We analysed the differences between the European and American groups using Bonferroni and Benjamini–Hochberg corrections (see “Materials and methods”). Because the Bonferroni method is stricter, the number of pathways that passed both thresholds was lower than that of the pathways that only passed the Benjamini–Hochberg threshold (Table 1; Supplementary Table 1). The pathways that passed both thresholds were caffeine metabolism, hematopoietic cell lineage, rheumatoid arthritis, intestinal immune network for IgA production, tuberculosis, Staphylococcus aureus infection, systemic lupus erythematosus, asthma, type I diabetes mellitus, leishmaniasis, Th17 cell differentiation, allograft rejection, Th1 and Th2 cell differentiation, graft-versus-host disease, and autoimmune thyroid disease (Table 1; Supplementary Table 1). The pathways that differed between the European and American groups and that only passed the Benjamini–Hochberg threshold were inflammatory bowel disease, Epstein–Barr virus infection, human T-cell leukaemia virus 1 infection, antigen processing and presentation, phagosome, cell adhesion molecules, viral myocarditis, Herpes simplex virus 1 infection, influenza A, pantothenate and CoA biosynthesis, Kaposi sarcoma-associated herpesvirus infection, natural killer cell-mediated cytotoxicity, and toxoplasmosis (Table 1; Supplementary Table 1). We excluded the caffeine metabolism pathway from further analysis because of the low number of genes and the uneven distribution of SNPs among these genes (see “Materials and methods”), and the pantothenate and CoA biosynthesis pathway because of the low number of SNPs/genes (20/11).

Interestingly, nearly all the pathways (with the two abovementioned exceptions) that were significantly different between the European and American groups, were related to the functioning of the immune system. The hematopoietic cell lineage, intestinal immune network for IgA production, antigen processing and presentation, cell adhesion molecules, phagosome, Th17 cell differentiation, Th1 and Th2 cell differentiation, and natural killer cell-mediated cytotoxicity pathways have basic immune functions. Systemic lupus erythematosus, rheumatoid arthritis, asthma, type I diabetes mellitus, autoimmune thyroid disease, and inflammatory bowel disease are pathways associated with immune system dysfunction (autoimmune disorders). Graft-versus-host disease and allograft rejection represent alloimmunity, which is an immune response to non-self-antigens from members of the same species. The Staphylococcus aureus infection, tuberculosis, leishmaniasis, Epstein-Barr virus infection, human T-cell leukaemia virus 1 infection, Herpes simplex virus 1 infection, influenza A, viral myocarditis, Kaposi sarcoma-associated herpesvirus infection, and toxoplasmosis are associated with the immune response to pathogens. An analysis of the intersecting genes between the identified pathways (Fig. 3) showed they all have common genes. There are two sets of genes. The first set contains genes HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB3, HLA-DRB4, and HLA-DRB5. These genes belong to the MHC (major histocompatibility complex or human leukocyte antigen, HLA) class II alleles. The second set contains genes HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, and HLA-G, which belong to the MHC class I alleles.Fig. 3 Heatmap showing intersecting genes among revealed pathways. The heatmap was created by Heatmap (v. 3.6.2.) (https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/heatmap).

The hematopoietic cell lineage, intestinal immune network for IgA production, leishmaniasis, toxoplasmosis, Staphylococcus aureus infection, tuberculosis, influenza A, asthma, inflammatory bowel disease, systemic lupus erythematosus, Th17 cell differentiation, Th1 and Th2 cell differentiation, and rheumatoid arthritis only included MHC-II genes (Table 2). The phagosome, cell adhesion molecules, antigen processing and presentation, type I diabetes mellitus, human T-cell leukaemia virus 1 infection, Herpes simplex virus 1 infection, Epstein-Barr virus infection, autoimmune thyroid disease, allograft rejection, graft-versus-host disease, and viral myocarditis pathways included both MHC-I and MHC-II genes (Table 2). Only two pathways, natural killer cell-mediated cytotoxicity and Kaposi sarcoma-associated herpes virus infection, included MHC-I genes (Table 2).Table 2 Analysed KEGG pathways containing MHC-I and MHC-II genes.

Pathways with MHCI genes only	Pathways with MHCI and MHCII genes	Pathways with MHCII genes only	
Cellular senescence	Allograft rejection	Asthma	
Endocytosis	Antigen processing and presentation	Hematopoietic cell lineage	
Human cytomegalovirus infection	Autoimmune thyroid disease	Inflammatory bowel disease	
Human immunodeficiency virus 1 infection	Cell adhesion molecules	Influenza A	
Human papillomavirus infection	Epstein–Barr virus infection	Intestinal immune network for IgA production	
Kaposi sarcoma-associated herpesvirus infection	Graft-versus-host disease	Leishmaniasis	
Natural killer cell mediated cytotoxicity	Herpes simplex virus 1 infection	Rheumatoid arthritis	
Viral carcinogenesis	Human T-cell leukemia virus 1 infection	Staphylococcus aureus infection	
	Phagosome	Systemic lupus erythematosus	
Type I diabetes mellitus	Th1 and Th2 cell differentiation	
Viral myocarditis	Th17 cell differentiation	
	Toxoplasmosis	
Tuberculosis	
Statistically significant different pathways between European and American groups are in bold.

Verification of the results

To ensure that our results reflected the selection pressure differences between the two groups and that they were not a statistical fluke, we created new samples by randomly mixing the data from both American and European samples and performed analogous tests on them 100 times. Population differentiation scores for pathways achieved statistically significant values less than in 5% randomly sampled populations. Therefore, the observed differences in biological pathways between the European and American groups were the result of different selective pressures acting on these two groups after their split.

Discussion

As a result of the migration of Europeans to the American continent, their further adaptation to local conditions, and their relative isolation after the split, Europeans and Americans of European origin have lived in different environments for several centuries. These environmental differences gave rise to cultural and biological variations between them.

Our findings showed that the European and American biological pathway differences were mainly related with the MHC, a system responsible for adaptive immunity in humans and other vertebrates. MHC genes are the most polymorphic genes in the human genome because of their main function: the development of immune responses against foreign antigens15–18. Two major groups of MHC genes, MHC group I and MHC group II, help develop humoral and cell-mediated immune responses by presenting antigens to T cells; however, there are significant differences between these two groups. MHC-I molecules are expressed in all types of nucleated cells in an organism and are responsible for protection from endogenous antigens. In contrast to MHC-I, MHC-II molecules are expressed only in antigen-presenting cells and are responsible for protection from exogenous antigens19,20.

Among the pathways that differed between the European and American groups, half only included MHC-II genes, another half included both MHC-I and MHC-II genes, and only two pathways (natural killer cell-mediated cytotoxicity and Kaposi sarcoma-associated herpesvirus infection) only included MHC-I genes; other pathways including only MHC-I genes did not show any differences between the European and American groups (Table 2). Therefore, we suggest that the ways of evolution were different for the MHC-I and MHC-II groups, at least in our sample of Europeans and Northern Americans of European origin.

Different ways of evolution for the MHC-I and MHC-II systems as well as high geographical variability of MHC SNPs have been shown in several studies20–25. Possibly, this may be due to the different mechanisms of action of the MHC-I and MHC-II immune responses. As mentioned above, the MHC-II system is more specialised than the MHC-I system; consequently, the MHC-II system may react faster to environmental changes. Moreover, the higher specialization of the MHC-II system can provide more possibilities for evolution under selective pressure because mutations in the MHC-II genes can affect only the processes connected with antigen-presenting cells, whereas mutations in the MHC-I genes can influence a wider spectrum of processes.

Possible triggers for such evolution pattern of the MHC genes could be different pathogen loads in Europe and North America, which could provoke different selective pressures on Europeans and Americans. One can suggest that plague epidemics could have played an important role in this evolutionary process because the European and North American continents were differentially affected by this disease26–28. In addition, many other diseases, such as measles, smallpox, and influenza, could have also contributed to the different patterns of evolution of the MHC genes observed in our samples ofEuropean and American populations29–32.

Significant events in the evolution of MHC genes and therefore of immune response during different stages of human history were indicated in other studies33–35. Here, we have found that more recent events in human history connected with massive migrations, changing the environment and pathogenic landscapes can also have great effect on the evolution of MHC genes.

In summary, we revealed significant differences between biological pathways related to the immune system in the samples of Europeans and Americans of European origin. Since these findings were observed only for non-synonymous and not for synonymous SNPs, we suggest that the differences were caused by selective pressure rather than by neutral population processes in these groups. Our results indicate possible different evolutionary mechanisms for the MHC-I and MHC-II systems in the European and American groups. Based on our current knowledge, we suggest that the main triggers for this diversity may be the different pathogen landscapes and pathogen loads in Europe and North America, although other possible causes cannot be excluded.

It should be noted that our method is entirely based on the grouping of genes according to genetic networks. This constitutes its primary advantage, but also a key flaw. Only SNPs within genes included in genetic networks were taken into account by our algorithm. Therefore, the results of our method are directly linked to the quality of genetic network annotation and could be significantly affected by it. For instance, if we consider even the most complete genetic network for the human genome, which comprises 17,929 genes36, this number is lower than the total number of genes predicted for the human genome. Consequently, working with such a network would reduce the number of checked SNPs, potentially leading to the loss of significant effects.

Noteworthy, our method only works with protein-coding genes because it relies on information about the type of substitution (synonymous/non-synonymous) present in the final protein, limiting the scope of its application and the range of data that can be used. The best data for the developed method are those from WGS, whereas the use of microarray data is very limited, as most of the SNPs obtained in this way can be chained with casual data and could not be correctly accounted for in our analysis. It should also be noted that we completely ignored SNPs that fall within regulatory sequences, although many approaches are already available to consider their functional effects.

And perhaps one of the main drawbacks and limitations of our work was the limited set of samples used for the studied population. As mentioned above, our method is preferably used with WGS datasets, and the only open access primary data source for such datasets is the 1000 Genomes Project. We could not find any other suitable data for our analysis. To address the issue of small population-specific datasets, we considered only highly frequent population alleles with a Minor Allele Frequency (MAF) > 5%. However, it is important to note that we may underrepresent the effects of rarer variants and incorporating them into the analysis could reveal more differences between populations.

Despite the above-mentioned shortcomings, the analysis approach developed in this study has allowed us to consider co-evolution from a new perspective, not only based on the spatial location of variants along the chromosome, but by analysing the co-evolution of genes in metabolic pathways. This makes it possible to find new information even among well-studied datasets. With an increase in the amount of functional information, this approach will allow new results to be achieved in the study of pathway evolution.

Our results suggest that the environmental conditions strongly influence modern human populations. Differences in immune biological pathways between the European and American groups may suggest different responses to pathogens. They can also be important for analysing processes related to allergies, organ transplantation, and autoimmune diseases. Our findings also indicate that, in studies of immune responses to vaccine antigens, it is important to consider not only socioeconomic factors and the quality of the health systems of different countries, but also the different genetic backgrounds of the populations. These conclusions could have been important for responding to current epidemics, and they may as well be useful for preventing and controlling possible future outbreaks. Our work, together with those of other studies, provides new opportunities for future research on the heterogeneity of immune responses in different human populations.

Methods

Data

To analyse the pathway differences, we used exome data from the latest release of the 1000 Genomes Project database (Genomes Project; http://www.internationalgenome.org). We used 92 samples from Americans of European ancestry (CEU). For the European population, we selected Iberian (IBS), Tuscan (TSI), British (GBR), and Finn (FIN) samples (404 in total). SNPs were functionally annotated with the SnpEff tool (4.3 T)37 using the hg19 (GRCh37.75) human genome annotation. The types of SNPs, that is, synonymous or non-synonymous SNPs, are listed in Table 3.Table 3 Types of SNPs used in this analysis.

Synonymous	Non-synonymous	
synonymous_variant	structural_interaction_variant, initiator_codon_variant,stop_gained,splice_region_variant	
stop_retained_variant	start_lost,sequence_feature	
	missense_variant	
	stop_lost, start_lost	
	stop_gained	
	splice_region_variant	
	5_prime_UTR_premature_start_codon_gain_variant	

A combined lists of (1) synonymous and (2) non-synonymous SNPs from Europeans and Utah residents of European ancestry was mapped onto 342 KEGG pathways38 (https://github.com/anulin/selection/blob/master/download%20Kegg.R). SNPs with allele frequencies < 5% were excluded. If not all the datasets from the samples from the American or European populations had the position for a SNP, such SNP was excluded.

For PCA analysis, we used whole-genome data from the same release of the 1000 Genomes Project database. We used African (AFR), Asian (EAS), and European (EUR) superpopulation samples (N = 1475).

Depth files correction

SAMtools 1.1339 was used for coverage depth calculation. For each SNP, samples were filtered to keep coverage above 35, and the resulting samples were used to calculate statistics. After this and minor allele frequency filtering, the final subsets contained 14,258 synonymous and 14,471 non-synonymous SNPs.

Analysis of selective pressure

We evaluated the following statistic for population differentiation40 at every available SNP:Ds2σDs2

where Ds=p1s-p2s represents the allele frequency difference at SNPS between population 1 and population 2, and σDs2 is the variance. After computing the scores for the SNPs, we calculated the 15th percentile value for each KEGG pathway using synonymous and non-synonymous SNPs. The percentile was chosen such that the method was not too sensitive and did not highlight individual SNPs under selection, but could detect smaller changes in all frequencies of the SNP groups. These values were utilized to assess the level of selection pressure acting on the pathway. Permutation tests, along with subsequent Bonferroni and Benjamini–Hochberg corrections41 for multiple testing, were employed to detect pathways exhibiting unexpected percentile values, indicative of pathways under selection. An adjusted p-value threshold of 0.05 was applied. The scheme for this procedure is provided as Supplementary Fig. 1.

Principal component analysis

Principal component analysis was carried out using PLINK2 for African (AFR), Asian (EAS), and European (EUR) superpopulation samples from the 1000 Genomes Project whole-genome data. Related individuals were preliminarily filtered out. SNPs with MAF > 5% were only considered. Data was LD pruned by using PLINK2 with parameter “-indep-pairwise 50 5 0.5”.

Validation of the method

We checked if the pathways did not have too few genes so that the SNPs from the percentile did not fall into only one or two genes. For this purpose, we used data from the SnpEff and KEGG databases. Based on these data, we excluded the caffeine metabolism pathway from the analysis because only 3 genes in this pathway contained SNPs, and 19 out of 25 SNPs were found in a single gene. The SNP distributions in the genes of each pathway are provided in the Supplementary Figures.

Calculation of gene length distributions

To check if there were any differences in gene lengths between the pathways that were significantly different between the European and American groups and other analysed pathways, we calculated gene length distributions using the GRCh37 annotation. The resulting distributions were identical (p = 0.2); therefore, gene length did not influence the results.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71803-7.

Acknowledgements

We are grateful to Dr. Alena Zolotarenko and Dr. Sergey Bruskin (Vavilov Institute of General Genetics, Russia) for valuable comments and fruitful discussion of the manuscript. This work was supported by the Ministry of Science and Higher Education of the Russian Federation, Agreement No. 075-15-2022-294 dated 15 April 2022.

Author contributions

The project was conceived by IM, AK, and AV. AV and EK performed calculations. AK, AV, EK, and IM analyzed the data. IM, AK, and AV wrote the manuscript.

Data availability

The tools for calculating p-values and scores may be found at https://github.com/anulin/selection.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Artem Kasianov and Irina Morozova.
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