Publications (2)7.02 Total impact
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Article: Relation of increased prebeta-1 high-density lipoprotein levels to risk of coronary heart disease.
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ABSTRACT: Preβ-1 high-density lipoprotein (HDL) plays a key role in reverse cholesterol transport by promoting cholesterol efflux. Our aims were (1) to test previous associations between preβ-1 HDL and coronary heart disease (CHD) and (2) to investigate whether preβ-1 HDL levels also are associated with risk of myocardial infarction (MI). Plasma preβ-1 HDL was measured by an ultrafiltration-isotope dilution technique in 1,255 subjects recruited from the University of California-San Francisco Lipid and Cardiovascular Clinics and collaborating cardiologists. Preβ-1 HDL was significantly and positively associated with CHD and MI even after adjustment for established risk factors. Inclusion of preβ-1 HDL in a multivariable model for CHD led to a modest improvement in reclassification of subjects (net reclassification index 0.15, p = 0.01; integrated discrimination improvement 0.003, p = 0.2). In contrast, incorporation of preβ-1 HDL into a risk model of MI alone significantly improved reclassification of subjects (net reclassification index 0.21, p = 0.008; integrated discrimination improvement 0.01, p = 0.02), suggesting that preβ-1 HDL has more discriminatory power for MI than for CHD in our study population. In conclusion, these results confirm previous associations between preβ-1 HDL and CHD in a large well-characterized clinical cohort. Also, this is the first study in which preβ-1 HDL was identified as a novel and independent predictor of MI above and beyond traditional CHD risk factors.The American journal of cardiology 08/2011; 108(3):360-6. · 3.58 Impact Factor -
Article: Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants
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ABSTRACT: Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling. Genet. Epidemiol. 35: 236-246, 2011. © 2011 Wiley-Liss, Inc.Genetic Epidemiology 02/2011; 35(4):236 - 246. · 3.44 Impact Factor
