Subtyping Severe Hypercholesterolemia by Genetic Determinant to Stratify Risk of Coronary Artery Disease.

BACKGROUND: Severe hypercholesterolemia, defined as LDL (low-density lipoprotein) cholesterol (LDL-C) measurement ≥190 mg/dL, is associated with increased risk for coronary artery disease (CAD). Causes of severe hypercholesterolemia include monogenic familial hypercholesterolemia, polygenic hypercholesterolemia, elevated lipoprotein(a) [Lp(a)] hypercholesteremia, polygenic hypercholesterolemia with elevated Lp(a) (two-hit), or nongenetic hypercholesterolemia. The added value of using a genetics approach to stratifying risk of incident CAD among those with severe hypercholesterolemia versus using LDL-C levels alone for risk stratification is not known. METHODS: To determine whether risk stratification by genetic cause provided better 10-year incident CAD risk stratification than LDL-C level, a retrospective cohort study comparing incident CAD risk among severe hypercholesterolemia subtypes (genetic and nongenetic causes) was performed among 130 091 UK Biobank participants. Analyses were limited to unrelated, White British or Irish participants with available exome sequencing data. Participants with cardiovascular disease at baseline were excluded from analyses of incident CAD. RESULTS: Of 130 091 individuals, 68 416 (52.6%) were women, and the mean (SD) age was 56.7 (8.0) years. Of the cohort, 9.0% met severe hypercholesterolemia criteria. Participants with LDL-C between 210 and 229 mg/dL and LDL-C ≥230 mg/dL showed modest increases in incident CAD risk relative to those with LDL-C between 190 and 209 mg/dL (210-229 mg/dL: hazard ratio [HR], 1.3 [95% CI, 1.1-1.7]; ≥230 mg/dL: HR, 1.3 [95% CI, 1.0-1.7]). In contrast, when risk was stratified by genetic subtype, monogenic familial hypercholesterolemia, elevated Lp(a), and two-hit hypercholesterolemia subtypes had increased rates of incident CAD relative to the nongenetic hypercholesterolemia subtype (monogenic familial hypercholesterolemia: HR, 2.3 [95% CI, 1.4-4.0]; elevated Lp(a): HR, 1.5 [95% CI, 1.2-2.0]; two-hit: HR, 1.9 [95% CI, 1.4-2.6]), while polygenic hypercholesterolemia did not. CONCLUSIONS: Genetics-based subtyping for monogenic familial hypercholesterolemia and Lp(a) in those with severe hypercholesterolemia provided better stratification of 10-year incident CAD risk than LDL-C-based stratification.

Yield of Familial Hypercholesterolemia Genetic and Phenotypic Diagnoses After Electronic Health Record and Genomic Data Screening.

Background Data mining of electronic health records to identify patients suspected of familial hypercholesterolemia (FH) has been limited by absence of both phenotypic and genomic data in the same cohort. Methods and Results Using the Geisinger MyCode Community Health Initiative cohort (n=130 257), we ran 2 screening algorithms (Mayo Clinic [Mayo] and flag, identify, network, deliver [FIND] FH) to determine FH genetic and phenotypic diagnostic yields. With 29 243 excluded by Mayo (for secondary causes of hypercholesterolemia, no lipid value in electronic health records), 52 034 excluded by FIND FH (insufficient data to run the model), and 187 excluded for prior FH diagnosis, a final cohort of 59 729 participants was created. Genetic diagnosis was based on presence of a pathogenic or likely pathogenic variant in FH genes. Charts from 180 variant-negative participants (60 controls, 120 identified by FIND FH and Mayo) were reviewed to calculate Dutch Lipid Clinic Network scores; a score ≥5 defined probable phenotypic FH. Mayo flagged 10 415 subjects; 194 (1.9%) had a pathogenic or likely pathogenic FH variant. FIND FH flagged 573; 34 (5.9%) had a pathogenic or likely pathogenic variant, giving a net yield from both of 197 out of 280 (70%). Confirmation of a phenotypic diagnosis was constrained by lack of electronic health record data on physical findings or family history. Phenotypic FH by chart review was present by Mayo and/or FIND FH in 13 out of 120 versus 2 out of 60 not flagged by either (P<0.09). Conclusions Applying 2 recognized FH screening algorithms to the Geisinger MyCode Community Health Initiative identified 70% of those with a pathogenic or likely pathogenic FH variant. Phenotypic diagnosis was rarely achievable due to missing data.

Frequency of truncating FLCN variants and Birt-Hogg-Dubé-associated phenotypes in a health care system population.

PURPOSE: Penetrance estimates of Birt-Hogg-Dubé syndrome (BHD)-associated cutaneous, pulmonary, and kidney manifestations are based on clinically ascertained families. In a health care system population, we used a genetics-first approach to estimate the prevalence of pathogenic/likely pathogenic (P/LP) truncating variants in FLCN, which cause BHD, and the penetrance of BHD-related phenotypes. METHODS: Exomes from 135,990 patient-participants in Geisinger's MyCode cohort were assessed for P/LP truncating FLCN variants. BHD-related phenotypes were evaluated from electronic health records. Association between P/LP FLCN variants and BHD-related phenotypes was assessed using Firth's logistic regression. RESULTS: P/LP truncating FLCN variants were identified in 35 individuals (1 in 3234 unrelated individuals), 68.6% of whom had BHD-related phenotype(s), including cystic lung disease (65.7%), pneumothoraces (17.1%), cutaneous manifestations (8.6%), and kidney cancer (2.9%). A total of 4 (11.4%) individuals had prior clinical BHD diagnoses. CONCLUSION: In this health care population, the frequency of P/LP truncating FLCN variants is 60 times higher than the previously reported prevalence. Although most variant-positive individuals had BHD-related phenotypes, a minority were previously clinically diagnosed, likely because cutaneous manifestations, pneumothoraces, and kidney cancer were observed at lower frequencies than in clinical cohorts. Improved clinical recognition of cystic lung disease and education concerning its association with FLCN variants could prompt evaluation for BHD.

Comprehensive identification of somatic nucleotide variants in human brain tissue.

BACKGROUND: Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. RESULTS: Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. CONCLUSIONS: This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

Developing and Optimizing Innovative Tools to Address Familial Hypercholesterolemia Underdiagnosis: Identification Methods, Patient Activation, and Cascade Testing for Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is the most common cardiovascular genetic disorder and, if left untreated, is associated with increased risk of premature atherosclerotic cardiovascular disease, the leading cause of preventable death in the United States. Although FH is common, fatal, and treatable, it is underdiagnosed and undertreated due to a lack of systematic methods to identify individuals with FH and limited uptake of cascade testing. METHODS AND RESULTS: This mixed-method, multi-stage study will optimize, test, and implement innovative approaches for both FH identification and cascade testing in 3 aims. To improve identification of individuals with FH, in Aim 1, we will compare and refine automated phenotype-based and genomic approaches to identify individuals likely to have FH. To improve cascade testing uptake for at-risk individuals, in Aim 2, we will use a patient-centered design thinking process to optimize and develop novel, active family communication methods. Using a prospective, observational pragmatic trial, we will assess uptake and effectiveness of each family communication method on cascade testing. Guided by an implementation science framework, in Aim 3, we will develop a comprehensive guide to identify individuals with FH. Using the Conceptual Model for Implementation Research, we will evaluate implementation outcomes including feasibility, acceptability, and perceived sustainability as well as health outcomes related to the optimized methods and tools developed in Aims 1 and 2. CONCLUSIONS: Data generated from this study will address barriers and gaps in care related to underdiagnosis of FH by developing and optimizing tools to improve FH identification and cascade testing.

Identification of Neuropsychiatric Copy Number Variants in a Health Care System Population.

Importance: Population screening for medically relevant genomic variants that cause diseases such as hereditary cancer and cardiovascular disorders is increasing to facilitate early disease detection or prevention. Neuropsychiatric disorders (NPDs) are common, complex disorders with clear genetic causes; yet, access to genetic diagnosis is limited. We explored whether inclusion of NPD in population-based genomic screening programs is warranted by assessing 3 key factors: prevalence, penetrance, and personal utility. Objective: To evaluate the suitability of including pathogenic copy number variants (CNVs) associated with NPD in population screening by determining their prevalence and penetrance and exploring the personal utility of disclosing results. Design, Setting, and Participants: In this cohort study, the frequency of 31 NPD CNVs was determined in patient-participants via exome data. Associated clinical phenotypes were assessed using linked electronic health records. Nine CNVs were selected for disclosure by licensed genetic counselors, and participants' psychosocial reactions were evaluated using a mixed-methods approach. A primarily adult population receiving medical care at Geisinger, a large integrated health care system in the United States with the only population-based genomic screening program approved for medically relevant results disclosure, was included. The cohort was identified from the Geisinger MyCode Community Health Initiative. Exome and linked electronic health record data were available for this cohort, which was recruited from February 2007 to April 2017. Data were collected for the qualitative analysis April 2017 through February 2018. Analysis began February 2018 and ended December 2019. Main Outcomes and Measures: The planned outcomes of this study include (1) prevalence estimate of NPD-associated CNVs in an unselected health care system population; (2) penetrance estimate of NPD diagnoses in CNV-positive individuals; and (3) qualitative themes that describe participants' responses to receiving NPD-associated genomic results. Results: Of 90 595 participants with CNV data, a pathogenic CNV was identified in 708 (0.8%; 436 women [61.6%]; mean [SD] age, 50.04 [18.74] years). Seventy percent (n = 494) had at least 1 associated clinical symptom. Of these, 28.8% (204) of CNV-positive individuals had an NPD code in their electronic health record, compared with 13.3% (11 835 of 89 887) of CNV-negative individuals (odds ratio, 2.21; 95% CI, 1.86-2.61; P < .001); 66.4% (470) of CNV-positive individuals had a history of depression and anxiety compared with 54.6% (49 118 of 89 887) of CNV-negative individuals (odds ratio, 1.53; 95% CI, 1.31-1.80; P < .001). 16p13.11 (71 [0.078%]) and 22q11.2 (108 [0.119%]) were the most prevalent deletions and duplications, respectively. Only 5.8% of individuals (41 of 708) had a previously known genetic diagnosis. Results disclosure was completed for 141 individuals. Positive participant responses included poignant reactions to learning a medical reason for lifelong cognitive and psychiatric disabilities. Conclusions and Relevance: This study informs critical factors central to the development of population-based genomic screening programs and supports the inclusion of NPD in future designs to promote equitable access to clinically useful genomic information.

Assessment of a pharmacogenomic marker panel in a polypharmacy population identified from electronic medical records.

BACKGROUND: The ADME Core Panel assays 184 variants across 34 pharmacogenes, many of which are difficult to accurately genotype with standard multiplexing methods. METHODS: We genotyped 326 frequently medicated individuals of European descent in Vanderbilt's biorepository linked to de-identified electronic medical records, BioVU, on the ADME Core Panel to assess quality and performance of the assay. We compared quality control metrics and determined the extent of direct and indirect marker overlap between the ADME Core Panel and the Illumina Omni1-Quad. RESULTS: We found the quality of the ADME Core Panel data to be high, with exceptions in select copy number variants and markers in certain genes (notably CYP2D6). Most of the common variants on the ADME panel are genotyped by the Omni1, but absent rare variants and copy number variants could not be accurately tagged by single markers. CONCLUSION: Our frequently medicated study population did not convincingly differ in allele frequency from reference populations, suggesting that heterogeneous clinical samples (with respect to medications) have similar allele frequency distributions in pharmacogenetics genes compared with reference populations.