Genetic Landscape of Familial Hypercholesterolemia in Southern India: Novel Mutations and Clinical Implications

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This study aimed to identify genetic variants associated with FH in patients from Telangana State, India. Methods: Probands with suspected FH were identified using the Dutch Lipid Clinic Network (DLCN) score, followed by cascade screening of their first-degree relatives. Targeted exome sequencing and pedigree analysis were performed to identify FH-associated genetic variants . Results: We identified both novel and known high-impact mutations in genes implicated in FH pathogenesis, including stop-gain mutations in LPL and LDLR, as well as splice donor site mutations in SLCO1B1 and CETP. Notably, a novel frameshift mutation in LDLR was identified in two siblings, one of whom exhibited a homozygous variant and met the "Definite FH" classification based on the DLCN criteria. Additionally, moderate-impact variants rs2075291 (APOA5) and rs193922571 (LDLR) showed strong correlations with the DLCN score, suggesting an increased susceptibility to FH. In contrast, rs6756629 (ABCG5) and rs11887534 (ABCG8) were strongly negatively correlated with LDL-C levels and the DLCN score, indicating potential protective effects against FH. Conclusions: These findings highlight the genetic heterogeneity of FH and emphasize the importance of identifying novel pathogenic variants. Moreover, the study underscores the role of moderate-impact variants in FH susceptibility. Overall, this research enhances our understanding of the genetic landscape of FH in the Indian population, with implications for improved diagnosis, risk assessment, and personalized management. Dutch Lipid Clinic Network Criteria Exome Sequencing Familial hypercholesterolemia Next-Generation Sequencing Single Nucleotide Polymorphisms Figures Figure 1 Figure 2 Background Familial hypercholesterolemia (FH) is primarily an autosomal dominant disorder of cholesterol metabolism characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels from an early age, predisposing individuals to premature atherosclerotic cardiovascular disease (Alonso et al., 2013). Early diagnosis and intervention are critical for mitigating FH-associated comorbidities and reducing cardiovascular mortality. The identification of an individual with FH often leads to the diagnosis of other first-degree relatives at risk, creating a positive cascade effect. Despite the widespread availability of cholesterol testing, FH remains largely underdiagnosed in the general Indian population, with many patients being diagnosed only at the time of hospitalization (Kalra et al., 2016). It is estimated that more than 95% of individuals with FH worldwide remain undiagnosed and untreated. Familial hypercholesterolemia (FH) is a common genetic disorder that affects approximately 1 in 311 individuals globally. Its prevalence is similar across different regions and is notably higher in people with atherosclerotic cardiovascular disease (ASCVD) (Hu et al., 2020). India bears a substantial burden of cardiovascular disease (CVD)-related deaths, accounting for one-fifth of the global total, particularly among the younger population (Murray et al., 1996). A global burden of disease study revealed an alarming age-standardized CVD death rate of 272 per 100,000 people in India, surpassing the global average of 235 (Kumar et al., 2020). Most FH cases are attributed to defects in the LDL receptor ( LDLR ) or apolipoprotein B-100 (APOB) genes, whereas gain-of-function variants in the PCSK9 gene leading to its overproduction are less common (Soutar et al., 2007). Additional variants in genes such as APOE have been sporadically linked to the FH phenotype (Santos et al., 2017). Homozygous FH can also arise from variants in the LDLRAP1 gene associated with an autosomal recessive form of the disease (Alnouri et al., 2018). The severity of FH is influenced by the type of LDLR gene variant, with "null variants" resulting in severely reduced LDL receptor activity and a more severe phenotype, whereas "defective variants" retain some receptor function (Vrablik et al., 2020). Pathogenic variants in the LDLR gene are the most prevalent cause of FH, exhibiting a wide spectrum of variations across populations. In addition to genetics, nongenetic factors such as age, sex, and lifestyle also influence LDL-C levels. Studies have shown that females with FH tend to have higher levels of total cholesterol, LDL-C, and HDL-C than males do, although both sexes present lower HDL-C levels than individuals without FH do (Schaefer et al., 1994). The average age of onset of coronary symptoms is delayed in females (55 years) compared with males (48 years) (Hill et al., 1991). In men, lower HDL-C levels and a history of smoking are associated with an increased risk of developing coronary artery disease (CAD). The prevalence of FH is estimated to be 1 in 250 individuals worldwide (Akioyamen et al., 2017), but its identification and management remain suboptimal in many regions, particularly in developing countries. Early diagnosis and treatment are crucial for reducing the risk of cardiovascular complications associated with FH, such as premature coronary artery disease, myocardial infarction, and stroke (Nordestgaard et al., 2013). Recent advancements include the development of a 12-SNP LDL-C "SNP score" on the basis of common variants associated with elevated LDL-C levels (Futema et al., 2018). This score has been validated in various populations and shows promise in identifying individuals with FH who lack identifiable mutations. In India, limited data are available on the genetic epidemiology and mutational spectrum of FH. In this work, we characterize the genetic variants associated with FH in a people from Telangana state, India. By combining targeted exome sequencing and detailed pedigree analysis, we identified novel and known high-impact mutations in genes implicated in FH pathogenesis. The identification of disease-causing mutations and the elucidation of familial inheritance patterns are crucial steps toward improving the diagnosis, risk stratification, and personalized management of FH in the Indian population. Furthermore, this study contributes to the growing body of knowledge on the genetic architecture of FH across diverse ethnic groups, facilitating a deeper understanding of the etiology of FH and potential therapeutic targets. The development of prognostic tools to detect FH in the population, especially in newborns and children, represents an unprecedented opportunity to initiate early treatment and reduce the burden of cardiovascular disease associated with FH. Methods Study Design and Participant Recruitment This study employed a cross-sectional study design, utilizing targeted exome sequencing and pedigree analysis of patients visiting AIIMS Bibinagar and healthcare centers in the Yadadri Bhuvanagiri district, Telangana, India. Eligibility criteria included asymptomatic children and adolescents (0–20 years old) exhibiting an LDL cholesterol level exceeding 160 mg/dL or having a first-degree relative with elevated cholesterol and premature coronary heart disease (men <50 years, women <60 years). Clinical features such as tendon xanthomas at any age, arcus corneae younger than 55 years, or xanthelasma younger than 25 years were also considered. Individuals with a history of diabetes mellitus, hypothyroidism, obesity, alcohol use, smoking, or metabolic syndrome were excluded. Proband Identification and Familial Screening Patients were prioritized on the basis of the Dutch Lipid Clinic Network score (DLCNC) for familial hypercholesterolemia, without considering genetic mutations (Tables 1-3) (Casula et al., 2018). A positive diagnosis according to any one of the criteria established a patient as a probable proband. Upon proband identification, informed consent was obtained for the screening of their first-degree relatives (parents, siblings, and children). Cascade screening A cascade screening approach was implemented to systematically identify individuals at risk for familial hypercholesterolemia (FH) within the families of identified probands. This strategy involved the sequential screening of first-degree relatives (parents, siblings, and children) of each proband diagnosed with FH. Upon identification of a new FH case among these relatives, the cascade screening process was reiterated, with the newly diagnosed individual becoming a proband for subsequent screening of their first-degree relatives. This iterative process aims to maximize the detection of at-risk individuals within families affected by FH. Sample collection, library preparation, and sequencing Peripheral blood samples from patients with familial hypercholesterolemia were collected in EDTA tubes and stored at -80°C. DNA was extracted, quantified, and assessed for purity and integrity. Thirty samples that passed quality control were processed to create whole-genome libraries via the Twist Library Preparation EF Kit, followed by exome capture. The enriched libraries were subjected to target enrichment via the Twist Comprehensive Exome Panel. The final libraries were quality checked, quantified, pooled, diluted, and sequenced on the Illumina NovaSeq 6000 system, generating 150 bp paired-end reads. Exome Sequence Analysis and Variant Annotation Fastp software (Chen et al., 2018) was used to process paired-end raw FASTQ files as part of a quality control and cleaning pipeline in next-generation sequencing (NGS) analysis. This included the automatic detection and trimming of adapter sequences often introduced during library preparation. To ensure high-quality data for downstream analysis, fastp filtered out reads falling below a specified quality threshold or containing a high percentage of low-quality bases. Paired-end reads in FASTQ format, previously cleaned and quality controlled via fastp, were aligned to the human reference genome, GRCh38.p14 (https://ftp.ncbi.nlm.nih.gov/genomes/), via the Burrows–Wheeler Aligner (BWA-MEM) algorithm (Li et al., 2013). The Picard tool (https://broadinstitute.github.io/picard/) AddOrReplaceReadGroups was used to modify the SAM file, adding read group information essential for downstream analysis tools. These tags provide crucial details about the sequencing data's origin and processing, aiding in quality control, alignment analysis, and variant calling. The command samtools sort (Li et al., 2009) sorts the BAM file by genomic coordinates, a critical step for many downstream analyses that require sorted input data for efficient processing, such as variant calling and visualization. The Picard tool MarkDuplicates was then employed to identify and mark potential PCR duplicates within the sorted BAM file. PCR duplicates, which arise during library preparation, can skew downstream analyses. The tool flags duplicate reads in the output BAM file and generates a metric file with statistics at the level of duplication. The human reference genome (GRCh38.p14) and a known variant file (VCF) were indexed via ‘samtools faidx’ and GATK's IndexFeatureFile (Van et al., 2013) to optimize data access and processing efficiency during variant calling. The Genome Analysis Toolkit's (GATK) BaseRecalibrator tool generated a base quality score recalibration (BQSR) table for the aligned sequencing data. This crucial step corrects systematic errors in base quality scores that can arise from sequencing technologies and library preparation, improving downstream variant calling accuracy. The recalibration table, which is specific to the input dataset, was created by referencing the human genome (GRCh38.p14) and incorporating information from a known variant file (VCF). This process ensures that base quality scores more accurately reflect the true probability of sequencing errors, enhancing the reliability of subsequent analyses. The GATK tool ApplyBQSR then recalibrated the base quality scores in the aligned and duplicate-marked BAM files. By utilizing the BQSR table and the human reference genome (GRCh38.p14), the base quality scores in the BAM file were adjusted to reflect the true probability of sequencing errors more accurately, improving the accuracy and reliability of downstream variant calling and analysis. The GATK tool HaplotypeCaller performs variant calling on the recalibrated BAM file, utilizing the human reference genome (GRCh38.p14). This process involved local reassembly of haplotypes and identification of potential variants in the dataset. The GATK tool GenotypeGVCFs then performs joint genotyping on the genomic VCF (gVCF) file produced by HaplotypeCaller. The human reference genome (GRCh38.p14) was used to consolidate genotype likelihoods across all genomic positions, resulting in a final VCF file with high-confidence variant calls. Compared with analyzing individual samples independently, this approach enhances variant detection accuracy and sensitivity. The GATK tool VariantFiltration was then applied to filter out potentially low-quality variant calls from the VCF file. Using the human reference genome (GRCh38.p14), variants were assessed on the basis of specific quality metrics, and filters were applied to remove variants not meeting quality thresholds. The BCFtools reheader command updated the header information in each original VCF file to reflect the sample source. Then, the BCFtools index command is used to index each VCF file for efficient random access to specific variants. Finally, the BCFtools merge (Danecek et al., 2017) command was used to combine the final VCF files of all samples into a single merged and compressed VCF file. This merged file was then indexed via GATK's IndexFeatureFile for efficient downstream analysis. Variant annotation The SnpEff variant annotation tool (Cingolani et al., 2012) was employed to build a database for the reference genome GCF_000001405.40. Subsequently, SnpEff annotated variants via the GRCh38.p14 reference genome, predicting their functional effects on genes, transcripts, and protein sequences. This annotation process provided valuable insights into the potential functional consequences of variants within the context of coding regions and protein structures. Furthermore, missense mutations with a moderate impact on protein function, as identified by SnpEff, were filtered via the point-accepted mutation or percent Accepted mutation (PAM250) score, which was calculated via the Biostrings R package (Pages et al., 2020). The scores reflect the likelihood of one amino acid being replaced by another during evolution. Higher scores indicate more probable substitutions, whereas lower scores indicate less likely substitutions. Only missense mutations with a PAM250 score less than one were retained. These missense mutations were also annotated via data from the SIFT (Vaser et al., 2016), PolyPhen-2 (Adzhubei et al., 2013), and ClinVar (Landrum et al., 2016) databases through the SNPnexus (Oscanoa et al., 2020). Results Patient Demographics and Clinical Characteristics The present study aimed to discover novel genetic variants associated with familial hypercholesterolemia (FH) in patients from Telangana, India. The study population comprised 30 patients from 15 families, with a mean age of 39 years (SD = 16). The gender distribution was skewed toward males, with 57% of the participants being male and 43% being female (Table 1 and Table S1). Table 1: Summary statistics of the demographics and clinical characteristics of patients Characteristic Value (n = 30) Age (years) Mean(SD): 39± 16 Sex Percent: M (57); F (43) Total cholesterol (mg/dL) Mean(SD): 280 ± 130 LDL-C (mg/dL) Mean(SD): 238± 135 Corneal Arcus 30% Tendon Xanthomas 20% Family history of CAD 93% The study population (n=30) had a mean age of 39 years (SD = 16), with 57% being male and 43% being female. The mean total cholesterol was 280 mg/dL (SD = 130), and the mean LDL-C was 238 mg/dL (SD = 135). A corneal arcus was present in 30% of the population, tendon xanthomas were present in 20%, and 93% reported a family history of coronary artery disease. Lipid profile analysis revealed significantly elevated levels of cholesterol in the study patients. The mean total cholesterol (TC) level was 280 mg/dL (SD = 130), which is well above the desirable range for cardiovascular health. Similarly, the mean low-density lipoprotein cholesterol (LDL-C) level was 238 mg/dL (SD = 135), indicating a substantial burden of atherogenic lipoproteins. Clinical examination of the participants revealed the presence of characteristic signs associated with FH. A corneal arcus, a gray or white opaque ring around the cornea due to cholesterol deposition, was observed in 30% of the participants. Additionally, 20% of the participants presented with tendon xanthomas, which are localized deposits of cholesterol in tendons and are most commonly observed in the Achilles tendon. Notably, a significant proportion (93%) of the patients reported a family history of coronary artery disease (FHx CAD), underscoring the genetic predisposition and familial nature of FH. This finding highlights the importance of comprehensive pedigree analysis in the diagnosis and management of FH. The participants were further classified on the basis of the Dutch Lipid Clinic Network Criteria (DLCNC), a widely accepted tool for diagnosing FH. The DLCNC scores categorize individuals into three groups: possible FH (DLNC > 3), probable FH (DLNC > 5), and definite FH (DLNC > 3). This classification system takes into account various factors, including lipid levels, physical signs and family history, to determine the likelihood of FH. As expected, LDL cholesterol levels were significantly higher in individuals with definite FH than in those with probable FH (p < 1e-05) (Figure 1). Similarly, total cholesterol levels were significantly different between the two groups, with definite FH cases exhibiting higher levels than probable FH cases (p < 4.6--e04). These findings suggest a strong correlation between FH severity, as determined by DLCNC scores, and elevated lipid levels. Interestingly, we found no significant difference in age distribution between the Probable and Definite FH groups (p = 0.16). These findings suggest that age may not be a primary factor in differentiating between these two groups. Our findings imply that genetic factors play a more substantial role in determining the severity of FH, as evidenced by the distinction between Probable and Definite FH. For all the comparisons, we employed nonparametric statistical methods (Mann–Whitney U test) to account for the potential nonnormal distribution of the data. The box plots with overlaid individual data points in our visualization (Figure 1) provide a comprehensive view of the data distribution, clearly illustrating the differences in lipid levels between the Probable and Definite FH groups, as well as the similarity in age distribution. These results support the validity of the DLCNC scoring system in differentiating between probable and definite FH on the basis of lipid profiles and highlight that age may not be a critical factor in this classification. This information may prove valuable for clinicians in understanding the relationships among FH severity, lipid levels, and patient age in the context of diagnosis and treatment planning. Pedigree Information for the Study Participants Detailed pedigree information was collected for each of the 30 study participants, spanning 15 families. Each individual was identified by their unique family ID (FAM ID) and patient ID (PID). The sex of each participant was recorded (1 denoting male, 2 denoting female), along with their Dutch Lipid Clinic Network Criteria (DLCNC) category (Table S2). The pedigree data included essential familial relationships, such as paternal ID (father), maternal ID (mother), and sibling IDs. This comprehensive information enabled us to construct detailed family trees and trace the inheritance patterns of FH within each family. By analyzing these pedigrees, we observed diverse patterns of FH transmission, including autosomal dominant inheritance, where the condition is passed down from one affected parent to their offspring, as well as sporadic cases where individuals developed FH without a clear family history. Elucidating these familial patterns through detailed pedigree analysis is crucial for understanding the genetic basis of FH and for providing personalized risk assessments and genetic counseling to affected families. By identifying individuals at high risk of FH owing to their family history, early interventions such as lifestyle modifications and lipid-lowering therapies can be implemented to prevent or delay the onset of cardiovascular complications. Moreover, the identification of specific genetic mutations responsible for FH in certain families can facilitate targeted screening and treatment strategies. High-impact variants identified in FH-associated genes High-impact variants cause major disruptions to genes and proteins, often rendering them nonfunctional or severely altered. These variants can have a range of consequences, including the complete loss of gene function, the production of shortened proteins, or disruptions in the protein production process. For example, frameshift variants alter the reading frame of a gene, leading to a drastically different amino acid sequence. Nonsense variants introduce premature stop signals, resulting in the formation of truncated proteins. Splice site variants disrupt RNA processing, potentially causing errors in protein production. Pedigree analysis revealed that the high-impact mutations identified in this study were distributed across multiple families, as detailed in Table 2. Targeted exome sequencing of the study population further revealed a range of high-impact genetic variants in genes previously associated with familial hypercholesterolemia (FH), as summarized in Table 3. Among the 5 high-impact mutations, four with different levels of clinical significance have already been reported in the dbSNP database (Sherry et al., 2001). Table 2: Pedigree information for familial hypercholesterolemia (FH) patients with high-impact mutations Family ID Patient ID Pat. ID Mat. ID Sibling ID Sex DLCNC Cat FAM01 F10 0 F9 F11 1 2 FAM02 F12 0 0 0 2 3 FAM03 F14 0 0 0 2 2 FAM05 F17 0 F18 0 1 2 FAM07 F1 F3 F2 0 1 3 FAM10 F23 0 0 0 1 2 FAM11 F24 0 0 0 1 2 FAM12 F25 0 0 0 1 2 FAM13 F26 0 F27 F28 1 2 FAM13 F27 0 0 0 2 3 FAM13 F28 0 F27 F26 2 3 FAM07 F2 F4 0 0 2 3 FAM07 F3 0 0 0 1 3 FAM07 F4 0 0 0 1 3 FAM14 F5 0 0 F6 1 3 FAM14 F6 0 0 F5 1 2 Each patient is identified by their family ID and patient ID, along with their sex (1: male, 2: female) and Dutch Lipid Clinic Network Criteria (DLCNC) category (1: possible, 2: probable, 3: definite FH). Relationships within families are indicated by paternal ID, maternal ID, and sibling IDs, elucidating the familial patterns of hypercholesterolemia within the study population. Table 3: High-impact variants identified in FH-associated genes. Family_ID Patient ID DLCNC Cat LPL (rs328) SLCO1B1 (rs571639279) CETP (rs2142001776) LDLR (rs769737896) LDLR (Novel) FAM01 F10 2 ./. G/A ./. ./. ./. FAM02 F12 3 C/G ./. ./. ./. ./. FAM03 F14 2 C/G ./. ./. ./. ./. FAM05 F17 2 C/G ./. ./. ./. ./. FAM07 F1 3 ./. ./. ./. T/T ./. FAM10 F23 2 ./. ./. T/C ./. ./. FAM11 F24 2 ./. ./. T/C ./. ./. FAM12 F25 2 ./. ./. T/C ./. ./. FAM13 F26 2 C/G ./. ./. ./. ./. FAM13 F27 3 C/G ./. ./. ./. ./. FAM13 F28 3 C/G ./. ./. ./. ./. FAM07 F2 3 ./. ./. ./. T/T ./. FAM07 F3 3 ./. ./. ./. C/T ./. FAM07 F4 3 ./. ./. ./. C/T ./. FAM14 F5 3 ./. ./. ./. ./. A/A FAM14 F6 2 ./. ./. ./. ./. AC/A Stop-gain mutations were identified in LPL (rs328) and LDLR (rs769737896), while splice donor site mutations were found in SLCO1B1 (rs571639279) and CETP (rs2142001776). A novel LDLR frameshift mutation (10-11129663-AC/A and 10-11129663-A/A) was detected in siblings F5 and F6 from FAM14, with F5 carrying a homozygous variant and classified as "Definite FH" per the Dutch Lipid Clinic Network Criteria (DLCNC). The rs328 (LPL) variant is benign, rs769737896 (LDLR) is unreported in ClinVar, and rs571639279 (SLCO1B1) is of uncertain significance. Additionally, rs769737896 (LDLR) was observed in FAM07, with heterozygous and homozygous stop-gain mutations. Stop-gain mutations: Notably, we identified stop-gain mutations in two key genes involved in lipid metabolism, lipoprotein lipase ( LPL , rs328, chr 8-19962213-C-G) and low-density lipoprotein receptor ( LDLR ) with heterozygous (rs769737896, chr 19-11110759-C-T) and homozygous stop gain mutations (chr 19-11110759-T-T) within the same family, FAM07. The variant rs328 is classified as benign, and another variant, rs769737896 (chr 19-11110759-C-T), resulting in p.Arg350Ter, is considered pathogenic in ClinVar (Landrum et al., 2016). The FinnGen database reports a significant association between the variant chr 8-19962213-C-G and various lipoprotein metabolism-related phenotypes, including statin medication usage, disorders of lipoprotein metabolism and other lipidemias, mixed hyperlipidemia, and pure hypercholesterolemia (https://r10.finngen.fi/). Stop-gain mutations introduce premature stop codons into gene sequences, resulting in the production of truncated proteins that are often nonfunctional or exhibit significantly reduced activity. In the context of the LPL and LDLR genes, LPL (lipoprotein lipase) is crucial for hydrolyzing triglycerides within lipoproteins, and its dysfunction can lead to elevated triglyceride levels, potentially contributing to hypercholesterolemia. Moreover, LDLR (low-density lipoprotein receptor) is essential for the cellular uptake of LDL cholesterol, and its dysfunction results in elevated LDL-C levels in the bloodstream, a hallmark of familial hypercholesterolemia. Splice Donor Site Mutations: In addition to stop-gain mutations, we observed splice donor site mutations in two other genes, SLCO1B1 (rs571639279, chr 12-21141659-G-A) and cholesteryl ester transfer protein ( CETP , rs2142001776, chr 16-56975153-T-C). The variant chr 12-21141659-G-A is classified as a variant of uncertain significance, and another variant, chr 16-56975153-T-C, is classified as likely pathogenic and has been associated with hyperalphalipoproteinemia 1 in the ClinVar database. Splice donor site mutations disrupt the normal splicing process of pre-mRNAs, often leading to the production of aberrant mRNA transcripts and potentially altered protein function. The SLCO1B1 gene encodes a protein called organic anion transporting polypeptide 1B1 (OATP1B1). OATP1B1 is a transmembrane receptor found in liver cells that transports compounds such as bilirubins and drugs from the blood into the liver for removal from the body. CETP is involved in the transfer of cholesterol esters between lipoproteins, and mutations in this gene can affect HDL cholesterol levels and overall cholesterol efflux capacity. Novel Frameshift Mutation in LDLR : A key finding in our analysis was a novel frameshift mutation in the LDLR gene in two siblings (F5 and F6) from family FAM14. This mutation, characterized by a nucleotide deletion in chromosome 10 at position 11129663, was present in a heterozygous state (chr 10-11129663-AC/A) in F6 and a homozygous state (A/A) in F5. The presence of the homozygous mutation in F5 resulted in a "Definite FH" classification according to the Dutch Lipid Clinic Network Criteria (DLCNC), underscoring the significant impact of this mutation on cholesterol metabolism and disease severity. The identification of this novel frameshift mutation expands the known mutational landscape of LDLR in FH and contributes to our understanding of the genetic heterogeneity of this disorder. Further investigation into the functional consequences of this mutation will be crucial to determine its precise impact on LDLR function and cholesterol metabolism. Moderately Impact Variants Identified in FH-Associated Genes Moderate impact variants are less likely to completely disrupt a gene or protein, but they can still lead to changes in function or effectiveness. For example, missense variants alter the amino acid sequence of a protein, whereas in-frame indels add or remove amino acids without shifting the reading frame. The specific impact of these changes can vary depending on the amino acid affected and its location within the protein. Since it is challenging to assess the functional impact of moderate-effect variants directly on proteins, unlike high-impact variants, we conducted a correlation analysis between moderate-effect variants and various clinical phenotypes, including age, LDL, DLCNC score and total cholesterol levels across the 30 individuals. Figure 2 presents a comprehensive analysis of the relationships between genetic markers and clinical phenotypes. We used Spearman rank correlation analysis to examine associations between genotype (SNP) data and key phenotypic variables. The p value significance of the correlations is provided in each cell of the heatmap. The SNPs rs2075291 (chr 11-116790675-C-A, APOA5 ) and rs193922571 (chr 10-11105268-G-A, LDLR ) showed strong correlations with the DLCNC score, suggesting that these variants may increase susceptibility to conditions related to elevated cholesterol, such as familial hypercholesterolemia. Conversely, the variants rs6756629 (chr 2-43837950-G-A, ABCG5 ) and rs11887534 (chr 2-43839107-G-C, ABCG8 ) exhibited strong negative correlations with LDL and DLCNC, indicating that these variants could be protective, potentially contributing to lower cholesterol levels and reduced FH risk. Further literature review revealed that the minor allele (A) of rs6756629 (chr 2-43837950-G-A, ABCG5 ) is associated with decreased LDL levels, increased HDL cholesterol levels, and decreased triglyceride levels (Cariaso et al., 2012). In contrast, the G allele is associated with a lower risk of gallstone disease (cholelithiasis) (Buch et al., 2017). Additionally, the C/C and GC variants of rs11887534 (chr 2-43839107-G-C, ABCG8 ) have been linked to an increased risk of gallstone disease in Egyptian patients (Aly et al., 2023), particularly among women who use hormones for various gynecological purposes (Keng-Wei et al., 2021). To assess the impact of these mutations on protein function, we employed the PAM250 matrix, SIFT, PolyPhen-2 software, and data from the ClinVar database, as described in the methods section. The variant rs2075291 (chr 11-116790675-C-A, APOA5 ) was classified as benign or deleterious with low confidence and considered a risk factor, whereas rs193922571 (chr 10-11105268-G-A in LDLR ) was likely pathogenic, probably damaging, and deleterious. In contrast, rs6756629 was considered benign, with low-confidence deleterious annotations, and likely benign, whereas rs11887534 was classified as possibly damaging or deleterious with low confidence, although it was also considered benign or likely benign and a risk factor. The moderate-impact mutation at position 11105268 on chromosome 19 (rs193922571) in the LDLR gene (c.362G>A, p.Cys121Tyr) was predicted to be damaging by SIFT software and probably damaging by PolyPhen-2. It was also classified as likely pathogenic in the ClinVar database. Patients F7 and F8 from family FAM15 carried this mutation and had a Dutch Lipid Clinic Network Criteria (DLCNC) score of 3, suggesting a likely genetic cause of familial hypercholesterolemia (FH) in these patients. Both patients were heterozygous (G/A), indicating that they may have had heterozygous autosomal dominant familial hypercholesterolemia. Discussion This study provides valuable insights into the genetic landscape of familial hypercholesterolemia through targeted exome sequencing and pedigree analysis of 30 patients from 15 families. We identified a novel frameshift mutation in the LDLR gene in two siblings, with one exhibiting a homozygous variant (chr 10-11129663-A/A) and a "Definite FH" classification according to the Dutch Lipid Clinic Network Criteria (DLCNC), underscoring its severe impact on cholesterol metabolism and highlighting the importance of identifying novel pathogenic variants. In addition to these novel findings, we also observed previously reported high-impact mutations, including stop-gain mutations in LPL (rs328, chr 8-19962213-C-G) and LDLR (rs769737896, chr 19-11110759-C-T and rs769737896, chr 19-11110759-T-T) and splice donor site mutations in SLCO1B1 (rs571639279, chr 12-21141659-G-A) and CETP (rs2142001776, chr 16-56975153-T-C), further emphasizing the genetic heterogeneity of FH. While some of these variants are classified as benign or of uncertain significance, their co-occurrence with FH phenotypes necessitates further investigation into their potential roles in disease pathogenesis. Additionally, the moderate-impact variants identified in this study, such as the missense mutation c.362G>A (p.Cys121Tyr, rs193922571, chr 10-11105268-G-A) in LDLR , were predicted to be damaging by in silico tools such as SIFT and PolyPhen-2 and were classified as likely pathogenic in ClinVar. Overall, these findings highlight the potential contribution of such variants to the FH phenotype, either independently or in combination with other genetic or environmental factors. Our comprehensive pedigree analysis enabled us to trace familial inheritance patterns and identify individuals at high risk of developing FH. This information is crucial for implementing early interventions such as lifestyle modifications and lipid-lowering therapies to prevent or delay cardiovascular complications. Moreover, the identification of specific pathogenic mutations within families can guide targeted screening and treatment strategies, such as more aggressive interventions for individuals with novel LDLR frameshift mutations. This study provides valuable insights into the genetic variants associated with familial hypercholesterolemia (FH) in a a patients from Telangana, India; however, several limitations must be considered. The small sample size of 30 individuals from 15 families limits the generalizability of the findings. The study's focus on a specific regional population restricts its applicability to broader ethnic groups, and potential selection bias may have influenced the severity of FH observed. Addressing these limitations in future research through larger, multi-ethnic cohorts, functional validation studies, and longitudinal designs will improve our understanding of FH genetics and its clinical implications. To conclude, this study provides a overview of the genetic variants associated with FH in a small sample of patients from Telangana, India. The identification of novel and known high-impact mutations, coupled with detailed pedigree analysis, enhances our understanding of the genetic landscape of FH and has significant implications for improved diagnosis, risk stratification, and personalized management of this disorder. Abbreviations Familial hypercholesterolemia (FH), Dutch Lipid Clinic Network Criteria (DLCNC), low-density lipoprotein cholesterol (LDL-C), cardiovascular disease (CVD). Declarations Data Availability The targeted exome sequence data of familial hypercholesterolemia patients, along with phenotypic information, are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1232422. This dataset includes raw sequencing reads and associated phenotypic data, which can be accessed through the NCBI BioProject database at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1232422. Ethics approval The study is approved by AIIMS Bibinagar ethics committee. The research was conducted in accordance with the Declaration of Helsinki. Human Ethics and Consent to Participate Informed consent was obtained from all patients prior to their participation in the study. For any participant under the age of 16, informed consent was obtained from their parents or legal guardians. Consent for Publication All participants/patients provided written informed consent for their clinical and demographic information to be published. For any participant under the age of 16, informed consent was obtained from a parent or legal guardian. Competing interests The authors declare that they have no competing interests Funding This is an intramural funded research project. Financial support was provided by AIIMS Bibinagar Hyderabad (AIIMS BBN/16/2021). Acknowledgments We acknowledge the BRAHM: High-Performance Computational facility of the Indian Biological Data Centre, Regional Centre for Biotechnology, Faridabad, INDIA (https://ibdc.rcb.res.in/; DBT Grant no. BT/TCB/IBDC/2019) to carry out the NGS data analysis and pathway enrichment analysis. Authors' contributions S.G. and S.Y. designed the study, performed data analysis. S.G., A.K. and S.Y. prepared the manuscript. S.V. identified FH patients and conducted clinical phenotyping, while S.G. led phenotype data collection, with patient contact primarily managed by S.G. S.G. and R.S. handled lipid profiling and blood collection. Genome sequencing coordination was managed by S.Y., R.S., and S.G. References Alonso R, Mata P, Zambón D, Mata N, Fuentes-Jiménez F. Early diagnosis and treatment of familial hypercholesterolemia: improving patient outcomes. Expert review of cardiovascular therapy. 2013 Mar 1;11(3):327-42. Kalra S, Sawhney JP, Sahay R. The Draupadi of dyslipidemia: familial hypercholesterolemia. Indian journal of endocrinology and metabolism. 2016 May;20(3):285. Hu P, Dharmayat KI, Stevens CA, Sharabiani MT, Jones RS, Watts GF, Genest J, Ray KK, Vallejo-Vaz AJ. Prevalence of familial hypercholesterolemia among the general population and patients with atherosclerotic cardiovascular disease: a systematic review and meta-analysis. Circulation. 2020 Jun 2;141(22):1742-59. Murray CJ, Lopez AD, World Health Organization. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: summary. World Health Organization; 1996. Kumar AS, Sinha N. Cardiovascular disease in India: A 360 degree overview. Medical Journal, Armed Forces India. 2020 Jan;76(1):1. Soutar AK, Naoumova RP. Mechanisms of disease: genetic causes of familial hypercholesterolemia. Nature clinical practice Cardiovascular medicine. 2007 Apr;4(4):214-25. Santos RD, Bourbon M, Alonso R, Cuevas A, Vasques-Cardenas NA, Pereira AC, Merchan A, Alves AC, Medeiros AM, Jannes CE, Krieger JE. Clinical and molecular aspects of familial hypercholesterolemia in Ibero-American countries. Journal of clinical lipidology. 2017 Jan 1;11(1):160-6. Alnouri F, Athar M, Al-Allaf FA, Abduljaleel Z, Taher MM, Bouazzaoui A, Al Ammari D, Karrar H, Albabtain M. 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Estimating the prevalence of heterozygous familial hypercholesterolaemia: a systematic review and meta-analysis. BMJ open. 2017 Sep 1;7(9):e016461. Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, Wiklund O, Hegele RA, Raal FJ, Defesche JC, Wiegman A. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. European heart journal. 2013 Dec 1;34(45):3478-90. Futema M, Bourbon M, Williams M, Humphries SE. Clinical utility of the polygenic LDL-C SNP score in familial hypercholesterolemia. Atherosclerosis. 2018 Oct 1;277:457-63. Casula M, Olmastroni E, Pirillo A, Catapano AL, Arca M, Averna M, Bertolini S, Calandra S, Tarugi P, Pellegatta F, Angelico F. Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study. Atherosclerosis. 2018 Oct 1;277:413-8. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultrafast all-in-one FASTQ preprocessor. Bioinformatics. 2018 Sep 1;34(17):i884-90. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. 2013 Mar 16. “Picard Toolkit.” 2019. Broad Institute, GitHub Repository. https://broadinstitute.github.io/picard/; Broad Institute. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup. The sequence alignment/map format and SAMtools. bioinformatics. 2009 Aug 15;25(16):2078-9. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy‐Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E. From FastQ data to high‐confidence variant calls: the genome analysis toolkit best practices pipeline. Current protocols in bioinformatics. 2013 Oct;43(1):11-0. Danecek P, McCarthy SA. BCFtools/csq: haplotype-aware variant consequences. Bioinformatics. 2017 Jul 1;33(13):2037-9. Cingolani P. Variant annotation and functional prediction: SnpEff. InVariant Calling: Methods and Protocols 2012 Feb 24 (pp. 289-314). New York, NY: Springer US. Pages H, Aboyoun P, Gentleman R, DebRoy S, Pages MH, DataImport D, BSgenome S, XStringSet-class R, MaskedXString-class R, XStringSet-io R. Package ‘Biostrings’. Bioconductor. 2013 Oct 9;18129. Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC. SIFT missense predictions for genomes. Nature protocols. 2016 Jan;11(1):1-9. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen‐2. Current protocols in human genetics. 2013 Jan;76(1):7-20. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Hoover J, Jang W. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic acids research. 2016 Jan 4;44(D1):D862-8. Oscanoa J, Sivapalan L, Gadaleta E, Dayem Ullah AZ, Lemoine NR, Chelala C. SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update). Nucleic acids research. 2020 Jul 2;48(W1):W185-92. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic acids research. 2001 Jan 1;29(1):308-11. Cariaso M, Lennon G. SNPedia: a wiki supporting personal genome annotation, interpretation and analysis. Nucleic acids research. 2012 Jan 1;40(D1):D1308-12. Buch S, Schafmayer C, Völzke H, Becker C, Franke A, von Eller-Eberstein H, Kluck C, Bässmann I, Brosch M, Lammert F, Miquel JF. A genome-wide association scan identifies the hepatic cholesterol transporter ABCG8 as a susceptibility factor for human gallstone disease. Nature genetics. 2007 Aug;39(8):995-9. Aly DM, Fteah AM, Al Assaly NM, Elashry MA, Youssef YF, Hedaya MS. Correlation of serum biochemical characteristics and ABCG8 genetic variant (rs 11887534) with gall stone compositions and risk of gallstone disease in Egyptian patients. Asian Journal of Surgery. 2023 Sep 1;46(9):3560-7. Liang KW, Huang HH, Wang L, Lu WY, Chou YH, Tantoh DM, Nfor ON, Chiu NY, Tyan YS, Liaw YP. Risk of gallstones based on ABCG8 rs11887534 single-nucleotide polymorphism among Taiwanese men and women. BMC gastroenterology. 2021 Dec;21:1-0. Additional Declarations No competing interests reported. Supplementary Files AddionalFile.pdf 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. 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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-6111843","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448951032,"identity":"cf940181-9ecd-410e-bf22-869ff15187d4","order_by":0,"name":"Supriya Garapati","email":"","orcid":"","institution":"All India Institute of Medical Sciences Bibinagar","correspondingAuthor":false,"prefix":"","firstName":"Supriya","middleName":"","lastName":"Garapati","suffix":""},{"id":448951033,"identity":"4310559f-ae33-4354-9e35-bee643dab310","order_by":1,"name":"SakthiVadivel V","email":"","orcid":"","institution":"All India Institute of Medical Sciences Bibinagar","correspondingAuthor":false,"prefix":"","firstName":"SakthiVadivel","middleName":"","lastName":"V","suffix":""},{"id":448951034,"identity":"cbd574fe-f759-4076-9950-22065910e2a2","order_by":2,"name":"Ariyanachi Kaliappan","email":"","orcid":"","institution":"All India Institute of Medical Sciences Bibinagar","correspondingAuthor":false,"prefix":"","firstName":"Ariyanachi","middleName":"","lastName":"Kaliappan","suffix":""},{"id":448951035,"identity":"d62a15e0-3ba5-4616-8985-9d575992c71a","order_by":3,"name":"Kishor Yadav","email":"","orcid":"","institution":"All India Institute of Medical Sciences Bibinagar","correspondingAuthor":false,"prefix":"","firstName":"Kishor","middleName":"","lastName":"Yadav","suffix":""},{"id":448951036,"identity":"8c1ede81-5bb1-4bc6-a7ba-19839a4face3","order_by":4,"name":"Rohit Saluja","email":"","orcid":"","institution":"All India Institute of Medical Sciences Bibinagar","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Saluja","suffix":""},{"id":448951037,"identity":"862c65bd-b909-4023-b960-5663ae701881","order_by":5,"name":"Sailu Yellaboina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACA2YwJSHDwN4A4loQr4WHgecAiCtBhBYozcMgkQDWS1iLOTvzAcYfNRY8BjefX93wo0CCgb+9OwGvFstmtgRmnmMSPAa3c8pu9gAdJnHm7Ab8DjvMA/QOG1hL2g0eoBYDiVzCWhh//ANquXkm7eYfYrUw8LYBtdxgP3abKFtAfjnM2yfBI3kmh+22jIEED0G/mPMfPvjwx7c6Ob7jx5/dfPPHRo6/vRe/FhA4AKF4wHHEQ1A5EmB/QIrqUTAKRsEoGEEAACvjQC8Zp6KIAAAAAElFTkSuQmCC","orcid":"","institution":"All India Institute of Medical Sciences Bibinagar","correspondingAuthor":true,"prefix":"","firstName":"Sailu","middleName":"","lastName":"Yellaboina","suffix":""}],"badges":[],"createdAt":"2025-02-26 09:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6111843/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6111843/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82065136,"identity":"72cb3bee-a335-4e9e-9b67-5c9b58f7e22c","added_by":"auto","created_at":"2025-05-06 12:28:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56688,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots comparing the LDL, total cholesterol, and age distributions between individuals classified as having probable or definite familial hypercholesterolemia (FH) on the basis of their Dutch Lipid Clinic Network Criteria (DLCNC) scores. The statistical significance of differences in the means of two distributions was calculated via the Mann–Whitney U test. (A) LDL levels are significantly different between Probable FH and Definite FH. (B) Total cholesterol levels are significantly different between Probable and Definite FH. (C) The difference in age between probable and definite familial hypercholesterolemia patients was not significant.\u003c/p\u003e","description":"","filename":"Binder31.png","url":"https://assets-eu.researchsquare.com/files/rs-6111843/v1/5b4f6612164628e52e5df32a.png"},{"id":82066231,"identity":"e46eb2f9-0333-4def-8ba9-1e26d58919d4","added_by":"auto","created_at":"2025-05-06 12:36:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":460502,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix heatmap showing Spearman correlations between genotype and phenotype variables. The correlation coefficients are represented by colors ranging from dark blue (strong negative correlation) through yellow (no correlation) to red (strong positive correlation). The variables are clustered hierarchically, with similar correlation patterns grouped together. Rectangles highlight major clusters of correlated variables. The P values of the correlation coefficients are displayed within each cell. The phenotype variables included LDL, total cholesterol, DLCNC and age. Only correlations with an absolute value greater than 0.6 are included in this plot to focus on stronger associations. The SNPs \u003cem\u003ers2075291\u003c/em\u003e (11-116790675-C-A, \u003cem\u003eAPOA5\u003c/em\u003e) and rs193922571 (10-11105268-G-A,\u003cem\u003e LDLR\u003c/em\u003e) are strongly correlated with the DLCNC score, whereas the variants rs6756629 (2-43837950-G-A, \u003cem\u003eABCG5\u003c/em\u003e) and rs11887534 (2-43839107-G-C, \u003cem\u003eABCG8\u003c/em\u003e) are strongly negatively correlated with LDL and DLCNC.\u003c/p\u003e","description":"","filename":"Binder32.png","url":"https://assets-eu.researchsquare.com/files/rs-6111843/v1/946f5211aad3529be7d4a1a6.png"},{"id":96801554,"identity":"95225b85-f339-4d58-8139-54c9354d0c8f","added_by":"auto","created_at":"2025-11-26 08:39:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1364717,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6111843/v1/e3068bb1-dce3-43b4-b1d6-aad3b5a938d4.pdf"},{"id":82066230,"identity":"06e4672b-5036-472e-bda4-f03fa4f2ea80","added_by":"auto","created_at":"2025-05-06 12:36:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":67005,"visible":true,"origin":"","legend":"","description":"","filename":"AddionalFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6111843/v1/011f61f5058eccf87891679c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Landscape of Familial Hypercholesterolemia in Southern India: Novel Mutations and Clinical Implications","fulltext":[{"header":"Background","content":"\u003cp\u003eFamilial hypercholesterolemia (FH) is primarily an autosomal dominant disorder of cholesterol metabolism characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels from an early age, predisposing individuals to premature atherosclerotic cardiovascular disease\u0026nbsp;(Alonso et al., 2013). Early diagnosis and intervention are critical for mitigating FH-associated comorbidities and reducing cardiovascular mortality. The identification of an individual with FH often leads to the diagnosis of other first-degree relatives at risk, creating a positive cascade effect. Despite the widespread availability of cholesterol testing, FH remains largely underdiagnosed in the general Indian population, with many patients being diagnosed only at the time of hospitalization (Kalra et al., 2016). It is estimated that more than 95% of individuals with FH worldwide remain undiagnosed and untreated.\u003c/p\u003e\n\u003cp\u003eFamilial hypercholesterolemia (FH) is a common genetic disorder that affects approximately 1 in 311 individuals globally. Its prevalence is similar across different regions and is notably higher in people with atherosclerotic cardiovascular disease (ASCVD) (Hu et al., 2020). India bears a substantial burden of cardiovascular disease (CVD)-related deaths, accounting for one-fifth of the global total, particularly among the younger population\u0026nbsp;(Murray et al., 1996). A global burden of disease study revealed an alarming age-standardized CVD death rate of 272 per 100,000 people in India, surpassing the global average of 235\u0026nbsp;(Kumar et al., 2020).\u003c/p\u003e\n\u003cp\u003eMost FH cases are attributed to defects in the LDL receptor (\u003cem\u003eLDLR\u003c/em\u003e) or apolipoprotein B-100 (APOB) genes, whereas gain-of-function variants in the \u003cem\u003ePCSK9\u0026nbsp;\u003c/em\u003egene leading to its overproduction are less common\u0026nbsp;(Soutar et al., 2007). Additional variants in genes such as APOE have been sporadically linked to the FH phenotype (Santos et al., 2017). Homozygous FH can also arise from variants in the \u003cem\u003eLDLRAP1\u0026nbsp;\u003c/em\u003egene associated with an autosomal recessive form of the disease (Alnouri et al., 2018). The severity of FH is influenced by the type of \u003cem\u003eLDLR\u0026nbsp;\u003c/em\u003egene variant, with \"null variants\" resulting in severely reduced LDL receptor activity and a more severe phenotype, whereas \"defective variants\" retain some receptor function (Vrablik et al., 2020). Pathogenic variants in the \u003cem\u003eLDLR\u0026nbsp;\u003c/em\u003egene are the most prevalent cause of FH, exhibiting a wide spectrum of variations across populations.\u003c/p\u003e\n\u003cp\u003eIn addition to genetics, nongenetic factors such as age, sex, and lifestyle also influence LDL-C levels. Studies have shown that females with FH tend to have higher levels of total cholesterol, LDL-C, and HDL-C than males do, although both sexes present lower HDL-C levels than individuals without FH do\u0026nbsp;(Schaefer et al., 1994). The average age of onset of coronary symptoms is delayed in females (55 years) compared with males (48 years) (Hill et al., 1991). In men, lower HDL-C levels and a history of smoking are associated with an increased risk of developing coronary artery disease (CAD).\u003c/p\u003e\n\u003cp\u003eThe prevalence of FH is estimated to be 1 in 250 individuals worldwide\u0026nbsp;(Akioyamen et al., 2017), but its identification and management remain suboptimal in many regions, particularly in developing countries. Early diagnosis and treatment are crucial for reducing the risk of cardiovascular complications associated with FH, such as premature coronary artery disease, myocardial infarction, and stroke (Nordestgaard et al., 2013).\u003c/p\u003e\n\u003cp\u003eRecent advancements include the development of a 12-SNP LDL-C \"SNP score\" on the basis of common variants associated with elevated LDL-C levels\u0026nbsp;(Futema et al., 2018). This score has been validated in various populations and shows promise in identifying individuals with FH who lack identifiable mutations.\u003c/p\u003e\n\u003cp\u003eIn India, limited data are available on the genetic epidemiology and mutational spectrum of FH. In this work, we characterize the genetic variants associated with FH in a people from Telangana state, India. By combining targeted exome sequencing and detailed pedigree analysis, we identified novel and known high-impact mutations in genes implicated in FH pathogenesis. The identification of disease-causing mutations and the elucidation of familial inheritance patterns are crucial steps toward improving the diagnosis, risk stratification, and personalized management of FH in the Indian population. Furthermore, this study contributes to the growing body of knowledge on the genetic architecture of FH across diverse ethnic groups, facilitating a deeper understanding of the etiology of FH and potential therapeutic targets. The development of prognostic tools to detect FH in the population, especially in newborns and children, represents an unprecedented opportunity to initiate early treatment and reduce the burden of cardiovascular disease associated with FH.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Design and Participant Recruitment\u003c/h3\u003e\n\u003cp\u003eThis study employed\u0026nbsp;a cross-sectional study design, utilizing targeted exome sequencing and pedigree analysis of patients visiting AIIMS Bibinagar and healthcare centers in the Yadadri Bhuvanagiri district, Telangana, India. Eligibility criteria included asymptomatic children and adolescents (0\u0026ndash;20 years old) exhibiting an LDL cholesterol level exceeding 160 mg/dL or having a first-degree relative with elevated cholesterol and premature coronary heart disease (men \u0026lt;50 years, women \u0026lt;60 years). Clinical features such as tendon xanthomas at any age, arcus corneae younger than 55 years, or xanthelasma younger than 25 years were also considered. Individuals with a history of diabetes mellitus, hypothyroidism, obesity, alcohol use, smoking, or metabolic syndrome were excluded.\u003c/p\u003e\n\u003ch3\u003eProband Identification and Familial Screening\u003c/h3\u003e\n\u003cp\u003ePatients were prioritized on the basis of the Dutch Lipid Clinic Network score (DLCNC) for familial hypercholesterolemia, without considering genetic mutations (Tables 1-3)\u0026nbsp;(Casula et al., 2018). A positive diagnosis according to any one of the criteria established a patient as a probable proband. Upon proband identification, informed consent was obtained for the screening of their first-degree relatives (parents, siblings, and children).\u003c/p\u003e\n\u003ch3\u003eCascade screening\u003c/h3\u003e\n\u003cp\u003eA cascade screening approach was implemented to systematically identify individuals at risk for familial hypercholesterolemia (FH) within the families of identified probands. This strategy involved the sequential screening of first-degree relatives (parents, siblings, and children) of each proband diagnosed with FH. Upon identification of a new FH case among these relatives, the cascade screening process was reiterated, with the newly diagnosed individual becoming a proband for subsequent screening of their first-degree relatives. This iterative process aims to maximize the detection of at-risk individuals within families affected by FH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection, library preparation, and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood samples from patients with familial hypercholesterolemia were collected in EDTA tubes and stored at -80\u0026deg;C. DNA was extracted, quantified, and assessed for purity and integrity. Thirty samples that passed quality control were processed to create whole-genome libraries via the Twist Library Preparation EF Kit, followed by exome capture. The enriched libraries were subjected to target enrichment via the Twist Comprehensive Exome Panel. The final libraries were quality checked, quantified, pooled, diluted, and sequenced on the Illumina NovaSeq 6000 system, generating 150 bp paired-end reads.\u003c/p\u003e\n\u003ch3\u003eExome Sequence Analysis and Variant Annotation\u003c/h3\u003e\n\u003cp\u003eFastp software\u0026nbsp;(Chen et al., 2018) was used to process paired-end raw FASTQ files as part of a quality control and cleaning pipeline in next-generation sequencing (NGS) analysis. This included the automatic detection and trimming of adapter sequences often introduced during library preparation. To ensure high-quality data for downstream analysis, fastp filtered out reads falling below a specified quality threshold or containing a high percentage of low-quality bases. Paired-end reads in FASTQ format, previously cleaned and quality controlled via fastp, were aligned to the human reference genome, GRCh38.p14 (https://ftp.ncbi.nlm.nih.gov/genomes/), via the Burrows\u0026ndash;Wheeler Aligner (BWA-MEM) algorithm (Li et al., 2013).\u003c/p\u003e\n\u003cp\u003eThe Picard tool\u0026nbsp;(https://broadinstitute.github.io/picard/) AddOrReplaceReadGroups was used to modify the SAM file, adding read group information essential for downstream analysis tools. These tags provide crucial details about the sequencing data\u0026apos;s origin and processing, aiding in quality control, alignment analysis, and variant calling. The command samtools sort (Li et al., 2009) sorts the BAM file by genomic coordinates, a critical step for many downstream analyses that require sorted input data for efficient processing, such as variant calling and visualization. The Picard tool MarkDuplicates was then employed to identify and mark potential PCR duplicates within the sorted BAM file. PCR duplicates, which arise during library preparation, can skew downstream analyses. The tool flags duplicate reads in the output BAM file and generates a metric file with statistics at the level of duplication.\u003c/p\u003e\n\u003cp\u003eThe human reference genome (GRCh38.p14) and a known variant file (VCF) were indexed via \u0026lsquo;samtools faidx\u0026rsquo; and GATK\u0026apos;s IndexFeatureFile\u0026nbsp;(Van et al., 2013)\u003csup\u003e\u0026nbsp;\u003c/sup\u003eto optimize data access and processing efficiency during variant calling. The Genome Analysis Toolkit\u0026apos;s (GATK) BaseRecalibrator tool generated a base quality score recalibration (BQSR) table for the aligned sequencing data. This crucial step corrects systematic errors in base quality scores that can arise from sequencing technologies and library preparation, improving downstream variant calling accuracy. The recalibration table, which is specific to the input dataset, was created by referencing the human genome (GRCh38.p14) and incorporating information from a known variant file (VCF). This process ensures that base quality scores more accurately reflect the true probability of sequencing errors, enhancing the reliability of subsequent analyses.\u003c/p\u003e\n\u003cp\u003eThe GATK tool ApplyBQSR then recalibrated the base quality scores in the aligned and duplicate-marked BAM files. By utilizing the BQSR table and the human reference genome (GRCh38.p14), the base quality scores in the BAM file were adjusted to reflect the true probability of sequencing errors more accurately, improving the accuracy and reliability of downstream variant calling and analysis. The GATK tool HaplotypeCaller performs variant calling on the recalibrated BAM file, utilizing the human reference genome (GRCh38.p14). This process involved local reassembly of haplotypes and identification of potential variants in the dataset. The GATK tool GenotypeGVCFs then performs joint genotyping on the genomic VCF (gVCF) file produced by HaplotypeCaller. The human reference genome (GRCh38.p14) was used to consolidate genotype likelihoods across all genomic positions, resulting in a final VCF file with high-confidence variant calls. Compared with analyzing individual samples independently, this approach enhances variant detection accuracy and sensitivity. The GATK tool VariantFiltration was then applied to filter out potentially low-quality variant calls from the VCF file. Using the human reference genome (GRCh38.p14), variants were assessed on the basis of specific quality metrics, and filters were applied to remove variants not meeting quality thresholds.\u003c/p\u003e\n\u003cp\u003eThe BCFtools reheader command updated the header information in each original VCF file to reflect the sample source. Then, the BCFtools index command is used to index each VCF file for efficient random access to specific variants. Finally, the BCFtools merge\u0026nbsp;(Danecek et al., 2017)\u003csup\u003e\u0026nbsp;\u003c/sup\u003ecommand was used to combine the final VCF files of all samples into a single merged and compressed VCF file. This merged file was then indexed via GATK\u0026apos;s IndexFeatureFile for efficient downstream analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariant annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SnpEff variant annotation tool\u0026nbsp;(Cingolani et al., 2012) was employed to build a database for the reference genome GCF_000001405.40. Subsequently, SnpEff annotated variants via the GRCh38.p14 reference genome, predicting their functional effects on genes, transcripts, and protein sequences. This annotation process provided valuable insights into the potential functional consequences of variants within the context of coding regions and protein structures.\u003c/p\u003e\n\u003cp\u003eFurthermore, missense mutations with a moderate impact on protein function, as identified by SnpEff, were filtered via the point-accepted mutation or percent Accepted mutation (PAM250) score, which was calculated via the Biostrings R package (Pages et al., 2020). The scores reflect the likelihood of one amino acid being replaced by another during evolution. Higher scores indicate more probable substitutions, whereas lower scores indicate less likely substitutions. Only missense mutations with a PAM250 score less than one were retained. These missense mutations were also annotated via data from the SIFT (Vaser et al., 2016), PolyPhen-2 (Adzhubei et al., 2013), and ClinVar (Landrum et al., 2016) databases through the SNPnexus (Oscanoa et al., 2020).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Demographics and Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study aimed to discover novel genetic variants associated with familial hypercholesterolemia (FH) in\u0026nbsp;patients from Telangana, India. The study population comprised 30 patients from 15 families, with a mean age of 39 years (SD = 16). The gender distribution was skewed toward males, with 57% of the participants being male and 43% being female (Table 1 and Table S1).\u003c/p\u003e\n\u003cp\u003eTable 1: Summary statistics of the demographics and clinical characteristics of patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"439\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eValue (n = 30)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMean(SD): 39± 16\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePercent: M (57); F (43)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMean(SD): 280 ± 130\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMean(SD): 238± 135\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eCorneal Arcus\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eTendon Xanthomas\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eFamily history of CAD\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eThe study population (n=30) had a mean age of 39 years (SD = 16), with 57% being male and 43% being female. The mean total cholesterol was 280 mg/dL (SD = 130), and the mean LDL-C was 238 mg/dL (SD = 135). A corneal arcus was present in 30% of the population, tendon xanthomas were present in 20%, and 93% reported a family history of coronary artery disease.\u003c/p\u003e\n\u003cp\u003eLipid profile analysis revealed significantly elevated levels of cholesterol in the study\u0026nbsp;patients. The mean total cholesterol (TC) level was 280 mg/dL (SD = 130), which is well above the desirable range for cardiovascular health. Similarly, the mean low-density lipoprotein cholesterol (LDL-C) level was 238 mg/dL (SD = 135), indicating a substantial burden of atherogenic lipoproteins.\u003c/p\u003e\n\u003cp\u003eClinical examination of the participants revealed the presence of characteristic signs associated with FH. A corneal arcus, a gray or white opaque ring around the cornea due to cholesterol deposition, was observed in 30% of the participants. Additionally, 20% of the participants presented with tendon xanthomas, which are localized deposits of cholesterol in tendons and are most commonly observed in the Achilles tendon.\u003c/p\u003e\n\u003cp\u003eNotably, a significant proportion (93%) of the patients reported a family history of coronary artery disease (FHx CAD), underscoring the genetic predisposition and familial nature of FH. This finding highlights the importance of comprehensive pedigree analysis in the diagnosis and management of FH.\u003c/p\u003e\n\u003cp\u003eThe participants were further classified on the basis of the Dutch Lipid Clinic Network Criteria (DLCNC), a widely accepted tool for diagnosing FH. The DLCNC scores categorize individuals into three groups: possible FH\u0026nbsp;(DLNC \u0026gt; 3), probable FH (DLNC \u0026gt; 5), and definite FH (DLNC \u0026gt; 3). This classification system takes into account various factors, including lipid levels, physical signs and family history, to determine the likelihood of FH.\u003c/p\u003e\n\u003cp\u003eAs expected, LDL cholesterol levels were significantly higher in individuals with definite FH than in those with probable FH (p \u0026lt; 1e-05) (Figure 1). Similarly, total cholesterol levels were significantly different between the two groups, with definite FH cases exhibiting higher levels than probable FH cases (p \u0026lt; 4.6--e04). These findings suggest a strong correlation between FH severity, as determined by DLCNC scores, and elevated lipid levels.\u003c/p\u003e\n\u003cp\u003eInterestingly, we found no significant difference in age distribution between the Probable and Definite FH groups (p = 0.16). These findings suggest that age may not be a primary factor in differentiating between these two groups. Our findings imply that genetic factors play a more substantial role in determining the severity of FH, as evidenced by the distinction between Probable and Definite FH. For all the comparisons, we employed nonparametric statistical methods (Mann–Whitney U test) to account for the potential nonnormal distribution of the data.\u003c/p\u003e\n\u003cp\u003eThe box plots with overlaid individual data points in our visualization (Figure 1) provide a comprehensive view of the data distribution, clearly illustrating the differences in lipid levels between the Probable and Definite FH groups, as well as the similarity in age distribution. These results support the validity of the DLCNC scoring system in differentiating between probable and definite FH on the basis of lipid profiles and highlight that age may not be a critical factor in this classification. This information may prove valuable for clinicians in understanding the relationships among FH severity, lipid levels, and patient age in the context of diagnosis and treatment planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePedigree Information for the Study Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed pedigree information was collected for each of the 30 study participants, spanning 15 families. Each individual was identified by their unique family ID (FAM ID) and patient ID (PID). The sex of each participant was recorded (1 denoting male, 2 denoting female), along with their Dutch Lipid Clinic Network Criteria (DLCNC) category (Table S2).\u003c/p\u003e\n\u003cp\u003eThe pedigree data included essential familial relationships, such as paternal ID (father), maternal ID (mother), and sibling IDs. This comprehensive information enabled us to construct detailed family trees and trace the inheritance patterns of FH within each family. By analyzing these pedigrees, we observed diverse patterns of FH transmission, including autosomal dominant inheritance, where the condition is passed down from one affected parent to their offspring, as well as sporadic cases where individuals developed FH without a clear family history.\u003c/p\u003e\n\u003cp\u003eElucidating these familial patterns through detailed pedigree analysis is crucial for understanding the genetic basis of FH and for providing personalized risk assessments and genetic counseling to affected families. By identifying individuals at high risk of FH owing to their family history, early interventions such as lifestyle modifications and lipid-lowering therapies can be implemented to prevent or delay the onset of cardiovascular complications. Moreover, the identification of specific genetic mutations responsible for FH in certain families can facilitate targeted screening and treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-impact variants identified in FH-associated genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-impact variants cause major disruptions to genes and proteins, often rendering them nonfunctional or severely altered. These variants can have a range of consequences, including the complete loss of gene function, the production of shortened proteins, or disruptions in the protein production process. For example, frameshift variants alter the reading frame of a gene, leading to a drastically different amino acid sequence. Nonsense variants introduce premature stop signals, resulting in the formation of truncated proteins. Splice site variants disrupt RNA processing, potentially causing errors in protein production.\u003c/p\u003e\n\u003cp\u003ePedigree analysis revealed that the high-impact mutations identified in this study were distributed across multiple families, as detailed in Table 2. Targeted exome sequencing of the study\u0026nbsp;population further revealed a range of high-impact genetic variants in genes previously associated with familial hypercholesterolemia (FH), as summarized in Table 3. Among the 5 high-impact mutations, four with different levels of clinical significance have already been reported in the dbSNP database (Sherry et al., 2001).\u003c/p\u003e\n\u003cp\u003eTable 2: Pedigree information for familial hypercholesterolemia (FH) patients with high-impact mutations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"533\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFamily ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ePatient ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePat. ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMat. ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eSibling ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003eDLCNC Cat\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM01\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF10\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF9\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eF11\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM02\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM03\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF14\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM05\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF17\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF18\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM10\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF23\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM11\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF24\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF25\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF26\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF27\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eF28\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF27\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF28\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF27\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eF26\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM14\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFAM14\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Each patient is identified by their family ID and patient ID, along with their sex (1: male, 2: female) and Dutch Lipid Clinic Network Criteria (DLCNC) category (1: possible, 2: probable, 3: definite FH). Relationships within families are indicated by paternal ID, maternal ID, and sibling IDs, elucidating the familial patterns of hypercholesterolemia within the study population.\u003c/p\u003e\n\u003cp\u003eTable 3: High-impact variants identified in FH-associated genes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFamily_ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePatient ID\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eDLCNC Cat\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eLPL\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(rs328)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eSLCO1B1\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(rs571639279)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eCETP\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(rs2142001776)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(rs769737896)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e (Novel)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM01\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF10\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM02\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM03\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF14\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM05\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF17\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003eT/T\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM10\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF23\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM11\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF24\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF25\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF26\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF27\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF28\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003eT/T\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM14\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFAM14\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e./.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eAC/A\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eStop-gain mutations were identified in LPL (rs328) and LDLR (rs769737896), while splice donor site mutations were found in SLCO1B1 (rs571639279) and CETP (rs2142001776). A novel LDLR frameshift mutation (10-11129663-AC/A and 10-11129663-A/A) was detected in siblings F5 and F6 from FAM14, with F5 carrying a homozygous variant and classified as \"Definite FH\" per the Dutch Lipid Clinic Network Criteria (DLCNC). The rs328 (LPL) variant is benign, rs769737896 (LDLR) is unreported in ClinVar, and rs571639279 (SLCO1B1) is of uncertain significance. Additionally, rs769737896 (LDLR) was observed in FAM07, with heterozygous and homozygous stop-gain mutations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStop-gain mutations:\u0026nbsp;\u003c/strong\u003eNotably, we identified stop-gain mutations in two key genes involved in lipid metabolism,\u0026nbsp;lipoprotein lipase (\u003cem\u003eLPL\u003c/em\u003e, rs328, chr 8-19962213-C-G) and low-density lipoprotein receptor (\u003cem\u003eLDLR\u003c/em\u003e) with heterozygous (rs769737896, chr 19-11110759-C-T) and homozygous stop gain mutations (chr 19-11110759-T-T) within the same family, FAM07. The variant rs328 is classified as benign, and another variant, rs769737896 (chr 19-11110759-C-T), resulting in p.Arg350Ter, is considered pathogenic in ClinVar (Landrum et al., 2016). The FinnGen database reports a significant association between the variant chr 8-19962213-C-G and various lipoprotein metabolism-related phenotypes, including statin medication usage, disorders of lipoprotein metabolism and other lipidemias, mixed hyperlipidemia, and pure hypercholesterolemia (https://r10.finngen.fi/). Stop-gain mutations introduce premature stop codons into gene sequences, resulting in the production of truncated proteins that are often nonfunctional or exhibit significantly reduced activity. In the context of the \u003cem\u003eLPL\u003c/em\u003e and \u003cem\u003eLDLR\u003c/em\u003e genes, \u003cem\u003eLPL\u003c/em\u003e (lipoprotein lipase) is crucial for hydrolyzing triglycerides within lipoproteins, and its dysfunction can lead to elevated triglyceride levels, potentially contributing to hypercholesterolemia. Moreover, \u003cem\u003eLDLR\u003c/em\u003e (low-density lipoprotein receptor) is essential for the cellular uptake of LDL cholesterol, and its dysfunction results in elevated LDL-C levels in the bloodstream, a hallmark of familial hypercholesterolemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSplice Donor Site Mutations:\u0026nbsp;\u003c/strong\u003eIn addition to stop-gain mutations, we observed splice donor site mutations in two other genes,\u0026nbsp;\u003cem\u003eSLCO1B1\u0026nbsp;\u003c/em\u003e(rs571639279, chr 12-21141659-G-A) and cholesteryl ester transfer protein (\u003cem\u003eCETP\u003c/em\u003e, rs2142001776, chr 16-56975153-T-C). The variant chr 12-21141659-G-A is classified as a variant of uncertain significance, and another variant, chr 16-56975153-T-C, is classified as likely pathogenic and has been associated with hyperalphalipoproteinemia 1 in the ClinVar database. Splice donor site mutations disrupt the normal splicing process of pre-mRNAs, often leading to the production of aberrant mRNA transcripts and potentially altered protein function. The \u003cem\u003eSLCO1B1\u003c/em\u003e gene encodes a protein called organic anion transporting polypeptide 1B1 (OATP1B1). OATP1B1 is a transmembrane receptor found in liver cells that transports compounds such as bilirubins and drugs from the blood into the liver for removal from the body. \u003cem\u003eCETP\u0026nbsp;\u003c/em\u003eis involved in the transfer of cholesterol esters between lipoproteins, and mutations in this gene can affect HDL cholesterol levels and overall cholesterol efflux capacity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNovel Frameshift Mutation in \u003cem\u003eLDLR\u003c/em\u003e:\u0026nbsp;\u003c/strong\u003eA key finding in our analysis was a novel frameshift mutation in the \u003cem\u003eLDLR\u0026nbsp;\u003c/em\u003egene in two siblings (F5 and F6) from family FAM14. This mutation, characterized by a nucleotide deletion in chromosome 10 at position 11129663, was present in a heterozygous state (chr 10-11129663-AC/A) in F6 and a homozygous state (A/A) in F5. The presence of the homozygous mutation in F5 resulted in a \"Definite FH\" classification according to the Dutch Lipid Clinic Network Criteria (DLCNC), underscoring the significant impact of this mutation on cholesterol metabolism and disease severity.\u003c/p\u003e\n\u003cp\u003eThe identification of this novel frameshift mutation expands the known mutational landscape of \u003cem\u003eLDLR\u003c/em\u003e in FH and contributes to our understanding of the genetic heterogeneity of this disorder. Further investigation into the functional consequences of this mutation will be crucial to determine its precise impact on \u003cem\u003eLDLR\u003c/em\u003e function and cholesterol metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerately Impact Variants Identified in FH-Associated Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModerate impact variants are less likely to completely disrupt a gene or protein, but they can still lead to changes in function or effectiveness. For example, missense variants alter the amino acid sequence of a protein, whereas in-frame indels add or remove amino acids without shifting the reading frame. The specific impact of these changes can vary depending on the amino acid affected and its location within the protein.\u003c/p\u003e\n\u003cp\u003eSince it is challenging to assess the functional impact of moderate-effect variants directly on proteins, unlike high-impact variants, we conducted a correlation analysis between moderate-effect variants and various clinical phenotypes, including age, LDL, DLCNC score and total cholesterol levels across the 30 individuals. Figure 2 presents a comprehensive analysis of the relationships between genetic markers and clinical phenotypes.\u003c/p\u003e\n\u003cp\u003eWe used Spearman rank correlation analysis to examine associations between genotype (SNP) data and key phenotypic variables. The p value significance of the correlations is provided in each cell of the heatmap. The SNPs rs2075291 (chr 11-116790675-C-A, \u003cem\u003eAPOA5\u003c/em\u003e) and rs193922571 (chr 10-11105268-G-A, \u003cem\u003eLDLR\u003c/em\u003e) showed strong correlations with the DLCNC score, suggesting that these variants may increase susceptibility to conditions related to elevated cholesterol, such as familial hypercholesterolemia. Conversely, the variants rs6756629 (chr 2-43837950-G-A, \u003cem\u003eABCG5\u003c/em\u003e) and rs11887534 (chr 2-43839107-G-C, \u003cem\u003eABCG8\u003c/em\u003e) exhibited strong negative correlations with LDL and DLCNC, indicating that these variants could be protective, potentially contributing to lower cholesterol levels and reduced FH risk.\u003c/p\u003e\n\u003cp\u003eFurther literature review revealed that the minor allele (A) of rs6756629 (chr 2-43837950-G-A, \u003cem\u003eABCG5\u003c/em\u003e) is associated with decreased LDL levels, increased HDL cholesterol levels, and decreased triglyceride levels (Cariaso et al., 2012). In contrast, the G allele is associated with a lower risk of gallstone disease (cholelithiasis) (Buch et al., 2017). Additionally, the C/C and GC variants of rs11887534 (chr 2-43839107-G-C, \u003cem\u003eABCG8\u003c/em\u003e) have been linked to an increased risk of gallstone disease in Egyptian patients (Aly et al., 2023), particularly among women who use hormones for various gynecological purposes (Keng-Wei et al., 2021).\u003c/p\u003e\n\u003cp\u003eTo assess the impact of these mutations on protein function, we employed the PAM250 matrix, SIFT, PolyPhen-2 software, and data from the ClinVar database, as described in the methods section. The variant rs2075291 (chr 11-116790675-C-A, \u003cem\u003eAPOA5\u003c/em\u003e) was classified as benign or deleterious with low confidence and considered a risk factor, whereas rs193922571 (chr 10-11105268-G-A in \u003cem\u003eLDLR\u003c/em\u003e) was likely pathogenic, probably damaging, and deleterious. In contrast, rs6756629 was considered benign, with low-confidence deleterious annotations, and likely benign, whereas rs11887534 was classified as possibly damaging or deleterious with low confidence, although it was also considered benign or likely benign and a risk factor.\u003c/p\u003e\n\u003cp\u003eThe moderate-impact mutation at position 11105268 on chromosome 19 (rs193922571) in the \u003cem\u003eLDLR\u0026nbsp;\u003c/em\u003egene (c.362G\u0026gt;A, p.Cys121Tyr) was predicted to be damaging by SIFT software and probably damaging by PolyPhen-2. It was also classified as likely pathogenic in the ClinVar database. Patients F7 and F8 from family FAM15 carried this mutation and had a Dutch Lipid Clinic Network Criteria (DLCNC) score of 3, suggesting a likely genetic cause of familial hypercholesterolemia (FH) in these patients. Both patients were heterozygous (G/A), indicating that they may have had heterozygous autosomal dominant familial hypercholesterolemia.\u003c/p\u003e\n\n\n\n\n\n"},{"header":"Discussion","content":"\u003cp\u003eThis study provides valuable insights into the genetic landscape of familial hypercholesterolemia through targeted exome sequencing and pedigree analysis of 30 patients from 15 families. We identified a novel frameshift mutation in the \u003cem\u003eLDLR\u0026nbsp;\u003c/em\u003egene in two siblings, with one exhibiting a homozygous variant (chr\u0026nbsp;10-11129663-A/A) and a \"Definite FH\" classification according to the Dutch Lipid Clinic Network Criteria (DLCNC), underscoring its severe impact on cholesterol metabolism and highlighting the importance of identifying novel pathogenic variants.\u003c/p\u003e\u003cp\u003eIn addition to these novel findings, we also observed previously reported high-impact mutations, including stop-gain mutations in \u003cem\u003eLPL\u0026nbsp;\u003c/em\u003e(rs328, chr 8-19962213-C-G) and \u003cem\u003eLDLR\u003c/em\u003e (rs769737896, chr 19-11110759-C-T and rs769737896, chr 19-11110759-T-T) and splice donor site mutations in \u003cem\u003eSLCO1B1\u0026nbsp;\u003c/em\u003e(rs571639279, chr 12-21141659-G-A)\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCETP\u0026nbsp;\u003c/em\u003e(rs2142001776, chr 16-56975153-T-C), further emphasizing the genetic heterogeneity of FH. While some of these variants are classified as benign or of uncertain significance, their co-occurrence with FH phenotypes necessitates further investigation into their potential roles in disease pathogenesis. Additionally, the moderate-impact variants identified in this study, such as the missense mutation c.362G\u0026gt;A (p.Cys121Tyr, rs193922571, chr 10-11105268-G-A) in \u003cem\u003eLDLR\u003c/em\u003e, were predicted to be damaging by in silico tools such as SIFT and PolyPhen-2 and were classified as likely pathogenic in ClinVar. Overall, these findings highlight the potential contribution of such variants to the FH phenotype, either independently or in combination with other genetic or environmental factors.\u003c/p\u003e\u003cp\u003eOur comprehensive pedigree analysis enabled us to trace familial inheritance patterns and identify individuals at high risk of developing FH. This information is crucial for implementing early interventions such as lifestyle modifications and lipid-lowering therapies to prevent or delay cardiovascular complications. Moreover, the identification of specific pathogenic mutations within families can guide targeted screening and treatment strategies, such as more aggressive interventions for individuals with novel \u003cem\u003eLDLR\u003c/em\u003e frameshift mutations.\u003c/p\u003e\u003cp\u003eThis study provides valuable insights into the genetic variants associated with familial hypercholesterolemia (FH) in a a patients from Telangana, India; however, several limitations must be considered. The small sample size of 30 individuals from 15 families limits the generalizability of the findings. The study's focus on a specific regional population restricts its applicability to broader ethnic groups, and potential selection bias may have influenced the severity of FH observed. Addressing these limitations in future research through larger, multi-ethnic cohorts, functional validation studies, and longitudinal designs will improve our understanding of FH genetics and its clinical implications.\u003c/p\u003e\u003cp\u003eTo conclude, this study provides a overview of the genetic variants associated with FH in a small sample of patients from Telangana, India. The identification of novel and known high-impact mutations, coupled with detailed pedigree analysis, enhances our understanding of the genetic landscape of FH and has significant implications for improved diagnosis, risk stratification, and personalized management of this disorder.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFamilial hypercholesterolemia (FH), Dutch Lipid Clinic Network Criteria (DLCNC), low-density lipoprotein cholesterol (LDL-C), cardiovascular disease (CVD).\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe targeted exome sequence data of familial hypercholesterolemia patients, along with phenotypic information, are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1232422. This dataset includes raw sequencing reads and associated phenotypic data, which can be accessed through the NCBI BioProject database at\u003ca href=\"https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1232422\"\u003e\u0026nbsp;\u003c/a\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA1232422.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is approved by AIIMS Bibinagar ethics committee. The research was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all patients prior to their participation in the study. For any participant under the age of 16, informed consent was obtained from their parents or legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants/patients provided written informed consent for their clinical and demographic information to be published. For any participant under the age of 16, informed consent was obtained from a parent or legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is an intramural funded research project. Financial support was provided by AIIMS Bibinagar Hyderabad (AIIMS BBN/16/2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the BRAHM: High-Performance Computational facility of the Indian Biological Data Centre, Regional Centre for Biotechnology, Faridabad, INDIA (https://ibdc.rcb.res.in/; DBT Grant no. BT/TCB/IBDC/2019) to carry out the NGS data analysis and pathway enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.G. and S.Y. designed the study, performed data analysis. S.G., A.K. and S.Y. prepared the manuscript. S.V. identified FH patients and conducted clinical phenotyping, while S.G. led phenotype data collection, with patient contact primarily managed by S.G. S.G. and R.S. handled lipid profiling and blood collection. Genome sequencing coordination was managed by S.Y., R.S., and S.G.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlonso R, Mata P, Zamb\u0026oacute;n D, Mata N, Fuentes-Jim\u0026eacute;nez F. Early diagnosis and treatment of familial hypercholesterolemia: improving patient outcomes. Expert review of cardiovascular therapy. 2013 Mar 1;11(3):327-42.\u003c/li\u003e\n \u003cli\u003eKalra S, Sawhney JP, Sahay R. The Draupadi of dyslipidemia: familial hypercholesterolemia. Indian journal of endocrinology and metabolism. 2016 May;20(3):285.\u003c/li\u003e\n \u003cli\u003eHu P, Dharmayat KI, Stevens CA, Sharabiani MT, Jones RS, Watts GF, Genest J, Ray KK, Vallejo-Vaz AJ. Prevalence of familial hypercholesterolemia among the general population and patients with atherosclerotic cardiovascular disease: a systematic review and meta-analysis. Circulation. 2020 Jun 2;141(22):1742-59.\u003c/li\u003e\n \u003cli\u003eMurray CJ, Lopez AD, World Health Organization. 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Clinical utility of the polygenic LDL-C SNP score in familial hypercholesterolemia. Atherosclerosis. 2018 Oct 1;277:457-63.\u003c/li\u003e\n \u003cli\u003eCasula M, Olmastroni E, Pirillo A, Catapano AL, Arca M, Averna M, Bertolini S, Calandra S, Tarugi P, Pellegatta F, Angelico F. Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study. Atherosclerosis. 2018 Oct 1;277:413-8.\u003c/li\u003e\n \u003cli\u003eChen S, Zhou Y, Chen Y, Gu J. fastp: an ultrafast all-in-one FASTQ preprocessor. Bioinformatics. 2018 Sep 1;34(17):i884-90.\u003c/li\u003e\n \u003cli\u003eLi H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. 2013 Mar 16.\u003c/li\u003e\n \u003cli\u003e\u0026ldquo;Picard Toolkit.\u0026rdquo; 2019. 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Correlation of serum biochemical characteristics and ABCG8 genetic variant (rs 11887534) with gall stone compositions and risk of gallstone disease in Egyptian patients. Asian Journal of Surgery. 2023 Sep 1;46(9):3560-7.\u003c/li\u003e\n \u003cli\u003eLiang KW, Huang HH, Wang L, Lu WY, Chou YH, Tantoh DM, Nfor ON, Chiu NY, Tyan YS, Liaw YP. Risk of gallstones based on ABCG8 rs11887534 single-nucleotide polymorphism among Taiwanese men and women. BMC gastroenterology. 2021 Dec;21:1-0.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Dutch Lipid Clinic Network Criteria, Exome Sequencing, Familial hypercholesterolemia, Next-Generation Sequencing, Single Nucleotide Polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-6111843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6111843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eFamilial hypercholesterolemia (FH) is a genetic disorder characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels, leading to premature cardiovascular disease (CVD). This study aimed to identify genetic variants associated with FH in patients from Telangana State, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eProbands with suspected FH were identified using the Dutch Lipid Clinic Network (DLCN) score, followed by cascade screening of their first-degree relatives. Targeted exome sequencing and pedigree analysis were performed to identify FH-associated genetic variants\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe identified both novel and known high-impact mutations in genes implicated in FH pathogenesis, including stop-gain mutations in LPL and LDLR, as well as splice donor site mutations in SLCO1B1 and CETP. Notably, a novel frameshift mutation in LDLR was identified in two siblings, one of whom exhibited a homozygous variant and met the \"Definite FH\" classification based on the DLCN criteria. Additionally, moderate-impact variants rs2075291 (APOA5) and rs193922571 (LDLR) showed strong correlations with the DLCN score, suggesting an increased susceptibility to FH. In contrast, rs6756629 (ABCG5) and rs11887534 (ABCG8) were strongly negatively correlated with LDL-C levels and the DLCN score, indicating potential protective effects against FH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThese findings highlight the genetic heterogeneity of FH and emphasize the importance of identifying novel pathogenic variants. Moreover, the study underscores the role of moderate-impact variants in FH susceptibility. Overall, this research enhances our understanding of the genetic landscape of FH in the Indian population, with implications for improved diagnosis, risk assessment, and personalized management.\u003c/p\u003e","manuscriptTitle":"Genetic Landscape of Familial Hypercholesterolemia in Southern India: Novel Mutations and Clinical Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 12:28:40","doi":"10.21203/rs.3.rs-6111843/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":"4a2549d5-31ea-40c3-a69a-8e103de1fc03","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T08:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 12:28:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6111843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6111843","identity":"rs-6111843","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}