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371 results on '"Ardlie, Kristin G."'

2. Multiset correlation and factor analysis enables exploration of multi-omics data

Author

Brown, Brielin C, Wang, Collin, Kasela, Silva, Aguet, François, Nachun, Daniel C, Taylor, Kent D, Tracy, Russell P, Durda, Peter, Liu, Yongmei, Johnson, W Craig, Van Den Berg, David, Gupta, Namrata, Gabriel, Stacy, Smith, Joshua D, Gerzsten, Robert, Clish, Clary, Wong, Quenna, Papanicolau, George, Blackwell, Thomas W, Rotter, Jerome I, Rich, Stephen S, Barr, R Graham, Ardlie, Kristin G, Knowles, David A, and Lappalainen, Tuuli

Subjects
Biological Sciences, Genetics, Aging, Biotechnology, Cardiovascular, Human Genome, 2.5 Research design and methodologies (aetiology), Aetiology, Good Health and Well Being
Abstract

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Published
2023

3. Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

Author

Buschur, Kristina L, Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G, Durda, Peter, Craig Johnson, W, Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S, Rotter, Jerome I, Smith, Josh, Taylor, Kent D, Tracy, Russell P, Lappalainen, Tuuli, Graham Barr, R, Sciurba, Frank, Hersh, Craig P, and Benos, Panayiotis V

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Chronic Obstructive Pulmonary Disease, Genetics, Lung, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Good Health and Well Being, Humans, Gene Regulatory Networks, Smokers, Genome-Wide Association Study, Pulmonary Disease, Chronic Obstructive, Prognosis, COPD, Graphical models, Gene expression, Disease subtypes, Cardiorespiratory Medicine and Haematology, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment.MethodsBlood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples.ResultsThe discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS).ConclusionsThe identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis.

Published
2023

5. Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits

Author

Patel, Roshni A, Musharoff, Shaila A, Spence, Jeffrey P, Pimentel, Harold, Tcheandjieu, Catherine, Mostafavi, Hakhamanesh, Sinnott-Armstrong, Nasa, Clarke, Shoa L, Smith, Courtney J, Program, VA Million Veteran, Durda, Peter P, Taylor, Kent D, Tracy, Russell, Liu, Yongmei, Johnson, W Craig, Aguet, Francois, Ardlie, Kristin G, Gabriel, Stacey, Smith, Josh, Nickerson, Deborah A, Rich, Stephen S, Rotter, Jerome I, Tsao, Philip S, Assimes, Themistocles L, and Pritchard, Jonathan K

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Atherosclerosis, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Cholesterol, LDL, Gene Expression, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, White People, V.A. Million Veteran Program, complex traits, genetic correlation, genome-wide association, population genetics, statistical genetics, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Published
2022

6. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Author

Weeks, Elle M., Ulirsch, Jacob C., Cheng, Nathan Y., Trippe, Brian L., Fine, Rebecca S., Miao, Jenkai, Patwardhan, Tejal A., Kanai, Masahiro, Nasser, Joseph, Fulco, Charles P., Tashman, Katherine C., Aguet, Francois, Li, Taibo, Ordovas-Montanes, Jose, Smillie, Christopher S., Biton, Moshe, Shalek, Alex K., Ananthakrishnan, Ashwin N., Xavier, Ramnik J., Regev, Aviv, Gupta, Rajat M., Lage, Kasper, Ardlie, Kristin G., Hirschhorn, Joel N., Lander, Eric S., Engreitz, Jesse M., and Finucane, Hilary K.

Published
2023
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7. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Author

Kasela, Silva, Aguet, François, Kim-Hellmuth, Sarah, Brown, Brielin C., Nachun, Daniel C., Tracy, Russell P., Durda, Peter, Liu, Yongmei, Taylor, Kent D., Johnson, W. Craig, Van Den Berg, David, Gabriel, Stacey, Gupta, Namrata, Smith, Joshua D., Blackwell, Thomas W., Rotter, Jerome I., Ardlie, Kristin G., Manichaikul, Ani, Rich, Stephen S., Barr, R. Graham, and Lappalainen, Tuuli

Published
2024
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8. Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies

Author

Zhang, Daiwei, Gao, Boran, Feng, Qidi, Manichaikul, Ani, Peloso, Gina M., Tracy, Russell P., Durda, Peter, Taylor, Kent D., Liu, Yongmei, Johnson, W. Craig, Gabriel, Stacey, Gupta, Namrata, Smith, Joshua D., Aguet, Francois, Ardlie, Kristin G., Blackwell, Thomas W., Gerszten, Robert E., Rich, Stephen S., Rotter, Jerome I., Scott, Laura J., Zhou, Xiang, and Lee, Seunggeun

Published
2024
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9. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Taliun, Daniel, Harris, Daniel N, Kessler, Michael D, Carlson, Jedidiah, Szpiech, Zachary A, Torres, Raul, Taliun, Sarah A Gagliano, Corvelo, André, Gogarten, Stephanie M, Kang, Hyun Min, Pitsillides, Achilleas N, LeFaive, Jonathon, Lee, Seung-been, Tian, Xiaowen, Browning, Brian L, Das, Sayantan, Emde, Anne-Katrin, Clarke, Wayne E, Loesch, Douglas P, Shetty, Amol C, Blackwell, Thomas W, Smith, Albert V, Wong, Quenna, Liu, Xiaoming, Conomos, Matthew P, Bobo, Dean M, Aguet, François, Albert, Christine, Alonso, Alvaro, Ardlie, Kristin G, Arking, Dan E, Aslibekyan, Stella, Auer, Paul L, Barnard, John, Barr, R Graham, Barwick, Lucas, Becker, Lewis C, Beer, Rebecca L, Benjamin, Emelia J, Bielak, Lawrence F, Blangero, John, Boehnke, Michael, Bowden, Donald W, Brody, Jennifer A, Burchard, Esteban G, Cade, Brian E, Casella, James F, Chalazan, Brandon, Chasman, Daniel I, Chen, Yii-Der Ida, Cho, Michael H, Choi, Seung Hoan, Chung, Mina K, Clish, Clary B, Correa, Adolfo, Curran, Joanne E, Custer, Brian, Darbar, Dawood, Daya, Michelle, de Andrade, Mariza, DeMeo, Dawn L, Dutcher, Susan K, Ellinor, Patrick T, Emery, Leslie S, Eng, Celeste, Fatkin, Diane, Fingerlin, Tasha, Forer, Lukas, Fornage, Myriam, Franceschini, Nora, Fuchsberger, Christian, Fullerton, Stephanie M, Germer, Soren, Gladwin, Mark T, Gottlieb, Daniel J, Guo, Xiuqing, Hall, Michael E, He, Jiang, Heard-Costa, Nancy L, Heckbert, Susan R, Irvin, Marguerite R, Johnsen, Jill M, Johnson, Andrew D, Kaplan, Robert, Kardia, Sharon LR, Kelly, Tanika, Kelly, Shannon, Kenny, Eimear E, Kiel, Douglas P, Klemmer, Robert, Konkle, Barbara A, Kooperberg, Charles, Köttgen, Anna, Lange, Leslie A, Lasky-Su, Jessica, Levy, Daniel, Lin, Xihong, Lin, Keng-Han, Liu, Chunyu, and Loos, Ruth JF

Subjects
Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Cytochrome P-450 CYP2D6, Genetic Variation, Genome, Human, Genomics, Haplotypes, Heterozygote, Humans, INDEL Mutation, Loss of Function Mutation, Mutagenesis, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Population Density, Precision Medicine, Quality Control, Sample Size, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, General Science & Technology
Abstract

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Published
2021

10. Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants.

Author

Zhao, Xutong, Qiao, Dandi, Yang, Chaojie, Kasela, Silva, Kim, Wonji, Ma, Yanlin, Shrine, Nick, Batini, Chiara, Sofer, Tamar, Taliun, Sarah A Gagliano, Sakornsakolpat, Phuwanat, Balte, Pallavi P, Prokopenko, Dmitry, Yu, Bing, Lange, Leslie A, Dupuis, Josée, Cade, Brian E, Lee, Jiwon, Gharib, Sina A, Daya, Michelle, Laurie, Cecelia A, Ruczinski, Ingo, Cupples, L Adrienne, Loehr, Laura R, Bartz, Traci M, Morrison, Alanna C, Psaty, Bruce M, Vasan, Ramachandran S, Wilson, James G, Taylor, Kent D, Durda, Peter, Johnson, W Craig, Cornell, Elaine, Guo, Xiuqing, Liu, Yongmei, Tracy, Russell P, Ardlie, Kristin G, Aguet, François, VanDenBerg, David J, Papanicolaou, George J, Rotter, Jerome I, Barnes, Kathleen C, Jain, Deepti, Nickerson, Deborah A, Muzny, Donna M, Metcalf, Ginger A, Doddapaneni, Harshavardhan, Dugan-Perez, Shannon, Gupta, Namrata, Gabriel, Stacey, Rich, Stephen S, O'Connor, George T, Redline, Susan, Reed, Robert M, Laurie, Cathy C, Daviglus, Martha L, Preudhomme, Liana K, Burkart, Kristin M, Kaplan, Robert C, Wain, Louise V, Tobin, Martin D, London, Stephanie J, Lappalainen, Tuuli, Oelsner, Elizabeth C, Abecasis, Goncalo R, Silverman, Edwin K, Barr, R Graham, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lung Working Group, Cho, Michael H, and Manichaikul, Ani

Subjects
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lung Working Group, Lung, Humans, Pulmonary Disease, Chronic Obstructive, Genetic Predisposition to Disease, Intracellular Signaling Peptides and Proteins, Calcium-Binding Proteins, Small Ubiquitin-Related Modifier Proteins, Follow-Up Studies, Feasibility Studies, Polymorphism, Single Nucleotide, Adult, Aged, Aged, 80 and over, Middle Aged, African Americans, Female, Male, Protein Inhibitors of Activated STAT, Respiratory Physiological Phenomena, Genome-Wide Association Study, Genetic Loci, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Whole Genome Sequencing, and over, Polymorphism, Single Nucleotide, Pulmonary Disease, Chronic Obstructive
Abstract

Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.

Published
2020

12. Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk

Author

Yu, Zhi, Fidler, Trevor P., Ruan, Yunfeng, Vlasschaert, Caitlyn, Nakao, Tetsushi, Uddin, Md Mesbah, Mack, Taralynn, Niroula, Abhishek, Heimlich, J. Brett, Zekavat, Seyedeh M., Gibson, Christopher J., Griffin, Gabriel K., Wang, Yuxuan, Peloso, Gina M., Heard-Costa, Nancy, Levy, Daniel, Vasan, Ramachandran S., Aguet, Francois, Ardlie, Kristin G., Taylor, Kent D., Rich, Stephen S., Rotter, Jerome I., Libby, Peter, Jaiswa, Siddhartha, Ebert, Benjamin L., Bick, Alexander G., Tall, Alan R., and Natarajan, Pradeep

Subjects
United States. National Heart, Lung and Blood Institute, Thermo Fisher Scientific Inc., Novartis AG, DNA repair, Scientific equipment and supplies industry, Gene expression, Inflammation -- Risk factors, Genes, Cardiovascular diseases -- Risk factors, Macrophages, DNA damage, Methyltransferases, Type 2 diabetes -- Risk factors, Genetically modified organisms, Pharmaceutical industry, Health care industry
Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVDs), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. We identified CHIP using whole-exome sequencing data of blood DNA and modeled as a composite, considering all driver genes together, as well as separately for common drivers (DNMT3A, TET2, ASXL1, and JAK2). We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identified IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2- and ASXLI-associated CVD risk. We show that CRISPR-induced Asx/1-mutated murine macrophages had a particularly heightened inflammatory response to AIM2 agonism, associated with an increased DNA damage response, as well as increased IL-10 secretion, mirroring a CVDprotective effect of IL10 expression in ASXL1 CHIP. Our study supports the role of inflammasomes in CHIP-associated CVD and provides evidence to support gene-specific strategies to address CHIP-associated CVD risk., Introduction Clonal hematopoiesis (CH) of indeterminate potential (CHIP) is the age-related acquisition and expansion of somatic mutations of genes frequently mutated in hematologic malignancies (e.g., DNMT3A, TET2, ASXL1, or JAK2) [...]

Published
2023
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13. Machine learning‐based clustering identifies obesity subgroups with differential multi‐omics profiles and metabolic patterns.

Author

Anwar, Mohammad Y., Highland, Heather, Buchanan, Victoria Lynn, Graff, Mariaelisa, Young, Kristin, Taylor, Kent D., Tracy, Russell P., Durda, Peter, Liu, Yongmei, Johnson, Craig W., Aguet, Francois, Ardlie, Kristin G., Gerszten, Robert E., Clish, Clary B., Lange, Leslie A., Ding, Jingzhong, Goodarzi, Mark O., Chen, Yii‐Der Ida, Peloso, Gina M., and Guo, Xiuqing

Subjects
HEART metabolism disorders, PATHOGENESIS, INSULIN resistance, BLOOD sugar, AGE differences
Abstract

Objective: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi‐omics approach might improve identification of subgroups of individuals with obesity who have distinct cardiometabolic disease patterns. Methods: We performed machine learning‐based, integrative unsupervised clustering to identify proteomics‐ and metabolomics‐defined subpopulations of individuals living with obesity (BMI ≥ 30 kg/m2), leveraging data from 243 individuals in the Multi‐Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to the observed clusters were functionally characterized. We performed multivariate regression to assess whether the individuals in each cluster demonstrated differential patterns of cardiometabolic traits. Results: We identified two distinct clusters (iCluster1 and 2). iCluster2 had significantly higher average BMI values, fasting blood glucose, and inflammation. iCluster1 was associated with higher levels of total cholesterol and high‐density lipoprotein cholesterol. Pathways mediating cell growth, lipogenesis, and energy expenditures were positively associated with iCluster1. Inflammatory response and insulin resistance pathways were positively associated with iCluster2. Conclusions: Although the two identified clusters may represent progressive obesity‐related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Histologic and Quality Assessment of Genotype-Tissue Expression (GTEx) Research Samples: A Large Postmortem Tissue Collection

Author

Sobin, Leslie, primary, Barcus, Mary, additional, Branton, Philip A., additional, Engel, Kelly B., additional, Keen, Judy, additional, Tabor, David, additional, Ardlie, Kristin G., additional, Greytak, Sarah R., additional, Roche, Nancy, additional, Luke, Brian, additional, Vaught, Jim, additional, Guan, Ping, additional, and Moore, Helen M., additional

Published
2024
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16. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

Author

Yang, Fan, Wang, Jiebiao, Consortium, The GTEx, Pierce, Brandon L, Chen, Lin S, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Jo, Brian, Kang, Eun Yong, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, and Wang, Gao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, Generic health relevance, Good Health and Well Being, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Selection, Genetic, Tissue Distribution, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Published
2017

17. Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Author

Saha, Ashis, Kim, Yungil, Gewirtz, Ariel DH, Jo, Brian, Gao, Chuan, McDowell, Ian C, Consortium, The GTEx, Engelhardt, Barbara E, Battle, Alexis, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Kang, Eun Yong, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, and Sul, Jae Hoon

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Generic health relevance, Bayes Theorem, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genotyping Techniques, Humans, Organ Specificity, Polymorphism, Single Nucleotide, RNA Splicing, Sequence Analysis, RNA, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Published
2017

18. Dynamic landscape and regulation of RNA editing in mammals

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Adenosine Deaminase, Animals, Female, Genotype, HEK293 Cells, Humans, Male, Mice, Muscles, Nuclear Proteins, Organ Specificity, Primates, Proteolysis, RNA Editing, RNA-Binding Proteins, Spatio-Temporal Analysis, Species Specificity, Transcriptome, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing.

Published
2017

19. Landscape of X chromosome inactivation across human tissues

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Genetics, Clinical Research, Human Genome, Generic health relevance, Good Health and Well Being, Chromosomes, Human, X, Female, Genes, X-Linked, Genome, Human, Genomics, Humans, Male, Organ Specificity, Phenotype, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, X Chromosome Inactivation, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.

Published
2017

20. The impact of rare variation on gene expression across tissues

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Published
2017

21. Mitochondrial genome copy number variation across tissues in mice and humans.

Author

Rath, Sneha P., Gupta, Rahul, Todres, Ellen, Hong Wang, Jourdain, Alexis A., Ardlie, Kristin G., Calvo, Sarah E., and Mootha, Vamsi K.

Subjects
MITOCHONDRIAL DNA, WHOLE genome sequencing, DATA libraries, MITOCHONDRIAL proteins, CELL lines
Abstract

The mammalian mitochondrial genome (mtDNA) is multicopy and its copy number (mtCN) varies widely across tissues, in development and in disease. Here, we systematically catalog this variation by assaying mtCN in 52 human tissues across 952 donors (10,499 samples from the Genotype-Tissue Expression project) and 20 murine tissues using qPCR, capturing 50-and 200-fold variation, respectively. We also estimate per cell mtCN across 173 human cell lines from the Cancer Cell Line Encyclopedia using whole-genome sequencing data and observe >50-fold variation. We then leverage the vast amount of genomics data available for these repositories to credential our resource and uncover mtDNA-related biology. Using already existing proteomics data, we show that variation in mtCN can be predicted by variation in TFAM, histone, and mitochondrial ribosome protein abundance. We also integrate mtCN estimates with the CRISPR gene dependency measurements to find that cell lines with high mtCN are resistant to loss of GPX4, a glutathione phospholipid hydroperoxidase. Our resource captures variation in mtCN across mammalian tissues and should be broadly useful to the research community. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Higher Maternal Body Mass Index Is Associated With Lower Placental Expression of EPYC: A Genome-Wide Transcriptomic Study.

Author

Sordillo, Joanne E., White, Frédérique, Majid, Sana, Aguet, François, Ardlie, Kristin G., Karumanchi, S. Ananth, Florez, Jose C., Powe, Camille E., Edlow, Andrea G., Bouchard, Luigi, Jacques, Pierre-Etienne, and Hivert, Marie-France

Subjects
BODY mass index, PREGNANCY, TRANSCRIPTOMES
Abstract

Context: Elevated body mass index (BMI) in pregnancy is associated with adverse maternal and fetal outcomes. The placental transcriptome may elucidate molecular mechanisms underlying these associations.Objective: We examined the association of first-trimester maternal BMI with the placental transcriptome in the Gen3G prospective cohort.Methods: We enrolled participants at 5 to 16 weeks of gestation and measured height and weight. We collected placenta samples at delivery. We performed whole-genome RNA sequencing using Illumina HiSeq 4000 and aligned RNA sequences based on the GTEx v8 pipeline. We conducted differential gene expression analysis of over 15 000 genes from 450 placental samples and reported the change in normalized gene expression per 1-unit increase in log2 BMI (kg/m²) as a continuous variable using Limma Voom. We adjusted models for maternal age, fetal sex, gestational age at delivery, gravidity, and surrogate variables accounting for technical variability. We compared participants with BMI of 18.5 to 24.9 mg/kg² (N = 257) vs those with obesity (BMI ≥30 kg/m², N = 82) in secondary analyses.Results: Participants’ mean ± SD age was 28.2 ± 4.4 years and BMI was 25.4 ± 5.5 kg/m² in early pregnancy. Higher maternal BMI was associated with lower placental expression of EPYC (slope = −1.94, false discovery rate [FDR]-adjusted P = 7.3 × 10−6 for continuous BMI; log2 fold change = −1.35, FDR-adjusted P = 3.4 × 10−3 for BMI ≥30 vs BMI 18.5-24.9 kg/m²) and with higher placental expression of IGFBP6, CHRDL1, and CXCL13 after adjustment for covariates and accounting for multiple testing (FDR < 0.05).Conclusion: Our genome-wide transcriptomic study revealed novel genes potentially implicated in placental biologic response to higher maternal BMI in early pregnancy. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Higher Maternal Body Mass Index Is Associated With Lower Placental Expression of EPYC: A Genome-Wide Transcriptomic Study

Author

Sordillo, Joanne E, primary, White, Frédérique, additional, Majid, Sana, additional, Aguet, François, additional, Ardlie, Kristin G, additional, Karumanchi, S Ananth, additional, Florez, Jose C, additional, Powe, Camille E, additional, Edlow, Andrea G, additional, Bouchard, Luigi, additional, Jacques, Pierre-Etienne, additional, and Hivert, Marie-France, additional

Published
2023
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25. Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Author

Zhang, Daiwei, primary, Gao, Boran, additional, Feng, Qidi, additional, Manichaikul, Ani W., additional, Peloso, Gina M., additional, Tracy, Russell, additional, Durda, Peter, additional, Taylor, Kent, additional, Liu, Yongmei, additional, Johnson, W. Craig, additional, Gabriel, Stacey, additional, Gupta, Namrata, additional, Smith, Joshua D., additional, Aguet, Francois, additional, Ardlie, Kristin G., additional, Blackwell, Thomas W., additional, Gerszten, Robert E., additional, Rich, Stephen S., additional, Rotter, Jerome I., additional, Scott, Laura, additional, Zhou, Xiang, additional, and Lee, Seunggeun, additional

Published
2023
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26. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Author

Kasela, Silva, primary, Aguet, François, additional, Kim-Hellmuth, Sarah, additional, Brown, Brielin C., additional, Nachun, Daniel C., additional, Tracy, Russell P., additional, Durda, Peter, additional, Liu, Yongmei, additional, Taylor, Kent D., additional, Johnson, W. Craig, additional, Van Den Berg, David, additional, Gabriel, Stacey, additional, Gupta, Namrata, additional, Smith, Joshua D., additional, Blackwell, Thomas W., additional, Rotter, Jerome I., additional, Ardlie, Kristin G., additional, Manichaikul, Ani, additional, Rich, Stephen S., additional, Barr, R. Graham, additional, and Lappalainen, Tuuli, additional

Published
2023
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28. A large-scale rheumatoid arthritis genetic study identifies association at chromosome 9q33.2.

Author

Chang, Monica, Rowland, Charles M, Garcia, Veronica E, Schrodi, Steven J, Catanese, Joseph J, van der Helm-van Mil, Annette HM, Ardlie, Kristin G, Amos, Christopher I, Criswell, Lindsey A, Kastner, Daniel L, Gregersen, Peter K, Kurreeman, Fina AS, Toes, Rene EM, Huizinga, Tom WJ, Seldin, Michael F, and Begovich, Ann B

Subjects
Chromosomes, Human, Pair 9, Humans, Arthritis, Rheumatoid, Genetic Predisposition to Disease, TNF Receptor-Associated Factor 1, Nuclear Proteins, DNA, Intergenic, Genetic Markers, Rheumatoid Factor, Logistic Models, Risk Factors, Case-Control Studies, Physical Chromosome Mapping, Genotype, Haplotypes, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Alleles, European Continental Ancestry Group, North America, Netherlands, Female, Male, Genetic Variation, DNA-Binding Proteins, Transcription Factors, Arthritis, Rheumatoid, Chromosomes, Human, Pair 9, DNA, Intergenic, Polymorphism, Single Nucleotide, Genetics, Developmental Biology
Abstract

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease affecting both joints and extra-articular tissues. Although some genetic risk factors for RA are well-established, most notably HLA-DRB1 and PTPN22, these markers do not fully account for the observed heritability. To identify additional susceptibility loci, we carried out a multi-tiered, case-control association study, genotyping 25,966 putative functional SNPs in 475 white North American RA patients and 475 matched controls. Significant markers were genotyped in two additional, independent, white case-control sample sets (661 cases/1322 controls from North America and 596 cases/705 controls from The Netherlands) identifying a SNP, rs1953126, on chromosome 9q33.2 that was significantly associated with RA (OR(common) = 1.28, trend P(comb) = 1.45E-06). Through a comprehensive fine-scale-mapping SNP-selection procedure, 137 additional SNPs in a 668 kb region from MEGF9 to STOM on 9q33.2 were chosen for follow-up genotyping in a staged-approach. Significant single marker results (P(comb) 5.41E-09). The observed association patterns for these SNPs had heightened statistical significance and a higher degree of consistency across sample sets. In addition, the allele frequencies for these SNPs displayed reduced variability between control groups when compared to other SNPs. Lastly, in combination with the other two known genetic risk factors, HLA-DRB1 and PTPN22, the variants reported here generate more than a 45-fold RA-risk differential.

Published
2008

30. Transcriptional and Cellular Diversity of the Human Heart

Author

Tucker, Nathan R., Chaffin, Mark, Fleming, Stephen J., Hall, Amelia W., Parsons, Victoria A., Bedi, Kenneth C., Jr, Akkad, Amer-Denis, Herndon, Caroline N., Arduini, Alessandro, Papangeli, Irinna, Roselli, Carolina, Aguet, François, Choi, Seung Hoan, Ardlie, Kristin G., Babadi, Mehrtash, Margulies, Kenneth B., Stegmann, Christian M., and Ellinor, Patrick T.

Published
2020
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32. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation

Author

Gamazon, Eric R., Segrè, Ayellet V., van de Bunt, Martijn, Wen, Xiaoquan, Xi, Hualin S., Hormozdiari, Farhad, Ongen, Halit, Konkashbaev, Anuar, Derks, Eske M., Aguet, François, Quan, Jie, GTEx Consortium, Nicolae, Dan L., Eskin, Eleazar, Kellis, Manolis, Getz, Gad, McCarthy, Mark I., Dermitzakis, Emmanouil T., Cox, Nancy J., and Ardlie, Kristin G.

Published
2018
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33. The origins and functional effects of postzygotic mutations throughout the human life span

Author

Rockweiler, Nicole B., primary, Ramu, Avinash, additional, Nagirnaja, Liina, additional, Wong, Wing H., additional, Noordam, Michiel J., additional, Drubin, Casey W., additional, Huang, Ni, additional, Miller, Brian, additional, Todres, Ellen Z., additional, Vigh-Conrad, Katinka A., additional, Zito, Antonino, additional, Small, Kerrin S., additional, Ardlie, Kristin G., additional, Cohen, Barak A., additional, and Conrad, Donald F., additional

Published
2023
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34. Data from Evaluation of the 8q24 Prostate Cancer Risk Locus and MYC Expression

Author

Pomerantz, Mark M., primary, Beckwith, Christine A., primary, Regan, Meredith M., primary, Wyman, Stacia K., primary, Petrovics, Gyorgy, primary, Chen, Yongmei, primary, Hawksworth, Dorota J., primary, Schumacher, Fredrick R., primary, Mucci, Lorelei, primary, Penney, Kathryn L., primary, Stampfer, Meir J., primary, Chan, Jennifer A., primary, Ardlie, Kristin G., primary, Fritz, Brian R., primary, Parkin, Rachael K., primary, Lin, Daniel W., primary, Dyke, Michelle, primary, Herman, Paula, primary, Lee, Steve, primary, Oh, William K., primary, Kantoff, Philip W., primary, Tewari, Muneesh, primary, McLeod, David G., primary, Srivastava, Shiv, primary, and Freedman, Matthew L., primary

Published
2023
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35. Supplementary Figure 1 from Evaluation of the 8q24 Prostate Cancer Risk Locus and MYC Expression

Author

Pomerantz, Mark M., primary, Beckwith, Christine A., primary, Regan, Meredith M., primary, Wyman, Stacia K., primary, Petrovics, Gyorgy, primary, Chen, Yongmei, primary, Hawksworth, Dorota J., primary, Schumacher, Fredrick R., primary, Mucci, Lorelei, primary, Penney, Kathryn L., primary, Stampfer, Meir J., primary, Chan, Jennifer A., primary, Ardlie, Kristin G., primary, Fritz, Brian R., primary, Parkin, Rachael K., primary, Lin, Daniel W., primary, Dyke, Michelle, primary, Herman, Paula, primary, Lee, Steve, primary, Oh, William K., primary, Kantoff, Philip W., primary, Tewari, Muneesh, primary, McLeod, David G., primary, Srivastava, Shiv, primary, and Freedman, Matthew L., primary

Published
2023
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36. Multiset correlation and factor analysis enables exploration of multi-omics data

Author

Brown, Brielin C., Wang, Collin, Kasela, Silva, Aguet, François, Nachun, Daniel C., Taylor, Kent D., Tracy, Russell P., Durda, Peter, Liu, Yongmei, Johnson, W. Craig, Van Den Berg, David, Gupta, Namrata, Gabriel, Stacy, Smith, Joshua D., Gerzsten, Robert, Clish, Clary, Wong, Quenna, Papanicolau, George, Blackwell, Thomas W., Rotter, Jerome I., Rich, Stephen S., Barr, R. Graham, Ardlie, Kristin G., Knowles, David A., Lappalainen, Tuuli, Brown, Brielin C., Wang, Collin, Kasela, Silva, Aguet, François, Nachun, Daniel C., Taylor, Kent D., Tracy, Russell P., Durda, Peter, Liu, Yongmei, Johnson, W. Craig, Van Den Berg, David, Gupta, Namrata, Gabriel, Stacy, Smith, Joshua D., Gerzsten, Robert, Clish, Clary, Wong, Quenna, Papanicolau, George, Blackwell, Thomas W., Rotter, Jerome I., Rich, Stephen S., Barr, R. Graham, Ardlie, Kristin G., Knowles, David A., and Lappalainen, Tuuli

Abstract

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets., QC 20231003

Published
2023
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37. Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

Author

Buschur, Kristina L., Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G., Durda, Peter, Craig Johnson, W., Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S., Rotter, Jerome I., Smith, Josh, Taylor, Kent D., Tracy, Russell P., Lappalainen, Tuuli, Graham Barr, R., Sciurba, Frank, Hersh, Craig P., Benos, Panayiotis V., Buschur, Kristina L., Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G., Durda, Peter, Craig Johnson, W., Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S., Rotter, Jerome I., Smith, Josh, Taylor, Kent D., Tracy, Russell P., Lappalainen, Tuuli, Graham Barr, R., Sciurba, Frank, Hersh, Craig P., and Benos, Panayiotis V.

Abstract

BackgroundChronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment.MethodsBlood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples.ResultsThe discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS).ConclusionsThe identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis., QC 20230303

Published
2023
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38. The landscape of expression and alternative splicing variation across human traits

Author

Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, García Pérez, Raquel, Ramírez Cardeñosa, José Miguel, Ripoll Cladellas, Aida, Chazarra Gil, Rubén, Oliveros Díez, Winona, Soldatkina, Oleksandra, Bosio, Mattia, Rognon, Paul Joris Denis, Capella Gutiérrez, Salvador, Calvo Llorca, Miguel, Reverter Comes, Ferran, Guigo Serra, Roderic, Aguet, François, Ferreira, Pedro G., Ardlie, Kristin G., Mele Messeguer, Marta, Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, García Pérez, Raquel, Ramírez Cardeñosa, José Miguel, Ripoll Cladellas, Aida, Chazarra Gil, Rubén, Oliveros Díez, Winona, Soldatkina, Oleksandra, Bosio, Mattia, Rognon, Paul Joris Denis, Capella Gutiérrez, Salvador, Calvo Llorca, Miguel, Reverter Comes, Ferran, Guigo Serra, Roderic, Aguet, François, Ferreira, Pedro G., Ardlie, Kristin G., and Mele Messeguer, Marta

Abstract

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease., This study was funded by the HumTranscriptom project with reference PID2019-107937GA-I00. R.G.-P. was supported by a Juan de la Cierva fellowship (FJC2020-044119-I) funded by MCIN/AEI/10.13039/501100011033 and ‘‘European Union NextGenerationEU/PRTR.’’ J.M.R. was supported by a predoctoral fellowship from ‘‘la Caixa’’ Foundation (ID 100010434) with code LCF/BQ/DR22/11950022. A.R.-C. was supported by a Formación Personal Investigador (FPI) fellowship (PRE2019-090193) funded by MCIN/AEI. R.C.-G. was supported by an FPI fellowship (PRE2020-092510) funded by MCIN/AEI. M.M. was supported by a Ramon y Cajal fellowship (RYC-2017-22249)., Peer Reviewed, Postprint (published version)

Published
2023

39. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Author

Massachusetts Institute of Technology. Department of Biology, Eraslan, Gökcen, Drokhlyansky, Eugene, Anand, Shankara, Fiskin, Evgenij, Subramanian, Ayshwarya, Slyper, Michal, Wang, Jiali, Van Wittenberghe, Nicholas, Rouhana, John M, Waldman, Julia, Ashenberg, Orr, Lek, Monkol, Dionne, Danielle, Win, Thet Su, Cuoco, Michael S, Kuksenko, Olena, Tsankov, Alexander M, Branton, Philip A, Marshall, Jamie L, Greka, Anna, Getz, Gad, Segrè, Ayellet V, Aguet, François, Rozenblatt-Rosen, Orit, Ardlie, Kristin G, Regev, Aviv, Massachusetts Institute of Technology. Department of Biology, Eraslan, Gökcen, Drokhlyansky, Eugene, Anand, Shankara, Fiskin, Evgenij, Subramanian, Ayshwarya, Slyper, Michal, Wang, Jiali, Van Wittenberghe, Nicholas, Rouhana, John M, Waldman, Julia, Ashenberg, Orr, Lek, Monkol, Dionne, Danielle, Win, Thet Su, Cuoco, Michael S, Kuksenko, Olena, Tsankov, Alexander M, Branton, Philip A, Marshall, Jamie L, Greka, Anna, Getz, Gad, Segrè, Ayellet V, Aguet, François, Rozenblatt-Rosen, Orit, Ardlie, Kristin G, and Regev, Aviv

Abstract

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Published
2023

40. Abstract P240: Higher Body Mass Index is Associated With Lower Placental Expression of EPYC: Results From a Genome-Wide Transcriptomic Study in the Gen3G Cohort

Author

Majid, Sana, primary, Sordillo, Joanne E, additional, White, Frédérique, additional, Aguet, Francois, additional, Ardlie, Kristin G, additional, Karumanchi, Ananth A, additional, Florez, Jose C, additional, Powe, Camille, additional, Edlow, Andrea G, additional, Bouchard, Luigi, additional, Jacques, Pierre-Étienne, additional, and Hivert, Marie-France, additional

Published
2023
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41. The impact of rare variation on gene expression across tissues

Author

Li, Xin, Kim, Yungil, Tsang, Emily K., Davis, Joe R., Damani, Farhan N., Chiang, Colby, Hess, Gaelen T., Zappala, Zachary, Strober, Benjamin J., Scott, Alexandra J., Li, Amy, Ganna, Andrea, Bassik, Michael C., Merker, Jason D., Aguet, Franois, Ardlie, Kristin G., Cummings, Beryl B., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Karczewski, Konrad J., Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segr, Ayellet V., Trowbridge, Casandra A., Tukiainen, Taru, Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Castel, Stephane E., Chen, Lin S., Conrad, Donald F., Cox, Nancy J., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frsard, Laure, Gamazon, Eric R., Garrido-Martn, Diego, Gewirtz, Ariel D.H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Sul, Jae Hoon, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Billy Li, Jin, Li, Qin, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Zhang, Rui, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, A. Roger, Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, W. James, Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, and Zhu, Jingchun

Subjects
Disease susceptibility -- Genetic aspects, Genetic variation -- Observations, Gene expression -- Observations, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Xin Li [1]; Yungil Kim [2]; Emily K. Tsang [1, 3]; Joe R. Davis [1, 4]; Farhan N. Damani [2]; Colby Chiang [5]; Gaelen T. Hess [4]; Zachary Zappala [...]

Published
2017
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42. Dynamic landscape and regulation of RNA editing in mammals

Author

Tan, Meng How, Li, Qin, Shanmugam, Raghuvaran, Piskol, Robert, Kohler, Jennefer, Young, Amy N., Liu, Kaiwen Ivy, Zhang, Rui, Ramaswami, Gokul, Ariyoshi, Kentaro, Gupte, Ankita, Keegan, Liam P., George, Cyril X., Ramu, Avinash, Huang, Ni, Pollina, Elizabeth A., Leeman, Dena S., Rustighi, Alessandra, Goh, Y. P. Sharon, Aguet, Franois, Ardlie, Kristin G., Cummings, Beryl B., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Karczewski, Konrad J., Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segr, Ayellet V., Trowbridge, Casandra A., Tukiainen, Taru, Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Castel, Stephane E., Chen, Lin S., Chiang, Colby, Conrad, Donald F., Cox, Nancy J., Damani, Farhan N., Davis, Joe R., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frsard, Laure, Gamazon, Eric R., Garrido-Martn, Diego, Gewirtz, Ariel D. H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J., Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Strober, Benjamin J., Sul, Jae Hoon, Tsang, Emily K., Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zappala, Zachary, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Li, Jin Billy, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, A. Roger, Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, W. James, Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, Zhu, Jingchun, Chawla, Ajay, Del Sal, Giannino, Peltz, Gary, Brunet, Anne, Samuel, Charles E., OConnell, Mary A., Walkley, Carl R., and Nishikura, Kazuko

Subjects
Genetic research, Mammals -- Genetic aspects, RNA processing -- Research, Genetic regulation, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Meng How Tan (corresponding author) [1, 2, 3]; Qin Li [1]; Raghuvaran Shanmugam [2, 3]; Robert Piskol [1]; Jennefer Kohler [1]; Amy N. Young [1]; Kaiwen Ivy Liu [3]; [...]

Published
2017
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43. Landscape of X chromosome inactivation across human tissues

Author

Tukiainen, Taru, Villani, Alexandra-Chlo, Yen, Angela, Rivas, Manuel A., Marshall, Jamie L., Satija, Rahul, Aguirre, Matt, Gauthier, Laura, Fleharty, Mark, Kirby, Andrew, Cummings, Beryl B., Castel, Stephane E., Karczewski, Konrad J., Aguet, Franois, Byrnes, Andrea, Ardlie, Kristin G., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segr, Ayellet V., Trowbridge, Casandra A., Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Chen, Lin S., Chiang, Colby, Conrad, Donald F., Cox, Nancy J., Damani, Farhan N., Davis, Joe R., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frsard, Laure, Gamazon, Eric R., Garrido-Martn, Diego, Gewirtz, Ariel D. H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J., Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Strober, Benjamin J., Sul, Jae Hoon, Tsang, Emily K., Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zappala, Zachary, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Li, Jin Billy, Li, Qin, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Zhang, Rui, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, A. Roger, Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, W. James, Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, Zhu, Jingchun, Regev, Aviv, and Hacohen, Nir

Subjects
Chromosomes -- Physiological aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Taru Tukiainen (corresponding author) [1, 2]; Alexandra-Chlo Villani [2, 3]; Angela Yen [2, 4]; Manuel A. Rivas [1, 2, 5]; Jamie L. Marshall [1, 2]; Rahul Satija [2, 6, [...]

Published
2017
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44. The human transcriptome across tissues and individuals

Author

The GTEx Consortium, Melé, Marta, Ferreira, Pedro G., Reverter, Ferran, DeLuca, David S., Monlong, Jean, Sammeth, Michael, Young, Taylor R., Goldmann, Jakob M, Pervouchine, Dmitri D., Sullivan, Timothy J., Johnson, Rory, Segrè, Ayellet V., Djebali, Sarah, Niarchou, Anastasia, Wright, Fred A., Lappalainen, Tuuli, Calvo, Miquel, Getz, Gad, Dermitzakis, Emmanouil T., Ardlie, Kristin G., and Guigó, Roderic

Published
2015

46. Multiset correlation and factor analysis enables exploration of multi-omic data

Author

Brown, Brielin C., primary, Wang, Collin, additional, Kasela, Silva, additional, Aguet, François, additional, Nachun, Daniel C., additional, Taylor, Kent D., additional, Tracy, Russell P., additional, Durda, Peter, additional, Liu, Yongmei, additional, Johnson, W. Craig, additional, Van Den Berg, David, additional, Gupta, Namrata, additional, Gabriel, Stacy, additional, Smith, Joshua D., additional, Gerzsten, Robert, additional, Clish, Clary, additional, Wong, Quenna, additional, Papanicolau, George, additional, Blackwell, Thomas W., additional, Rotter, Jerome I., additional, Rich, Stephen S., additional, Ardlie, Kristin G., additional, Knowles, David A., additional, and Lappalainen, Tuuli, additional

Published
2022
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47. Genetic drivers of m6A methylation in human brain, lung, heart and muscle

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Xiong, Xushen, Hou, Lei, Park, Yongjin P, Molinie, Benoit, Ardlie, Kristin G, Aguet, François, Gregory, Richard I, Kellis, Manolis, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Xiong, Xushen, Hou, Lei, Park, Yongjin P, Molinie, Benoit, Ardlie, Kristin G, Aguet, François, Gregory, Richard I, and Kellis, Manolis

Abstract

The most prevalent post-transcriptional mRNA modification, N6-methyladenosine (m6A), plays diverse RNA-regulatory roles, but its genetic control in human tissues remains uncharted. Here we report 129 transcriptome-wide m6A profiles, covering 91 individuals and 4 tissues (brain, lung, muscle and heart) from GTEx/eGTEx. We integrate these with interindividual genetic and expression variation, revealing 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs), which are modestly enriched in, but mostly orthogonal to, expression QTLs. We integrate m6A QTLs with disease genetics, identifying 184 GWAS-colocalized m6A QTL, including brain m6A QTLs underlying neuroticism, depression, schizophrenia and anxiety; lung m6A QTLs underlying expiratory flow and asthma; and muscle/heart m6A QTLs underlying coronary artery disease. Last, we predict novel m6A regulators that show preferential binding in m6A QTLs, protein interactions with known m6A regulators and expression correlation with the m6A levels of their targets. Our results provide important insights and resources for understanding both cis and trans regulation of epitranscriptomic modifications, their interindividual variation and their roles in human disease.

Published
2022

48. Genetic effects on gene expression across human tissues

Author

Aguet, François, Ardlie, Kristin G., Cummings, Beryl B., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Karczewski, Konrad J., Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segrè, Ayellet V., Trowbridge, Casandra A., Tukiainen, Taru, Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Castel, Stephane E., Chen, Lin S., Chiang, Colby, Conrad, Donald F., Cox, Nancy J., Damani, Farhan N., Davis, Joe R., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frésard, Laure, Gamazon, Eric R., Garrido-Martín, Diego, Gewirtz, Ariel D.H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Jo, Brian, Kang, Eun Yong, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muñoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J., Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Strober, Benjamin J., Sul, Jae Hoon, Tsang, Emily K., Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zappala, Zachary, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Li, Jin Billy, Li, Qin, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Zhang, Rui, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, Roger A., Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, James W., Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, Zhu, Jingchun, Brown, Andrew A., Nguyen, Duyen Y., Sullivan, Timothy J., Addington, Anjene, Koester, Susan, Lockhart, Nicole C., Roe, Bryan, Valley, Dana, Gewirtz, Ariel D. H., He, Amy Z., Quon, Gerald, Ripke, Stephan, Shimko, Tyler C., Teran, Nicole A., Zhang, Hailei, Bustamante, Carlos D., and Guigó, Roderic

Published
2017
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49. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Author

Eraslan, Gökcen, primary, Drokhlyansky, Eugene, additional, Anand, Shankara, additional, Fiskin, Evgenij, additional, Subramanian, Ayshwarya, additional, Slyper, Michal, additional, Wang, Jiali, additional, Van Wittenberghe, Nicholas, additional, Rouhana, John M., additional, Waldman, Julia, additional, Ashenberg, Orr, additional, Lek, Monkol, additional, Dionne, Danielle, additional, Win, Thet Su, additional, Cuoco, Michael S., additional, Kuksenko, Olena, additional, Tsankov, Alexander M., additional, Branton, Philip A., additional, Marshall, Jamie L., additional, Greka, Anna, additional, Getz, Gad, additional, Segrè, Ayellet V., additional, Aguet, François, additional, Rozenblatt-Rosen, Orit, additional, Ardlie, Kristin G., additional, and Regev, Aviv, additional

Published
2022
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