Evaluation of DNA Methylation Episignatures for Diagnosis and Phenotype Correlations in 42 Mendelian Neurodevelopmental Disorders.

Genetic syndromes frequently present with overlapping clinical features and inconclusive or ambiguous genetic findings which can confound accurate diagnosis and clinical management. An expanding number of genetic syndromes have been shown to have unique genomic DNA methylation patterns (called "episignatures"). Peripheral blood episignatures can be used for diagnostic testing as well as for the interpretation of ambiguous genetic test results. We present here an approach to episignature mapping in 42 genetic syndromes, which has allowed the identification of 34 robust disease-specific episignatures. We examine emerging patterns of overlap, as well as similarities and hierarchical relationships across these episignatures, to highlight their key features as they are related to genetic heterogeneity, dosage effect, unaffected carrier status, and incomplete penetrance. We demonstrate the necessity of multiclass modeling for accurate genetic variant classification and show how disease classification using a single episignature at a time can sometimes lead to classification errors in closely related episignatures. We demonstrate the utility of this tool in resolving ambiguous clinical cases and identification of previously undiagnosed cases through mass screening of a large cohort of subjects with developmental delays and congenital anomalies. This study more than doubles the number of published syndromes with DNA methylation episignatures and, most significantly, opens new avenues for accurate diagnosis and clinical assessment in individuals affected by these disorders.

Long-read sequencing for molecular diagnostics in constitutional genetic disorders.

Long-read sequencing (LRS) has been around for more than a decade, but widespread adoption of the technology has been slow due to the perceived high error rates and high sequencing cost. This is changing due to the recent advancements to produce highly accurate sequences and the reducing costs. LRS promises significant improvement over short read sequencing in four major areas: (1) better detection of structural variation (2) better resolution of highly repetitive or nonunique regions (3) accurate long-range haplotype phasing and (4) the detection of base modifications natively from the sequencing data. Several successful applications of LRS have demonstrated its ability to resolve molecular diagnoses where short-read sequencing fails to identify a cause. However, the argument for increased diagnostic yield from LRS remains to be validated. Larger cohort studies may be required to establish the realistic boundaries of LRS's clinical utility and analytical validity, as well as the development of standards for clinical applications. We discuss the limitations of the current standard of care, and contrast with the applications and advantages of two major LRS platforms, PacBio and Oxford Nanopore, for molecular diagnostics of constitutional disorders, and present a critical argument about the potential of LRS in diagnostic settings.

Characterization of a possible founder synonymous variant in TECTA in multiple individuals with autosomal recessive hearing loss.

Synonymous variants have been shown to alter the correct splicing of pre-mRNAs and generate disease-causing transcripts. These variants are not an uncommon etiology of genetic disease; however, they are frequently overlooked during genetic testing in the absence of functional and clinical data. Here, we describe the occurrence of a synonymous variant [NM_005422.4 (TECTA):c.327C>T, p.(Gly109=)] in seven individuals with hearing loss from six unrelated families. The variant is not located near exonic/intronic boundaries but is predicted to impact splicing by activating a cryptic splicing donor site in exon 4 of TECTA. In vitro minigene assays show that the variant disrupts the reading frame of the canonical transcript, which is predicted to cause a premature termination codon 48 amino acids downstream of the variant, leading to nonsense-mediated decay. The variant is present in population databases, predominantly in Latinos of African ancestry, but is rare in other ethnic groups. Our findings suggest that this synonymous variant is likely pathogenic for TECTA-associated autosomal recessive hearing loss and seems to have arisen as a founder variant in this specific Latino subpopulation. This study demonstrates that synonymous variants need careful splicing assessment and support from additional testing methodologies to determine their clinical impact.

Functional annotation of genomic variation: DNA methylation episignatures in neurodevelopmental Mendelian disorders.

The breadth and complexity of genetic testing in patients with suspected Mendelian neurodevelopmental disorders has rapidly expanded in the past two decades. However, in spite of advances in genomic technologies, genetic diagnosis remains elusive in more than half of these patients. Epigenomics, and in particular genomic DNA methylation profiles, are now known to be associated with the underpinning genetic defects in a growing number of Mendelian disorders. These often highly specific and sensitive molecular biomarkers have been used to screen these patient populations, resolve ambiguous clinical cases and interpret genetic variants of unknown clinical significance. Increasing the diagnostic yield beyond genomic sequencing technologies has rapidly propelled epigenomics to clinical utilization, with recent introduction of DNA methylation 'EpiSign' analysis in clinical diagnostic laboratories. This review provides an overview of the principles, applications and limitations of DNA methylation episignature analysis in patients with neurodevelopmental Mendelian disorders, and discusses clinical implications of this emerging diagnostic technology.

Diagnostic Utility of Genome-wide DNA Methylation Testing in Genetically Unsolved Individuals with Suspected Hereditary Conditions.

Conventional genetic testing of individuals with neurodevelopmental presentations and congenital anomalies (ND/CAs), i.e., the analysis of sequence and copy number variants, leaves a substantial proportion of them unexplained. Some of these cases have been shown to result from DNA methylation defects at a single locus (epi-variants), while others can exhibit syndrome-specific DNA methylation changes across multiple loci (epi-signatures). Here, we investigate the clinical diagnostic utility of genome-wide DNA methylation analysis of peripheral blood in unresolved ND/CAs. We generate a computational model enabling concurrent detection of 14 syndromes using DNA methylation data with full accuracy. We demonstrate the ability of this model in resolving 67 individuals with uncertain clinical diagnoses, some of whom had variants of unknown clinical significance (VUS) in the related genes. We show that the provisional diagnoses can be ruled out in many of the case subjects, some of whom are shown by our model to have other diseases initially not considered. By applying this model to a cohort of 965 ND/CA-affected subjects without a previous diagnostic assumption and a separate assessment of rare epi-variants in this cohort, we identify 15 case subjects with syndromic Mendelian disorders, 12 case subjects with imprinting and trinucleotide repeat expansion disorders, as well as 106 case subjects with rare epi-variants, a portion of which involved genes clinically or functionally linked to the subjects' phenotypes. This study demonstrates that genomic DNA methylation analysis can facilitate the molecular diagnosis of unresolved clinical cases and highlights the potential value of epigenomic testing in the routine clinical assessment of ND/CAs.

Advanced approach for comprehensive mtDNA genome testing in mitochondrial disease.

Mitochondrial disease diagnosis requires interrogation of both nuclear and mitochondrial (mtDNA) genomes for single-nucleotide variants (SNVs) and copy number alterations, both in the proband and often maternal relatives, together with careful phenotype correlation. We developed a comprehensive mtDNA sequencing test ('MitoGenome') using long-range PCR (LR-PCR) to amplify the full length of the mtDNA genome followed by next generation sequencing (NGS) to accurately detect SNVs and large-scale mtDNA deletions (LSMD), combined with droplet digital PCR (ddPCR) for LSMD heteroplasmy quantification. Overall, MitoGenome tests were performed on 428 samples from 394 patients with suspected or confirmed mitochondrial disease. The positive yield was 11% (43/394), including 34 patients with pathogenic or likely pathogenic SNVs (the most common being m.3243A > G in 8/34 (24%) patients), 8 patients with single LSMD, and 3 patients with multiple LSMD exceeding 10% heteroplasmy levels. Two patients with both LSMD and pathogenic SNV were detected. Overall, this LR-PCR/NGS assay provides a highly accurate and comprehensive diagnostic method for simultaneous mtDNA SNV detection at heteroplasmy levels as low as 1% and LSMD detection at heteroplasmy levels below 10%. Inclusion of maternal samples for variant classification and ddPCR to quantify LSMD heteroplasmy levels further enables accurate pathogenicity assessment and clinical correlation interpretation of mtDNA genome sequence variants and copy number alterations.

Genomic DNA Methylation Signatures Enable Concurrent Diagnosis and Clinical Genetic Variant Classification in Neurodevelopmental Syndromes.

Pediatric developmental syndromes present with systemic, complex, and often overlapping clinical features that are not infrequently a consequence of Mendelian inheritance of mutations in genes involved in DNA methylation, establishment of histone modifications, and chromatin remodeling (the "epigenetic machinery"). The mechanistic cross-talk between histone modification and DNA methylation suggests that these syndromes might be expected to display specific DNA methylation signatures that are a reflection of those primary errors associated with chromatin dysregulation. Given the interrelated functions of these chromatin regulatory proteins, we sought to identify DNA methylation epi-signatures that could provide syndrome-specific biomarkers to complement standard clinical diagnostics. In the present study, we examined peripheral blood samples from a large cohort of individuals encompassing 14 Mendelian disorders displaying mutations in the genes encoding proteins of the epigenetic machinery. We demonstrated that specific but partially overlapping DNA methylation signatures are associated with many of these conditions. The degree of overlap among these epi-signatures is minimal, further suggesting that, consistent with the initial event, the downstream changes are unique to every syndrome. In addition, by combining these epi-signatures, we have demonstrated that a machine learning tool can be built to concurrently screen for multiple syndromes with high sensitivity and specificity, and we highlight the utility of this tool in solving ambiguous case subjects presenting with variants of unknown significance, along with its ability to generate accurate predictions for subjects presenting with the overlapping clinical and molecular features associated with the disruption of the epigenetic machinery.

Clinical epigenomics: genome-wide DNA methylation analysis for the diagnosis of Mendelian disorders.

PURPOSE: We describe the clinical implementation of genome-wide DNA methylation analysis in rare disorders across the EpiSign diagnostic laboratory network and the assessment of results and clinical impact in the first subjects tested. METHODS: We outline the logistics and data flow between an integrated network of clinical diagnostics laboratories in Europe, the United States, and Canada. We describe the clinical validation of EpiSign using 211 specimens and assess the test performance and diagnostic yield in the first 207 subjects tested involving two patient subgroups: the targeted cohort (subjects with previous ambiguous/inconclusive genetic findings including genetic variants of unknown clinical significance) and the screening cohort (subjects with clinical findings consistent with hereditary neurodevelopmental syndromes and no previous conclusive genetic findings). RESULTS: Among the 207 subjects tested, 57 (27.6%) were positive for a diagnostic episignature including 48/136 (35.3%) in the targeted cohort and 8/71 (11.3%) in the screening cohort, with 4/207 (1.9%) remaining inconclusive after EpiSign analysis. CONCLUSION: This study describes the implementation of diagnostic clinical genomic DNA methylation testing in patients with rare disorders. It provides strong evidence of clinical utility of EpiSign analysis, including the ability to provide conclusive findings in the majority of subjects tested.

Clinical and technical assessment of MedExome vs. NGS panels in patients with suspected genetic disorders in Southwestern Ontario.

The adaptation of a broad genomic sequencing approach in the clinical setting has been accompanied by considerations regarding the clinical utility, technical performance, and diagnostic yield compared to targeted genetic approaches. We have developed MedExome, an integrated framework for sequencing, variant calling (SNVs, Indels, and CNVs), and clinical assessment of ~4600 medically relevant genes. We compared the technical performance of MedExome with the whole-exome and targeted gene-panel sequencing, assessed the reasons for discordance, and evaluated the added clinical yield of MedExome in a cohort of unresolved subjects suspected of genetic disease. Our analysis showed that despite a higher average read depth in panels (3058 vs. 855), MedExome yielded full coverage of the enriched regions (>20X) and 99% variant concordance rate with panels. The discordance rate was associated with low-complexity regions, high-GC content, and low allele fractions, observed in both platforms. MedExome yielded full sensitivity in detecting clinically actionable variants, and the assessment of 138 patients with suspected genetic conditions resulted in 76 clinical reports (31 full [22.1%], 3 partial, and 42 uncertain/possible molecular diagnoses). MedExome sequencing has comparable performance in variant detection to gene panels. Added diagnostic yield justifies expanded implementation of broad genomic approaches in unresolved patients; however, cost-benefit and health systems impact warrants assessment.

Detection of a DNA Methylation Signature for the Intellectual Developmental Disorder, X-Linked, Syndromic, Armfield Type.

A growing number of genetic neurodevelopmental disorders are known to be associated with unique genomic DNA methylation patterns, called episignatures, which are detectable in peripheral blood. The intellectual developmental disorder, X-linked, syndromic, Armfield type (MRXSA) is caused by missense variants in FAM50A. Functional studies revealed the pathogenesis to be a spliceosomopathy that is characterized by atypical mRNA processing during development. In this study, we assessed the peripheral blood specimens in a cohort of individuals with MRXSA and detected a unique and highly specific DNA methylation episignature associated with this disorder. We used this episignature to construct a support vector machine model capable of sensitive and specific identification of individuals with pathogenic variants in FAM50A. This study contributes to the expanding number of genetic neurodevelopmental disorders with defined DNA methylation episignatures, provides an additional understanding of the associated molecular mechanisms, and further enhances our ability to diagnose patients with rare disorders.

Glucose-induced, duration-dependent genome-wide DNA methylation changes in human endothelial cells.

DNA methylation, a critical epigenetic mechanism, plays an important role in governing gene expressions during biological processes such as aging, which is well known to be accelerated in hyperglycemia (diabetes). In the present study, we investigated the effects of glucose on whole genome DNA methylation in small [human retinal microvascular endothelial cells (HRECs)] and large [human umbilical vein endothelial cells (HUVECs)] vessel endothelial cell (EC) lines exposed to basal or high glucose-containing media for variable lengths of time. Using the Infinium EPIC array, we obtained 773,133 CpG sites (probes) for analysis. Unsupervised clustering of the top 5% probes identified four distinct clusters within EC groups, with significant methylation differences attributed to EC types and the duration of cell culture rather than glucose stimuli alone. When comparing the ECs incubated for 2 days versus 7 days, hierarchical clustering analyses [methylation change >10% and false discovery rate (FDR) <0.05] identified 17,354 and 128 differentially methylated CpGs for HUVECs and HRECs, respectively. Predominant DNA hypermethylation was associated with the length of culture and was enriched for gene enhancer elements and regions surrounding CpG shores and shelves. We identified 88 differentially methylated regions (DMRs) for HUVECs and 8 DMRs for HRECs (all FDR <0.05). Pathway enrichment analyses of DMRs highlighted involvement of regulators of embryonic development (i.e., HOX genes) and cellular differentiation [transforming growth factor-ß (TGF-ß) family members]. Collectively, our findings suggest that DNA methylation is a complex process that involves tightly coordinated, cell-specific mechanisms. Such changes in methylation overlap genes critical for cellular differentiation and embryonic development.

De novo SMARCA2 variants clustered outside the helicase domain cause a new recognizable syndrome with intellectual disability and blepharophimosis distinct from Nicolaides-Baraitser syndrome.

PURPOSE: Nontruncating variants in SMARCA2, encoding a catalytic subunit of SWI/SNF chromatin remodeling complex, cause Nicolaides-Baraitser syndrome (NCBRS), a condition with intellectual disability and multiple congenital anomalies. Other disorders due to SMARCA2 are unknown. METHODS: By next-generation sequencing, we identified candidate variants in SMARCA2 in 20 individuals from 18 families with a syndromic neurodevelopmental disorder not consistent with NCBRS. To stratify variant interpretation, we functionally analyzed SMARCA2 variants in yeasts and performed transcriptomic and genome methylation analyses on blood leukocytes. RESULTS: Of 20 individuals, 14 showed a recognizable phenotype with recurrent features including epicanthal folds, blepharophimosis, and downturned nasal tip along with variable degree of intellectual disability (or blepharophimosis intellectual disability syndrome [BIS]). In contrast to most NCBRS variants, all SMARCA2 variants associated with BIS are localized outside the helicase domains. Yeast phenotype assays differentiated NCBRS from non-NCBRS SMARCA2 variants. Transcriptomic and DNA methylation signatures differentiated NCBRS from BIS and those with nonspecific phenotype. In the remaining six individuals with nonspecific dysmorphic features, clinical and molecular data did not permit variant reclassification. CONCLUSION: We identified a novel recognizable syndrome named BIS associated with clustered de novo SMARCA2 variants outside the helicase domains, phenotypically and molecularly distinct from NCBRS.

Genetic and epigenetic profiling of BRCA1/2 in ovarian tumors reveals additive diagnostic yield and evidence of a genomic BRCA1/2 DNA methylation signature.

Poly-ADP-ribose-polymerase inhibitor (PARPi) treatment is indicated for advanced-stage ovarian tumors with BRCA1/2 deficiency. The "BRCAness" status is thought to be attributed to a tumor phenotype associated with a specific epigenomic DNA methylation profile. Here, we examined the diagnostic impact of combined BRCA1/2 sequence, copy number, and promoter DNA methylation analysis, and evaluated whether genomic DNA methylation patterns can predict the BRCAness in ovarian tumors. DNA sequencing of 172 human tissue samples of advanced-stage ovarian adenocarcinoma identified 36 samples with a clinically significant tier 1/2 sequence variants (point mutations and in/dels) and 9 samples with a CNV causing a loss of function in BRCA1/2. DNA methylation analysis of the promoter of BRCA1/2 identified promoter hypermethylation of BRCA1 in two mutation-negative samples. Computational modeling of genome-wide methylation markers, measured using Infinium EPIC arrays, resulted in a total accuracy of 0.75, sensitivity: 0.83, specificity: 0.64, positive predictive value: 0.76, negative predictive value: 0.74, and area under the receiver's operating curve (AUC): 0.77, in classifying tumors harboring a BRCA1/2 defect from the rest. These findings indicate that the assessment of CNV and promoter DNA methylation in BRCA1/2 increases the cumulative diagnostic yield by 10%, compared with the 20% yield achieved by sequence variant analysis alone. Genomic DNA methylation data can partially predict BRCAness in ovarian tumors; however, further investigation in expanded BRCA1/2 cohorts is needed, and the effect of other double strand DNA repair gene defects in these tumors warrants further investigations.

Diagnostic Utility of Genome-Wide DNA Methylation Analysis in Mendelian Neurodevelopmental Disorders.

Mendelian neurodevelopmental disorders customarily present with complex and overlapping symptoms, complicating the clinical diagnosis. Individuals with a growing number of the so-called rare disorders exhibit unique, disorder-specific DNA methylation patterns, consequent to the underlying gene defects. Besides providing insights to the pathophysiology and molecular biology of these disorders, we can use these epigenetic patterns as functional biomarkers for the screening and diagnosis of these conditions. This review summarizes our current understanding of DNA methylation episignatures in rare disorders and describes the underlying technology and analytical approaches. We discuss the computational parameters, including statistical and machine learning methods, used for the screening and classification of genetic variants of uncertain clinical significance. Describing the rationale and principles applied to the specific computational models that are used to develop and adapt the DNA methylation episignatures for the diagnosis of rare disorders, we highlight the opportunities and challenges in this emerging branch of diagnostic medicine.

Genome-wide DNA methylation and RNA analyses enable reclassification of two variants of uncertain significance in a patient with clinical Kabuki syndrome.

Nontruncating sequence variants represent a major challenge in variant interpretation and classification. Here, we report a patient with features of Kabuki syndrome who carries two rare heterozygous variants in KMT2D: c.12935C>T, p.(Ser4312Phe) and c.15785-10T>G. The clinical significance of these variants were discordantly interpreted by different diagnostic laboratories. Parental testing showed that the missense variant was inherited from the father with a mild Kabuki phenotype and the intronic variant from the mother with mosaic status. Through genome-wide DNA methylation analysis of peripheral blood, we confirmed that the proband exhibited a previously described episignature of Kabuki syndrome. Parental samples had normal DNA methylation profiles, thus ruling out the involvement of the paternally inherited missense variant. RNA analysis revealed that the intronic change resulted in exon 49 skipping and frameshift, thereby providing a molecular diagnosis of Kabuki syndrome. This study demonstrates the utility of epigenomic and RNA analyses in resolving ambiguous clinical cases.

Implementation of an NGS-based sequencing and gene fusion panel for clinical screening of patients with suspected hematologic malignancies.

OBJECTIVES: The diagnosis of hematologic malignancies integrates multiple diagnostic and clinical disciplines. Historically, targeted (single-analyte) genetic testing has been used as reflex to initial prescreening by other diagnostic modalities including flow cytometry, anatomic pathology, and clinical cytogenetics. Given the wide range of mutations associated with hematologic malignancies a DNA/RNA-based NGS panel can provide a more effective and economical approach to comprehensive testing of patients as an initial, tier-1 screen. METHODS: Using a cohort of 380 patients, we performed clinical validation of a gene panel designed to assess 40 genes (DNA), and 29 fusion driver genes with over 600 gene fusion partners (RNA), including sample exchange data across three clinical laboratories, and correlation with cytogenetic testing results. RESULTS: The clinical validation of this technology demonstrated that its accuracy, sensitivity, and specificity are comparable to the majority of targeted single-gene approaches, while assessment of the initial patient cohort data demonstrated a high diagnostic yield of 50.5%. CONCLUSIONS: Implementation of a tier-1 NGS-based protocol for gene panel screening provides a comprehensive alternative to targeted molecular testing in patients with suspected hematologic malignancies, with increased diagnostic yield, scalability, reproducibility, and cost effectiveness, making it ideally suited for implementation in clinical laboratories.

Genetic associations in community context: a mixed model approach identifies a functional variant in the RBP4 gene associated with HDL-C dyslipidemia.

BACKGROUND: The objective of this study was to examine individual and community factors that influence high-density lipoprotein cholesterol (HDL-C) dyslipidemia in Newfoundland and Labrador (NL), a genetically isolated population in Canada with a high prevalence of HDL-C dyslipidemia. METHODS: First, a group of single nucleotide polymorphisms from 10 metabolic trait candidate genes was tested using a multivariate logistic regression model. The significant SNPs were entered into the second phase, where a mixed logistic model incorporated the community disease risk factors together with the individual factors as the fixed part of the model and the geographic region as a random effect. RESULTS: Analysis of 1489 subjects (26.9% HDL-C dyslipidemia) identified rs3758539, a non-coding variant in the 5'UTR of RBP4, to be associated with HDL-C dyslipidemia (odds ratio = 1.45, 95% confidence interval = 1.08-1.97, p = 0.01). The association remained significant, and the effect size did not change after the incorporation of individual and community risk factors from 17 geographic regions (odds ratio: 1.41, 95% confidence interval = 1.03-1.93, p = 0.03) in NL. Besides this variant, sex, BMI, and smoking also showed significant associations with HDL-C dyslipidemia, whereas no role was identified for the community factors. CONCLUSIONS: This study demonstrates the use of community-level data in a genetic association testing. It reports a functional variant in the promoter of RBP4, a gene directly involved in lipoprotein metabolism, to be associated with HDL-C dyslipidemia. These findings indicate that individual factors are the main reason for a higher prevalence of HDL-C dyslipidemia in the NL population.

Six-year time-trend analysis of dyslipidemia among adults in Newfoundland and Labrador: findings from the laboratory information system between 2009 and 2014.

BACKGROUND: Dyslipidemia, an increased level of total cholesterol (TC), triglycerides (TG), low-density-lipoprotein cholesterol (LDL-C) and decreased level of high-density-lipoprotein cholesterol (HDL-C), is one of the most important risk factors for cardiovascular disease. We examined the six-year trend of dyslipidemia in Newfoundland and Labrador (NL), a Canadian province with a historically high prevalence of dyslipidemia. METHODS: A serial cross-sectional study on all of the laboratory lipid tests available from 2009 to 2014 was performed. Dyslipidemia for every lipid component was defined using the Canadian Guidelines for the Diagnosis and Treatment of Dyslipidemia. The annual dyslipidemia rates for each component of serum lipid was examined. A fixed and random effect model was applied to adjust for confounding variables (sex and age) and random effects (residual variation in dyslipidemia over the years and redundancies caused by individuals being tested multiple times during the study period). RESULTS: Between 2009 and 2014, a total of 875,208 records (mean age: 56.9 ± 14.1, 47.6% males) containing a lipid profile were identified. The prevalence of HDL-C and LDL-C dyslipidemia significantly decreased during this period (HDL-C: 35.8% in 2009 [95% CI 35.5-36.1], to 29.0% in 2014 [95% CI: 28.8-29.2], P = 0.03, and LDL-C: 35.2% in 2009 [95% CI: 34.9-35.4] to 32.1% in 2014 [95% CI: 31.9-32.3], P = 0.02). A stratification by sex, revealed no significant trend for any lipid element in females; however, in men, the previously observed trends were intensified and a new decreasing trend in dyslipidemia of TC was appeared (TC: 34.1% [95% CI 33.7-34.5] to 32.3% [95%CI: 32.0-32.6], p < 0.02, HDL-C: 33.8% (95%CI: 33.3-34.2) to 24.0% (95% CI: 23.7-24.3)], P < 0.01, LDL-C: 32.9% (95%CI:32.5-33.3) to 28.6 (95%CI: 28.3-28.9), P < 0.001). Adjustment for confounding factors and removing the residual noise by modeling the random effects did not change the significance. CONCLUSION: This study demonstrates a significant downward trend in the prevalence of LDL-C, HDL-C, and TC dyslipidemia, exclusively in men. These trends could be the result of males being the primary target for cardiovascular risk management.

Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database.

The objective of this study was to define the optimal algorithm to identify patients with dyslipidemia using electronic medical records (EMRs). EMRs of patients attending primary care clinics in St. John's, Newfoundland and Labrador (NL), Canada during 2009-2010, were studied to determine the best algorithm for identification of dyslipidemia. Six algorithms containing three components, dyslipidemia ICD coding, lipid lowering medication use, and abnormal laboratory lipid levels, were tested against a gold standard, defined as the existence of any of the three criteria. Linear discriminate analysis, and bootstrapping were performed following sensitivity/specificity testing and receiver's operating curve analysis. Two validating datasets, NL records of 2011-2014, and Canada-wide records of 2010-2012, were used to replicate the results. Relative to the gold standard, combining laboratory data together with lipid lowering medication consumption yielded the highest sensitivity (99.6%), NPV (98.1%), Kappa agreement (0.98), and area under the curve (AUC, 0.998). The linear discriminant analysis for this combination resulted in an error rate of 0.15 and an Eigenvalue of 1.99, and the bootstrapping led to AUC: 0.998, 95% confidence interval: 0.997-0.999, Kappa: 0.99. This algorithm in the first validating dataset yielded a sensitivity of 97%, Negative Predictive Value (NPV) = 83%, Kappa = 0.88, and AUC = 0.98. These figures for the second validating data set were 98%, 93%, 0.95, and 0.99, respectively. Combining laboratory data with lipid lowering medication consumption within the EMR is the best algorithm for detecting dyslipidemia. These results can generate standardized information systems for dyslipidemia and other chronic disease investigations using EMRs.