Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Jb, Nielsen; Rom, Oren; Surakka, Ida; Graham, Sarah E.; Zhou, Wei; Roychowdhury, Tanmoy; Fritsche, Lars G.; Taliun, Sarah A. Gagliano; Sidore, Carlo; Wang, Kun; Gabrielsen, Maiken Elvestad; Skogholt, Anne Heidi; Wolford, Brooke N.; Overton, William; Zhao, Ying; Chen, Jin; He, Zhonghu; Hornsby, Whitney E.; Acheampong, Akua; Grooms, Austen; Schaefer, Amanda M.; Zajac, Gregory J.M.; Villacorta, Luis; Zhang, Jifeng; Brumpton, Ben; Løset, Mari; Rai, Vivek; Lundegaard, Pia R.; Olesen, Morten S.; Taylor, Kent D.; Palmer, Nicholette D.; Chen, Yii Der; Choi, Seung Hoan; Lubitz, Steven A.; Ellinor, Patrick T.; Barnes, Kathleen C.; Daya, Michelle; Rafaels, Nicholas; Weiss, Scott T.; Lasky‐Su, Jessica; Tracy, Russell P.; Vasan, Ramachandran S.; Cupples, L. Adrienne; Mathias, Rasika A.; Yanek, Lisa R.; Becker, Lewis C.; Peyser, Patricia A.; Bielak, Lawrence F.; Smith, Jennifer A.; Aslibekyan, Stella; Hidalgo, Bertha; Arnett, Donna K.; Irvin, Marguerite R.; Wilson, James G.; Musani, Solomon K.; Correa, Adolfo; Rich, Stephen S.; Guo, Xiuqing; Rotter, Jerome I.; Konkle, Barbara A.; Johnsen, Jill M.; Ashley–Koch, Allison; Telen, Marilyn J.; Sheehan, Vivien A.; Blangero, John; Curran, Joanne E.; Peralta, Juan M.; Montgomery, Courtney; Sheu, Wayne H‐H; Chung, Ren Hua; Schwander, Karen; Nouraie, Mehdi; Gordeuk, Victor R.; Zhang, Yingze; Kooperberg, Charles; Reiner, Alexander P.; Jackson, Rebecca D.; Bleecker, Eugene R.; Meyers, Deborah A.; Li, Xingnan; S, Das; Yu, Ketian; LeFaive, Jonathon; Smith, Albert V.; Blackwell, Tom; Taliun, Daniel; Zöllner, Sebastian; Forer, Lukas; Schoenherr, Sebastian; Fuchsberger, Christian; Pandit, Anita; Zawistowski, Matthew; Kheterpal, Sachin; Brummett, Chad M.; Natarajan, Pradeep; Schlessinger, David; Lee, Seunggeun; Kang, Hyun Min; Cucca, Francesco; Holmen, Oddgeir L.; Åsvold, Bjørn Olav; Boehnke, Michael; Kathiresan, Sekar; Abecasis, Gonçalo R.; Chen, Y. Eugene; Willer, Cristen J.; Hveem, Kristian

doi:10.1038/s41467-020-20086-3

Cited by 46 publications

(39 citation statements)

References 55 publications

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“…In addition to the PAGE GWAS, independent GWAS have shown SNPs at the APOE locus associated with C-reactive protein [37][38][39], HDL cholesterol [37,38,[40][41][42][43], LDL cholesterol [37,38,[40][41][42]44], and total cholesterol [37,38,40,41,45]. In our study, increased predicted abundance of CRP associated with increased measured C-reactive protein, effectively acting as a positive control for our method.…”

Section: Population-matched Protein Prediction Models Map the Most Trait Associationsmentioning

confidence: 64%

“…It is not currently possible to differentiate between true differences in abundance of Apo E from differences in binding affinity among isoforms. The protein abundance mechanisms underlying the well established APOE genetic associations [1,[37][38][39][40][41]45] remain to be elucidated. Among other proteins, common (MAF>0.01) PAVs tend to be relatively rare.…”

Section: Discussionmentioning

confidence: 99%

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Protein prediction for trait mapping in diverse populations

Schubert

Geoffroy

Gregga

et al. 2021

Preprint

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Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ≈50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837328.

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Section: Population-matched Protein Prediction Models Map the Most Trait Associationsmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Protein prediction for trait mapping in diverse populations

Schubert

Geoffroy

Gregga

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…For the "Cardiovascular" phenotypes (apolipoprotein A, apolipoprotein B, C-reactive protein, total cholesterol, HDL cholesterol, LDL cholesterol, lipoprotein A, and triglycerides), MRP recovers array associations between: PLG, LPA, and lipoprotein A 43 ; APOC3 and triglycerides 44 ; ANGPTL3 and triglycerides 45 ; APOB and apolipoprotein B 46 and LDL cholesterol 47 ; ABCA1 and apolipoprotein A 44 and HDL cholesterol 47 ; PCSK9 and total cholesterol 48 ; and CRP and C-reactive protein 49 . Exome-only signals recover associations such as between ZPR1 50 and SIK3 44 and triglycerides.…”

Section: Simulationsmentioning

confidence: 96%

“…MRP applied to liver-related phenotypes recover known associations between: UGT genes and bilirubins 52 ; GOT1 and aspartate aminotransferase 53 ; FCGRT and albumin 42 ; and GPT and AST-ALT ratio 49 . in the exome sequencing, we additionally recover associations between GGT1 and gamma glutamyltransferase 54 , TMEM236 and aspartate aminotransferase 42 , and SLCO1B3 and bilirubin.…”

Section: Simulationsmentioning

confidence: 99%

Bayesian model comparison for rare variant association studies

Venkataraman

DeBoever

Tanigawa

et al. 2018

Preprint

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Whole genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery and inference that are not addressed by the traditional one variant-one phenotype association study. Here we introduce a model comparison approach we refer to as MRP for rare variant association studies that considers correlation, scale, and location of genetic effects across a group of genetic variants, phenotypes, and studies. We consider the use of summary statistic data to apply univariate and multivariate gene-based meta-analysis models for identifying rare variant associations with an emphasis on protective protein-truncating variants that can expedite drug discovery. Through simulation studies, we demonstrate that the proposed model comparison approach can improve ability to detect rare variant association signals. We also apply the model to two groups of phenotypes from the UK Biobank: 1) asthma diagnosis, eosinophil counts, forced expiratory volume, and forced vital capacity; and 2) glaucoma diagnosis, intra-ocular pressure, and corneal resistance factor. We are able to recover known associations such as the protective association between rs146597587 in IL33 and asthma. We also find evidence for novel protective associations between rare variants in ANGPTL7 and glaucoma. Overall, we show that the MRP model comparison approach is able to retain and improve upon useful features from widely-used meta-analysis approaches for rare variant association analyses and prioritize protective modifiers of disease risk.

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“…For instance, the mRNA vaccine produced by Moderna is based on lipid nanoparticles [ 67 ]. There are other examples of vaccine delivery using nano platform such as emulsions, dendrimers, polysaccharide-based nanoparticles [ 9 , 40 , 67 , 81 , 112 ].…”

Section: Nanotechnology As a Potential Tool To Fight Sars-cov-2mentioning

confidence: 99%

SARS-CoV-2: phylogenetic origins, pathogenesis, modes of transmission, and the potential role of nanotechnology

et al. 2021

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The COVID-19 pandemic has elicited a rapid response from the scientific community with significant advances in understanding the causative pathogen (SARS-CoV-2). Mechanisms of viral transmission and pathogenesis, as well as structural and genomic details, have been reported, which are essential in guiding containment, treatment, and vaccine development efforts. Here, we present a concise review of the recent research in these domains and an exhaustive analysis of the genomic origins of SARS-CoV-2. Particular emphasis has been placed on the pathology and disease progression of COVID-19 as documented by recent clinical studies, in addition to the characteristic immune responses involved therein. Furthermore, we explore the potential of nanomaterials and nanotechnology to develop diagnostic tools, drug delivery systems, and personal protective equipment design within the ongoing pandemic context. We present this as a ready resource for researchers to gain succinct, up-to-date insights on SARS-CoV-2.

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Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Cited by 46 publications

References 55 publications

Protein prediction for trait mapping in diverse populations

Protein prediction for trait mapping in diverse populations

Bayesian model comparison for rare variant association studies

SARS-CoV-2: phylogenetic origins, pathogenesis, modes of transmission, and the potential role of nanotechnology

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