@article {7448, title = {PCSK9 Loss-of-Function Variants, Low-Density Lipoprotein Cholesterol, and Risk of Coronary Heart Disease and Stroke: Data From 9 Studies of Blacks and Whites.}, journal = {Circ Cardiovasc Genet}, volume = {10}, year = {2017}, month = {2017 Aug}, pages = {e001632}, abstract = {

BACKGROUND: PCSK9 loss-of-function (LOF) variants allow for the examination of the effects of lifetime reduced low-density lipoprotein cholesterol (LDL-C) on cardiovascular events. We examined the association of PCSK9 LOF variants with LDL-C and incident coronary heart disease and stroke through a meta-analysis of data from 8 observational cohorts and 1 randomized trial of statin therapy.

METHODS AND RESULTS: These 9 studies together included 17 459 blacks with 403 (2.3\%) having at least 1 Y142X or C679X variant and 31 306 whites with 955 (3.1\%) having at least 1 R46L variant. Unadjusted odds ratios for associations between PCSK9 LOF variants and incident coronary heart disease (851 events in blacks and 2662 events in whites) and stroke (523 events in blacks and 1660 events in whites) were calculated using pooled Mantel-Haenszel estimates with continuity correction factors. Pooling results across studies using fixed-effects inverse-variance-weighted models, PCSK9 LOF variants were associated with 35 mg/dL (95\% confidence interval [CI], 32-39) lower LDL-C in blacks and 13 mg/dL (95\% CI, 11-16) lower LDL-C in whites. PCSK9 LOF variants were associated with a pooled odds ratio for coronary heart disease of 0.51 (95\% CI, 0.28-0.92) in blacks and 0.82 (95\% CI, 0.63-1.06) in whites. PCSK9 LOF variants were not associated with incident stroke (odds ratio, 0.84; 95\% CI, 0.48-1.47 in blacks and odds ratio, 1.06; 95\% CI, 0.80-1.41 in whites).

CONCLUSIONS: PCSK9 LOF variants were associated with lower LDL-C and coronary heart disease incidence. PCSK9 LOF variants were not associated with stroke risk.

}, issn = {1942-3268}, doi = {10.1161/CIRCGENETICS.116.001632}, author = {Kent, Shia T and Rosenson, Robert S and Avery, Christy L and Chen, Yii-der I and Correa, Adolfo and Cummings, Steven R and Cupples, L Adrienne and Cushman, Mary and Evans, Daniel S and Gudnason, Vilmundur and Harris, Tamara B and Howard, George and Irvin, Marguerite R and Judd, Suzanne E and Jukema, J Wouter and Lange, Leslie and Levitan, Emily B and Li, Xiaohui and Liu, Yongmei and Post, Wendy S and Postmus, Iris and Psaty, Bruce M and Rotter, Jerome I and Safford, Monika M and Sitlani, Colleen M and Smith, Albert V and Stewart, James D and Trompet, Stella and Sun, Fangui and Vasan, Ramachandran S and Woolley, J Michael and Whitsel, Eric A and Wiggins, Kerri L and Wilson, James G and Muntner, Paul} } @article {8626, title = {Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index.}, journal = {JAMA Netw Open}, volume = {3}, year = {2020}, month = {2020 10 01}, pages = {e2023242}, abstract = {

Importance: Obesity is a global health challenge and a risk factor for atherosclerotic cardiovascular disease (ASVCD). Performance of the pooled cohort equations (PCE) for ASCVD risk by body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is unknown.

Objective: To assess performance of the PCE across clinical BMI categories.

Design, Setting, and Participants: This cohort study used pooled individual-level data from 8 community-based, prospective, longitudinal cohort studies with 10-year ASCVD event follow-up from 1996 to 2016. We included all adults ages 40 to 79 years without baseline ASCVD or statin use, resulting in a sample size of 37 311 participants. Data were analyzed from August 2017 to July 2020.

Exposures: Participant BMI category: underweight (<18.5), normal weight (18.5 to <25), overweight (25 to <30), mild obesity (30 to <35), and moderate to severe obesity (>=35).

Main Outcomes and Measures: Discrimination (Harrell C statistic) and calibration (Nam-D{\textquoteright}Agostino χ2 goodness-of-fit test) of the PCE across BMI categories. Improvement in discrimination and net reclassification with addition of BMI, waist circumference, and high-sensitivity C-reactive protein (hsCRP) to the PCE.

Results: Among 37 311 participants (mean [SD] age, 58.6 [11.8] years; 21 897 [58.7\%] women), 380 604 person-years of follow-up were conducted. Mean (SD) baseline BMI was 29.0 (6.2), and 360 individuals (1.0\%) were in the underweight category, 9937 individuals (26.6\%) were in the normal weight category, 13 601 individuals (36.4\%) were in the overweight category, 7783 individuals (20.9\%) were in the mild obesity category, and 5630 individuals (15.1\%) were in the moderate to severe obesity category. Median (interquartile range [IQR]) 10-year estimated ASCVD risk was 7.1\% (2.5\%-15.4\%), and 3709 individuals (9.9\%) developed ASCVD over a median (IQR) 10.8 [8.5-12.6] years. The PCE overestimated ASCVD risk in the overall cohort (estimated/observed [E/O] risk ratio, 1.22; 95\% CI, 1.18-1.26) and across all BMI categories except the underweight category. Calibration was better near the clinical decision threshold in all BMI groups but worse among individuals with moderate or severe obesity (E/O risk ratio, 1.36; 95\% CI, 1.25-1.47) and among those with the highest estimated ASCVD risk >=20\%. The PCE C statistic overall was 0.760 (95\% CI, 0.753-0.767), with lower discrimination in the moderate or severe obesity group (C statistic, 0.742; 95\% CI, 0.721-0.763) compared with the normal-range BMI group (C statistic, 0.785; 95\% CI, 0.772-0.798). Waist circumference (hazard ratio, 1.07 per 1-SD increase; 95\% CI, 1.03-1.11) and hsCRP (hazard ratio, 1.07 per 1-SD increase; 95\% CI, 1.05-1.09), but not BMI, were associated with increased ASCVD risk when added to the PCE. However, these factors did not improve model performance (C statistic, 0.760; 95\% CI, 0.753-0.767) with or without added metrics.

Conclusions and Relevance: These findings suggest that the PCE had acceptable model discrimination and were well calibrated at clinical decision thresholds but overestimated risk of ASCVD for individuals in overweight and obese categories, particularly individuals with high estimated risk. Incorporation of the usual clinical measures of obesity did not improve risk estimation of the PCE. Future research is needed to determine whether incorporation of alternative high-risk obesity markers (eg, weight trajectory or measures of visceral or ectopic fat) into the PCE may improve risk prediction.

}, keywords = {Adult, Aged, Body Mass Index, Cardiovascular Diseases, Cohort Studies, Correlation of Data, Female, Humans, Longitudinal Studies, Male, Middle Aged, Proportional Hazards Models, Prospective Studies, Risk Assessment, Risk Factors}, issn = {2574-3805}, doi = {10.1001/jamanetworkopen.2020.23242}, author = {Khera, Rohan and Pandey, Ambarish and Ayers, Colby R and Carnethon, Mercedes R and Greenland, Philip and Ndumele, Chiadi E and Nambi, Vijay and Seliger, Stephen L and Chaves, Paulo H M and Safford, Monika M and Cushman, Mary and Xanthakis, Vanessa and Vasan, Ramachandran S and Mentz, Robert J and Correa, Adolfo and Lloyd-Jones, Donald M and Berry, Jarett D and de Lemos, James A and Neeland, Ian J} } @article {9239, title = {Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Jan}, pages = {154-164}, abstract = {

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

}, keywords = {Exome Sequencing, Genome-Wide Association Study, Lipids, Phenotype, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-022-01225-6}, author = {Li, Xihao and Quick, Corbin and Zhou, Hufeng and Gaynor, Sheila M and Liu, Yaowu and Chen, Han and Selvaraj, Margaret Sunitha and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Haessler, Jeffrey and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Manichaikul, Ani and Martin, Lisa W and McGarvey, Stephen T and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Vasan, Ramachandran S and Willer, Cristen J and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Natarajan, Pradeep and Peloso, Gina M and Li, Zilin and Lin, Xihong} }