@article {160, title = {SVEP1 is a human coronary artery disease locus that promotes atherosclerosis.}, journal = {Sci Transl Med}, volume = {13}, year = {2021}, month = {2021 Mar 24}, abstract = {

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 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.

}, issn = {1946-6242}, doi = {10.1126/scitranslmed.abe0357}, author = {Jung, In-Hyuk and Elenbaas, Jared S and Alisio, Arturo and Santana, Katherine and Young, Erica P and Kang, Chul Joo and Kachroo, Puja and Lavine, Kory J and Razani, Babak and Mecham, Robert P and Stitziel, Nathan O} } @article {113, title = {Serum sphingolipids and incident diabetes in a US population with high diabetes burden: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).}, journal = {Am J Clin Nutr}, volume = {112}, year = {2020}, month = {2020 07 01}, pages = {57-65}, abstract = {

BACKGROUND: Genetic or pharmacological inhibition of de novo sphingolipid synthases prevented diabetes in animal studies.

OBJECTIVES: We sought to evaluate prospective associations of serum sphingolipids with incident diabetes in a population-based cohort.

METHODS: We included 2010 participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) aged 18-74 y who were free of diabetes and other major chronic diseases at baseline (2008-2011). Metabolomic profiling of fasting serum was performed using a global, untargeted approach. A total of 43 sphingolipids were quantified and, considering subclasses and chemical structures of individual species, 6 sphingolipid scores were constructed. Diabetes status was assessed using standard procedures including blood tests. Multivariable survey Poisson regressions were applied to estimate RR and 95\% CI of incident diabetes associated with individual sphingolipids or sphingolipid scores.

RESULTS: There were 224 incident cases of diabetes identified during, on average, 6 y of follow-up. After adjustment for socioeconomic and lifestyle factors, a ceramide score (RR Q4 versus Q1~=~2.40; 95\% CI: 1.24, 4.65; P-trend~=~0.003) and a score of sphingomyelins with fully saturated sphingoid-fatty acid pairs (RR Q4 versus Q1~=~3.15; 95\% CI: 1.75, 5.67; P-trend~<0.001) both were positively associated with risk of diabetes, whereas scores of glycosylceramides, lactosylceramides, or other unsaturated sphingomyelins (even if having an SFA base) were not associated with risk of diabetes. After additional adjustment for numerous traditional risk factors (especially triglycerides), both associations were attenuated and only the saturated-sphingomyelin score remained associated with risk of diabetes (RR Q4 versus Q1~=~1.98; 95\% CI: 1.09, 3.59; P-trend~=~0.031).

CONCLUSIONS: Our findings suggest that a cluster of saturated sphingomyelins may be associated with elevated risk of diabetes beyond traditional risk factors, which needs to be verified in other population studies. This study was registered at clinicaltrials.gov as NCT02060344.

}, keywords = {Adolescent, Adult, Aged, Diabetes Mellitus, Female, Hispanic Americans, Humans, Male, Middle Aged, Prospective Studies, Risk Factors, Sphingolipids, United States, Young Adult}, issn = {1938-3207}, doi = {10.1093/ajcn/nqaa114}, author = {Chen, Guo-Chong and Chai, Jin Choul and Yu, Bing and Michelotti, Gregory A and Grove, Megan L and Fretts, Amanda M and Daviglus, Martha L and Garcia-Bedoya, Olga L and Thyagarajan, Bharat and Schneiderman, Neil and Cai, Jianwen and Kaplan, Robert C and Boerwinkle, Eric and Qi, Qibin} } @article {94, title = {Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data.}, journal = {Genet Med}, volume = {22}, year = {2020}, month = {2020 05}, pages = {945-953}, abstract = {

PURPOSE: Spinal muscular atrophy (SMA), caused by loss of the SMN1 gene, is a leading cause of early childhood death. Due to the near identical sequences of SMN1 and SMN2, analysis of this region is challenging. Population-wide SMA screening to quantify the SMN1 copy number (CN) is recommended by the American College of Medical Genetics and Genomics.

METHODS: We developed a method that accurately identifies the CN of SMN1 and SMN2 using genome sequencing (GS) data by analyzing read depth and eight informative reference genome differences between SMN1/2.

RESULTS: We characterized SMN1/2 in 12,747 genomes, identified 1568 samples with SMN1 gains or losses and 6615 samples with SMN2 gains or losses, and calculated a pan-ethnic carrier frequency of 2\%, consistent with previous studies. Additionally, 99.8\% of our SMN1 and 99.7\% of SMN2 CN calls agreed with orthogonal methods, with a recall of 100\% for SMA and 97.8\% for carriers, and a precision of 100\% for both SMA and carriers.

CONCLUSION: This SMN copy-number caller can be used to identify both carrier and affected status of SMA, enabling SMA testing to be offered as a comprehensive test in neonatal care and an accurate carrier screening tool in GS sequencing projects.

}, keywords = {Base Sequence, Child, Child, Preschool, Humans, Muscular Atrophy, Spinal, Survival of Motor Neuron 1 Protein}, issn = {1530-0366}, doi = {10.1038/s41436-020-0754-0}, author = {Chen, Xiao and Sanchis-Juan, Alba and French, Courtney E and Connell, Andrew J and Delon, Isabelle and Kingsbury, Zoya and Chawla, Aditi and Halpern, Aaron L and Taft, Ryan J and Bentley, David R and Butchbach, Matthew E R and Raymond, F Lucy and Eberle, Michael A} } @article {114, title = {A structural variation reference for medical and population genetics.}, journal = {Nature}, volume = {581}, year = {2020}, month = {2020 05}, pages = {444-451}, abstract = {

Structural variants (SVs) rearrange large segments of DNA and can have profound consequences in evolution and human disease. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral in the interpretation of single-nucleotide variants (SNVs). However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54\% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29\% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9\% of samples, and estimate that 0.13\% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings. This SV resource is freely distributed via the gnomAD browser and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

}, keywords = {Continental Population Groups, Disease, Female, Genetic Testing, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genotyping Techniques, Humans, Male, Middle Aged, Mutation, Polymorphism, Single Nucleotide, Reference Standards, Selection, Genetic, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2287-8}, author = {Collins, Ryan L and Brand, Harrison and Karczewski, Konrad J and Zhao, Xuefang and Alf{\"o}ldi, Jessica and Francioli, Laurent C and Khera, Amit V and Lowther, Chelsea and Gauthier, Laura D and Wang, Harold and Watts, Nicholas A and Solomonson, Matthew and O{\textquoteright}Donnell-Luria, Anne and Baumann, Alexander and Munshi, Ruchi and Walker, Mark and Whelan, Christopher W and Huang, Yongqing and Brookings, Ted and Sharpe, Ted and Stone, Matthew R and Valkanas, Elise and Fu, Jack and Tiao, Grace and Laricchia, Kristen M and Ruano-Rubio, Valentin and Stevens, Christine and Gupta, Namrata and Cusick, Caroline and Margolin, Lauren and Taylor, Kent D and Lin, Henry J and Rich, Stephen S and Post, Wendy S and Chen, Yii-Der Ida and Rotter, Jerome I and Nusbaum, Chad and Philippakis, Anthony and Lander, Eric and Gabriel, Stacey and Neale, Benjamin M and Kathiresan, Sekar and Daly, Mark J and Banks, Eric and MacArthur, Daniel G and Talkowski, Michael E} } @article {84, title = {Structural variant calling: the long and the short of it.}, journal = {Genome Biol}, volume = {20}, year = {2019}, month = {2019 11 20}, pages = {246}, abstract = {

Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.

}, issn = {1474-760X}, doi = {10.1186/s13059-019-1828-7}, author = {Mahmoud, Medhat and Gobet, Nastassia and Cruz-D{\'a}valos, Diana Ivette and Mounier, Ninon and Dessimoz, Christophe and Sedlazeck, Fritz J} } @article {85, title = {svtools: population-scale analysis of structural variation.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 Nov 01}, pages = {4782-4787}, abstract = {

SUMMARY: Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps-including deletions, duplications, mobile element insertions, inversions and other rearrangements-in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of >=100~000 genomes. These tools will help enable the next generation of human genetics studies.

AVAILABILITY AND IMPLEMENTATION: svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btz492}, author = {Larson, David E and Abel, Haley J and Chiang, Colby and Badve, Abhijit and Das, Indraniel and Eldred, James M and Layer, Ryan M and Hall, Ira M} } @article {26, title = {Simultaneous epitope and transcriptome measurement in single cells.}, journal = {Nat Methods}, volume = {14}, year = {2017}, month = {2017 Sep}, pages = {865-868}, abstract = {

High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels. Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout. CITE-seq is compatible with existing single-cell sequencing approaches and scales readily with throughput increases.

}, keywords = {Epitope Mapping, Epitopes, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Sequence Analysis, RNA, Tissue Array Analysis, Transcriptome}, issn = {1548-7105}, doi = {10.1038/nmeth.4380}, author = {Stoeckius, Marlon and Hafemeister, Christoph and Stephenson, William and Houck-Loomis, Brian and Chattopadhyay, Pratip K and Swerdlow, Harold and Satija, Rahul and Smibert, Peter} } @article {20, title = {SVScore: an impact prediction tool for structural variation.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Apr 01}, pages = {1083-1085}, abstract = {

SUMMARY: Here we present SVScore, a tool for in silico structural variation (SV) impact prediction. SVScore aggregates per-base single nucleotide polymorphism (SNP) pathogenicity scores across relevant genomic intervals for each SV in a manner that considers variant type, gene features and positional uncertainty. We show that the allele frequency spectrum of high-scoring SVs is strongly skewed toward lower frequencies, suggesting that they are under purifying selection, and that SVScore identifies deleterious variants more effectively than alternative methods. Notably, our results also suggest that duplications are under surprisingly strong selection relative to deletions, and that there are a similar number of strongly pathogenic SVs and SNPs in the human population.

AVAILABILITY AND IMPLEMENTATION: SVScore is implemented in Perl and available freely at {{ http://www.github.com/lganel/SVScore }} for use under the MIT license.

CONTACT: ihall@wustl.edu.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Gene Frequency, Genomic Structural Variation, Genomics, Humans, Polymorphism, Single Nucleotide, Sequence Deletion, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw789}, author = {Ganel, Liron and Abel, Haley J and Hall, Ira M} }