Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts. Alzheimer’s Disease (AD) is a complex neurodegenerative disorder characterized by continuous cognitive impairment and eventual amyloid plaques, neurofibrillary tangles, and atrophy patterns in the brain ,2,3. As Many previous AD studies use GWAS and pathway enrichment analysis to explore the genetic basis of the AD diagnosis ,12,13,14Conventional brain imaging genetics studies perform massive pairwise association analyses between each SNP-QT pair. These individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To bridge this gap, some studies attempt to interpret these results on a macroscopic level or derive high-level understandings. For example, Yao et al. used a two-dimensional enrichment analysis to address this challenge, grouping similar brain regions and genes together via a biclustering approach . Yao’s wIn this work, instead of using the knowledge-driven, predefined imaging genetic modules, we propose an alternative data-driven approach to identify high-level imaging genetic patterns. Based on the detailed SNP-QT associations, we develop a graph-cut algorithm to cluster similar SNPs together so that SNPs within the same cluster tend to have similar associations with QTs across the brain. We construct multiple SNP networks based on different similarity measurements. Each similarity network can be viewed as a weighted graph with a specific similarity measure defined as the edge weight. We employ a multigraph clustering method derived from min-max graph cut to discover SNP clusters that take into consideration of all the studied similarity measures. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern to provide valuable biological insights at a high level.We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for subsequent imaging and genetics studies in independent cohorts.www.adni-info.org. In this study, participants (N = 971) include 202 AD, 218 late MCI (LMCI), 296 early MCI (EMCI), and 255 healthy control (HC) subjects. The baseline structural magnetic resonance imaging (MRI) scans, AV45, and FDG positron-emission tomography (PET) scans, genotyping data, demographic information and clinical assessments are downloaded from the ADNI database (adni.loni.usc.edu). Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) . The ADNThe genotyping data were downloaded and analyzed using PLINK v1.90 . We perfanalysis ,6,7. TabStructural MRI scans are processed with voxel-based morphometry (VBM) using the Statistical Parametric Mapping (SPM) software. All scans are aligned to a T1-weighted template image, segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) maps, normalized to the standard Montreal Neurological Institute (MNI) space as 2 × 2 × 2 mmPreprocessed F-18 florbetapir (AV45) PET scans are collected and aligned to the Montreal Neurological Institute space as The (18)F-fluorodeoxyglucose (FDG) PET measurements are also registered into the same MNI space as 2 × 2 × 2 mmG be a set of SNPs and Y be a set of imaging QTs . We perform a linear regression model to estimate the additive effect of each SNP g has a significant effect  ,28,29. TAfter analyzing the genetic domain, the brain pattern corresponding to each SNP cluster can be analyzed through mapping the average effect of each SNP group onto the brain. This brain association pattern can be analyzed by Neurovault and Neurosynth , which gp-value) from the linear regression before normalization. After an initial SNP-QT map is created, each 116-dimensional vector of a given SNP is normalized such that the Euclidean norm is 1. This step is performed so that each SNP is represented as a directional unit vector to facilitate subsequent analysis.Each entry of the map corresponds to While such an imaging genetic map describes detailed associations for each SNP-QT pair, it is not straightforward to detect any general trend in these maps. The goal of the subsequent steps is to extract high-level information from these maps and help provide biological interpretation to aid biomarker discovery and therapeutic target identification.The multigraph vs. single-graph averaged Silhouette scores are shown in A higher Silhouette score indicates a better clustering quality. A lower number of clusters is preferred in this study when the Silhouette scores are similar since our goal is to provide a high-level understanding. As a result, cluster number = 2 is chosen for the subsequent analyses.The SNP networks constructed by the normalized cosine scoring function are shown in The similarity network entries are normalized so that the minimum is 0 and the maximum is 1. Each SNP has a maximum similarity of 1 with itself as observed from the diagonal. Good partition of SNPs is indicated by strong similarity within each cluster and weak similarity between the clusters. A balanced size of the two clusters is preferred so that we can identify multiple high-level patterns instead of one single high-level pattern coupled with a small number of outliers; therefore, the clustering result on the AV45 measures for the LMCI vs. HC comparison group as well as the clustering result on the VBM measures for the AD vs. HC comparison group are selected for subsequent analysis.Among all the results in modality AV45, the most balanced one is generated by analyzing the LMCI vs. HC comparison group, and this result is shown in Among all the results in the modality VBM, the most significant and balanced result is generated by analyzing the AD vs. HC comparison group, and this result is shown in In this study, multiple scoring functions have been selected to evaluate the similarity between different AD-related SNPs in terms of their effects on 116 ROIs across the brain. Each scoring function quantifies the similarity between SNPs from a specific perspective. Multigraph clustering is used to output a clustering result that combines the effects of multiple scoring functions. The purpose of building SNP–SNP networks through different scoring methods is to evaluate the SNP similarity in terms of their effects on 116 ROIs traits across the brain from multiple perspectives. Given two vectors  and , their Pearson correlation, Spearman correlation, and cosine similarity are all 1 (corresponding to the largest similarity), since they focus on comparing the vector directionality instead of the vector magnitude; however, their Manhattan distance and Euclidean distance are very sensitive to the vector magnitude, and thus are both large, leading to very small similarity. Our multigraph approach combines the effects of all these scoring functions, and takes into consideration both vector directionality and magnitude when performing multigraph clustering.Several single-graph and multigraph clusterings with a varying number of clusters from 2 to 9 are performed. Averaged Silhouette analysis scores are used to quantify clustering quality under a given cluster condition. In With these difficulties addressed in single graph clustering, the use of multigraph clustering is very promising for various reasons. The first advantage of multigraph clustering is that at a given number of clusters, it is able to selectively use scoring functions that behave well. For example, at cluster number = 2, the Pearson and Spearman methods have low Silhouette scores (<0.062) across all three modalities, while the Manhattan, Euclidean, and cosine methods have high ones (>0.11). In this case, the multigraph clustering yields an average Silhouette score of 0.1016 , resultiThe second advantage of multigraph clustering for this study is that it behaves the best for AV45 and VBM at the number of clusters = 2 see . As discThe third advantage of multigraph clustering is that the analysis is more efficient and consistent than a collection of single-graph clusterings. Instead of doing five single-graph clusterings with inconsistent results among different scoring functions, multigraph clustering is able to return a single set of clustering result. This feature provides a novel way of analysis for future studies with a large number of candidate evaluation functions and no prior knowledge of their performances.In the AV45 row of Comparison group EMCI vs. HC has one cluster group of 2 SNPs and another cluster group of 52 SNPs. Again, this can be roughly viewed as a single group. The smaller cluster group contains rs4575098 and rs4663105. There is no prior research of rs4575098, but rs4663105 mapped to BINI gene was identified as having a significant association among APOE Comparison group LMCI vs. HC has the most balanced cluster group for AV45 with one cluster of 20 SNPs and another cluster of 34 SNPs (with APOE rs429358). The partition will provide us with insights of how two groups of SNPs each plays a different role in the LMCI stage. This finding is promising given that (1) LMCI is the transitional stage between EMCI and AD, (2) there are no significant partitions at EMCI and AD, and (3) there is a significant pattern at LMCI. This suggests a potential stage-specific imaging genetic pattern during AD progression, which warrants further investigation. See In the FDG row of In the VBM row of Comparison group EMCI vs. HC has a smaller group of six SNPs: rs10808026, rs7810606, rs10498633, rs12881735, rs12590654, and rs113260531. Comparison group LMCI vs. HC has a smaller group of five SNPs: rs4236673, rs9331896, rs10498633, rs12881735, and rs12590654. The SNPs rs10498633, rs12881735, and rs12590654 lie in the intersection of these two groups, potentially having an impact throughout the MCI stage. As mentioned in the FDG section, rs10498633 is also found to be distant from the other AD-related SNPs for VBM modality, which reinforces its unique role associated with anisotropy in the MCI stage.Comparison group AD vs. HC has the most balanced cluster result with one group of 16 SNPs and another group of 38 SNPs. This provides us with insights about how the two groups of AD-related SNPs each play a different role in AD patients. Functional annotation of this high-level imaging genetic pattern are discussed in In  peptide . The dis present .A relevant observation from our pathway analysis is Group 1’s association with amyloid beta and APP intracellular transport in AD and amyloid beta traffic and degradation in extracellular matrix in AD and Group 2’s association with APP processing. Another indicator of Group 1’s role on radation . The mosradation . The com-amyloid .https://neurosynth.org/analyses/terms/, accessed on 16 June 2022 for definition of these terms). Cerebellar and cerebellum are responsible for motor functions and balance. It is also associated with the visual system. Vermis and some subsequent correlated brain regions are also associated with maintaining posture. So, this group is primarily associated with brain regions that are responsible for balance, motor functions, and visual functions. Group 2 is correlated with prefrontal, medial prefrontal, medial, prefrontal cortex, and social. All these regions control cognitive ability, memory management, and emotional impulse. The affected brain regions and their respective functions of two groups of SNPs show a great difference, demonstrating the promise of our clustering result.In https://neurosynth.org/analyses/terms/, accessed on 16 June 2022 for definition of these terms). The putamen is involved in learning and motor control, including speech articulation, language functions, and cognitive functions. Similar to the Group 1 result of the AV45 analysis above, this group is associated with balance, motor functions, and visual functions. The brain association pattern corresponding to SNP Group 2, on the other hand, is related to premotor, parietal motor, movements, and primary motor. The primary function of the premotor cortex is to assist in integration of sensory and motor information of the performance of an action. The parietal lobes integrate somatosensory signals and information from different modalities. The difference between the two brain maps in this case is less significant than the AV45 analysis above.In A data-driven analysis pipeline has been proposed in this work to identify high-level imaging genetic patterns. Based on the detailed SNP-QT associations, we develop a graph-cut algorithm to cluster similar SNPs together so that SNPs within the same cluster tend to have similar associations with QTs across the brain. We construct multiple SNP networks based on different similarity measurements. Each similarity network can be viewed as a weighted graph with a specific similarity measure defined as the edge weight. We employ a multigraph clustering method derived from min-max graph cut to discover SNP clusters that take into consideration of all the studied similarity measures. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern to provide valuable biological insights at a high level.Our genetic analysis of the AV45 imaging QTs in the LMCI vs. HC comparison yields a prominent clustering pattern in the cosine SNP network. The pathway analysis shows that the identified SNP Group 1 is associated with amyloid beta clearances while the SNP Group 2 is related to amyloid beta formation. The functional annotation using Neurosynth shows that the brain regions associated with SNP Group 1 are related to motor and balance functions while the brain regions associated with SNP Group 2 are related to memory and cognitive functions. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.


Then, qGL3.5 was mapped to the genomic interval between the flanking markers M2 and M3 using 2786 BC4F2 individuals. Because the region from B5 to B6 was not the associated region under BSA-seq analysis, qGL3.5 was narrowed down to the interval between B6 and M3, which spanned 24.0-kb. Of all 37 genes with non-synonymous single-nucleotide polymorphisms (SNPs) between KJ01 and Huaye 3 based on BSA-seq analysis, only one complete annotated gene, ORF18 (Gene ID: LOC_Os03g42790.1) was found. ORF18 encodes an IBR-RING zinc-finger-related protein, with one really interesting new gene (RING) and two in between ring finger (IBR) domains. The knockout of ORF18 derived from Huaye 3 using clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9) editing technology increased the GL of the mutant by approximately 2.2 mm. The novel locus qGL3.5 negatively regulated GL by promoting elongation of the longitudinal cell of the grain outer glume. These results provide a new genetic resource for rice grain shape breeding and a starting point for the functional characterization of the wild rice GL gene.Grain length (GL) directly affects the yield and quality of rice. Very few cloned GL-related genes are applied in production because their yield-increasing effects are not obvious, and the overall regulatory networks underlying the associated processes remain poorly understood. DNA samples from two bulk DNA pools (L-pool and S-pool) and their parents (KJ01 and Huaye 3) were subjected to high-throughput sequencing. Using bulked segregant analysis (BSA),  Rice is among the most important staple crop species and feeds approximately one-third of the global population. The rice grain yield is determined by three main characteristics: grain weight, number of filled grains per panicle, and number of panicles per plant . In termqGL3/GL3.1 encodes a PPKL family of serine/threonine phosphatases and restricts cell division in spikelet hulls by dephosphorylating cyclin T1;3   and two other Gγ proteins to regulate grain size  transcription factor; this protein has a premature stop codon and reduces the expression or causes the loss of function of GL6, thereby affecting rice GL  . GS3, a ain size . SG3, whop codon . GLW7 ennd yield . GL7 reged genes . GL6 enc rice GL .Although some genes regulating GL have been cloned, few genes have been applied in production, and the overall regulatory networks underlying the associated processes remain poorly understood. With the development of high-throughput sequencing in recent years, high-efficiency, low-cost whole-genome sequencing has become available. Approaches based on bulked segregant analysis (BSA) coupled with whole-genome sequencing (BSA-seq) have been widely applied for mapping several important agronomic-related genes in rice , and genOryza rufipogon Griff.), which presented a very short GL of only 7.03 mm , namely, qGL3.5, was found to regulate GL via clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9). Our study provides a new gene resource for rice grain shape breeding and a starting point for the functional characterization of the wild rice GL gene.Previously, we obtained the wild rice inbred line Huaye 3  procedure and purified by RNase A. The DNA concentration and quality were estimated with a Nanodrop 2000 UV–Vis spectrophotometer , and the DNA concentration was diluted to 3 μg/μL; the total DNA was not less than 150 μg. The individual genomic DNA of the 50 L-pool individuals was mixed together equally, constituting the long-seed bulk DNA pool (L-pool), and the individual genomic DNA of the 50 S-pool individuals was mixed together equally, constituting the short-seed bulk DNA pool (S-pool). The genomic DNA of the two bulk DNA pools and both parents was prepared for subsequent high-throughput sequencing.Total genomic DNA was extracted from young healthy leaves of both parents and 100 selected F1The four samples were subjected to high-throughput sequencing on an Illumina HiSeq™ 2500 instrument  at Biomarker Technologies Corporation in Beijing. Real-time monitoring was performed for each cycle during sequencing, and the percentage of high-quality reads with quality scores greater than Q30  among the raw reads and the GC content were calculated for quality control. Afterward, the sequencing data were compared with the sequence of the reference genome of Nipponbare.2 was used for preprocessing, such as marking duplicates, and GATK was used for local realignment and base recalibration to ensure the accuracy of detected SNPs. GATK was then used for SNP detection and filtering, after which a final set of SNPs was obtained.The detection of SNPs was mainly performed using the GATK software toolkit . AccordiThe relative marker abundance in the L-pool was calculated as the number of reads of the maternal allele divided by the number of reads of the paternal allele, whereas in the S-pool, the relative marker abundance was calculated as the number of reads of the paternal allele divided by that of the maternal allele. It was expected that the larger the relative abundance was, the greater the possibility that the marker was associated with GL. SNP index association analysis  and EuclThe idea behind SNP index association analysis was recently published; this analysis is a type of method used to calculate genotype frequency differences between two bulks satisfied by Δ(SNP index). A relatively close marker is associated with traits, while a relatively close Δ(SNP index) is associated with the value 1. The Δ(SNP index) was calculated as follows: Laa is the depth of the aa group derived from L, while Saa indicates the depth of the aa group derived from S. Lab means the depth of the ab group derived from L, while Sab represents the depth of the ab group derived from S.Euclidean distance association analysis is a method for calculating ED (the quadratic sum root of differences between bulks based on the mutation frequency of the four types of bases). In theory, the higher the ED value is, the closer the object site.Euclidean distance was calculated as described below. Aaa, Caa, Taa, and Gaa represent the frequency of bases A, C, T, and G, respectively, at a particular site in the large seed bulk. Aab, Cab, Tab, and Gab represent the frequency of bases A, C, T, and G, respectively, at a particular site in the small seed bulk.In this study, high-throughput sequencing combined with BSA was used to detect SNP tags between the two bulked DNA pools and to rapidly identify marker-intensive hotspots associated with GL in the rice genome.ORF18 via the CRISPR-GE website.3 The expression cassette vector was constructed by multiple rounds of PCR  to identify positive transgenic plants. Three wild-type plants and 23 transformed plants were selected at maturity to measure their GL.Two targets were designed for the potential candidate gene s of PCR . The tarSpikelet hulls from WT and mutant lines were collected after ripening. Glumes with a flat shape were selected and dried completely, and the samples were then laid flat with conductive glue and fixed on the loading table. After solidification, the outer surface of the glumes of the spikelets was sputter-coated with gold and observed with a Verios 460 scanning electron microscope. The cell size was measured using ImageJ software. The cell number and density were calculated along the longitudinal axis.1 was 9.40 mm. A genetic analysis of GL for 1,307 separate F2 individuals showed that the frequency distribution of GL was continuous, that the means deviated from those of the long-grain parent and that the variation range of GL was very large, from 7.07 to 13.33 mm, in the F2 population  and short-seed pool (S-pool). In total, 100.60 Gb of raw data were obtained from four samples , and 100.22 Gbp of clean data were attained after filtering. Of the high-quality data, 55,999,600 reads were obtained from KJ01, and 45,059,434 reads were obtained from Huaye 3. The read numbers for the S-pool and L-pool were 127,409,412 and 106,871,512, respectively. The average percentage between the sample and the reference genome was 96.64%, the average coverage depth was 55×, and the genome coverage was 96.51% (at least one base coverage). Four samples , with 16.72, 13.46, 31.93, and 38.09 Gb, respectively, were sequenced, and their GC contents were 45.72, 45.54, 46.04, and 46.03%, respectively.Fifty individuals that produced extremely long grains (11.57–13.33 mm) and 50 individuals that produced extremely short grains (7.07–8.13 mm) from the FOryza sativa japonica genome, which comprised 374 Mb and had a GC content of 43.55%, was used as the reference genome. The average depth and Cov-ratio-10x (%) of KJ01 and Huaye 3 were 38x (88.40%) and 31x (90.19%), respectively. Similarly, the average depth and Cov-ratio-10x (%) of the L-pool and the S-pool were 70x (95.23%) and 83x (95.89%), respectively  is, the higher the accuracy of mutation detection is.Sequencing quality statistics showed that our sequencing data were of high quality, showed good consistency and could be used for subsequent bioinformatics analysis.Single-nucleotide polymorphism mutations can be divided into transition and transversion types. Mutations involving the same type of bases, such as changes between a purine and another purine and between a pyrimidine and another pyrimidine, are called transitions. Mutations involving different types of bases, such as changes between a purine and pyrimidine, are called transversions. Generally, transitions are more likely to occur than transversions, so the transition/transversion (Ti/Tv) ratio is generally greater than one. For diploid species, if a certain SNP site on homologous chromosomes is the same base, the SNP site is referred to as homozygous. If an SNP site on a homologous chromosome involves different types of bases, the SNP site is referred to as a heterozygous SNP.In the present study, the higher the number of homozygous SNPs was, the greater the difference between the sample and the reference genome was, and the greater the number of heterozygous SNPs was, the higher the degree of heterozygosity of the sample was. These results are related to the material used. Compared with the number of SNPs in the reference genome, the number of SNPs in KJ01 and Huaye 3 was 1,443,316 and 1,245,956, respectively. For the two bulked pools, the numbers of SNPs in the S-pool and L-pool were 2,216,645 and 2,136,482, respectively . The numFor SNP index association analysis, 5,201 SNPs with multiple mutations were filtered: 772,175 SNPs were the same in the two bulk pools, 23,119 SNPs had a read number of less than four, and 311,069 SNPs were non-existent in the parents. These SNPs were all filtered out. A total of 1,225,946 SNPs were ultimately selected for further analysis.For ED association analysis, 45,000 SNPs with read support less than four in any bulk pool were filtered out, 513,705 identical SNPs between bulk pools were filtered out, and 4,806 SNPs with multiple alignments were filtered out. Finally, 1,773,999 SNPs were selected for ED association analysis.via a 10-kb sliding window  were developed in the preliminarily mapped interval by BSA based on the sequencing data of KJ01 and the wild rice mutant  was selected as the candidate gene, named qGL3.5, and its function was verified. ORF18 comprised five exons and four introns and encoded an IBR-RING zinc-finger-related protein with one really interesting new gene (RING) and two in between ring finger (IBR) domains  that controls the 1,000-grain weight of rice  signaling pathway, the ubiquitin–proteasome pathway, phytohormone signaling, and transcriptional regulatory factors . GS3 wasand GGC2 . The ovehenotype . qGL3 alignaling . OsARF6 ignaling .GS6 encodes a GRAS domain protein and affects lemma development by regulating the number of cells in glumes and thus negatively regulating the size of rice grains , which encodes an E3 ligase containing a CHY zinc-finger domain and a C3H2C3-type RING domain, regulates GL by directly interacting with GS3  can be found below: LW: conceptualization, methodology, data analysis, and writing—original draft. XL: conceptualization, methodology, writing—review and editing, and developing Huaye 3. YL, HZ, YZ, FB, and SD: performing the experiments. ZC and JW: writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Candida auris is a recently emerged, multidrug-resistant pathogenic yeast capable of causing a diversity of human infections worldwide. Genetic analyses based on whole-genome sequences have clustered strains in this species into five divergent clades, with each clade containing limited genetic variation and one of two mating types, MTLa or MTLα. The patterns of genetic variations suggest simultaneous emergence and clonal expansion of multiple clades of this pathogen across the world. At present, it is unclear whether recombination has played any role during the evolution of C. auris. In this study, we analyzed patterns of associations among single nucleotide polymorphisms in both the nuclear and the mitochondrial genomes of 1,285 strains to investigate potential signatures of recombination in natural C. auris populations. Overall, we found that polymorphisms in the nuclear and mitochondrial genomes clustered the strains similarly into the five clades, consistent with a lack of evidence for recombination among the clades after their divergence. However, variable percentages of SNP pairs showed evidence of phylogenetic incompatibility and linkage equilibrium among samples in both the nuclear and the mitochondrial genomes, with the percentages higher in the total population than those within individual clades. Our results are consistent with limited but greater frequency of recombination before the divergence of the clades than afterwards. SNPs at loci related to antifungal resistance showed frequencies of recombination similar to or lower than those observed for SNPs in other parts of the genome. Together, though very limited, evidence for the observed recombination for both before and after the divergence of the clades suggests the possibility for continuous genetic exchange in natural populations of this important yeast pathogen. Since the first report in 2009, C. auris has been reported to cause a large number of hospital-related infection outbreaks in many countries, with a high mortality rate C. auris is similar to several other species in the genus CandidaC. auris can grow at a high temperature (42 °C) and high salinity conditions, and most strains are resistant to at least one class of antifungal drugs C. auris include the ability to form biofilm and to persist in hospital environments including medical devices C. auris and its rapid spread have alarmed the World Health Organization and many national public health agencies C. auris strains belonged to five distinct clades, Clades I to V. While multiple strains have been reported for each of the five clades, only four of the five clades, Clades I to IV, have whole-genome sequences available from multiple strains each. In contrast, the whole-genome sequence of only one Clade V strain is available. The five clades differ from each other by 20,000 to over 200,000 nuclear genome SNPs. In contrast, within each clade, most strains were genetically very similar to each other, with pairwise SNP differences between them ranging from 0 to ∼3,800 SNPs Whole-genome sequence analyses have revealed that the global C. auris. Among all strains of C. auris analyzed so far, those from Clades I and IV have the MTLa mating type while those in Clades II and III have the alternative MTLα mating type. The lack of alternative mating types within individual clades is consistent with clonal divergence among clades and asexual reproduction within clades in C. auris. In addition, within Clade I, all strains sequenced so far contain a deletion of two nucleotides at positions 3,309 and 3,310 at the STE6 gene. STE6 is a pheromone transporter, essential for mating in yeasts. The deletion of these two nucleotides makes STE6 non-functional and consequently the strains in Clade I most likely sterile DMC1, RAD55, RAD57, and MSH4/5 were not detected in the reference strains of Clades I-IV RAD57 was found in the draft genome of a different strain C. auris. Nevertheless, as it has been observed in a related yeast Candida lusitaniaeC. auris may be capable of mating and sexual reproduction in nature.So far, no evidence for mating and sexual reproduction have been reported in C. auris identified geographic clustering among strains of the five clades. Specifically, strains of Clade I were isolated predominantly from South Asia, Clade II predominantly from East Asia, Clade III predominantly from Africa, Clade IV predominantly from the Americas, and several strains of Clade V from Iran in central Asia C. auris from different clades to generate genetically diverse hybrid offspring Earlier reports of C. auris to investigate the potential signatures of recombination of this species in nature. The strains were from diverse geographic regions across the world and all known ecological niches such as different body sites in humans, hospital settings, and natural environments. Both the nuclear and mitochondrial genomes were analyzed. Different from the nuclear genome, inheritance of the mitochondrial genome in sexual crosses typically do not follow Mendelian laws, and with both uniparental and biparental inheritance having been observed in ascomycetes C. auris, the different inheritance mechanisms between the nuclear and mitochondrial genomes will result in cytonuclear genome recombination within and between C. auris clades. To investigate the cytonuclear genome recombination, we compared the nuclear and mitochondrial phylogenies. Here, since C. auris is a haploid species, associations between alleles at the same SNP site  cannot be analyzed. Instead, we focus on analyzing associations between alleles at different single nucleotide polymorphism (SNP) sites. Following traditional tests of recombination, pairs of SNP sites that show evidence for phylogenetic incompatibility (PI) are considered as putative evidence for recombination. SNP pairs showing PI are further tested to determine if the relative proportions of the respective genotypes deviate from random association . Here, SNP sites from both the nuclear and mitochondrial genomes are identified and analyzed. In addition, we separately analyze SNPs that are shared among clades from those that are found only within individual clades. The details of the population samples, the two tests of recombination, the results and implications of our analyses are described below.In this study, we analyze the genome-wide allelic associations based on data from over 1,200 strains of 22.1C. auris strains were downloaded from the National Center for Biotechnology Information (NCBI) Sequence Read Archive. These strains were from five continents, including 673 from the Americas, 141 from Europe, 179 from Africa, 281 from Asia, 10 from Oceania, and two whose location information is unknown. In total, these samples were distributed among 27 countries and were collected from various sources from 1997 up to May 2022. The datasets were from 40 BioProjects that have been linked to 29 studies Whole-genome sequence data for 1,286 2.2C. auris reference strain B8441 (of Clade I) was chosen as the reference to derive the whole-genome SNP dataset for the whole population. The whole-genome SNP dataset was also used to confirm the clade affiliation of each strain. After clade confirmation, strains within each clade were then compared to their corresponding reference strain from the same clade to derive the SNPs for each clade. Specifically, the following four strains were used as references for Clades I-IV respectively, B8441 , B11220 , B11221 , and B11245 . These four strains were chosen because of their completeness in the genome assemblies and annotations C. auris, including three circular mitogenomes of Clade I strains (including that of Clade I reference strain B8441 with its mitogenome assembled as MT849287.1), one circular mitogenome of a Clade II strain, and one linear mitogenome of a Clade III strain which likely represents an incomplete mitogenome assembly. Thus, to generate the mitogenome references for Clades II, III and IV, we used raw sequencing reads from strains B11220, B11221, and B11245 and assembled their mitogenomes into circular molecules using NOVOPlasty4.2 To analyze signature of recombination, we first obtained the biallelic SNPs in the genomes of all strains. Here, the completely assembled genome of a commonly used For SNP identification, we used the NASP pipeline 2.3https://itol.embl.de/).To investigate whether there is evidence for mitochondrial and nuclear genome phylogenetic incongruence at the clade level, we compared the phylogenetic relationships among strains separately derived based on nuclear and mitochondrial genome SNPs. Incongruent relationships among clades between nuclear and mitochondrial genome phylogenies would be consistent with recombination/hybridization among clades. In this analysis, we used strain B8441 as the reference, SNPs of all strains were determined for the nuclear genome. Strains were clustered into representative clades based on nuclear SNPs. Similarly, the mitochondrial SNPs of each strain were determined based on comparison with the mitochondrial genome of strain B8441. Samples with ambiguous calls at over 10% of the SNP sites were removed from the dataset. Then, SNP sites with ambiguous calls of less than 5% of the remaining samples were concatenated. The phylogeny was inferred using FastTree with the GTR+CAT model 2.4C. auris, a pair of SNP sites with two alternative bases each is considered phylogenetically compatible if ≤3 possible genotype combinations are observed in the population. For example, one SNP site has two alternative nucleotides A and G in the population and another SNP site has two alternative nucleotides C and T in the population, if three or fewer of the four possible SNP combinations  are found in the population, these two SNP sites are considered phylogenetically compatible and be consistent with asexual reproduction in the population. In contrast, if all four possible SNP combinations are found in the population, the two SNPs are considered PI and consistent with recombination between the two SNP sites. [An alternative explanation is parallel mutation at the two SNP sites.] PI is determined using the four-gamete test (FGT). For pairs of SNPs that are PI, we further test whether the observed frequencies of the four genotypes deviate significantly from those expected under random recombination, following the LE test protocol commonly used for haploid organisms (Xu 2006). Different from that of the PI test, the null hypothesis for LE test is random allelic association between SNP sites.To investigate evidence for potential recombination between SNPs, we used two approaches that had different null hypotheses For both tests, we examined the following five datasets. The first dataset contains SNPs in the whole population of 1,285 strains. Here, every SNP site is included if the site has an alternative base in at least one of the 1,285 strains. The SNPs in this dataset are determined based on the alignments of their sequence reads to the genome assembly of strain B8441, the Clade I reference for both the nuclear and mitochondrial genomes. The second dataset consists of four sub-datasets, one for each of four clades and includes nuclear genome SNPs. The third dataset also consists of four sub-datasets, one for each of four clades and includes only the mitogenome SNPs. The fourth dataset again consists of four sub-datasets, one for each of the four clades but included both the nuclear and mitochondrial SNPs where each nuclear SNP was compared with each mitochondrial SNP within each of the four clades in the tests of recombination. The fifth dataset contains only SNPs that are shared among all four clades. In the fifth dataset, each SNP must be polymorphic within each of the four clades.P-values were adjusted to minimize false discovery rate (FDR) for multiple testing For each of the above datasets, we first used the FGT to identify pairs of SNP sites that show evidence for PI. The identified PI SNP pairs were then tested for their LE. These tests were performed using in-house scripts written in R3.5 2.5C. auris from the analyzed genomes, using SnpEff5.0 As described above, SNP pairs showing evidence of PI and recombination could have been derived due to convergent/parallel mutations among different strains. In general, convergent mutations are more likely to happen to genes under strong selection pressure. To examine this possibility, we annotated the SNPs showing the strongest evidence of recombination to determine whether they are located on genes known to be associated with stress response. We also extracted SNPs from known or putative drug-resistance genes in 33.1C. auris isolates into five clades. This result is consistent with those reported previously  was similar to that of the nuclear genome, a much higher percentage of those mitochondrial SNP pairs (38.76%) were in LE than those nuclear SNP pairs  of all SNP pairs were in PI and two of the seven incompatible pairs were in LE . Specifically, if a SNP was in PI with over 20% of the total SNPs in the population, the SNP is considered as putative recombinogenic SNP. Our search found that 20, 8, 0, and 23 SNP sites were recombinogenic in Clades I, II, III, and IV respectively. Overall, Clades II and III, the two I and IV . Annotat3.5Among the 232,179 nuclear genome SNPs, 13 were polymorphic within each of the four clades. These 13 SNPs were located in three genomic regions and all three regions were intergenic . Two of 3.6FUR, TAC1B, ERG3, ERG5, CDR1, PMS1, HOG1 ERG7, ERG11, STE6, YMC1, PDR1, and MSH2. Here we hypothesize that if parallel mutations were responsible for the observed PI, we would see a significantly higher percentages of SNPs in these drug resistance related genes being PI than those in other parts of the genome. A total of 801 SNPs were identified among 1,285 strains in these 13 genes. Interestingly, among the 801 SNPs, 10,029 SNP pairs  showed PI. This rate is similar to that found between all SNPs in the nuclear genome  to select for parallel mutations across the clades to account for the observed PI, especially for Clade IV. However, such selection cannot explain most of the observed PI across the clades. In addition, none of the SNPs in the drug resistance-related genes, including those in Clade IV, belonged to the recombinogenic SNPs nor were they shared among all four clades.4C. auris strains collected from across the world over the past 20+ years to investigate the potential signatures of recombination in this species. SNPs in both the nuclear and mitochondrial genomes were analyzed, both in the total sample as well as for each of the four clades where multiple strains have been sequenced. Our analyses revealed signatures of infrequent recombination in both the total sample as well as within each of the four individual clades. In addition, specific groups of SNPs, including those in genes involved in antifungal drug resistance as well as those that are shared among all four clades, were separately analyzed to help identify the potential contributors to the observed signatures of recombination. Different patterns of allelic associations were found among the sample types and between the nuclear and mitochondrial genomes. Below we discuss the main findings of our analyses and the major implications of our results.In this study, we analyzed the whole genome SNPs of 1,286 4.1C. auris that have been sequenced and deposited in GenBank by researchers, up to May 2022. Multiple studies have analyzed variable numbers of strains, with the largest number of strains analyzed by Muñoz et al. C. auris could be grouped into five distinct clades, with Clades I to IV represented by multiple strains in each while Clade V was represented by only one whole-genome sequenced strain (so far). However, one previous study analyzed mitogenome variations. Based on mitogenome SNPs of 130 C. auris strains, Misas et al. C. auris into five clades for both the nuclear genome and the mitochondrial genome was observed as previously reported , our analyses also revealed several notable features. Specifically, first, despite having more than twice as many strains as the earlier study (210 vs 86), we found no unambiguous SNP within the mitogenome of Clade IV, similar to that found by Misas et al. 6), about six times of that of the mitochondrial genome .The 1,285 genomes analyzed here represented all the strains of Cryptococcus gattii species complex and the ectomycorrhizal mushroom Tricholoma matsutake species complex C. auris remain to be elucidated.The lower observed genetic variation in the mitochondrial genome than in the nuclear genome has been reported in several other fungal species, including the human pathogen 4.2C. auris after the divergence of clades than before their divergence. Our observed pattern is largely consistent with the expectations of each clade having only one mating type and therefore less likely to mate and recombine among strains of the same clade.At both the species and individual clade levels, though the frequencies were generally low, evidence for PI was observed in both the nuclear and the mitochondrial genomes, with a significant proportion of those PI SNP pairs also in linkage equilibrium. However, the frequencies of PI SNP pairs differed among the samples. Overall, the frequency of nuclear PI SNP pairs at the species level was from twice to over 30 times of those within individual clades , Table 4Armillaria gallica[74], the commercial button mushroom Agaricus bisporusRussula virescens species complex Cryptococcus gattii species complex A. bisporus, the observed frequency of mitochondrial loci with PI was correlated with the life cycles of two varieties within the species, with the outcrossing heterothallic population showing more evidence of mitochondrial genome recombination than the secondarily homothallic populations Relative to the frequent reports of recombination in the nuclear genomes of fungal populations, reports of mitochondrial genome recombination are still rare. However, the list of fungal species and populations showing evidence of mitogenome recombination is growing. For example, since 1998, mitochondrial DNA recombination has been reported for the honey mushroom C. auris contained strains of both mating types, evidence for recombination in the total sample was expected. The higher rate of SNP pairs that showed evidence for PI than those within individual clades is consistent with sexual recombination in the ancient population of this species. The absence of incongruent relationships among clades between nuclear and mitochondrial genome phylogenies is consistent with the absence of mating and recombination among the four clades after their divergence from each other. However, the observed PIs among SNP pairs within individual clades after their divergence are puzzling. Specifically, each clade is known to contain strains of only one mating type, MTLa for Clades I and IV, and MTLα for Clades II and III. In addition, we found limited evidence of parallel mutations in the genes that are most likely under parallel selective pressure, the antifungal drug resistance-related genes. While we cannot completely exclude the possibility that convergent mutations might have contributed to some of the observed PIs, our analyses revealed that even if they existed, such an effect would likely be minimal. However, evidence for recombination have been found in natural fungal populations known to contain only a single mating type. For example, same-sex mating has been reported in the human fungal pathogen Cryptococcus neoformans species complex and such mating can generate genetic recombinants, similar to what have been reported for opposite-sex mating and to natural populations containing strains of both mating types C. auris clades to generate the observed PIs and linkage equilibrium. Alternatively, low frequency strains of the alternative mating type may exist within each of the four clades in nature and mating between strains of opposite mating types could have generated the observed signatures of recombination. Indeed, these two possibilities are not mutually exclusive, and both could have contributed to the observed signatures of recombination. Broader and more intensive sampling as well as experimental investigations of genetic crosses are needed in order to test these two possibilities.Because the ancestral population of 4.3TORC1) signaling. TORC1 is a multiprotein signaling complex functions as the organizer that incorporate internal and external cues to regulate cell growth and cell cycle progression TORC1 signaling plays an important role in controlling NaCl resistance through Sir2 in Saccharomyces cerevisiaeTORC1 gene may be involved in regulating the expression levels of TORC1.Our analyses revealed three clade-shared SNP regions, with SNPs in two of these regions showing high frequency of PI with other SNPs in the genome. Interestingly, all three clade-shared SNP regions are in intergenic regions between genes coding for hydrolases, oxidoreductases, and transcription factors with potential impacts on cell growth and lifespan . For exaC. auris in different clades could have contributed to the observed distributions of clade-shared polymorphisms and PIs. Our BLAST searches revealed that based on the amino acid sequence, the closest match to B9J08_002254 was in the bacterial genus Achromobacter, with a 99% query coverage and an E value of 1e-54.Interestingly, the downstream gene of clade-shared SNP region 2, B9J08_002254, codes for a protein containing a putative FMN-binding domain which is known to be most frequently found in bacteria. It has been hypothesized that proteins containing such a domain in fungi may have been horizontally transferred from bacterial to fungal genomes C. albicans, the zinc finger protein CZF1 is one of the DNA-binding proteins of the Cys6Zn2 class of transcriptional regulators with a multitude of functions such as biofilm induction, hyphal growth regulation, white-opaque switch, and yeast cell adherence The two genes located upstream and downstream of the clade-shared SNP region #3 were B9J08_003771 and B9J08_003772. Gene B9J08_003771 has a predicted unfolded protein-binding activity, while B9J08_003772 has a predicted DNA-binding transcription factor activity, zinc ion binding activity, and transcriptional regulation activity. The unfolded protein response is known to help human fungal pathogens survive in the host through balancing the load of proteins entering the endoplasmic reticulum and the protein-folding capacity of the organelle 4.4This study identified limited but unambiguous evidence of recombination in both the total sample and within individual clades. In addition, evidence of recombination was found in both the nuclear and mitochondrial genomes, as well as between the nuclear and mitochondrial genomes. Overall, signatures of recombination were more prominent in the total sample than within individual clades, consistent with greater frequencies of recombination before the divergence of the four clades than after their divergence. At present, while several possibilities were suggested, the mechanism(s) for the observed recombination is not known. Nevertheless, the signatures of recombination identified here suggested a number of avenues from which further investigations could be conducted, including more extensive sampling for alternative mating types within each clade, laboratory attempts of both same-sex and opposite-sex mating, and identifying the adaptive significance of clade-shared SNPs. Such investigations should allow us to better understand the genetic architecture of virulence and drug resistance evolution within and among the divergent clades of this pathogen in natural and clinical environments.Natural Sciences and Engineering Research Council of Canada (Grant No. CRDPJ 474638-14) and the Faculty of Science’s Global Science Initiative of McMaster University. Y.W. is supported by a MacData Fellowship.This research was supported by grants from the The authors declare no conflict of interest.


Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients. Identifying these biomarkers, albeit elucidating only a portion of the entire underpinnings of complex diseases, could nevertheless aid in increasing patients’ chances of survival by allowing a more personalized and advanced disease risk assessment4.Understanding the emergence and progression of complex diseases incessantly pose challenges to researchers due to its intricate and multifactorial nature. These diseases are caused by interplays between genetics and environmental factors leading to a plethora of combinations that need to be considered in modeling. From the genetics’ aspect, understanding the etiology of complex diseases necessitates an extensive localization of significant genomic variations due to its polygenic nature5. GWAS has been successful in identifying several risk loci for a wide array of illnesses including cancer6, Type 2 diabetes mellitus7, Crohn’s disease8, and coronary artery disease9, among others. However, despite these achievements, GWAS faces limitations due to its individual-SNP analysis approach exacerbated by the high dimensionality of genomic datasets. As multitudinous individual association tests are performed, stringent thresholds must be adopted to account for error rates leading to underpowered detection10. This increases the probability of not detecting SNPs with small effects that are truly associated with a trait and could significantly contribute to phenotypic variability11. The traditional GWAS approach also fails to capture SNP-SNP interactions as it only tests for the marginal effects of SNPs and disregards the variants’ joint contributions to phenotypic expression. These interactions require explicit analysis since they are vital in addressing the “missing heritability” problem12 which states that single genetic variations are insufficient in explaining the entire heritability of a trait.A genome-wide association study (GWAS) is the traditional approach employed to discover genetic biomarkers, i.e. single nucleotide polymorphisms (SNPs), associated with various traits and diseases13 and reducing the number of tests employed14, is crucial in increasing the chances of discovering true associations. Empirical evidence16 has shown that as sample size increases, GWAS continues to yield more novel trait-associated loci. However, this approach is not always feasible14 especially for studies involving small populations and diseases with low prevalence. For this reason, it is more viable to reduce the number of tests employed to relax the stringent conditions used to consider genomic variants as significant. Existing approaches to this latter strategy include haplotype-based association analysis and SNP-set analysis, both of which also address the inability of GWAS to capture SNP-SNP interactions18. Haplotype-based analysis19 accounts for linkage disequilibrium between SNPs; while SNP-set analysis, e.g. gene-based20 and pathway-based analyses21, considers the joint effects of variants on phenotypic expression. Aside from addressing the aforementioned GWAS’ limitations, SNP-set analysis further permits hypothesis testing on associations possibly existing between wider loci and traits18. However, when this type of analysis groups SNPs based on prior biological knowledge, a study’s success may be hampered when information on genetic variations and competitive pathways related to the trait are insufficient. To allow a less restricted analysis, it is necessary to explore other methods of forming SNP-sets using information independent of a priori biological knowledge.Under the “polygenic paradigm”, refining statistical models, such as increasing sample sizes24. Several studies have already demonstrated the usability of ML in genomic datasets27; however, to our knowledge, there is only a handful of existing literature discussing its application to SNP-set formation31. These studies employed cluster analysis to form SNP-sets in a data-driven manner. This approach could subsequently lead to the identification of novel risk loci associated with a trait31, albeit there may be problems related to computational complexity and cost. As genomic datasets are usually of high dimension, it is susceptible to the “curse of dimensionality”33, a problem that could be addressed by solely clustering the SNPs found in certain genomic regions that are known to play a role in trait development30. However, this approach defeats the purpose of performing an inclusive analysis as the search for significant biomarkers is restricted by relatively narrow regions. For a more varied selection of SNPs to analyze, dimensionality reduction techniques based on random forest (RF) could be used to reduce dataset dimensions before conducting cluster analysis. RF has been widely incorporated in SNP research36 due to its significant properties: (1) a nonparametric nature that allows the establishment of predictive models without the need for preliminary statistical assumptions, and (2) the capability to provide an importance score, i.e. variable importance measure (VIM) for each SNP, which increases the probability of detecting highly relevant biomarkers.Machine learning (ML) is an innovative and powerful approach used in solving complex problems in various fields and disciplines due to its capability to handle and analyze high-dimensional datasets37. However, based on the literature review, the integration of these approaches has not been explored on SNP data. This study aims to incorporate these two techniques to augment previous GWAS findings and allow the discovery of novel trait-associated susceptibility loci. The study implements the proposed integrated framework using the following three-step algorithm:Dimensionality reduction through RF;SNP-set formation through cluster analysis involving top-ranking SNPs from Step 1 and SNPs considered by GWAS to be significantly associated with the trait of interest (termed in this study as ‘GWAS-identified SNPs’); andAssociation testing on the resulting SNP-sets from Step 2.Cluster analysis and random forest have already been proven applicable and effective in genomic data analysis, specifically in identifying predictive and presumably disease-associated SNPs35. In Step 2, top-ranking SNPs determined from the results of Step 1 and GWAS-identified SNPs are subjected to cluster analysis to evaluate shared similarities among the variants and form SNP-sets. Finally, Step 3 involves testing the SNP-sets derived from Step 2 for trait-association. The proposed methodology was applied to the GWAS data by38 wherein the phenotype of interest is hepatitis B virus surface antigen (HBsAg) seroclearance, a marker for clearance of chronic hepatitis B virus (HBV) infection.In Step 1, dimension reduction is implemented using random forest feature selection to circumvent the “curse of dimensionality” problem associated with analyzing high-dimensional SNP datasetsThis study aims to discover novel trait-associated susceptibility loci through a machine learning-based SNP-set analysis approach built on the integration of RF, cluster analysis, and previous GWAS findings. The entire analysis is divided into three main parts: dimension reduction, SNP-set formation, and association testing. Figure 38 which aimed to identify susceptibility loci associated with HBsAg seroclearance among patients with chronic hepatitis B. The dataset is composed of 1,365,088 SNPs collected from 200 subjects of Korean ethnicity. The subjects were further divided into two groups: the cases (n = 100), which consist of patients who had experienced HBsAg seroclearance before the age of 60, and the controls (n = 100) comprising of patients who exhibited high levels (> 1000 IU/mL) of HBsAg at The data used in this study was adopted from the GWAS conducted by39. In RF, the predictive abilities of multiple decision trees, which are trained on bootstrap samples of the data, are consolidated to generate the final output prediction. In addition, randomization is not only induced by bootstrapping but also introduced at the node level when growing a tree. It selects a random subset of SNPs at each node of the tree as candidates to find the best split for the node. In estimating the importance of SNPs, RF calculates the Gini importance which quantifies the difference between a node’s impurity and the weighted sum of the impurities of the two descendent nodes.Dimension reduction is commonly a prerequisite in analyzing SNP datasets as large amounts of features impedes the capability of analytical approaches in performing fast and effective analyses. In this study, features are only selected for cluster analysis if they were considered by a previous GWAS to be statistically significant or if they are one of the top-ranking SNPs as per RF. RF ranks SNPs based on their feature importance scores which is a measure of the usefulness of a marker in predicting a target variable, in this case, trait occurrence. RF has been widely utilized in analyzing SNP data primarily due to its capacity to build a predictive model without making any assumptions about the underlying relationship between genotype and phenotypeMathematically, the importance of N-1) dataset, where N is the number of observations, and a corresponding score (the Gini variable importance) for each SNP is calculated. The scores obtained by a SNP for all folds are then averaged and the result would be the final feature importance score of that variant. In symbols,jth fold, N is the number of subjects, and SNPi is the final SNP score of the ith SNP. The final scores are then used to rank the SNPs. The number of top biomarkers included in the clustering process is determined by the researchers as it is already outside the scope of RF. A detailed description on how SNPs are ranked is provided in the ‘Since one round of calculations is not enough to ensure robustness of scores, the Leave-One-Out cross-validation (LOOCV) strategy was adopted. For each fold in the LOOCV, RF is trained on the , the more similar the variants are and more likely to cluster30.This study exploited the similarities shared among SNPs to identify novel susceptibility loci associated with HBsAg seroclearance. The analysis utilized the unsupervised machine learning method known as cluster analysis which aims to separate data points into distinct groups such that more similarities are shared among objects within the same group than objects belonging to different groups. Similarities between SNPs can be quantified in terms of 38 and the top biomarkers identified by random forest (starting from top 1000 to top 5000 SNPs in increments of 1000). Each implementation resulted in candidate SNP-sets identified using the following parameters: percentile cut which specifies the height wherein a dendrogram will be cut and minimum cluster size which dictates the minimum number of SNPs for all clusters.Multiple cluster analyses were performed exclusively on GWAS-identified and top-ranking SNPs obtained by random forest. As shown in Table 30 were employed to identify the candidate SNP-sets significantly associated with HBsAg seroclearance. The presence of association depends on the amount of difference in the biomarkers found in cases and controls. Minor alleles were incorporated in the equations as it reveals more similarities in the genomes of two individuals than common alleles43. A comprehensive discussion of the equations used in HDAT can be found in30. Permutation test, a non-parametric test used to evaluate the statistical significance of a model through randomization, is used to compute the p-value of each SNP-set. The test calculates the p-value by permuting the dataset and constructing a test-statistic distribution and evaluating the probability that a test-statistic would be equal to or more extreme than the initial computed value.Hamming distance-based association tests (HDAT)38 which was a project approved by the ethics committees at Korea University Anam Hospital (ED13220) and conducted in agreement with the ethical principles of the Declaration of Helsinki. According to the project’s ethical declaration, all patients provided written informed consent for participation and use of their data for research purposes.This study used the data provided in46, a disease that can develop due to the presence of the hepatitis B virus.This study used random forest feature selection to reduce dataset dimensions prior to conducting cluster analysis. Specifically, random forest was employed to rank SNPs based on their feature importance score, a measure which determines a variant’s relevance in making accurate phenotype predictions. SNPs are assigned a feature importance score based on the average scores for every fold in LOOCV to eliminate bias and ensure robustness. Investigation into the functional significance of three of the top five biomarkers ranked by RF led to possible connections between the variants and HBsAg seroclearance. SNPs rs28588178 (top-ranking SNP), rs1994209 (3rd-ranking SNP), and rs7958186 (5th-ranking SNP) are linked with Cadherin 4 (CDH4), PIG11, and PCED1B, respectively—genes reported to be associated with hepatocellular carcinoma (HCC)Upon performing multiple cluster analyses, a total of 108 candidate SNP-sets were identified at a percentile cut of 0.9 and a minimum cluster size of 3. SNP-sets with the maximum number of SNPs were chosen in cases where there were overlaps to maximize the information obtained from the analyses.–4 in Kim et. al.’s GWAS38). Genes were retrieved from dbSNP47 and38.) No information regarding possible association existing between the latter five SNPs and the phenotype of interest was found; meanwhile, the opposite was true for rs2399971. Notably, albeit rs2399971 had not reached the cut-off value used in the GWAS performed by Kim et al.38 on the whole study population, it was nevertheless found to be significantly associated with HBsAg seroclearance in the subjects who had received antiviral treatment38. Figure SNP-sets containing SNPs which were considered significant in a previous GWAS were investigated as the variants sharing high degrees of similarity with GWAS-identified SNPs may also provide insights into trait etiology. As shown in Table p-value < 0.0005) were identified, the majority of which (7 out of 11) were found to harbor at least one of the GWAS-identified SNPs. Among the SNP-sets obtaining the lowest p-values, the set which obtained the highest test statistic is the one composed of rs1809862, rs10769023, rs10838245, rs2017434, rs2047456, rs7945342, and rs872751—all GWAS-identified SNPs38. All these variants reside in 11p15.4, a region that shows a possible correlation with HBsAg seroclearance. In a study by48, it was observed that among hepatocellular carcinoma cases, more than 20 percent loss of heterozygosity (LOH) was shown for locus 11p, wherein region 11p15 was commonly affected. Moreover, a significant correlation was found to exist between LOH on 11p and HBsAg positivity. Specifically, results showed that there is a significantly higher frequency of LOH on 11p among hepatitis B virus carriers48.Hamming distance-based association test (HDAT) was performed on the candidate SNP-sets to further identify SNPs possibly associated with HBsAg seroclearance. After performing a Bonferroni correction for multiple tests, 11 SNP-sets significantly associated with HBsAg seroclearance . No supporting evidence was found regarding possible associations between the individual variants belonging to the five SNP-sets and HBsAg seroclearance. Nonetheless, interesting findings were discovered when SNPs were analyzed collectively. Results showed that three out of the five SNP-sets in Table 49, LINC00578 was reported to be a prognostic marker for pancreatic cancer (PC), a disease for which hepatitis B has been suggested to be a risk factor52, increasing the likelihood of PC by 24%53.Table This study aims to discover novel trait-associated susceptibility loci by augmenting previous GWAS findings using a machine learning-based SNP-set analysis approach built on the integration of RF and cluster analysis. By analyzing SNP-sets instead of individual variants, we increase the chances of discovering other existing true associations in two ways. First, by exploiting the similarities shared among the variants, SNPs that are truly associated with the trait of interest but which did not pass the threshold of significance can still be detected when grouped with statistically significant SNPs. Second, by reducing the unit of analysis into groups, a substantial decrease in the number of tests ensues which eliminates the necessity to adopt stringent thresholds used in considering a SNP significant. Investigation into the functional relevance of variants found in the same SNP-set containing GWAS-identified SNPs and SNP-sets obtaining significant p-values led to the discovery of loci that may also contribute to phenotypic expression yet overlooked by GWAS as a consequence of its individual SNP analysis approach. The novelty in our proposed method lies in the GWAS-based and data-driven approach in feature selection prior to cluster analyses. This study did not restrict the discovery of susceptibility loci to a certain genomic region alone as the criteria for selecting SNPs depend on statistical significance and predictive powers. As a result, the resulting SNP-sets implicated a varied selection of genes and cytobands.38 and was able to enhance the GWAS findings in two ways. First is through the discovery of SNPs highly similar with GWAS-identified variants. As shown in Table p-value of 1.05 × 10–4 wherein the cut-off p-value used was 1.00 × 10–4). Nevertheless, it was found to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment38. The other way in which the proposed approach has successfully enhanced the previous GWAS findings is through the identification of SNP-sets significantly associated with the trait of interest. Variants in Table 30. Identifying these significant SNP-sets also allows hypothesis generation not only on SNPs but also on other larger biological units such as genes or cytobands29. For instance, gene LINC00578 and locus 11p15, regions implicated by two of the SNP-sets with the lowest p-values, have shown potential in understanding HBsAg seroclearance as both are linked with diseases associated with the presence of hepatitis B virus infection. By mapping out these implicated regions and identifying shared susceptibility loci with a well-researched phenotype, a better understanding of the intricate underpinnings of the trait of interest could be achieved. For instance, some of the SNPs associated with height may be considered in understanding the etiology of HBsAg seroclearance as 11p15 has been reported to harbor genes responsible for growth and development54. Furthermore, elevations in alanine transaminase (ALT) level, a consideration in declaring HBsAg seroclearance, was found to be an important factor for growth impairment in children55.The proposed method was applied on an HBsAg seroclearance GWAS data57 to ensure an optimal number and a more cohesive composition of SNP-sets. Thirdly, the type of clustering procedure and association test employed on the SNP-sets should be modified depending on the goals of a study. HDAT, the association test used herein, only evaluates whether a SNP-set could distinguish different disease phenotypes. It does not determine if there is a presence of interaction in the set and even more so if that interaction is significant. Accounting for complex SNP interactions and nonlinear effects would require employing a different type of test on the SNP-sets such as the logistic kernel-machine-based test by18.Despite the advantages of the proposed method, several issues remain to be resolved. First, the total number of SNPs to consider in the clustering process should be optimized in future implementations of the approach so that variants possibly associated with the trait but obtaining low feature importance scores could still have higher chances of being discovered. Secondly, parameter values would still have to be tuned by utilizing specific measures such as gap statistics58. However, the results of these techniques could be distorted when there is a large number of correlated markers due to linkage disequilibrium (LD)58. To address this problem, one could employ a clustering-based strategy on the SNPs initially in order to minimize the number of markers to only the most informative ones59. Following these premises, a proposed approach that could be utilized to avoid unreliable results is to perform clustering on the SNPs first to select representative markers, then on the patients to identify subpopulations, and lastly on the variants once again to identify the final list of SNP-sets that would then be subjected to the association tests. Although correcting for population structure is a prerequisite for any genetic data analysis, it should be proceeded with caution when the dataset being analyzed contains only a few observations . In these settings, handling population stratification could be more complicated than usual especially if statistical power is at stake30. Mentioned that even though it is intuitive to address population stratification first before conducting association tests on the case and control groups of the same population, if the stratified populations are only of smaller sizes, then it could just lead to unstable findings.Aside from optimizing the settings directly involved in the actual data analysis, another fundamental issue that needs to be addressed in this study is the possible presence of population structure in the dataset which could negatively affect the clustering results and could lead to spurious associations. Some approaches that could be used to correct for this include principal component analysis (PCA) and clustering techniques which utilize similarity measures such as the allele-sharing distance (ASD)38. Thereafter, the researchers were able to detect rs2399971, a variant that was not considered to be significantly associated with the phenotype in the main GWAS, but which obtained a significantly low p-value in a subgroup analysis38. Results of the association tests conducted on the generated SNP-sets led to the implication of gene LINC00578 and locus 11p15. The former was linked with pancreatic cancer49 and the latter with hepatocellular carcinoma48, diseases associated with hepatitis B virus infection. There are three ways in which readers could reinforce their findings using the proposed approach. The first one could be done during the dimension reduction phase wherein random forest is employed to identify SNPs which are highly predictive of the trait of interest. If a researcher found that the variant they discovered to be associated with a phenotype is also one of the top-ranking SNPs as per RF, then this could provide strong evidence for follow-up investigations on the said variant. Predictive importance and association do not always coincide and so if they do, it could show important promise for clinical translation. The second way that readers could reinforce their findings is by looking at the SNP-sets which are significantly associated with the trait of interest. By identifying the genomic regions implicated by these sets, further evidence is provided to studies reporting on the significance of the said regions on a given phenotype. And lastly, one could check if the variant discovered to be associated with a phenotype belongs to a set which contains GWAS-identified SNPs. For instance, SNP rs2399971 was discovered in this study as it shared a high degree of similarity with variants significantly associated with HBsAg seroclearance. This somehow supports38’s finding on the association existing between rs2399971 and HBsAg seroclearance on patients who had received antiviral treatment38. Researchers who aim to extend this study could experiment on different supervised learning techniques for feature selection and utilize other similarity measures for clustering SNPs. With further investigation and validation, insights gleaned using the proposed framework could also be integrated into prediction models to aid in quantifying patients’ risks for trait or disease development.This study aims to identify disease-associated susceptibility loci by augmenting previous GWAS findings using the integration of RF and cluster analysis. The proposed approach was applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS datahttps://figshare.com/articles/dataset/gtReport_txt/6614975. The Python code used for implementing random forest can be found in https://github.com/jdgaudillo/SNP-ML.git while the R codes for clustering and association tests are available at http://homepage.ntu.edu.tw/~ckhsiao/HammingDistance/HD.htm.The dataset used in this study can be accessed through this link: Supplementary Information.


The Omicron variant is currently ravaging the world, raising serious concern globally. Monitoring genomic variations and determining their influence on biological features are critical for tracing its ongoing transmission and facilitating effective measures. Based on large-scale sequences from different continents, this study found that: (i) The genetic diversity of Omicron is much lower than that of the Delta variant. Still, eight deletions (Del 1–8) and 1 insertion, as well as 130 SNPs, were detected on the Omicron genomes, with two deletions (Del 3 and 4) and 38 SNPs commonly detected on all continents and exhibiting high-occurring frequencies. (ii) Four groups of tightly linked SNPs (linkage I–IV) were detected, among which linkage I, containing 38 SNPs, with 6 located in the RBD, increased its occurring frequency remarkably over time. (iii) The third codons of the Omicron shouldered the most mutation pressures, while the second codons presented the least flexibility. (iv) Four major mutants with amino acid substitutions in the RBD were detected, and further structural analysis suggested that the substitutions did not alter the viral receptor binding ability greatly. It was inferred that though the Omicron genome harbored great changes in antigenicity and remarkable ability to evade immunity, it was immune-pressure selected. This study tracked mutational signatures of Omicron variant and the potential biological significance of the SNPs, and the linkages await further functional verification. Since the beginning of COVID-19, five variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were designated as variants of concern (VOC) by the World Health Organization (WHO): Alpha, Beta, Gamma, Delta, and Omicron. The Omicron variant B.1.1.529) was first identified in Botswana and South Africa in November 2021 and quickly became the predominant epidemic variant in the world within 2 months. Compared with other VOCs, Omicron is more rapidly spreading and more concealed, which brings great challenges to the prevention and control of COVID-19. It was highly mutated, and the spike glycoprotein (S) protein alone contains at least 31 mutations, while nsp4, nsp14, nsp3, membrane protein E, membrane protein M, and nuclear protein N also contain large numbers of mutations 29 was fi. The varThe receptor-binding domain (RBD) and N-terminal domain (NTD) in the S of SARS-CoV-2 are respectively the main and the auxiliary interactions with the host cell; mutations in the domains can confer resistance against multiple neutralizing antibodies and change affinity between the virus and its receptor ,8,9,10. VOC Omicron has been circulating for nearly one year since the emergence, and it is unclear how it will establish its evolutionary pattern throughout the world. Multi-locus genome surveillance of the Omicron variant is very important for monitoring viral transmission globally, and a comprehensive understanding of global and regional variation dynamics of the virus is also critical to guide current and future interventions. In this study, by using large-scale SARS-CoV-2 Omicron genomes retrieved from the Global Initiative on Sharing Avian Influenza Data (GISAID) database , we uncoSARS-CoV-2 genomic sequences in this study were all retrieved from GISAID by selecting the “VOC Omicron GRA (B.1.1.529+BA.*)” as the “Variants” search terms. The top 1 or 2 countries of Omicron sequence number in each continent before April 2022 were selected as the representative countries of each continent, i.e., Japan in Asia, South Africa in Africa, Portugal and England in Europe, the USA and Mexico in North America, Chile and Peru in South America, and Australia and New Zealand in Oceania. The sequences were downloaded, ensuring that no less than 15 sequences were collected from the same day and the same country. After preliminary analysis, it was found that the number of sequences in South America were relatively low, so Colombia and Peru were added as representative countries there.http://mafft.cbrc.jp/alignment/software, accessed on 25 July 2021) and adjusted manually by MEGA 7 software. The Omicron genomic sequence from South Africa in the very early time  was used as reference for mutation analysis in this study.The sequences were annotated with the accession number, clear sampling time, and location. Low-quality sequences with Ns or gaps were removed, and all the sequences were aligned by using MAFFT 7  calling and SNP-linkage analysis were developed, and the compiled R codes were deposited in Github as previously described . Data wahttp://hdock.phys.hust.edu.cn/, accessed on 13 November 2021), based on a hybrid algorithm of template modeling with default parameters. The structures were further calculated and annotated using PyMOL software (version 2).To evaluate the effect of substitutions in the RBD, the interactions of the RBD with the human angiotensin-converting enzyme 2 (hACE2) in host cells were characterized using the HDOCK server  were collected. After removing the low-quality sequences with more than 10 Ns, 9796 sequences were left, among which 1509, 1178, 1062, 1725, 2187, and 2135 were respectively downloaded from North America, Oceania, Africa, South America, Europe, and Asia. Due to unbalanced and poor-quality of sample sequencing in Africa, only South Africa had a relatively enough number of sequences, and it was the only country in Africa included in this study, so the number of sequences from Africa was the smallest; while due to the large-scale and high-quality of sequencing in Europe and Asia, their sequence numbers were relatively large. The period covered in this study was calculated every two months, and it was revealed that in the very early stage (November to December in 2021), Africa harbored the most sequences Nucleotide deletions and insertions with different lengths on the Omicron genomes were detected in this study. Eight deletions (Del) and 1 insertion (Inser) were found, with Del 1–3 located in the NSP1, and Del 4–8 and Inser 1 in the S. Of the deletions, Del 4 and 6 were commonly detected with high-occurring frequencies on the six continents ; Del 2 was absent in North America and South America and had low frequencies in other continents; Del 1 was Del 2 without the leading and trailing amino acid, which exhibited a low frequency and only occurred in Africa; Del 3 occurred on all the continents except Oceania, and the frequencies ranged from 2.18% in Asia to 19.17% in Africa; Del 5 and 8 occurred simultaneously and only presented in Asia, while Del 7 occurred only in South America, and the frequencies of Del 5/8 and Del 7 were 12.89% and 13.79%, respectively. In addition, the insertion (Inser 1) added an amino acid between the amino acid position 206 and 207 on the S protein, and it only happened in Europe and Asia, with frequencies of 38.69% and 37.35%, respectively .Temporal distribution of the deletions and insertions on the six continents were analyzed. To avoid bias caused by small numbers, early months with fewer than 30 sequences were excluded in this study, including all the regions except Africa in November 2021 and Asia and Europe in December 2021. Results showed that the distributions of the deletions/insertions exhibited commonality and characteristics across continents. Specifically, both Del 4 and Del 6 had low frequencies in the early stage but increased significantly over time and rapidly reached 100%. Del 3 presented a higher frequency in Africa than in other continents; the frequency increased markedly from February and reached a peak in May (with a frequency about 60%), then declined to the same level as South America. Both Del 1 and 2 exhibited low occurring frequencies in each month, and Del 1 appeared only in May to July in Africa. Del 5/8 and 7 only presented in June in Asia and in January in South America, respectively, and occurred with 100% frequency. Inser 1 started to appear in Europe and Asia in June with occurring frequency of 100% .SNPs on the Omicron genomes from different continents were analyzed. To exclude the coincidental factors such as sequencing error, we defined mutation site with occurring frequency higher than 1% as an SNP. In total, 130 SNPs were found, among which more than half (72 SNPs) were low-frequency (<10%) and 38 were high-frequency (>50%). The S gene is a hotspot region for mutation, and 41 SNPs were located on it, 14 of which were high-frequency, and 15 SNPs were located in the RBD A. AlthouWe also analyzed distributions of SNPs with frequencies more than 5% in the Nsp12, S, and N genes on the six continents . ResultsWe analyzed mutation directions of the substituted base and found the pattern was similar among the six continents. Mutation from C to U occurred the most, with frequencies ranging from 33.4% (in Africa) to 43.45% (in South America), accounting for more than one third of the 12 different mutation directions. The frequency of mutations from U to C, A to G, and G to A were all about 10%, while the total frequency of the other eight directions was less than 30% A. NotablWe analyzed SNP linkages and their distributions on the six continents. To rule out the possibility that the linkages were caused by occasional factors, the ones containing only two SNPs or frequencies lower than 5% were not calculated in this study. With this criterion, four SNP linkages  were found on all the six continents. Linkage I contained 38 high-frequency SNPs, the highest number among all, with 1, 20, and 17 located in the intergenic region, nonstructural and structural genes, respectively, among which 14 were on the S gene and 6 in the RBD. All 17 linked SNPs on the structural genes caused nonsynonymous mutations, while 8 of the 20 linked SNPs on the nonstructural genes caused synonymous mutations. Linkage II involved three SNPs, one located in the nsp8 gene causing synonymous mutation and the other two in the S gene causing nonsynonymous mutation. Linkage III involved four SNPs; one caused synonymous mutation and was located in the M gene, and the other three were located in the ORF6 gene and coded the same amino acid, and the mutations caused an amino acid substitution of D to L. Linkage IV involved three SNPs, which were located on the M, ORF6, and ORF7b genes, respectively .Temporal distribution revealed that the occurring frequencies of linkages I, II, and III sharply increased in a short time, and then all maintained at a high level, while the occurring frequency of linkage IV increased remarkably, but the high frequency only lasted for two months before it quickly decreased to a very low level. The four linkages were all first detected in Africa, followed by Europe, and the detections in North America, South America, and Asia were the latest. For linkage II, it was detected in Europe two months following being detected in Africa, and the other three were all detected in Europe one month following Africa . In thisAs stated above, ten commonly detected SNPs on the six continents were located in RBD, among which 6  were tightly linked and exhibited a high-occurring frequency, while the other four  were not linked together. We calculated the proportions of different amino acid combinations on these substituted sites on the six continents. For convenience, the tightly linked sites were marked together and recorded as 493. The amino acids corresponding to positions 343, 483, 490, 449, and 493 in the reference sequence were RFRLS. We found four major different amino acid combinations, i.e., 1-RFRLG, 2-KFRLS, 3-RFRQG, and 4-RVQRG. Specifically, in South and North America, all four combinations were widely detected, while in Oceania, Europe, and Asia, 1-RFRLG, 2-KFRLS, and 4-RVQRG were the major detections. In Africa, it was 1-RFRLG and 4-RVQRG A. In totFor the four major combinations of amino acid substitutions in the RBD, we evaluated the impact of their amino acid changes on receptor binding ability. Docking structure revealed that compared with the early Omicron virus (designated as wild-type here), the mutants did not remarkably alter the binding of the virus with the hACE2 receptor. Specifically, for the 1-RFRLG mutant, it only harbored the six-linked amino acid substitutions in linkage I, but compared with wild-type Omicron, there were seven pairs of atoms that weakened their interactions versus eight enhanced, which may slightly have enhanced the receptor binding ability. For the other three mutants , their interaction forces decreased overall, and the pairs of interacting atoms with weakened and enhanced forces were: 6 versus 2, 6 versus 3, and 5 versus 1, respectively . The Omicron variant is posing major public health challenges; it is the most heavily mutated among all the pandemic variants . While sPrevious studies revealed that insertions and deletions are important driving forces contributing to the diversity of SARS-CoV-2, and some are highly relevant to the emergence of variants with changed viral biological or antigenic properties. For example, the 382-nucleotide deletion in Singapore may result in a milder infection , the 69–SNP calling is important for understanding the genetic variations of the virus. Our previous studies have shown that the Delta variant had a much greater genetic diversity at early stage than at later stage ,22. The RNA-editing enzyme families in host cells, such as APOBEC and ADAR, are able to induce point mutations to suppress viral infection ,24. In tIt is important to detect co-occurring mutations to study the cooperative manner of different genes at the genetic level ,32. Our The amino acid changes in SARS-CoV-2 RBD would potentially affect viral biological properties ,35. The In conclusion, this study revealed genetic variation signatures on the genome of the Omicron variant during viral transmission. The results suggested that the Omicron genome was relatively stable, it contained small number of SNPs, and all the high-frequency SNPs were tightly linked. A significant proportion of the SNPs underwent strong immune pressure and occurred on the S-RBD, which may potentially change the biological properties of the virus. There is still much to learn about the function and mechanism of RBD substitutions and SNP linkages. Due to the sampling differences and limitation of sequencing capacity, the conclusions in this study may be biased. Nevertheless, this study tracked mutation dynamics of the Omicron temporally and spatially, and it could provide scientific data for the control and prevention of SARS-CoV-2.