ANGPTL3 Deficiency and Risk of Hepatic Steatosis.

BACKGROUND: ANGPTL3 (angiopoietin-like 3) is a therapeutic target for reducing plasma levels of triglycerides and low-density lipoprotein cholesterol. A recent trial with vupanorsen, an antisense oligonucleotide targeting hepatic production of ANGPTL3, reported a dose-dependent increase in hepatic fat. It is unclear whether this adverse effect is due to an on-target effect of inhibiting hepatic ANGPTL3. METHODS: We recruited participants with ANGPTL3 deficiency related to ANGPTL3 loss-of-function (LoF) mutations, along with wild-type (WT) participants from 2 previously characterized cohorts located in Campodimele, Italy, and St. Louis, MO. Magnetic resonance spectroscopy and magnetic resonance proton density fat fraction were performed to measure hepatic fat fraction and the distribution of extrahepatic fat. To estimate the causal relationship between ANGPTL3 and hepatic fat, we generated a genetic instrument of plasma ANGPTL3 levels as a surrogate for hepatic protein synthesis and performed Mendelian randomization analyses with hepatic fat in the UK Biobank study. RESULTS: We recruited participants with complete (n=6) or partial (n=32) ANGPTL3 deficiency related to ANGPTL3 LoF mutations, as well as WT participants (n=92) without LoF mutations. Participants with ANGPTL3 deficiency exhibited significantly lower total cholesterol (complete deficiency, 78.5 mg/dL; partial deficiency, 172 mg/dL; WT, 188 mg/dL; P<0.05 for both deficiency groups compared with WT), along with plasma triglycerides (complete deficiency, 26 mg/dL; partial deficiency, 79 mg/dL; WT, 88 mg/dL; P<0.05 for both deficiency groups compared with WT) without any significant difference in hepatic fat (complete deficiency, 9.8%; partial deficiency, 10.1%; WT, 9.9%; P>0.05 for both deficiency groups compared with WT) or severity of hepatic steatosis as assessed by magnetic resonance imaging. In addition, ANGPTL3 deficiency did not alter the distribution of extrahepatic fat. Results from Mendelian randomization analyses in 36 703 participants from the UK Biobank demonstrated that genetically determined ANGPTL3 plasma protein levels were causally associated with low-density lipoprotein cholesterol (P=1.7×10-17) and triglycerides (P=3.2×10-18) but not with hepatic fat (P=0.22). CONCLUSIONS: ANGPTL3 deficiency related to LoF mutations in ANGPTL3, as well as genetically determined reduction of plasma ANGPTL3 levels, is not associated with hepatic steatosis. Therapeutic approaches to inhibit ANGPTL3 production in hepatocytes are not necessarily expected to result in the increased risk for hepatic steatosis that was observed with vupanorsen.

SVEP1 is an endogenous ligand for the orphan receptor PEAR1.

Sushi, von Willebrand factor type A, EGF and pentraxin domain containing 1 (SVEP1) is an extracellular matrix protein that causally promotes vascular disease and associates with platelet reactivity in humans. Here, using a human genomic and proteomic approach, we identify a high affinity, disease-relevant, and potentially targetable interaction between SVEP1 and the orphan receptor Platelet and Endothelial Aggregation Receptor 1 (PEAR1). This interaction promotes PEAR1 phosphorylation and disease associated AKT/mTOR signaling in vascular cells and platelets. Mice lacking SVEP1 have reduced platelet activation, and exogenous SVEP1 induces PEAR1-dependent activation of platelets. SVEP1 and PEAR1 causally and concordantly relate to platelet phenotypes and cardiovascular disease in humans, as determined by Mendelian Randomization. Targeting this receptor-ligand interaction may be a viable therapeutic strategy to treat or prevent cardiovascular and thrombotic disease.

Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences.

BACKGROUND: Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS: We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION: These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.

Association of structural variation with cardiometabolic traits in Finns.

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

SVEP1 is a human coronary artery disease locus that promotes atherosclerosis.

A low-frequency variant of sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SVEP1), an extracellular matrix protein, is associated with risk of coronary disease in humans independent of plasma lipids. Despite a robust statistical association, if and how SVEP1 might contribute to atherosclerosis remained unclear. Here, using Mendelian randomization and complementary mouse models, we provide evidence that SVEP1 promotes atherosclerosis in humans and mice and is expressed by vascular smooth muscle cells (VSMCs) within the atherosclerotic plaque. VSMCs also interact with SVEP1, causing proliferation and dysregulation of key differentiation pathways, including integrin and Notch signaling. Fibroblast growth factor receptor transcription increases in VSMCs interacting with SVEP1 and is further increased by the coronary disease-associated SVEP1 variant p.D2702G. These effects ultimately drive inflammation and promote atherosclerosis. Together, our results suggest that VSMC-derived SVEP1 is a proatherogenic factor and support the concept that pharmacological inhibition of SVEP1 should protect against atherosclerosis in humans.

Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

A sample selection strategy for next-generation sequencing.

Next-generation sequencing technology provides us with vast amounts of sequence data. It is efficient and cheaper than previous sequencing technologies, but deep resequencing of entire samples is still expensive. Therefore, sensible strategies for choosing subsets of samples to sequence are required. Here we describe an algorithm for selection of a sub-sample of an existing sample if one has either of two possible goals in mind: maximizing the number of new polymorphic sites that are detected, or improving the efficiency with which the remaining unsequenced individuals can have their types imputed at newly discovered polymorphisms. We then describe a variation on our algorithm that is more focused on detecting rarer variants. We demonstrate the performance of our algorithm using simulated data and data from the 1000 Genomes Project.

Exact coalescent simulation of new haplotype data from existing reference haplotypes.

MOTIVATION: We introduce a coalescent-based method (RECOAL) for the simulation of new haplotype data from a reference population of haplotypes. A coalescent genealogy for the reference haplotype data is sampled from the appropriate posterior probability distribution, then a coalescent genealogy is simulated which extends the sampled genealogy to include new haplotype data. The new haplotype data will, therefore, contain both some of the existing polymorphic sites and new polymorphisms added based on the structure of the simulated coalescent genealogy. This allows exact coalescent simulation of new haplotype data, compared with other methods which are more approximate in nature. RESULTS: We demonstrate the performance of our method using a variety of data simulated under a coalescent model, before applying it to data from the 1000 Genomes project.

Inference of population mutation rate and detection of segregating sites from next-generation sequence data.

We live in an age in which our ability to collect large amounts of genome-wide genetic variation data offers the promise of providing the key to the understanding and treatment of genetic diseases. Over the next few years this effort will be spearheaded by so-called next-generation sequencing technologies, which provide vast amounts of short-read sequence data at relatively low cost. This technology is often used to detect unknown variation in regions that have been linked with a given disease or phenotype. However, error rates are significant, leading to some nontrivial issues when it comes to interpreting the data. In this article, we present a method with which to address questions of widespread interest: calling variants and estimating the population mutation rate. We show performance of the method using simulation studies before applying our approach to an analysis of data from the 1000 Genomes project.

Methods for detecting associations between phenotype and aggregations of rare variants.

Although genome-wide association studies have uncovered variants associated with more than 150 traits, the percentage of phenotypic variation explained by these associations remains small. This has led to the search for the dark matter that explains this missing genetic component of heritability. One potential explanation for dark matter is rare variants, and several statistics have been devised to detect associations resulting from aggregations of rare variants in relatively short regions of interest, such as candidate genes. In this paper we investigate the feasibility of extending this approach in an agnostic way, in which we consider all variants within a much broader region of interest, such as an entire chromosome or even the entire exome. Our method searches for subsets of variant sites using either Markov chain Monte Carlo or genetic algorithms. The analysis was performed with knowledge of the Genetic Analysis Workshop 17 answers.