Population differences in allele frequencies modify the clinical interpretation of genetic variants associated with rare diseases in Chilean patients

Population differences in allele frequencies modify the clinical interpretation of genetic variants associated with rare diseases in Chilean patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population differences in allele frequencies modify the clinical interpretation of genetic variants associated with rare diseases in Chilean patients Pablo Alarcón-Arias, Rosa Pardo-Vargas, Patricia Castro-Santos, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8682805/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate interpretation of genetic variants relies heavily on population allele frequencies derived from large international reference databases. However, these resources largely underrepresent Latin American populations, raising concerns about the generalizability of variant interpretation and the potential for diagnostic inequities, particularly in admixed and indigenous populations. Here, we evaluated how population-specific allele frequencies influence the clinical interpretation of variants associated with rare diseases in Chilean patients. Results We generated a pilot exomic allele frequency dataset from 60 Chilean individuals without rare diseases and systematically compared it with major international reference databases. Among the 252,169 variants identified, 73,608 were rare or of low frequency in international datasets, of which 10,417 (14.2%) were common in the Chilean population. In addition, 6,043 variants were absent from all the international databases analyzed. Using Chilean allele frequencies as a population reference in standard variant interpretation workflows led to the reclassification of 364 nonbenign variants toward benignity in a cohort of 41 patients with suspected rare diseases. Importantly, two pathogenic and two likely pathogenic variants were reclassified as benign, modifying the diagnostic interpretation in six patients and directly impacting the clinical reports returned to the treating physicians. Conclusions Population-specific genomic diversity significantly influences the clinical interpretation of exome sequencing data. The systematic underrepresentation of Latin American populations in global reference databases can negatively affect diagnostic accuracy and equity in genomic medicine. Our results demonstrate that even pilot population-specific datasets can substantially improve variant classification and support more accurate and equitable genetic diagnoses in underrepresented populations. Rare diseases ACMG/AMP guidelines Underrepresented populations Human genetic diversity Figures Figure 1 Figure 2 BACKGROUND Definitions of rare diseases (RDs) or infrequent diseases vary widely across countries. In Europe, they are defined as life-threatening or chronically debilitating disorders that affect fewer than 5 in 10,000 individuals. In the USA, the criteria include fewer than 200,000 affected individuals nationwide or fewer than 7.5 in 10,000 individuals. In China, the prevalence is defined as less fewer 1 in 10,000 or a total of 140,000 individuals in the country ( 1 – 3 ). These diseases represent 6–8% of the population ( 4 ), with approximately 80% being of genetic origin, corresponding to approximately 6,000 to 7,000 different entities ( 5 – 7 ). Another relevant issue is the difficulty in diagnosis. Many patients take an average of 5 years from the onset of symptoms to molecular confirmation of diagnosis, a process known as the “diagnostic odyssey” ( 8 ). Moreover, it has been estimated that 50% of individuals with genetically originated RD fail to receive a diagnosis ( 8 ). Various techniques are used for diagnosis. Cytogenetics and molecular karyotyping techniques detect large rearrangements and copy number variants (CNVs), which together explain approximately 20% of cases ( 9 ). For single-nucleotide variants (SNVs) and small insertions and deletions (indels), sequencing techniques such as Sanger or next-generation sequencing (NGS) are used. NGS allows the simultaneous analysis of multiple genes and testing for different diseases, thereby improving diagnostic precision. The usefulness of NGS has been described in a series of patients with suspected genetic diseases without a clear diagnosis, for whom an accurate diagnostic rate of 25–52% through whole-exome sequencing (WES) or whole-genome sequencing (WGS) has been reported ( 10 – 13 ). Additionally, the initial use of WES has been shown to reduce the total cost of the diagnostic procedure, the time required for the diagnostic process, and the number of complementary procedures or analyses ( 11 ). To standardize the interpretation of the results, the American College of Medical Genetics and Genomics (ACMG) together with the Association for Molecular Pathology (AMP) published a consensus in 2015 on the nomenclature and criteria for classifying gene variants ( 14 ). Variants are classified as pathogenic (P), likely pathogenic (LP), likely benign (LB), benign (B), or as an intermediate category called variants of uncertain significance (VUSs). However, most allele frequency databases have been constructed using populations of European origin, leaving the frequency in other populations unknown. The databases commonly used as references for the analysis of NGS in RD, such as the 1000 Genomes Project (1000g) ( 15 ), Exome Aggregation Consortium (ExAC) ( 16 ), Exome Variant Server–NHLBI GO Exome Sequencing Project (ESP) ( 17 ), and the Genome Aggregation Database (gnomAD) ( 18 ), do not include individuals of Chilean origin (Supplementary Table 1). Therefore, some variants that are very rare or absent in published NGS might have a higher allele frequency in the population to which the patient belongs. Consequently, variants that are rare in published populations but common in others might be erroneously interpreted as clinically relevant. Including studies from other populations can help reclassify benign variants that are mistakenly interpreted as pathogenic ( 19 ). Chilean ancestry has been studied based on genome-wide genetic variation. By genotyping with an Affymetrix 6.0 GeneChip Array in 313 Chileans from all regions, Eyheramendy et al., in 2015, reported a global average of 54.38% European, 43.22% Native American, and 2.4% African ancestry ( 20 ). These percentages are consistent with those of previous studies but do not distinguish between the subcontinental components of Native American ancestry ( 21 ). A later study with a reduced set of 150 Ancestry Informative Markers in 2843 individuals recruited by the ChileGenomico Initiative across Chile revealed that this component could be separated into 18% Northern and 25% Southern components that share close ancestry with the Aymara and Mapuche people ( 22 ). However, neither of these ancestries are represented in the global datasets of genome-wide genetic variation commonly used for clinical interpretation of variants found in patients with RDs. The aim of this study was to evaluate the usefulness of using national data on genetic variants in the clinical interpretation of NGS tests performed on Chilean patients with RD. The effect on clinical interpretation was determined by considering Chilean allele frequency data obtained from sources with local population data, in addition to international reference data. METHODS Recruitment and data Sixty adult Chileans without any RDs living in the Maule region of Chile were recruited for the study. We collected 5 ml of venous blood in plastic vacutainer tubes containing EDTA, which was subsequently stored at − 80°C. DNA was extracted from these blood samples using the GeneJET Genomic DNA Purification Kit #K0722 (ThermoScientific) following the manufacturer’s protocol. All individuals gave their written informed consent prior to enrolling in the study. Informed consent for the rheumatoid arthritis study was approved by the Ethical Committee of the “Servicio de Salud del Maule” (registration number 04/2014), Chile. Informed consent for patients with RD was obtained from the Ethics Committee of the Faculty of Medicine of the University of Chile. This was given in writing to the patients or their legal guardians, authorizing the anonymous use of their genomic data for research purposes. Data generation and analysis Exome libraries were created using SureSelect XT V5 and sequenced on an Illumina platform by Theragen Etex, Inc. (Seoul, Korea). The Bcbio-nextgen pipeline ( https://bcbio-nextgen.readthedocs.io/ ) was used for quality control, which included FastQC v0.10.1, read filtering with Cutadapt v1.8.1, mapping, alignment against GRCh38, and BAM generation with BWA v0.7.12, Picard v1.92, SAMtools v1.2 and GATK v2.3-9. Variant calling was performed using UnifiedGenotyper, and quality control of the alignments was conducted with Qualimap v2.1 (Supplementary Table 2). Only variants in autosomes were analyzed to avoid unreliable allele frequency estimations in sex chromosomes because of the differing proportions of X and Y chromosomes in each pool. Variants were annotated using ANNOVAR with the Hg38 genome. Allele frequency data were derived from 1000g (version August 2015), ExAC, ESP, gnomAD version 2.1.1 genomes and exomes, and gnomAD v4.1 RefSeq (refGene), while variant identification attributes were derived from avsnp150. Prediction attributes were annotated with dbNSFP version 3.5c (dbnsfp35c) and version 2.6 (ljb26_all) for CADD annotation, along with the ACMG/AMP recommendations pathogenicity attributes provided by the InterVar tool. Allele frequencies of the variants were estimated for the entire CL60 set, combining cases and controls. This was calculated by dividing the number of reads per allele by the total number of reads at the allele position across all pools. International allele frequency values were compared with the highest allele frequency observed for the alternative allele among various international databases commonly used as references. Three international reference groups were determined: International maximum 1 (max_int_1) : 1000g + ExAC + ESP; International maximum 2 (max_int_2) : 1000g + ExAC + ESP + gnomAD v2.1.1 genome + gnomAD v2.1.1 exome; International maximum 3 (max_int_3) : 1000g + ExAC + ESP + gnomAD v2.1.1 genome + gnomAD v2.1.1 exome + gnomAD v3. Max_int_3 was used as the reference frequency because of its inclusion of variants in gnomAD v3. A comparison analysis of allele frequencies was carried out for the alternative alleles of the CL60 set and the different international databases, both alone and aggregated, and their changes depending on depth (DP). The comparison was visualized using the ggplot2 package in RStudio version 1.2.1335 (Fig. 1 ). Variants were selected on the basis of depth filters > 100× and allele frequency (≤ 0.05) in 1000g, ExAC, ESP, and gnomAD version 2 and 3. Variants meeting these criteria were termed “CL60 with a change in allele frequency.” These variants were then classified using ACMG/AMP criteria via InterVar v.2.1.3 ( 23 ), available at https://wintervar.wglab.org , and VarSome v8.0, available at https://varsome.com ( 24 ). Of the 28 ACMG/AMP criteria, 18 were automatically evaluable. Variants were subsequently reclassified considering allele frequencies in the Chilean population using the web tools http://wInterVar.wglab.org/ and VarSome, applying the BA1 criterion (benign/standard alone: allele frequency > 0.05 in ExAC, 1000g and ESP). InterVar interprets only exonic substitution variants and does not allow the interpretation of indels. Evaluation in Chilean patients The study included 41 clinical cases of RD, with 15 from the “Exo_22” project at Laboratorio ChileGenomico and 26 from the “Exoma Chile: genetic characterization of Chilean patients with rare diseases” project (Supplementary Table 3). Full clinical information was recorded and coded according to the Human Phenotype Ontology (HPO). Variant Call Format (VCF) files for the Exo_22 group were generated by the ChileGenomico Laboratory team, while the Exoma Chile cases were processed by the respective clinical laboratories. Each case was analyzed on the VarStation platform using the Uncommon Variants filter (depth ≥ 20× and allele frequency ≤ 0.05 in 1000g, ExAC, ESP, and gnomAD v2 and v3). Variants from real cases were identified in the CL60 set by transforming coordinates from Hg38 to Hg37 using LiftOver from the UCSC Genomics Institute ( 25 ). The ACMG/AMP classification for each variant was determined, and nonbenign variants (P, LP, VUS, and LB) that changed their pathogenicity classification were identified. This intersection was calculated using RStudio software, considering the patient’s symptoms and filtering by genes associated with their HPO codes. RESULTS The CL60 database of exomic allele frequencies in Chileans Exomic sequencing data from 60 Chilean individuals (30 rheumatoid arthritis patients and 30 healthy controls; 17 males and 43 females; age range: 23–65 years) were used. All individuals were from Talca and declared nonnative ancestry and had not been diagnosed with any RD. Given that the general objective of this research is to evaluate monogenic diseases, this set of cases and controls of multifactorial diseases forms a pilot database of allele frequencies for the Chilean population. Among the 588,742 total variants identified, 568,390 were located on autosomes, and 252,169 had a read depth greater than 100× (Table 1 ). Most variants (53.62%) were found in intronic regions, followed by exonic regions (26.35%), which include protein-coding sequences. Smaller proportions of variants were identified in noncoding RNA (ncRNA) regions (5.76%), untranslated regions (UTRs) (5.42%), and intergenic regions (7.12%). The upstream and downstream regions contained 1.58% of the variants, and the splicing regions contained 0.12% of the variants. Among the 252,169 variants with a read depth greater than 100×, a subset of 10,417 (4.1%) variants had an allele frequency greater than 0.05 in the CL60 set but less than or equal to 0.05 in the aggregated international databases (max_int_3). This subset was particularly interesting because these variants might be common in the Chilean population but rare or low in other populations, highlighting the importance of considering population-specific allele frequencies in genetic studies. Table 1 Variants of the CL60 set with depth equal to or greater than 100× Location Called variants % CL60 > 0.05 and max_int_3 < = 0.05 variants % exonic 66,453 26.35 2,340 22.46 splicing 306 0.12 16 0.15 UTR 13,680 5.42 498 4.78 intronic 135,223 53.62 5,401 51.84 ncRNA 14,546 5.76 815 7.82 upstream, downstream 3,985 1.58 158 1.51 intergenic 17,976 7.12 1,189 11.41 total 100x 252,169 100 10,417 100 The Chilean set contains variants absent in international databases To understand how the genetic variants identified in the Chilean population compare to those found in other populations, we analyzed the presence and frequency of these variants in several widely used international databases. Among the 252,169 variants, 223,284 were found in 1000g, 128,375 in ExAC, 108,833 in ESP, 235,269 in the gnomAD v2 genome, 129,895 in the gnomAD v2 exome, and 244,697 in gnomAD v4.1. There were 6,043 variants absent in all these databases, i.e., 2.4% of the called variants. The Chilean set contains changes in allele frequencies that changed the ACMG/AMP pathogenicity classification of its variants A subset of 10,417 variants met the criteria of an allele frequency > 0.05 in the Chilean set and ≤ 0.05 in the aggregate of international databases. These were defined as “CL60 with Change in Allele Frequency”. This subset represented 14.15% of the 73,608 variants with allele frequencies ≤ 0.05 in international databases. This percentage reflects the “degree of importance of CL60", where 0% indicates that no CL60 variant has the power to change the ACMG/AMP BA1 criterion, and 100% indicates that all CL60 variants change the BA1 criterion. Of these, 9,228 were in gene regions, and 2,356 variants were of high interest because they were exonic (n = 2,340) or spliced (n = 16). These genes were classified using ACMG/AMP and reclassified by adding the CL60 allele frequencies. Using InterVar, one variant changed from LP to VUS, and 663 from VUS along with 1,547 LB were reclassified as B. When VarSome was used, one P variant changed to VUS, two LP to VUS, 109 VUS to B, 394 LB to B, and 3 VUS did not change classification. Discrepancies were observed between the automatic classifications of InterVar and VarSome, with VarSome classifying more variants as B, causing a greater number of variants to switch using InterVar (Table 2 ). Table 2 Classification of exonic and splicing variants of the CL60 set with changes in allele frequency Classification InterVar Modified InterVar VarSome Modified VarSome Pathogenic (P) 0 0 1 0 Likely Pathogenic (LP) 1 0 2 0 Uncertain Significance (VUS) 663 1 112 6 Likely Benign (LB) 1,547 0 394 0 Benign (B) 50 2,260 1,847 2,350 Not classified 95 95 0 0 Abbreviations: InterVar: automatic pathogenicity classification by InterVar. Modified InterVar: pathogenicity reclassification of InterVar variants using CL60. VarSome: automatic classification by VarSome. Modified VarSome: reclassification of VarSome using CL60. Most Chilean patients have variants whose ACMG/AMP pathogenicity classification has changed A total of 41 clinical cases of Chilean patients with suspected RD, with ages in the range of 1 to 49 years, were included in the study. Twenty-six patients were treated at the Genetics Service at the Clinical Hospital of the University of Chile between 2019 and 2020 (Supplementary Table 3), and 15 were from multiple national centers (Fundación Debra Chile, Fundación Diagnosis, Hospital Clínico Magallanes, and Hospital Padre Hurtado). Clinical exomes were analyzed using the VarStation platform, considering all genes or those related to the patient’s phenotype. In the exome analysis, 26 patients presented with at least one P variant, while all presented LP, VUS, and LB variants that were susceptible to changes in ACMG/AMP classification. In the phenotype-related analysis, 25 cases showed classification changes in their variants. The analysis flow of a clinical case as an individual example is shown in Fig. 2 . The use of the reference set managed to change the ACMG/AMP classification to 0.48% of variants in Chilean patients For a global analysis, the number of nonbenign variants in the 41 cases present in the CL60-Reclassified set was calculated. In the 41 cases, 76,848 variants met the Uncommon Variants filter condition (DP ≥ 20×, FA ≤ 0.05 in 1000g, ExAC, ESP, and gnomAD). Of these, 2,106 were B, and 74,742 were nonbenign (P: 137; LP: 750; VUS: 62,054; LB: 11,800). In total, 364 variants (P: 2, LP: 2, VUS: 288, LB: 72) changed their pathogenicity classification, all toward benign, resulting in a percentage change of 0.48%. The same analysis considering the gene filters related to phenotype resulted in a percentage change of 0.16% (Table 3 ). Variants that changed from P or LP to B in genes related to the patients’ phenotypes impact the report returned to the treating physician. This occurred in several cases, as shown in Table 4 . Therefore, the revaluation of clinical significance using the CL60 database altered the report that would have been returned to 6 out of 41 patients with a diagnostic test result (14.6%). Table 3 Uncommon variants of 41 cases analyzed with VarStation considering Exome genes or genes related to phenotype Exome Related to phenotype Classification Total variants Changed using CL60 Total variants Changed using CL60 Benign 2,106 0 678 0 Not Benign P 137 2 12 0 LP 750 2 60 0 VUS 62,054 288 5,898 10 LB 11,800 72 2,101 3 Subtotal 74,742 364 8,071 13 Total variants Percentage of change (%) 76,848 8,749 0.48 0.16 Abbreviations: Exome: exonic or splicing variants. Uncommon variants: DP ≥ 20×, FA ≤ 0.05 in all 1000g, ExAC, ESP and gnomAD. Related to phenotype: genes related to HPO of the case. P: Pathogenic. LP: Likely pathogenic. VUS: Variant of uncertain significance. LB: Likely benign. Table 4 Pathogenic and likely pathogenic exome variants that changed their ACMG/AMP pathogenicity classification when using the CL60 set in the 41 patients analyzed with VarStation Gene Variant CL60 allele frequency max_int_3 allele frequency VarStation InterVar Modified InterVar VarSome Modified VarSome Case ASB15 NM_080928: c.844C > T: p.(Arg282Ter) 0.0529 0.0096 P VUS B VUS B Exo22_05 ZNF544 NM_014480: c.1843C > T: p.(Arg615Ter) 0.0596 0.0166 P VUS B LB B Exo22_05 NPAP1L NM_001282301: c.262C > T: p.(Gln88Ter) 0.0939 0.0086 LP VUS B VUS B Exo22_06 SCN7A NM_002976: c.2696delA: p.(Asn899fs) 0.0795 0.0024 LP N/A N/A LB B Exo22_08 , Exo22_09 , Exo22_10 , Exo22_11 Abbreviations: InterVar: automatic pathogenicity classification by InterVar. Modified InterVar: pathogenicity reclassification of InterVar variants using CL60. VarSome: automatic classification by VarSome. Modified VarSome: reclassification of VarSome using CL60. P: Pathogenic. LP: Likely pathogenic. VUS: Variant of uncertain significance. LB: Likely benign. N/A: Classification not available using InterVar. DISCUSSION The clinical interpretation of genetic variants is essential for precision medicine, particularly in the diagnosis of RD. Current guidelines established by the ACMG/AMP depend greatly on population allele frequencies as a criterion for variant classification. Databases such as gnomAD, ExAC, ESP, and 1000g have therefore become fundamental references for the evaluation of pathogenicity. However, these resources are largely composed of individuals of European ancestry, and most Latin American populations, including Chileans, are underrepresented. Therefore, alleles that are rare in global datasets may be common in specific regional populations, leading to potential misclassification and inequities in genetic diagnosis. Different studies have highlighted this limitation. Manrai et al. reported that pathogenic assertions based on frequency data from nonrepresentative populations can lead to false-positive diagnoses ( 19 ). Subsequent work in hereditary cancer syndromes has demonstrated that the rate of VUS is higher in individuals from non-European ethnic groups and that the dynamics of variant reclassification over time also differ by ancestry ( 26 , 27 ). These findings underscore that the interpretation of variants on the basis of allele frequencies derived from predominantly European datasets can disproportionately affect patients from underrepresented populations. In the present study, we analyzed how population differences in allele frequencies influence the clinical interpretation of variants in genes associated with RD in Chilean patients. Using a pilot exomic dataset from 60 Chilean individuals, we compared local allele frequencies with those in international databases and evaluated their effects on the automatic ACMG/AMP classification generated by InterVar and VarSome. We then assessed how these differences modified the genetic diagnosis in 41 Chilean patients with suspected RDs. Our findings show that population-specific genomic data can substantially modify the clinical interpretation of sequence variants in RD patients. We identified 504 variants whose ACMG/AMP classification changed when local frequencies were considered, resulting in the formation of a set of 2 VUSs and 502 variants classified as benign after re-evaluation (Supplementary Table 4). From a clinical perspective, these differences were translated into modified diagnostic interpretations for six out of 41 patients (14.6%). These findings illustrate how the lack of regional frequency data may lead to false pathogenic assertions and, consequently, to misdiagnoses that could affect patient management or genetic counseling. Our results are in line with those of previous reports showing that ancestry-informed analyses can correct a meaningful fraction of misclassified variants. Naslavsky et al. demonstrated that in an elderly admixed Brazilian cohort, the incorporation of local allele frequencies and the application of ACMG/AMP criteria related to population data (BA1, BS1, and PM2) resulted in the reclassification of previously reported pathogenic or likely pathogenic variants, thereby challenging their clinical interpretation in admixed individuals ( 28 ). Park et al. reported that using allele frequencies from 1,314 Korean exomes as ethnic controls allowed the reclassification of 9 of 36 BRCA1/2 variants by applying BS1 ( 29 ). Similarly, Ağaoğlu et al. reported that in Turkish breast cancer patients, the use of allele frequencies from 3,362 Turkish individuals led to the reclassification of 5 of 75 VUSs (6.7%) in cancer susceptibility genes ( 30 ). Together, these studies reinforce that local allele frequencies are essential for preventing systematic biases in variant interpretation in admixed populations. Other Latin American studies have described experiences of variant curation and reclassification in hereditary cancer genes but have not used population-specific reference frequency datasets ( 31 ). To our knowledge, our work presents the first practical application, in Latin American patients, of a reference dataset of allele frequencies derived from the same population to reclassify variants associated with RD. The Chilean population is characterized by a complex admixture of European, Native American (primarily Mapuche and Aymara) and African ancestry ( 20 – 22 ). None of these specific Native American components are adequately represented in global reference datasets that are commonly used in clinical genomics. Therefore, the development of a Chilean-specific allele frequency resource addresses a critical gap and provides a framework that could be extended to other underrepresented populations in the region. The underrepresentation of many populations, including those from Latin America, in global databases of genomic diversity has been identified as a pervasive source of bias, limiting scientific progress and potentially perpetuating disparities in access to high-quality health care in the postgenomic era ( 32 ). Our findings suggest that using datasets composed of individuals from populations not included in commonly used reference databases can substantially benefit the interpretation of patient variants. Furthermore, there is a need for more diverse reference datasets as well as for population-level and, where possible, ancestry-stratified resources. Otherwise, relatively common alleles in a particular population may be diluted in large heterogeneous datasets, resulting in missed opportunities to reclassify variants in patients from that population. Beyond technical benefits, these efforts have important ethical dimensions. The systematic underrepresentation of Latin American populations in global databases perpetuates inequities in the postgenomic era, as it limits both the accuracy and the accessibility of precision medicine for patients from these regions. Experiences in other fields have already highlighted how a lack of diversity in genomic resources can translate into unequal diagnostic performance and therapeutic opportunities ( 33 , 34 ). Expanding local and regional genomic resources is therefore both a scientific and a social imperative. On the other hand, several limitations in our design should be considered, which preclude us from drawing solid conclusions. First, the CL60 reference dataset is based on pooled sequencing of 60 individuals, which restricts the precision of allele frequency estimation for very-low-frequency variants and precludes the evaluation of individual ancestries or relatedness. The CL60 dataset represents the first Chilean exome-based allele frequency reference specifically applied to clinical variant interpretation. Despite its modest scale, it provides proof of principle for the need for regional genomic data to improve diagnostic accuracy. Second, the dataset includes both RA cases and controls. Although RA is a complex disease with a polygenic architecture, this design could increase the frequency of alleles associated with RA or autoimmune traits. Nonetheless, given that the focus of this work is on monogenic variants related to RD and that the individuals included had no known RD diagnoses, we consider that any potential bias in allele frequencies is unlikely to drive the main conclusions. Future versions of Chilean reference datasets should ideally prioritize individuals without known genetic disorders and include larger and more geographically diverse cohorts. Third, given that sequencing was performed in pools of DNA, we could not perform a formal quantification of ancestry for the individuals in CL60 or for the RD patients. The Chilean population presents a complex admixture pattern, with substantial variability in the proportions of European, Native American and African ancestry across regions ( 20 – 22 ). Consequently, allele frequencies for some variants may vary geographically according to local ancestry proportions. Incorporating ancestry-informed analyses in future studies will be important for refining frequency estimates, defining ancestry-stratified thresholds, and improving interpretation in highly admixed contexts. CONCLUSIONS In summary, the results of this study provide evidence that population differences in allele frequencies can affect the clinical interpretation of variants in Chilean patients with RD. The incorporation of local genomic data led to the reclassification of hundreds of variants and altered the diagnostic report for approximately one in seven patients analyzed. The inclusion of population-specific allele frequencies can directly improve diagnostic reliability in RDs. Even a small fraction of reclassified variants can have substantial consequences at the individual level. Our findings emphasize the need to systematically incorporate local frequency data into variant interpretation workflows in Chile and other underrepresented regions. Collaborative data-sharing frameworks across Latin America will be essential for increasing the representation of admixed populations in genomic reference databases. This is not only a technical challenge but also an ethical commitment to ensure that the benefits of genomic medicine are distributed equitably across populations. Abbreviations 1000g 1000 Genomes Project ACMG American College of Medical Genetics and Genomics AF Allele frequency AIMs Ancestry Informative Markers AMP Association for Molecular Pathology B Benign BA1 Benign standalone criterion 1 (ACMG/AMP) BS1 Benign strong criterion 1 (ACMG/AMP) CADD Combined Annotation Dependent Depletion CL60 Chilean allele frequency dataset generated from 60 individuals CNVs Copy number variants DP Read depth ESP Exome Variant Server – NHLBI GO Exome Sequencing Project ExAC Exome Aggregation Consortium GATK Genome Analysis Toolkit gnomAD Genome Aggregation Database HPO Human Phenotype Ontology indels Insertions and deletions LB Likely benign LP Likely pathogenic max_int_1 Maximum allele frequency across 1000g, ExAC and ESP max_int_2 Maximum allele frequency across 1000g, ExAC, ESP and gnomAD v2 max_int_3 Maximum allele frequency across 1000g, ExAC, ESP and gnomAD v2 and v3 NCBI National Center for Biotechnology Information ncRNA Non-coding RNA NGS Next-generation sequencing P Pathogenic PM2 Pathogenic moderate criterion 2 (ACMG/AMP) RD Rare disease RefSeq Reference Sequence database SNVs Single-nucleotide variants UTRs Untranslated regions VCF Variant Call Format VUS Variant of uncertain significance WES Whole-exome sequencing WGS Whole-genome sequencing Declarations DATA AVAILABILITY The data that support the findings of this study are available from the corresponding authors upon reasonable request. ETHICS APPROVAL AND CONSENT TO PARTICIPATE The study protocol was reviewed and approved by the Ethics Committee of the “Servicio de Salud del Maule” (registration number 04/2014), Chile, and the ethics committee of the Faculty of Medicine of the University of Chile. COMPETING INTERESTS All authors declare no competing interests. FUNDING This work was supported by grants and support from the Fondecyt grant Nº 1220540, Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union (PI22/00804), and GAIN Proyectos de Excelencia (IN607D2022/06). AUTHORS´ CONTRIBUTORS PAA, RPV and PCS contributed to the conception and design of the study and wrote the main manuscript text. RPV, GLS, MLB, MM, PK and IF contributed to patient recruitment, clinical data collection, phenotypic characterization and interpretation of genetic variants. PAA, PCS and RAV contributed to data analysis and interpretation of results. RDP and RAV supervised the study, provided critical input, and revised the manuscript. All authors reviewed and approved the final version of the manuscript. ACKNOWLEDGMENTS Roberto Díaz-Peña is supported by the Miguel Servet (CP21/00003) contract, funded by the ISCIII and co-funded by the European Union. References Stolk P, Willemen MJC, Leufkens HGM. Rare essentials: drugs for rare diseases as essential medicines. Bull World Health Organ. 2006;84(9):745–51. Rodwell C, Aymé S. Rare disease policies to improve care for patients in Europe. Biochim Biophys Acta. 2015;1852(10 Pt B):2329–35. Lu Y, Han J. The definition of rare disease in China and its prospects. Intractable Rare Dis Res. 2022;11(1):29–30. Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2020;28(2):165–73. Sawyer SL, Hartley T, Dyment DA, Beaulieu CL, Schwartzentruber J, Smith A, et al. 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Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91. Auer PL, Johnsen JM, Johnson AD, Logsdon BA, Lange LA, Nalls MA, et al. Imputation of exome sequence variants into population- based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project. Am J Hum Genet. 2012;91(5):794–808. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43. Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, Szolovits P, et al. Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med. 2016;375(7):655–65. Eyheramendy S, Martinez FI, Manevy F, Vial C, Repetto GM. Genetic structure characterization of Chileans reflects historical immigration patterns. Nat Commun. 2015;6:6472. Fuentes M, Pulgar I, Gallo C, Bortolini MC, Canizales-Quinteros S, Bedoya G, et al. [Gene geography of Chile: regional distribution of American, European and African genetic contributions]. Rev Med Chil. 2014;142(3):281–9. Verdugo RA, Di Genova A, Herrera L, Moraga M, Acuña M, Berríos S et al. Development of a small panel of SNPs to infer ancestry in Chileans that distinguishes Aymara and Mapuche components. Biological Research. 16 de abril de. 2020;53(1):15. Li Q, Wang K, InterVar. Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet. 2017;100(2):267–80. Kopanos C, Tsiolkas V, Kouris A, Chapple CE, Albarca Aguilera M, Meyer R, et al. VarSome: the human genomic variant search engine. Bioinformatics. 2019;35(11):1978–80. Kuhn RM, Haussler D, Kent WJ. The UCSC genome browser and associated tools. Brief Bioinform. 2013;14(2):144–61. Caswell-Jin JL, Gupta T, Hall E, Petrovchich IM, Mills MA, Kingham KE, et al. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. Genet Med. 2018;20(2):234–9. Slavin TP, Van Tongeren LR, Behrendt CE, Solomon I, Rybak C, Nehoray B, et al. Prospective Study of Cancer Genetic Variants: Variation in Rate of Reclassification by Ancestry. J Natl Cancer Inst. 2018;110(10):1059–66. Naslavsky MS, Scliar MO, Yamamoto GL, Wang JYT, Zverinova S, Karp T, et al. Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil. Nat Commun. 2022;13(1):1004. Park JS, Nam EJ, Park HS, Han JW, Lee JY, Kim J, et al. Identification of a Novel BRCA1 Pathogenic Mutation in Korean Patients Following Reclassification of BRCA1 and BRCA2 Variants According to the ACMG Standards and Guidelines Using Relevant Ethnic Controls. Cancer Res Treat. 2017;49(4):1012–21. Agaoglu NB, Unal B, Hayes CP, Walker M, Ng OH, Doganay L, et al. Genomic disparity impacts variant classification of cancer susceptibility genes in Turkish breast cancer patients. Cancer Med. 2024;13(3):e6852. Manotas MC, Rivera AL, Sanabria-Salas MC. Variant curation and interpretation in hereditary cancer genes: An institutional experience in Latin America. Mol Genet Genomic Med. 2023;11(5):e2141. Bustamante CD, Burchard EG, De la Vega FM. Genomics for the world. Nature. 2011;475(7355):163–5. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–91. Díaz-Peña R, Adelowo O. Advancing equity in genomic medicine for rheumatology. Nat Rev Rheumatol. 2024;20(10):595–6. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.doc Supplementarymaterial2.doc Supplementarymaterial3.doc Supplementarymaterial4.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8682805","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580278044,"identity":"3906ba60-530f-418b-9c63-f905be5cbf01","order_by":0,"name":"Pablo Alarcón-Arias","email":"","orcid":"","institution":"Hospital Clínico de la Universidad de Chile","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Alarcón-Arias","suffix":""},{"id":580278045,"identity":"475c698a-2331-4650-b804-002564799a70","order_by":1,"name":"Rosa Pardo-Vargas","email":"","orcid":"","institution":"Hospital Clínico de la Universidad de Chile","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"","lastName":"Pardo-Vargas","suffix":""},{"id":580278046,"identity":"92773bc4-0542-466a-a5e9-c41302cac90b","order_by":2,"name":"Patricia Castro-Santos","email":"","orcid":"","institution":"Universidad Autónoma de Chile","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Castro-Santos","suffix":""},{"id":580278047,"identity":"42afce84-cc17-41fc-980e-d60881d9df13","order_by":3,"name":"Guillermo Lay-Son","email":"","orcid":"","institution":"Hospital Padre Hurtado","correspondingAuthor":false,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Lay-Son","suffix":""},{"id":580278048,"identity":"b12e7b5b-d9de-412c-99e7-45792fbf1630","order_by":4,"name":"M Leonor Bustamante","email":"","orcid":"","institution":"University of Chile","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"Leonor","lastName":"Bustamante","suffix":""},{"id":580278051,"identity":"1b6c5ae9-1a44-4301-8f39-c12e6dfc6a48","order_by":5,"name":"Marcelo Miranda","email":"","orcid":"","institution":"Fundación Diagnosis","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Miranda","suffix":""},{"id":580278053,"identity":"6e6d95f9-9e7b-43e9-88d4-40464595b282","order_by":6,"name":"Paola Krall","email":"","orcid":"","institution":"University of Chile","correspondingAuthor":false,"prefix":"","firstName":"Paola","middleName":"","lastName":"Krall","suffix":""},{"id":580278054,"identity":"d2e53738-3f88-47bf-912e-b5e85d63284e","order_by":7,"name":"Ignacia Fuentes","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Ignacia","middleName":"","lastName":"Fuentes","suffix":""},{"id":580278055,"identity":"24e3dd06-f778-4650-965d-e6b4f994ef42","order_by":8,"name":"Roberto Díaz-Peña","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFACHih9GIg/IEvw4dWSANHCOAPJCAY2gloOMDAw8xCjxby99+Djyh82DHzHmR9/tm2zS9zP3nvw0Y0aBnlcWmTOnEs2PJOQxiB5mM3AOLctObGH51yycc4xBsM2HFokJHLMJBsSDjMYAFFybhtzYg9QRDqHjSEBly1ALeY/IVrYPxy2bKuHavmHV4sZI0QLj2EzY9thiJbcNjxagC6XbEhL45E8zFPM2HPuuHHPmTPGxrl9Erj9Agyfjw02NnJ8549v/vCjrFq2vb3H8HHONxt5fhxaYIAHXUCCgIZRMApGwSgYBfgAAJVPUBT6x5ElAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Autónoma de Chile","correspondingAuthor":true,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Díaz-Peña","suffix":""},{"id":580278056,"identity":"ac5ace71-5d21-40b0-a33b-b4d6ba039b8e","order_by":9,"name":"Ricardo A. Verdugo","email":"","orcid":"","institution":"University of Chile","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"A.","lastName":"Verdugo","suffix":""}],"badges":[],"createdAt":"2026-01-23 23:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8682805/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8682805/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101792552,"identity":"7100bf25-86e2-44b7-b71b-74151fa79082","added_by":"auto","created_at":"2026-02-03 16:12:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86380,"visible":true,"origin":"","legend":"\u003cp\u003eVariants found in Chilean patients present in reference datasets by allele frequency category.\u003c/p\u003e\n\u003cp\u003eVariants in red are rare in international datasets but frequent in Chileans. Abbreviations: AF: Allele frequency in the international dataset. CL60: Allele frequency in this study. max_int_1: 1000g + ExAC + ESP, max_int_2: max_int_1 + gnomAD v2.1.1 genome + gnomAD v2.1.1 exome, max_int_3: max_int_2 + gnomAD v3.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/d17686de92a3d6909f2fbf9d.png"},{"id":101792518,"identity":"490497db-d5aa-4224-afcc-1457bdc4df33","added_by":"auto","created_at":"2026-02-03 16:12:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":954997,"visible":true,"origin":"","legend":"\u003cp\u003eExample of an analyzed case. Abbreviations: P: Pathogenic. LP: Likely pathogenic. VUS: Variant of uncertain significance. LB: Likely benign.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/70de601402ac3b9de4af1fb1.png"},{"id":101792627,"identity":"d8e1019b-d2d4-44cc-b645-36ad37649e95","added_by":"auto","created_at":"2026-02-03 16:12:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2259442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/0bd5d3cc-b8bc-4f2f-afde-670ef96ee81b.pdf"},{"id":101792440,"identity":"6e75dc11-5add-4fd6-b28f-d8056b8eb02e","added_by":"auto","created_at":"2026-02-03 16:12:28","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33792,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.doc","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/baa3830d8c47365a38e4e623.doc"},{"id":101792493,"identity":"965095e5-5cfe-4723-be9b-81225a23a86e","added_by":"auto","created_at":"2026-02-03 16:12:40","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44032,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial2.doc","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/d01bbe16e10689273dfba8ae.doc"},{"id":101792470,"identity":"e7cf9b8e-49bb-454f-98d3-bedbdac8caa2","added_by":"auto","created_at":"2026-02-03 16:12:36","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":91648,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial3.doc","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/29b6949f62446cae632e6566.doc"},{"id":101792434,"identity":"a25683ab-5817-4370-943f-fd5d939742db","added_by":"auto","created_at":"2026-02-03 16:12:26","extension":"doc","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":622080,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial4.doc","url":"https://assets-eu.researchsquare.com/files/rs-8682805/v1/b1f6dfbf0da7e244210045e0.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population differences in allele frequencies modify the clinical interpretation of genetic variants associated with rare diseases in Chilean patients","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eDefinitions of rare diseases (RDs) or infrequent diseases vary widely across countries. In Europe, they are defined as life-threatening or chronically debilitating disorders that affect fewer than 5 in 10,000 individuals. In the USA, the criteria include fewer than 200,000 affected individuals nationwide or fewer than 7.5 in 10,000 individuals. In China, the prevalence is defined as less fewer 1 in 10,000 or a total of 140,000 individuals in the country (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These diseases represent 6\u0026ndash;8% of the population (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), with approximately 80% being of genetic origin, corresponding to approximately 6,000 to 7,000 different entities (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother relevant issue is the difficulty in diagnosis. Many patients take an average of 5 years from the onset of symptoms to molecular confirmation of diagnosis, a process known as the \u0026ldquo;diagnostic odyssey\u0026rdquo; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Moreover, it has been estimated that 50% of individuals with genetically originated RD fail to receive a diagnosis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Various techniques are used for diagnosis. Cytogenetics and molecular karyotyping techniques detect large rearrangements and copy number variants (CNVs), which together explain approximately 20% of cases (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For single-nucleotide variants (SNVs) and small insertions and deletions (indels), sequencing techniques such as Sanger or next-generation sequencing (NGS) are used. NGS allows the simultaneous analysis of multiple genes and testing for different diseases, thereby improving diagnostic precision. The usefulness of NGS has been described in a series of patients with suspected genetic diseases without a clear diagnosis, for whom an accurate diagnostic rate of 25\u0026ndash;52% through whole-exome sequencing (WES) or whole-genome sequencing (WGS) has been reported (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Additionally, the initial use of WES has been shown to reduce the total cost of the diagnostic procedure, the time required for the diagnostic process, and the number of complementary procedures or analyses (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo standardize the interpretation of the results, the American College of Medical Genetics and Genomics (ACMG) together with the Association for Molecular Pathology (AMP) published a consensus in 2015 on the nomenclature and criteria for classifying gene variants (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Variants are classified as pathogenic (P), likely pathogenic (LP), likely benign (LB), benign (B), or as an intermediate category called variants of uncertain significance (VUSs). However, most allele frequency databases have been constructed using populations of European origin, leaving the frequency in other populations unknown.\u003c/p\u003e \u003cp\u003eThe databases commonly used as references for the analysis of NGS in RD, such as the 1000 Genomes Project (1000g) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), Exome Aggregation Consortium (ExAC) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), Exome Variant Server\u0026ndash;NHLBI GO Exome Sequencing Project (ESP) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and the Genome Aggregation Database (gnomAD) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), do not include individuals of Chilean origin (Supplementary Table\u0026nbsp;1). Therefore, some variants that are very rare or absent in published NGS might have a higher allele frequency in the population to which the patient belongs. Consequently, variants that are rare in published populations but common in others might be erroneously interpreted as clinically relevant. Including studies from other populations can help reclassify benign variants that are mistakenly interpreted as pathogenic (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChilean ancestry has been studied based on genome-wide genetic variation. By genotyping with an Affymetrix 6.0 GeneChip Array in 313 Chileans from all regions, Eyheramendy et al., in 2015, reported a global average of 54.38% European, 43.22% Native American, and 2.4% African ancestry (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These percentages are consistent with those of previous studies but do not distinguish between the subcontinental components of Native American ancestry (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A later study with a reduced set of 150 Ancestry Informative Markers in 2843 individuals recruited by the ChileGenomico Initiative across Chile revealed that this component could be separated into 18% Northern and 25% Southern components that share close ancestry with the Aymara and Mapuche people (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, neither of these ancestries are represented in the global datasets of genome-wide genetic variation commonly used for clinical interpretation of variants found in patients with RDs.\u003c/p\u003e \u003cp\u003eThe aim of this study was to evaluate the usefulness of using national data on genetic variants in the clinical interpretation of NGS tests performed on Chilean patients with RD. The effect on clinical interpretation was determined by considering Chilean allele frequency data obtained from sources with local population data, in addition to international reference data.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment and data\u003c/h2\u003e \u003cp\u003eSixty adult Chileans without any RDs living in the Maule region of Chile were recruited for the study. We collected 5 ml of venous blood in plastic vacutainer tubes containing EDTA, which was subsequently stored at \u0026minus;\u0026thinsp;80\u0026deg;C. DNA was extracted from these blood samples using the GeneJET Genomic DNA Purification Kit #K0722 (ThermoScientific) following the manufacturer\u0026rsquo;s protocol. All individuals gave their written informed consent prior to enrolling in the study. Informed consent for the rheumatoid arthritis study was approved by the Ethical Committee of the \u0026ldquo;Servicio de Salud del Maule\u0026rdquo; (registration number 04/2014), Chile. Informed consent for patients with RD was obtained from the Ethics Committee of the Faculty of Medicine of the University of Chile. This was given in writing to the patients or their legal guardians, authorizing the anonymous use of their genomic data for research purposes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData generation and analysis\u003c/h3\u003e\n\u003cp\u003eExome libraries were created using SureSelect XT V5 and sequenced on an Illumina platform by Theragen Etex, Inc. (Seoul, Korea). The \u003cem\u003eBcbio-nextgen\u003c/em\u003e pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bcbio-nextgen.readthedocs.io/\u003c/span\u003e\u003cspan address=\"https://bcbio-nextgen.readthedocs.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for quality control, which included FastQC v0.10.1, read filtering with Cutadapt v1.8.1, mapping, alignment against GRCh38, and BAM generation with BWA v0.7.12, Picard v1.92, SAMtools v1.2 and GATK v2.3-9. Variant calling was performed using UnifiedGenotyper, and quality control of the alignments was conducted with Qualimap v2.1 (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eOnly variants in autosomes were analyzed to avoid unreliable allele frequency estimations in sex chromosomes because of the differing proportions of X and Y chromosomes in each pool. Variants were annotated using ANNOVAR with the Hg38 genome. Allele frequency data were derived from 1000g (version August 2015), ExAC, ESP, gnomAD version 2.1.1 genomes and exomes, and gnomAD v4.1 RefSeq (refGene), while variant identification attributes were derived from avsnp150. Prediction attributes were annotated with dbNSFP version 3.5c (dbnsfp35c) and version 2.6 (ljb26_all) for CADD annotation, along with the ACMG/AMP recommendations pathogenicity attributes provided by the InterVar tool.\u003c/p\u003e \u003cp\u003eAllele frequencies of the variants were estimated for the entire CL60 set, combining cases and controls. This was calculated by dividing the number of reads per allele by the total number of reads at the allele position across all pools.\u003c/p\u003e \u003cp\u003eInternational allele frequency values were compared with the highest allele frequency observed for the alternative allele among various international databases commonly used as references. Three international reference groups were determined:\u003c/p\u003e \u003cp\u003e \u003cb\u003eInternational maximum 1 (max_int_1)\u003c/b\u003e: 1000g\u0026thinsp;+\u0026thinsp;ExAC\u0026thinsp;+\u0026thinsp;ESP; \u003cb\u003eInternational maximum 2 (max_int_2)\u003c/b\u003e: 1000g\u0026thinsp;+\u0026thinsp;ExAC\u0026thinsp;+\u0026thinsp;ESP\u0026thinsp;+\u0026thinsp;gnomAD v2.1.1 genome\u0026thinsp;+\u0026thinsp;gnomAD v2.1.1 exome; \u003cb\u003eInternational maximum 3 (max_int_3)\u003c/b\u003e: 1000g\u0026thinsp;+\u0026thinsp;ExAC\u0026thinsp;+\u0026thinsp;ESP\u0026thinsp;+\u0026thinsp;gnomAD v2.1.1 genome\u0026thinsp;+\u0026thinsp;gnomAD v2.1.1 exome\u0026thinsp;+\u0026thinsp;gnomAD v3.\u003c/p\u003e \u003cp\u003eMax_int_3 was used as the reference frequency because of its inclusion of variants in gnomAD v3. A comparison analysis of allele frequencies was carried out for the alternative alleles of the CL60 set and the different international databases, both alone and aggregated, and their changes depending on depth (DP). The comparison was visualized using the ggplot2 package in RStudio version 1.2.1335 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVariants were selected on the basis of depth filters\u0026thinsp;\u0026gt;\u0026thinsp;100\u0026times; and allele frequency (\u0026le;\u0026thinsp;0.05) in 1000g, ExAC, ESP, and gnomAD version 2 and 3. Variants meeting these criteria were termed \u0026ldquo;CL60 with a change in allele frequency.\u0026rdquo; These variants were then classified using ACMG/AMP criteria via InterVar v.2.1.3 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wintervar.wglab.org\u003c/span\u003e\u003cspan address=\"https://wintervar.wglab.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and VarSome v8.0, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://varsome.com\u003c/span\u003e\u003cspan address=\"https://varsome.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Of the 28 ACMG/AMP criteria, 18 were automatically evaluable. Variants were subsequently reclassified considering allele frequencies in the Chilean population using the web tools \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wInterVar.wglab.org/\u003c/span\u003e\u003cspan address=\"http://wInterVar.wglab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and VarSome, applying the BA1 criterion (benign/standard alone: allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in ExAC, 1000g and ESP). InterVar interprets only exonic substitution variants and does not allow the interpretation of indels.\u003c/p\u003e\n\u003ch3\u003eEvaluation in Chilean patients\u003c/h3\u003e\n\u003cp\u003eThe study included 41 clinical cases of RD, with 15 from the \u0026ldquo;Exo_22\u0026rdquo; project at Laboratorio ChileGenomico and 26 from the \u0026ldquo;Exoma Chile: genetic characterization of Chilean patients with rare diseases\u0026rdquo; project (Supplementary Table\u0026nbsp;3). Full clinical information was recorded and coded according to the Human Phenotype Ontology (HPO). Variant Call Format (VCF) files for the Exo_22 group were generated by the ChileGenomico Laboratory team, while the Exoma Chile cases were processed by the respective clinical laboratories. Each case was analyzed on the VarStation platform using the Uncommon Variants filter (depth\u0026thinsp;\u0026ge;\u0026thinsp;20\u0026times; and allele frequency\u0026thinsp;\u0026le;\u0026thinsp;0.05 in 1000g, ExAC, ESP, and gnomAD v2 and v3).\u003c/p\u003e \u003cp\u003eVariants from real cases were identified in the CL60 set by transforming coordinates from Hg38 to Hg37 using LiftOver from the UCSC Genomics Institute (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The ACMG/AMP classification for each variant was determined, and nonbenign variants (P, LP, VUS, and LB) that changed their pathogenicity classification were identified. This intersection was calculated using RStudio software, considering the patient\u0026rsquo;s symptoms and filtering by genes associated with their HPO codes.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eThe CL60 database of exomic allele frequencies in Chileans\u003c/h2\u003e \u003cp\u003eExomic sequencing data from 60 Chilean individuals (30 rheumatoid arthritis patients and 30 healthy controls; 17 males and 43 females; age range: 23\u0026ndash;65 years) were used. All individuals were from Talca and declared nonnative ancestry and had not been diagnosed with any RD. Given that the general objective of this research is to evaluate monogenic diseases, this set of cases and controls of multifactorial diseases forms a pilot database of allele frequencies for the Chilean population.\u003c/p\u003e \u003cp\u003eAmong the 588,742 total variants identified, 568,390 were located on autosomes, and 252,169 had a read depth greater than 100\u0026times; (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most variants (53.62%) were found in intronic regions, followed by exonic regions (26.35%), which include protein-coding sequences. Smaller proportions of variants were identified in noncoding RNA (ncRNA) regions (5.76%), untranslated regions (UTRs) (5.42%), and intergenic regions (7.12%). The upstream and downstream regions contained 1.58% of the variants, and the splicing regions contained 0.12% of the variants. Among the 252,169 variants with a read depth greater than 100\u0026times;, a subset of 10,417 (4.1%) variants had an allele frequency greater than 0.05 in the CL60 set but less than or equal to 0.05 in the aggregated international databases (max_int_3). This subset was particularly interesting because these variants might be common in the Chilean population but rare or low in other populations, highlighting the importance of considering population-specific allele frequencies in genetic studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariants of the CL60 set with depth equal to or greater than 100\u0026times;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalled variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCL60\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and\u003c/p\u003e \u003cp\u003emax_int_3\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.05 variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66,453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esplicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003encRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupstream, downstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal 100x\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe Chilean set contains variants absent in international databases\u003c/h2\u003e \u003cp\u003eTo understand how the genetic variants identified in the Chilean population compare to those found in other populations, we analyzed the presence and frequency of these variants in several widely used international databases. Among the 252,169 variants, 223,284 were found in 1000g, 128,375 in ExAC, 108,833 in ESP, 235,269 in the gnomAD v2 genome, 129,895 in the gnomAD v2 exome, and 244,697 in gnomAD v4.1. There were 6,043 variants absent in all these databases, i.e., 2.4% of the called variants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Chilean set contains changes in allele frequencies that changed the ACMG/AMP pathogenicity classification of its variants\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA subset of 10,417 variants met the criteria of an allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in the Chilean set and \u0026le;\u0026thinsp;0.05 in the aggregate of international databases. These were defined as \u0026ldquo;CL60 with Change in Allele Frequency\u0026rdquo;. This subset represented 14.15% of the 73,608 variants with allele frequencies\u0026thinsp;\u0026le;\u0026thinsp;0.05 in international databases. This percentage reflects the \u0026ldquo;degree of importance of CL60\", where 0% indicates that no CL60 variant has the power to change the ACMG/AMP BA1 criterion, and 100% indicates that all CL60 variants change the BA1 criterion. Of these, 9,228 were in gene regions, and 2,356 variants were of high interest because they were exonic (n\u0026thinsp;=\u0026thinsp;2,340) or spliced (n\u0026thinsp;=\u0026thinsp;16). These genes were classified using ACMG/AMP and reclassified by adding the CL60 allele frequencies. Using InterVar, one variant changed from LP to VUS, and 663 from VUS along with 1,547 LB were reclassified as B. When VarSome was used, one P variant changed to VUS, two LP to VUS, 109 VUS to B, 394 LB to B, and 3 VUS did not change classification. Discrepancies were observed between the automatic classifications of InterVar and VarSome, with VarSome classifying more variants as B, causing a greater number of variants to switch using InterVar (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of exonic and splicing variants of the CL60 set with changes in allele frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterVar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified InterVar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVarSome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModified VarSome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathogenic (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikely Pathogenic (LP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain Significance (VUS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikely Benign (LB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot classified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: InterVar: automatic pathogenicity classification by InterVar. Modified InterVar: pathogenicity reclassification of InterVar variants using CL60. VarSome: automatic classification by VarSome. Modified VarSome: reclassification of VarSome using CL60.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMost Chilean patients have variants whose ACMG/AMP pathogenicity classification has changed\u003c/h3\u003e\n\u003cp\u003eA total of 41 clinical cases of Chilean patients with suspected RD, with ages in the range of 1 to 49 years, were included in the study. Twenty-six patients were treated at the Genetics Service at the Clinical Hospital of the University of Chile between 2019 and 2020 (Supplementary Table\u0026nbsp;3), and 15 were from multiple national centers (Fundaci\u0026oacute;n Debra Chile, Fundaci\u0026oacute;n Diagnosis, Hospital Cl\u0026iacute;nico Magallanes, and Hospital Padre Hurtado). Clinical exomes were analyzed using the VarStation platform, considering all genes or those related to the patient\u0026rsquo;s phenotype. In the exome analysis, 26 patients presented with at least one P variant, while all presented LP, VUS, and LB variants that were susceptible to changes in ACMG/AMP classification. In the phenotype-related analysis, 25 cases showed classification changes in their variants. The analysis flow of a clinical case as an individual example is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe use of the reference set managed to change the ACMG/AMP classification to 0.48% of variants in Chilean patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor a global analysis, the number of nonbenign variants in the 41 cases present in the CL60-Reclassified set was calculated. In the 41 cases, 76,848 variants met the Uncommon Variants filter condition (DP\u0026thinsp;\u0026ge;\u0026thinsp;20\u0026times;, FA\u0026thinsp;\u0026le;\u0026thinsp;0.05 in 1000g, ExAC, ESP, and gnomAD). Of these, 2,106 were B, and 74,742 were nonbenign (P: 137; LP: 750; VUS: 62,054; LB: 11,800). In total, 364 variants (P: 2, LP: 2, VUS: 288, LB: 72) changed their pathogenicity classification, all toward benign, resulting in a percentage change of 0.48%. The same analysis considering the gene filters related to phenotype resulted in a percentage change of 0.16% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Variants that changed from P or LP to B in genes related to the patients\u0026rsquo; phenotypes impact the report returned to the treating physician. This occurred in several cases, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Therefore, the revaluation of clinical significance using the CL60 database altered the report that would have been returned to 6 out of 41 patients with a diagnostic test result (14.6%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUncommon variants of 41 cases analyzed with VarStation considering Exome genes or genes related to phenotype\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eExome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRelated to phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChanged using CL60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChanged using CL60\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Benign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTotal variants\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePercentage of change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e76,848\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8,749\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: Exome: exonic or splicing variants. Uncommon variants: DP\u0026thinsp;\u0026ge;\u0026thinsp;20\u0026times;, FA\u0026thinsp;\u0026le;\u0026thinsp;0.05 in all 1000g, ExAC, ESP and gnomAD. Related to phenotype: genes related to HPO of the case. P: Pathogenic. LP: Likely pathogenic. VUS: Variant of uncertain significance. LB: Likely benign.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathogenic and likely pathogenic exome variants that changed their ACMG/AMP pathogenicity classification when using the CL60 set in the 41 patients analyzed with VarStation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCL60 allele frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emax_int_3 allele frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVarStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterVar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModified InterVar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVarSome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModified VarSome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eASB15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_080928:\u003c/p\u003e \u003cp\u003ec.844C\u0026thinsp;\u0026gt;\u0026thinsp;T: p.(Arg282Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExo22_05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZNF544\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_014480:\u003c/p\u003e \u003cp\u003ec.1843C\u0026thinsp;\u0026gt;\u0026thinsp;T: p.(Arg615Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExo22_05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPAP1L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_001282301:\u003c/p\u003e \u003cp\u003ec.262C\u0026thinsp;\u0026gt;\u0026thinsp;T: p.(Gln88Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExo22_06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSCN7A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_002976:\u003c/p\u003e \u003cp\u003ec.2696delA: p.(Asn899fs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExo22_08 ,\u003c/p\u003e \u003cp\u003eExo22_09 ,\u003c/p\u003e \u003cp\u003eExo22_10 ,\u003c/p\u003e \u003cp\u003eExo22_11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: InterVar: automatic pathogenicity classification by InterVar. Modified InterVar: pathogenicity reclassification of InterVar variants using CL60. VarSome: automatic classification by VarSome. Modified VarSome: reclassification of VarSome using CL60. P: Pathogenic. LP: Likely pathogenic. VUS: Variant of uncertain significance. LB: Likely benign. N/A: Classification not available using InterVar.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe clinical interpretation of genetic variants is essential for precision medicine, particularly in the diagnosis of RD. Current guidelines established by the ACMG/AMP depend greatly on population allele frequencies as a criterion for variant classification. Databases such as gnomAD, ExAC, ESP, and 1000g have therefore become fundamental references for the evaluation of pathogenicity. However, these resources are largely composed of individuals of European ancestry, and most Latin American populations, including Chileans, are underrepresented. Therefore, alleles that are rare in global datasets may be common in specific regional populations, leading to potential misclassification and inequities in genetic diagnosis.\u003c/p\u003e \u003cp\u003eDifferent studies have highlighted this limitation. Manrai et al. reported that pathogenic assertions based on frequency data from nonrepresentative populations can lead to false-positive diagnoses (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Subsequent work in hereditary cancer syndromes has demonstrated that the rate of VUS is higher in individuals from non-European ethnic groups and that the dynamics of variant reclassification over time also differ by ancestry (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These findings underscore that the interpretation of variants on the basis of allele frequencies derived from predominantly European datasets can disproportionately affect patients from underrepresented populations.\u003c/p\u003e \u003cp\u003eIn the present study, we analyzed how population differences in allele frequencies influence the clinical interpretation of variants in genes associated with RD in Chilean patients. Using a pilot exomic dataset from 60 Chilean individuals, we compared local allele frequencies with those in international databases and evaluated their effects on the automatic ACMG/AMP classification generated by InterVar and VarSome. We then assessed how these differences modified the genetic diagnosis in 41 Chilean patients with suspected RDs.\u003c/p\u003e \u003cp\u003eOur findings show that population-specific genomic data can substantially modify the clinical interpretation of sequence variants in RD patients. We identified 504 variants whose ACMG/AMP classification changed when local frequencies were considered, resulting in the formation of a set of 2 VUSs and 502 variants classified as benign after re-evaluation (Supplementary Table\u0026nbsp;4). From a clinical perspective, these differences were translated into modified diagnostic interpretations for six out of 41 patients (14.6%). These findings illustrate how the lack of regional frequency data may lead to false pathogenic assertions and, consequently, to misdiagnoses that could affect patient management or genetic counseling. Our results are in line with those of previous reports showing that ancestry-informed analyses can correct a meaningful fraction of misclassified variants. Naslavsky et al. demonstrated that in an elderly admixed Brazilian cohort, the incorporation of local allele frequencies and the application of ACMG/AMP criteria related to population data (BA1, BS1, and PM2) resulted in the reclassification of previously reported pathogenic or likely pathogenic variants, thereby challenging their clinical interpretation in admixed individuals (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Park et al. reported that using allele frequencies from 1,314 Korean exomes as ethnic controls allowed the reclassification of 9 of 36 \u003cem\u003eBRCA1/2\u003c/em\u003e variants by applying BS1 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Similarly, Ağaoğlu et al. reported that in Turkish breast cancer patients, the use of allele frequencies from 3,362 Turkish individuals led to the reclassification of 5 of 75 VUSs (6.7%) in cancer susceptibility genes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Together, these studies reinforce that local allele frequencies are essential for preventing systematic biases in variant interpretation in admixed populations.\u003c/p\u003e \u003cp\u003eOther Latin American studies have described experiences of variant curation and reclassification in hereditary cancer genes but have not used population-specific reference frequency datasets (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). To our knowledge, our work presents the first practical application, in Latin American patients, of a reference dataset of allele frequencies derived from the same population to reclassify variants associated with RD. The Chilean population is characterized by a complex admixture of European, Native American (primarily Mapuche and Aymara) and African ancestry (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). None of these specific Native American components are adequately represented in global reference datasets that are commonly used in clinical genomics. Therefore, the development of a Chilean-specific allele frequency resource addresses a critical gap and provides a framework that could be extended to other underrepresented populations in the region.\u003c/p\u003e \u003cp\u003eThe underrepresentation of many populations, including those from Latin America, in global databases of genomic diversity has been identified as a pervasive source of bias, limiting scientific progress and potentially perpetuating disparities in access to high-quality health care in the postgenomic era (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Our findings suggest that using datasets composed of individuals from populations not included in commonly used reference databases can substantially benefit the interpretation of patient variants. Furthermore, there is a need for more diverse reference datasets as well as for population-level and, where possible, ancestry-stratified resources. Otherwise, relatively common alleles in a particular population may be diluted in large heterogeneous datasets, resulting in missed opportunities to reclassify variants in patients from that population. Beyond technical benefits, these efforts have important ethical dimensions. The systematic underrepresentation of Latin American populations in global databases perpetuates inequities in the postgenomic era, as it limits both the accuracy and the accessibility of precision medicine for patients from these regions. Experiences in other fields have already highlighted how a lack of diversity in genomic resources can translate into unequal diagnostic performance and therapeutic opportunities (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Expanding local and regional genomic resources is therefore both a scientific and a social imperative.\u003c/p\u003e \u003cp\u003eOn the other hand, several limitations in our design should be considered, which preclude us from drawing solid conclusions. First, the CL60 reference dataset is based on pooled sequencing of 60 individuals, which restricts the precision of allele frequency estimation for very-low-frequency variants and precludes the evaluation of individual ancestries or relatedness. The CL60 dataset represents the first Chilean exome-based allele frequency reference specifically applied to clinical variant interpretation. Despite its modest scale, it provides proof of principle for the need for regional genomic data to improve diagnostic accuracy. Second, the dataset includes both RA cases and controls. Although RA is a complex disease with a polygenic architecture, this design could increase the frequency of alleles associated with RA or autoimmune traits. Nonetheless, given that the focus of this work is on monogenic variants related to RD and that the individuals included had no known RD diagnoses, we consider that any potential bias in allele frequencies is unlikely to drive the main conclusions. Future versions of Chilean reference datasets should ideally prioritize individuals without known genetic disorders and include larger and more geographically diverse cohorts. Third, given that sequencing was performed in pools of DNA, we could not perform a formal quantification of ancestry for the individuals in CL60 or for the RD patients. The Chilean population presents a complex admixture pattern, with substantial variability in the proportions of European, Native American and African ancestry across regions (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Consequently, allele frequencies for some variants may vary geographically according to local ancestry proportions. Incorporating ancestry-informed analyses in future studies will be important for refining frequency estimates, defining ancestry-stratified thresholds, and improving interpretation in highly admixed contexts.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, the results of this study provide evidence that population differences in allele frequencies can affect the clinical interpretation of variants in Chilean patients with RD. The incorporation of local genomic data led to the reclassification of hundreds of variants and altered the diagnostic report for approximately one in seven patients analyzed. The inclusion of population-specific allele frequencies can directly improve diagnostic reliability in RDs. Even a small fraction of reclassified variants can have substantial consequences at the individual level. Our findings emphasize the need to systematically incorporate local frequency data into variant interpretation workflows in Chile and other underrepresented regions. Collaborative data-sharing frameworks across Latin America will be essential for increasing the representation of admixed populations in genomic reference databases. This is not only a technical challenge but also an ethical commitment to ensure that the benefits of genomic medicine are distributed equitably across populations.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e1000g\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1000 Genomes Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACMG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican College of Medical Genetics and Genomics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAllele frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAncestry Informative Markers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAssociation for Molecular Pathology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBA1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBenign standalone criterion 1 (ACMG/AMP)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBS1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBenign strong criterion 1 (ACMG/AMP)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCADD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCombined Annotation Dependent Depletion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCL60\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChilean allele frequency dataset generated from 60 individuals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy number variants\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRead depth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExome Variant Server \u0026ndash; NHLBI GO Exome Sequencing Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eExAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExome Aggregation Consortium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGATK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome Analysis Toolkit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003egnomAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome Aggregation Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Phenotype Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eindels\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsertions and deletions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLikely benign\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLikely pathogenic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emax_int_1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum allele frequency across 1000g, ExAC and ESP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emax_int_2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum allele frequency across 1000g, ExAC, ESP and gnomAD v2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emax_int_3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum allele frequency across 1000g, ExAC, ESP and gnomAD v2 and v3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003encRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-coding RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNext-generation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePathogenic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePathogenic moderate criterion 2 (ACMG/AMP)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRare disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRefSeq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReference Sequence database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-nucleotide variants\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUntranslated regions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVCF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariant Call Format\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariant of uncertain significance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole-exome sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole-genome sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ethics Committee of the \u0026ldquo;Servicio de Salud del Maule\u0026rdquo; (registration number 04/2014), Chile, and the ethics committee of the Faculty of Medicine of the University of Chile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants and support from the Fondecyt grant N\u0026ordm; 1220540, Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union (PI22/00804), and GAIN Proyectos de Excelencia (IN607D2022/06).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026acute; CONTRIBUTORS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePAA, RPV and PCS contributed to the conception and design of the study and wrote the main manuscript text. RPV, GLS, MLB, MM, PK and IF contributed to patient recruitment, clinical data collection, phenotypic characterization and interpretation of genetic variants. PAA, PCS and RAV contributed to data analysis and interpretation of results. RDP and RAV supervised the study, provided critical input, and revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRoberto D\u0026iacute;az-Pe\u0026ntilde;a is supported by the Miguel Servet (CP21/00003) contract, funded by the ISCIII and co-funded by the European Union.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStolk P, Willemen MJC, Leufkens HGM. Rare essentials: drugs for rare diseases as essential medicines. Bull World Health Organ. 2006;84(9):745\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodwell C, Aym\u0026eacute; S. Rare disease policies to improve care for patients in Europe. Biochim Biophys Acta. 2015;1852(10 Pt B):2329\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Han J. The definition of rare disease in China and its prospects. Intractable Rare Dis Res. 2022;11(1):29\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2020;28(2):165\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawyer SL, Hartley T, Dyment DA, Beaulieu CL, Schwartzentruber J, Smith A, et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin Genet. 2016;89(3):275\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZastrow DB, Kohler JN, Bonner D, Reuter CM, Fernandez L, Grove ME, et al. A toolkit for genetics providers in follow-up of patients with nondiagnostic exome sequencing. J Genet Couns. 2019;28(2):213\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoller JK. The Challenge of Rare Diseases. Chest. 2018;153(6):1309\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShashi V, McConkie-Rosell A, Rosell B, Schoch K, Vellore K, McDonald M, et al. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders. Genet Med. 2014;16(2):176\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, et al. 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N Engl J Med. 2013;369(16):1502\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Muzny DM, Xia F, Niu Z, Person R, Ding Y, et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA. 2014;312(18):1870\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuer PL, Johnsen JM, Johnson AD, Logsdon BA, Lange LA, Nalls MA, et al. Imputation of exome sequence variants into population- based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project. Am J Hum Genet. 2012;91(5):794\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alf\u0026ouml;ldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, Szolovits P, et al. Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med. 2016;375(7):655\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEyheramendy S, Martinez FI, Manevy F, Vial C, Repetto GM. Genetic structure characterization of Chileans reflects historical immigration patterns. Nat Commun. 2015;6:6472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuentes M, Pulgar I, Gallo C, Bortolini MC, Canizales-Quinteros S, Bedoya G, et al. [Gene geography of Chile: regional distribution of American, European and African genetic contributions]. Rev Med Chil. 2014;142(3):281\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerdugo RA, Di Genova A, Herrera L, Moraga M, Acu\u0026ntilde;a M, Berr\u0026iacute;os S et al. Development of a small panel of SNPs to infer ancestry in Chileans that distinguishes Aymara and Mapuche components. Biological Research. 16 de abril de. 2020;53(1):15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Wang K, InterVar. Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet. 2017;100(2):267\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKopanos C, Tsiolkas V, Kouris A, Chapple CE, Albarca Aguilera M, Meyer R, et al. VarSome: the human genomic variant search engine. Bioinformatics. 2019;35(11):1978\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhn RM, Haussler D, Kent WJ. The UCSC genome browser and associated tools. Brief Bioinform. 2013;14(2):144\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaswell-Jin JL, Gupta T, Hall E, Petrovchich IM, Mills MA, Kingham KE, et al. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. Genet Med. 2018;20(2):234\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlavin TP, Van Tongeren LR, Behrendt CE, Solomon I, Rybak C, Nehoray B, et al. Prospective Study of Cancer Genetic Variants: Variation in Rate of Reclassification by Ancestry. J Natl Cancer Inst. 2018;110(10):1059\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaslavsky MS, Scliar MO, Yamamoto GL, Wang JYT, Zverinova S, Karp T, et al. Whole-genome sequencing of 1,171 elderly admixed individuals from S\u0026atilde;o Paulo, Brazil. Nat Commun. 2022;13(1):1004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JS, Nam EJ, Park HS, Han JW, Lee JY, Kim J, et al. Identification of a Novel BRCA1 Pathogenic Mutation in Korean Patients Following Reclassification of BRCA1 and BRCA2 Variants According to the ACMG Standards and Guidelines Using Relevant Ethnic Controls. Cancer Res Treat. 2017;49(4):1012\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgaoglu NB, Unal B, Hayes CP, Walker M, Ng OH, Doganay L, et al. Genomic disparity impacts variant classification of cancer susceptibility genes in Turkish breast cancer patients. Cancer Med. 2024;13(3):e6852.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManotas MC, Rivera AL, Sanabria-Salas MC. Variant curation and interpretation in hereditary cancer genes: An institutional experience in Latin America. Mol Genet Genomic Med. 2023;11(5):e2141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBustamante CD, Burchard EG, De la Vega FM. Genomics for the world. Nature. 2011;475(7355):163\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az-Pe\u0026ntilde;a R, Adelowo O. Advancing equity in genomic medicine for rheumatology. Nat Rev Rheumatol. 2024;20(10):595\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rare diseases, ACMG/AMP guidelines, Underrepresented populations, Human genetic diversity","lastPublishedDoi":"10.21203/rs.3.rs-8682805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8682805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate interpretation of genetic variants relies heavily on population allele frequencies derived from large international reference databases. However, these resources largely underrepresent Latin American populations, raising concerns about the generalizability of variant interpretation and the potential for diagnostic inequities, particularly in admixed and indigenous populations. Here, we evaluated how population-specific allele frequencies influence the clinical interpretation of variants associated with rare diseases in Chilean patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe generated a pilot exomic allele frequency dataset from 60 Chilean individuals without rare diseases and systematically compared it with major international reference databases. Among the 252,169 variants identified, 73,608 were rare or of low frequency in international datasets, of which 10,417 (14.2%) were common in the Chilean population. In addition, 6,043 variants were absent from all the international databases analyzed. Using Chilean allele frequencies as a population reference in standard variant interpretation workflows led to the reclassification of 364 nonbenign variants toward benignity in a cohort of 41 patients with suspected rare diseases. Importantly, two pathogenic and two likely pathogenic variants were reclassified as benign, modifying the diagnostic interpretation in six patients and directly impacting the clinical reports returned to the treating physicians.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePopulation-specific genomic diversity significantly influences the clinical interpretation of exome sequencing data. The systematic underrepresentation of Latin American populations in global reference databases can negatively affect diagnostic accuracy and equity in genomic medicine. Our results demonstrate that even pilot population-specific datasets can substantially improve variant classification and support more accurate and equitable genetic diagnoses in underrepresented populations.\u003c/p\u003e","manuscriptTitle":"Population differences in allele frequencies modify the clinical interpretation of genetic variants associated with rare diseases in Chilean patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:11:00","doi":"10.21203/rs.3.rs-8682805/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c07a60ee-58fd-48e0-b0de-7f4776709c9c","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T16:11:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:11:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8682805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8682805","identity":"rs-8682805","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}