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Angiopoietin-like protein (ANGPTL)3 regulates plasma lipids by inhibiting LPL and endothelial lipase (EL). ANGPTL3 inactivation lowers LDL-C independently of the classical LDLR-mediated pathway and represents a promising therapeutic approach for individuals with homozygous familial hypercholesterolemia due to LDLR mutations. Yet, how ANGPTL3 regulates LDL-C levels is unknown. Here, we demonstrate in hyperlipidemic humans and mice that ANGPTL3 controls VLDL catabolism upstream of LDL. Using kinetic, lipidomic, and biophysical studies, we show that ANGPTL3 inhibition reduces VLDL-lipid content and size, generating remnant particles that are efficiently removed from the circulation. This suggests that ANGPTL3 inhibition lowers LDL-C by limiting LDL particle production. Mechanistically, we discovered that EL is a key mediator of ANGPTL3's novel pathway. Our experiments revealed that, although dispensable in the presence of LDLR, EL-mediated processing of VLDL becomes critical for LDLR-independent particle clearance. In the absence of EL and LDLR, ANGPTL3 inhibition perturbed VLDL catabolism, promoted accumulation of atypical remnants, and failed to reduce LDL-C. Taken together, we uncover ANGPTL3 at the helm of a novel EL-dependent pathway that lowers LDL-C in the absence of LDLR.

The growing number of individuals who are affected by undiagnosed rare genetic disorders and participating in genomics research—to identify unknown relationships between genes and disease—enable analyses using statistical approaches, such as exome-wide or genome-wide association analyses. This chapter reviews the statistical methods historically used to study rare diseases such as linkage analysis and transmission disequilibrium tests and explores the characteristics of rare disease, research participant ascertainment and availability, and the constellation of peripheral information that may determine whether modern analytical methods may be useful and well powered to enable disease variant and gene discovery. The characteristics that drive the choice of analytical approach and the strengths and limitations of these methods for the agnostic identification of relationships between genes and rare genetic disorders are also discussed.

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.