Kullo, Iftikhar

Background: The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict the effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate the implementation of MES, and validated it using ClinVar and UK-Biobank (UKBB) data. Method: We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid-lowering medication use. Results: MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. Conclusion: MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.

Introduction: We aimed to identify and characterize carotid artery stenosis (CAS), defined as ≥40% stenosis in the internal carotid artery (ICA) or the common carotid artery (CCA), among participants of the Mayo Vascular Disease Biorepository (VDB, n = 11,814). Hypothesis: A string-based text processing (TP) tool coded in R can analyze radiology text reports to ascertain and characterize CAS. Methods: After importing a radiology report into R, it was segmented for analysis and each segment was tested for 22 unique features. The TP tool was able to i) identify stenosis in the ICA or the CCA while excluding stenosis of vertebral arteries, subclavian arteries, or external carotid arteries, ii) recognize both quantitative and qualitative descriptions of CAS, iii) interpret numerical expressions by considering the surrounding text, iv) detect negations, and v) ascertain previous revascularization procedures if mentioned in the text. We manually ascertained CAS and characterized it in 800 randomly selected radiology reports to assess the performance of the TP tool in terms of precision, recall, and F-score. Results: We retrieved 21,651 imaging reports for 6,342 unique patients including 20,461 ultrasonography reports, 812 magnetic resonance imaging reports, 357 computed tomography scan reports, and 21interventional radiology reports. The performance of TP is outlined in Table 1 . Conclusion: We demonstrate that a string-based TP tool coded in R is efficient and accurate in identifying and characterizing CAS from carotid radiology reports. The tool does not require natural language processing and could be used in a wide range of settings for genetic and biomarker studies.

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) refers to the formation of genetically distinct subpopulations of blood cells due to somatic mutations. CHIP is associated with a variety of adverse outcomes, including hematological cancers, cardiovascular disease, and death. We sought to investigate the association of CHIP with incident venous thromboembolism (VTE). Methods: We analyzed exome sequence data from 200,628 individuals in the UK Biobank cohort using Mutect2 to identify a prespecified list of somatic mutations in DNMT3A, TET2, and ASXL1, which are known genetic drivers of CHIP. CHIP was defined as harboring a somatic mutation at a variant allele fraction (VAF) ≥10% and individuals with VAF <2% were deemed as non-carriers. VTE was ascertained using verbal interview data and diagnostic/procedural codes and defined as a composite of the following: thrombophlebitis of deep veins of lower limbs, embolism/thrombosis of vena cava, pulmonary embolism, venous thrombectomy of lower limb veins or IVC filter insertion. We implemented a nested case-control design with incidence density sampling to identify incident cases and controls matched (1:8 ratio) for sex, smoking status, and history of malignancy at baseline. Conditional logistic regression analysis adjusted for age, BMI, and genetic ancestry was performed to assess the association between CHIP and incident VTE. Results: Among 200,628 individuals, we identified 3,885 individuals with CHIP at baseline and 748 incident VTE events during a median follow-up of 9.2 years. In 748 incident cases and 5984 matched controls, CHIP was associated with increased risk of incident VTE independent of conventional risk factors for VTE, namely, age, sex, BMI, smoking, and history of malignancy (incidence rate ratio [95% confidence interval]; 1.60 [1.04-2.46], p=0.032). When we restricted the cases to individuals with incident pulmonary embolism (n=441), we observed a stronger association with CHIP (1.80 [1.08-3.05], p=0.025). Conclusions: CHIP is associated with increased risk of VTE independent of history of malignancy and other conventional risk factors. The pathophysiological basis for this association needs further investigation.

Introduction: The prevalence of atrial fibrillation (AF) increases with age and inflammation plays a key pathophysiologic role. Somatic mosaicism increases with age and promotes inflammation. Therefore, we investigated whether Clonal Hematopoiesis of Indeterminate Potential (CHIP) is a risk factor for incident AF. Methods: We assessed exome sequence (ES) data from 200,628 participants in the UK biobank. Using the Genome Analysis Toolkit pipeline, we ascertained a predefined list of CHIP mutations in DNMT3A , TET2 , and ASXL1 . CHIP was defined as variant allele frequency (VAF) >10% and those with VAF <2% served as controls. AF was ascertained based on self-report and ICD codes. Lone AF was defined as AF in the absence of heart failure, mitral stenosis, mitral valve repair/valvotomy/replacement, and congenital heart disease. Those with AF prior to or at the time of enrollment were excluded from analyses to ascertain incident events. Cox proportional hazard models adjusted for age, sex, self-reported race/ethnicity, diabetes, hypertension, smoking, and obesity were used to identify associations between CHIP and incident AF. Results: Of 200,628 participants, 3885 (1.9%) had CHIP. The number of participants with mutations in DNMT3A , TET2 , and ASXL1 was 2512, 1065, and 439, respectively. During a median follow-up of 9 years, 4,676 incident AF and 3,584 incident lone AF occurred. CHIP was an independent risk factor for developing incident AF and also for developing lone AF. CHIP in TET2 and ASXL1 but not DNMT3A was associated with AF ( Table 1 ). Conclusions: The presence of CHIP involving TET2 and ASXL1 is associated with an increased risk of incident AF including the subset with lone AF. Our findings reveal somatic mosaicism in blood cells to be a novel risk factor for AF.

The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate implementation of MES and validated it using ClinVar and UK-Biobank (UKBB) data. We tested concordance of MaxSpliZer predictions with ClinVar classifications of pathogenicity of variants in LDLR and FBN1 with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid lowering medication use. MaxSpliZer prediction was concordant with the ClinVar classification in 96% of LDLR variants and 98% of FBN1 variants. In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify splice variants in a rapid automated fashion.

The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate implementation of MES and validated it using ClinVar and UK-Biobank (UKBB) data. We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid lowering medication use. MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.

Background: Whether disclosing genetic risk for coronary heart disease (CHD) to individuals influences information seeking and information sharing is not known. Methods: The myocardial infarction genes (MI-GENES) trial randomized participants aged 45-65 years who were at intermediate risk for CHD based on conventional risk factors and not on statins, to receive their conventional risk score (CRS) or their CRS plus a genetic risk score (GRS) based on 28 susceptibility variants. CHD risk was disclosed by a genetic counselor and then discussed with a physician. Surveys to assess information seeking (including internet use and accessing electronic health records (EHR)) were completed before and three and six months after risk disclosure. Information sharing parameters were assessed after risk disclosure. We assessed whether these behaviors differed by GRS disclosure, or by high (≥1.1) or low (<1.1) GRS. Adjustments were made for age, sex, family history of CHD, baseline CRS and GRS, and education. Results were reported as the mean difference (and standard error) in the score for each survey response between the GRS and CRS participants, with significance determined by regression analysis. Results: GRS participants accessed their EHR to obtain information related to their CHD risk more than CRS participants (0.14 ± 0.06, p=0.03). Overall internet use (0.61 ± 0.23, p=0.01), as well as internet use to seek information about heart disease (0.14 ± 0.06, p=0.02) and how genetic factors affect risk of having a heart attack (0.23 ± 0.07, p=0.002), was significantly higher in the GRS participants. GRS participants shared information about heart attack risk with others (0.35 ± 0.13, p=0.007), particularly family members (0.1 ± 0.04, p=0.02), (V4: 0.10 ± 0.05, p=0.05), and their primary care provider (V4: 0.15 ± 0.07, p=0.03) more than CRS participants. Internet use, EHR access, and information sharing did not differ significantly between the high and low GRS groups. Conclusions: Disclosure of GRS for CHD resulted in greater information seeking (including internet use and EHR access) and information sharing by study participants. Disclosure of genetic risk for CHD may help advance patient engagement in precision medicine.

Fibromuscular dysplasia (FMD) is a nonatherosclerotic vascular disease leading to arterial stenosis, aneurysm and dissection, mainly in renal and carotid arteries. FMD has higher prevalence in females (80-90%) and is associated with hypertension and stroke. The pathophysiology of FMD is unclear and a genetic origin is suspected. We performed a genetic association study in European ancestry individuals. The discovery included 249 cases and 689 controls, in which we analyzed ∼26K common variants (MAF>0.05) using an exome-chip array. We followed up 13 loci (P<10-4) in 393 cases and 2537 controls and replicated a signal on Chr6. Three additional studies (combined Ncases=512, Ncontrols=669) confirmed this association, with an overall OR of 1.39, (P=7.4х10e-10 , All Ncases=1154, All Ncontrols=3895). The FMD risk variant is intronic to the phosphatase and actin regulator 1 gene (PHACTR1), involved in angiogenesis and cell migration. PHACTR1 is a risk locus for coronary artery disease, migraine, and cervical artery dissection, which may occur in FMD. We found a significant association between the risk allele and higher central pulse pressure (P=0.0009), increased intima media thickness (P=0.001) and wall cross-sectional area (P=0.003) of carotids assessed by echotracking in 3800 population-based individuals. The correlation of genotypes with the expression of PHACTR1 in primary cultured human fibroblasts showed higher expression in FMD risk allele carriers, compared to non carriers (N=57, P=0.02). Finally, Phactr1 knockdown of zebrafish showed significantly dilated vessels (P=0.003) indicating impaired vascular development. In conclusion, we report the first risk locus for FMD with the largest genetic association study conducted so far. Our data reveal a common genetic variant at PHACTR1 providing indices of shared pathophysiology between FMD and other cardiovascular and neurovascular diseases.

A challenge in returning genomic test results to research participants is how best to communicate complex and clinically nuanced findings to participants in a manner that is scalable to the large numbers of participants enrolled. The purpose of this study was to examine the features of genetic results letters produced at each Electronic Medical Records and Genomics (eMERGE3) Network site to assess their readability and content. Letters were collected from each site, and a qualitative analysis of letter content and a quantitative analysis of readability statistics were performed. Because letters were produced independently at each eMERGE site, significant heterogeneity in readability and content was found. The content of letters varied widely from a baseline of notifying participants that results existed to more detailed information about positive or negative results, as well as materials for sharing with family members. Most letters were significantly above the Centers for Disease Control-suggested reading level for health communication. While continued effort should be applied to make letters easier to understand, the ongoing challenge of explaining complex genomic information, the implications of negative test results, and the uncertainty that comes with some types of test and result makes simplifying letter text challenging.