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1. Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Author

Mack, Taralynn M, Raddatz, Michael A, Pershad, Yash, Nachun, Daniel C, Taylor, Kent D, Guo, Xiuqing, Shuldiner, Alan R, O’Connell, Jeffrey R, Kenny, Eimear E, Loos, Ruth JF, Redline, Susan, Cade, Brian E, Psaty, Bruce M, Bis, Joshua C, Brody, Jennifer A, Silverman, Edwin K, Yun, Jeong H, Cho, Michael H, DeMeo, Dawn L, Levy, Daniel, Johnson, Andrew D, Mathias, Rasika A, Yanek, Lisa R, Heckbert, Susan R, Smith, Nicholas L, Wiggins, Kerri L, Raffield, Laura M, Carson, April P, Rotter, Jerome I, Rich, Stephen S, Manichaikul, Ani W, Gu, C Charles, Chen, Yii-Der Ida, Lee, Wen-Jane, Shoemaker, M Benjamin, Roden, Dan M, Kooperberg, Charles, Auer, Paul L, Desai, Pinkal, Blackwell, Thomas W, Smith, Albert V, Reiner, Alexander P, Jaiswal, Siddhartha, Weinstock, Joshua S, and Bick, Alexander G

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Hematology, Genetics, Biotechnology, Aging, Human Genome, Stem Cell Research, Precision Medicine, Aetiology, 2.1 Biological and endogenous factors, Cancer, Good Health and Well Being, Clinical sciences
Abstract

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Published
2024

2. Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Author

Mack, Taralynn M., Raddatz, Michael A., Pershad, Yash, Nachun, Daniel C., Taylor, Kent D., Guo, Xiuqing, Shuldiner, Alan R., O’Connell, Jeffrey R., Kenny, Eimear E., Loos, Ruth J. F., Redline, Susan, Cade, Brian E., Psaty, Bruce M., Bis, Joshua C., Brody, Jennifer A., Silverman, Edwin K., Yun, Jeong H., Cho, Michael H., DeMeo, Dawn L., Levy, Daniel, Johnson, Andrew D., Mathias, Rasika A., Yanek, Lisa R., Heckbert, Susan R., Smith, Nicholas L., Wiggins, Kerri L., Raffield, Laura M., Carson, April P., Rotter, Jerome I., Rich, Stephen S., Manichaikul, Ani W., Gu, C. Charles, Chen, Yii-Der Ida, Lee, Wen-Jane, Shoemaker, M. Benjamin, Roden, Dan M., Kooperberg, Charles, Auer, Paul L., Desai, Pinkal, Blackwell, Thomas W., Smith, Albert V., Reiner, Alexander P., Jaiswal, Siddhartha, Weinstock, Joshua S., and Bick, Alexander G.

Published
2024
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3. PRC2-AgeIndex as a universal biomarker of aging and rejuvenation

Author

Moqri, Mahdi, Cipriano, Andrea, Simpson, Daniel J., Rasouli, Sajede, Murty, Tara, de Jong, Tineke Anna, Nachun, Daniel, de Sena Brandine, Guilherme, Ying, Kejun, Tarkhov, Andrei, Aberg, Karolina A., van den Oord, Edwin, Zhou, Wanding, Smith, Andrew, Mackall, Crystal, Gladyshev, Vadim N., Horvath, Steve, Snyder, Michael P., and Sebastiano, Vittorio

Published
2024
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4. Organ aging signatures in the plasma proteome track health and disease.

Author

Oh, Hamilton, Rutledge, Jarod, Nachun, Daniel, Pálovics, Róbert, Abiose, Olamide, Moran-Losada, Patricia, Channappa, Divya, Urey, Deniz, Kim, Kate, Sung, Yun, Wang, Lihua, Timsina, Jigyasha, Western, Dan, Liu, Menghan, Kohlfeld, Pat, Budde, John, Wilson, Edward, Guen, Yann, Maurer, Taylor, Haney, Michael, He, Zihuai, Greicius, Michael, Andreasson, Katrin, Sathyan, Sanish, Weiss, Erica, Milman, Sofiya, Barzilai, Nir, Cruchaga, Carlos, Wagner, Anthony, Mormino, Elizabeth, Lehallier, Benoit, Henderson, Victor, Longo, Frank, Montgomery, Stephen, Wyss-Coray, Tony, and Yang, Andrew

Subjects
Adult, Animals, Humans, Alzheimer Disease, Proteome, Aging, Brain, Cognitive Dysfunction, Biomarkers
Abstract

Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimers disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Published
2023

5. 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

6. Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders

Author

Zhou, Bo, Arthur, Joseph G., Guo, Hanmin, Kim, Taeyoung, Huang, Yiling, Pattni, Reenal, Wang, Tao, Kundu, Soumya, Luo, Jay X.J., Lee, HoJoon, Nachun, Daniel C., Purmann, Carolin, Monte, Emma M., Weimer, Annika K., Qu, Ping-Ping, Shi, Minyi, Jiang, Lixia, Yang, Xinqiong, Fullard, John F., Bendl, Jaroslav, Girdhar, Kiran, Kim, Minsu, Chen, Xi, Greenleaf, William J., Duncan, Laramie, Ji, Hanlee P., Zhu, Xiang, Song, Giltae, Montgomery, Stephen B., Palejev, Dean, zu Dohna, Heinrich, Roussos, Panos, Kundaje, Anshul, Hallmayer, Joachim F., Snyder, Michael P., Wong, Wing H., and Urban, Alexander E.

Published
2024
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7. Clonal hematopoiesis of indeterminate potential, DNA methylation, and risk for coronary artery disease

Author

Uddin, MD Mesbah, Nguyen, Ngoc Quynh H, Yu, Bing, Brody, Jennifer A, Pampana, Akhil, Nakao, Tetsushi, Fornage, Myriam, Bressler, Jan, Sotoodehnia, Nona, Weinstock, Joshua S, Honigberg, Michael C, Nachun, Daniel, Bhattacharya, Romit, Griffin, Gabriel K, Chander, Varuna, Gibbs, Richard A, Rotter, Jerome I, Liu, Chunyu, Baccarelli, Andrea A, Chasman, Daniel I, Whitsel, Eric A, Kiel, Douglas P, Murabito, Joanne M, Boerwinkle, Eric, Ebert, Benjamin L, Jaiswal, Siddhartha, Floyd, James S, Bick, Alexander G, Ballantyne, Christie M, Psaty, Bruce M, Natarajan, Pradeep, and Conneely, Karen N

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Cardiovascular, Stem Cell Research - Nonembryonic - Non-Human, Aging, Heart Disease - Coronary Heart Disease, Atherosclerosis, Human Genome, Hematology, Stem Cell Research, Heart Disease, 2.1 Biological and endogenous factors, Aetiology, Cancer, Good Health and Well Being, Clonal Hematopoiesis, Coronary Artery Disease, DNA Methylation, Hematopoiesis, Hematopoietic Stem Cells, Humans
Abstract

Age-related changes to the genome-wide DNA methylation (DNAm) pattern observed in blood are well-documented. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by the age-related acquisition and expansion of leukemogenic mutations in hematopoietic stem cells (HSCs), is associated with blood cancer and coronary artery disease (CAD). Epigenetic regulators DNMT3A and TET2 are the two most frequently mutated CHIP genes. Here, we present results from an epigenome-wide association study for CHIP in 582 Cardiovascular Health Study (CHS) participants, with replication in 2655 Atherosclerosis Risk in Communities (ARIC) Study participants. We show that DNMT3A and TET2 CHIP have distinct and directionally opposing genome-wide DNAm association patterns consistent with their regulatory roles, albeit both promoting self-renewal of HSCs. Mendelian randomization analyses indicate that a subset of DNAm alterations associated with these two leading CHIP genes may promote the risk for CAD.

Published
2022

8. Loss-of-function mutations in Dnmt3a and Tet2 lead to accelerated atherosclerosis and concordant macrophage phenotypes

Author

Rauch, Philipp J., Gopakumar, Jayakrishnan, Silver, Alexander J., Nachun, Daniel, Ahmad, Herra, McConkey, Marie, Nakao, Tetsushi, Bosse, Marc, Rentz, Thiago, Vivanco Gonzalez, Nora, Greenwald, Noah F., McCaffrey, Erin F., Khair, Zumana, Gopakumar, Manu, Rodrigues, Kameron B., Lin, Amy E., Sinha, Eti, Fefer, Maia, Cohen, Drew N., Vromman, Amélie, Shvartz, Eugenia, Sukhova, Galina, Bendall, Sean, Angelo, Michael, Libby, Peter, Ebert, Benjamin L., and Jaiswal, Siddhartha

Published
2023
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9. Clonal hematopoiesis is associated with protection from Alzheimer’s disease

Author

Bouzid, Hind, Belk, Julia A., Jan, Max, Qi, Yanyan, Sarnowski, Chloé, Wirth, Sara, Ma, Lisa, Chrostek, Matthew R., Ahmad, Herra, Nachun, Daniel, Yao, Winnie, Beiser, Alexa, Bick, Alexander G., Bis, Joshua C., Fornage, Myriam, Longstreth, Jr., William T., Lopez, Oscar L., Natarajan, Pradeep, Psaty, Bruce M., Satizabal, Claudia L., Weinstock, Joshua, Larson, Eric B., Crane, Paul K., Keene, C. Dirk, Seshadri, Sudha, Satpathy, Ansuman T., Montine, Thomas J., and Jaiswal, Siddhartha

Published
2023
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10. The mitochondrial multi-omic response to exercise training across rat tissues

Author

Adkins, Joshua N., Armenteros, Jose Juan Almagro, Amper, Mary Anne S., Bae, Dam, Bamman, Marcas, Bararpour, Nasim, Barnes, Jerry, Bergman, Bryan C., Bessesen, Daniel H., Broskey, Nicholas T., Buford, Thomas W., Carr, Steven, Chambers, Toby L., Chavez, Clarisa, Chiu, Roxanne, Clark, Natalie, Cutter, Gary, Evans, Charles R., Franczak, Edziu, Gagne, Nicole, Ge, Yongchao, Hennig, Krista M., Houmard, Joseph A., Huffman, Kim M., Hung, Chia-Jui, Hutchinson-Bunch, Chelsea, Ilkayeva, Olga, Jackson, Bailey E., Jankowski, Catherine M., Jin, Christopher A., Johannsen, Neil M., Katz, Daniel H., Keshishian, Hasmik, Kohrt, Wendy M., Kramer, Kyle S., Kraus, William E., Lester, Bridget, Li, Jun Z., Lira, Ana K., Lowe, Adam, Mani, D.R., Many, Gina M., May, Sandy, Melanson, Edward L., Moore, Samuel G., Moreau, Kerrie L., Musi, Nicolas, Nachun, Daniel, Nair, Venugopalan D., Newgard, Christopher, Nudelman, German, Piehowski, Paul D., Pincas, Hanna, Qian, Wei-Jun, Rankinen, Tuomo, Rasmussen, Blake B., Ravussin, Eric, Rooney, Jessica L., Rushing, Scott, Samdarshi, Mihir, Sanford, James A., Schauer, Irene E., Sealfon, Stuart C., Smith, Kevin S., Smith, Gregory R., Snyder, Michael, Stowe, Cynthia L., Talton, Jennifer W., Teng, Christopher, Thalacker-Mercer, Anna, Tracy, Russell, Trappe, Scott, Trappe, Todd A., Vasoya, Mital, Vetr, Nikolai G., Volpi, Elena, Walkup, Michael P., Wiel, Laurens, Wu, Si, Yan, Zhen, Yu, Jiye, Zaslavsky, Elena, Zebarjadi, Navid, Zhen, Jimmy, Amar, David, Gay, Nicole R., Jimenez-Morales, David, Jean Beltran, Pierre M., Ramaker, Megan E., Raja, Archana Natarajan, Zhao, Bingqing, Sun, Yifei, Marwaha, Shruti, Gaul, David A., Hershman, Steven G., Ferrasse, Alexis, Xia, Ashley, Lanza, Ian, Fernández, Facundo M., Montgomery, Stephen B., Hevener, Andrea L., Ashley, Euan A., Walsh, Martin J., Sparks, Lauren M., Burant, Charles F., Rector, R. Scott, Thyfault, John, Wheeler, Matthew T., Goodpaster, Bret H., Coen, Paul M., Schenk, Simon, Bodine, Sue C., and Lindholm, Malene E.

Published
2024
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11. Methylation differences in Alzheimer’s disease neuropathologic change in the aged human brain

Author

Lang, Anna-Lena, Eulalio, Tiffany, Fox, Eddie, Yakabi, Koya, Bukhari, Syed A, Kawas, Claudia H, Corrada, Maria M, Montgomery, Stephen B, Heppner, Frank L, Capper, David, Nachun, Daniel, and Montine, Thomas J

Subjects
Biological Sciences, Genetics, Neurodegenerative, Neurosciences, Aging, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Dementia, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Aetiology, Neurological, Aged, 80 and over, Humans, Middle Aged, Aged, Alzheimer Disease, Amyloid beta-Peptides, Neuropathology, Brain, Plaque, Amyloid, DNA Methylation, Methylation, Deconvolution, plus, Old age, Alzheimer's disease, AD, PEN-2, Gamma secretase, Alzheimer’s disease, Biochemistry and Cell Biology, Clinical Sciences, Biochemistry and cell biology
Abstract

Alzheimer's disease (AD) is the most common cause of dementia with advancing age as its strongest risk factor. AD neuropathologic change (ADNC) is known to be associated with numerous DNA methylation changes in the human brain, but the oldest old (> 90 years) have so far been underrepresented in epigenetic studies of ADNC. Our study participants were individuals aged over 90 years (n = 47) from The 90+ Study. We analyzed DNA methylation from bulk samples in eight precisely dissected regions of the human brain: middle frontal gyrus, cingulate gyrus, entorhinal cortex, dentate gyrus, CA1, substantia nigra, locus coeruleus and cerebellar cortex. We deconvolved our bulk data into cell-type-specific (CTS) signals using computational methods. CTS methylation differences were analyzed across different levels of ADNC. The highest amount of ADNC related methylation differences was found in the dentate gyrus, a region that has so far been underrepresented in large scale multi-omic studies. In neurons of the dentate gyrus, DNA methylation significantly differed with increased burden of amyloid beta (Aβ) plaques at 5897 promoter regions of protein-coding genes. Amongst these, higher Aβ plaque burden was associated with promoter hypomethylation of the Presenilin enhancer 2 (PEN-2) gene, one of the rate limiting genes in the formation of gamma-secretase, a multicomponent complex that is responsible in part for the endoproteolytic cleavage of amyloid precursor protein into Aβ peptides. In addition to novel ADNC related DNA methylation changes, we present the most detailed array-based methylation survey of the old aged human brain to date. Our open-sourced dataset can serve as a brain region reference panel for future studies and help advance research in aging and neurodegenerative diseases.

Published
2022

12. Transcriptomic profiling of whole blood in 22q11.2 reciprocal copy number variants reveals that cell proportion highly impacts gene expression

Author

Lin, Amy, Forsyth, Jennifer K, Hoftman, Gil D, Kushan-Wells, Leila, Jalbrzikowski, Maria, Dokuru, Deepika, Coppola, Giovanni, Fiksinski, Ania, Zinkstok, Janneke, Vorstman, Jacob, Nachun, Daniel, and Bearden, Carrie E

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Biotechnology, Pediatric, Human Genome, Brain Disorders, Prevention, Clinical Research, Neurosciences, Genetics, Behavioral and Social Science, Basic Behavioral and Social Science, Mental Health, Aetiology, 2.1 Biological and endogenous factors, 22q11, 2, Copy number variation, Transcriptome, Differential expression, Whole blood, Cell type composition, 22q11.2, Clinical sciences
Abstract

22q11.2 reciprocal copy number variants (CNVs) offer a powerful quasi-experimental "reverse-genetics" paradigm to elucidate how gene dosage (i.e., deletions and duplications) disrupts the transcriptome to cause further downstream effects. Clinical profiles of 22q11.2 CNV carriers indicate that disrupted gene expression causes alterations in neuroanatomy, cognitive function, and psychiatric disease risk. However, interpreting transcriptomic signal in bulk tissue requires careful consideration of potential changes in cell composition. We first characterized transcriptomic dysregulation in peripheral blood from reciprocal 22q11.2 CNV carriers using differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify modules of co-expressed genes. We also assessed for group differences in cell composition and re-characterized transcriptomic differences after accounting for cell type proportions and medication usage. Finally, to explore whether CNV-related transcriptomic changes relate to downstream phenotypes associated with 22q11.2 CNVs, we tested for associations of gene expression with neuroimaging measures and behavioral traits, including IQ and psychosis or ASD diagnosis. 22q11.2 deletion carriers (22qDel) showed widespread expression changes at the individual gene as well as module eigengene level compared to 22q11.2 duplication carriers (22qDup) and controls. 22qDup showed increased expression of 5 genes within the 22q11.2 locus, and CDH6 located outside of the locus. Downregulated modules in 22qDel implicated altered immune and inflammatory processes. Celltype deconvolution analyses revealed significant differences between CNV and control groups in T-cell, mast cell, and macrophage proportions; differential expression of individual genes between groups was substantially attenuated after adjusting for cell composition. Individual gene, module eigengene, and cell proportions were not significantly associated with psychiatric or neuroanatomic traits. Our findings suggest broad immune-related dysfunction in 22qDel and highlight the importance of understanding differences in cell composition when interpreting transcriptomic changes in clinical populations. Results also suggest novel directions for future investigation to test whether 22q11.2 CNV effects on macrophages have implications for brain-related microglial function that may contribute to psychiatric phenotypes in 22q11.2 CNV carriers.

Published
2021

13. 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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14. Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Author

Nachun, Daniel, Lu, Ake T, Bick, Alexander G, Natarajan, Pradeep, Weinstock, Joshua, Szeto, Mindy D, Kathiresan, Sekar, Abecasis, Goncalo, Taylor, Kent D, Guo, Xiuqing, Tracy, Russ, Durda, Peter, Liu, Yongmei, Johnson, Craig, Rich, Stephen S, Van Den Berg, David, Laurie, Cecilia, Blackwell, Tom, Papanicolaou, George J, Correa, Adolfo, Raffield, Laura M, Johnson, Andrew D, Murabito, Joanne, Manson, JoAnn E, Desai, Pinkal, Kooperberg, Charles, Assimes, Themistocles L, Levy, Daniel, Rotter, Jerome I, Reiner, Alex P, Whitsel, Eric A, Wilson, James G, Horvath, Steve, Jaiswal, Siddhartha, and Consortium, the NHLBI Trans‐Omics for Precision Medicine

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Aging, Heart Disease, Cardiovascular, Patient Safety, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Clonal Hematopoiesis, Epigenomics, Humans, Risk Factors, Treatment Outcome, clonal hematopoiesis, epigenomics, heart disease, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is a common precursor state for blood cancers that most frequently occurs due to mutations in the DNA-methylation modifying enzymes DNMT3A or TET2. We used DNA-methylation array and whole-genome sequencing data from four cohorts together comprising 5522 persons to study the association between CHIP, epigenetic clocks, and health outcomes. CHIP was strongly associated with epigenetic age acceleration, defined as the residual after regressing epigenetic clock age on chronological age, in several clocks, ranging from 1.31 years (GrimAge, p 0 in both Hannum and GrimAge (referred to as AgeAccelHG+). This group was at high risk of all-cause mortality (hazard ratio 2.90, p

Published
2021

15. DNA Methylation Analysis Validates Organoids as a Viable Model for Studying Human Intestinal Aging

Author

Lewis, Sophia K, Nachun, Daniel, Martin, Martin G, Horvath, Steve, Coppola, Giovanni, and Jones, D Leanne

Subjects
Medical Biotechnology, Biological Sciences, Biomedical and Clinical Sciences, Digestive Diseases, Aging, Regenerative Medicine, Colo-Rectal Cancer, Stem Cell Research - Nonembryonic - Non-Human, Stem Cell Research, Cancer, Genetics, Adolescent, Adult, Adult Stem Cells, Aged, Aged, 80 and over, Cells, Cultured, Child, Preschool, Colon, DNA Methylation, Epigenesis, Genetic, Humans, Infant, Infant, Newborn, Intestinal Mucosa, Intestine, Small, Middle Aged, Organoids, Primary Cell Culture, Spheroids, Cellular, Young Adult, Intestinal Stem Cells, Epigenetic Clock, Biochemistry and cell biology, Clinical sciences
Abstract

Background & aimsThe epithelia of the intestine and colon turn over rapidly and are maintained by adult stem cells at the base of crypts. Although the small intestine and colon have distinct, well-characterized physiological functions, it remains unclear if there are fundamental regional differences in stem cell behavior or region-dependent degenerative changes during aging. Mesenchyme-free organoids provide useful tools for investigating intestinal stem cell biology in vitro and have started to be used for investigating age-related changes in stem cell function. However, it is unknown whether organoids maintain hallmarks of age in the absence of an aging niche. We tested whether stem cell-enriched organoids preserved the DNA methylation-based aging profiles associated with the tissues and crypts from which they were derived.MethodsTo address this, we used standard human methylation arrays and the human epigenetic clock as a biomarker of age to analyze in vitro-derived, 3-dimensional, stem cell-enriched intestinal organoids.ResultsWe found that human stem cell-enriched organoids maintained segmental differences in methylation patterns and that age, as measured by the epigenetic clock, also was maintained in vitro. Surprisingly, we found that stem cell-enriched organoids derived from the small intestine showed striking epigenetic age reduction relative to organoids derived from colon.ConclusionsOur data validate the use of organoids as a model for studying human intestinal aging and introduce methods that can be used when modeling aging or age-onset diseases in vitro.

Published
2020

16. Synaptic and Gene Regulatory Mechanisms in Schizophrenia, Autism, and 22q11.2 Copy Number Variant–Mediated Risk for Neuropsychiatric Disorders

Author

Forsyth, Jennifer K, Nachun, Daniel, Gandal, Michael J, Geschwind, Daniel H, Anderson, Ariana E, Coppola, Giovanni, and Bearden, Carrie E

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Genetics, Biological Psychology, Psychology, Pharmacology and Pharmaceutical Sciences, Brain Disorders, Pediatric, Human Genome, Prevention, Neurosciences, Serious Mental Illness, Biotechnology, Mental Health, Autism, Intellectual and Developmental Disabilities (IDD), Schizophrenia, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Autism Spectrum Disorder, Autistic Disorder, DNA Copy Number Variations, Genome-Wide Association Study, Humans, MicroRNAs, RNA-Binding Proteins, 22q11.2 copy number variants, Autism spectrum disorder, Brain development, Functional genomics, Genetic risk, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

Background22q11.2 copy number variants are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear.MethodsUsing a functional genomics approach, we integrated transcriptomic data from the developing human brain, genome-wide association findings for SCZ and ASD, protein interaction data, and gene expression signatures from SCZ and ASD postmortem cortex to 1) organize genes into the developmental cellular and molecular systems within which they operate, 2) identify neurodevelopmental processes associated with polygenic risk for SCZ and ASD across the allelic frequency spectrum, and 3) elucidate pathways and individual genes through which 22q11.2 copy number variants may confer risk for each disorder.ResultsPolygenic risk for SCZ and ASD converged on partially overlapping neurodevelopmental modules involved in synaptic function and transcriptional regulation, with ASD risk variants additionally enriched for modules involved in neuronal differentiation during fetal development. The 22q11.2 locus formed a large protein network during development that disproportionately affected SCZ-associated and ASD-associated neurodevelopmental modules, including loading highly onto synaptic and gene regulatory pathways. SEPT5, PI4KA, and SNAP29 genes are candidate drivers of 22q11.2 synaptic pathology relevant to SCZ and ASD, and DGCR8 and HIRA are candidate drivers of disease-relevant alterations in gene regulation.ConclusionsThis approach offers a powerful framework to identify neurodevelopmental processes affected by diverse risk variants for SCZ and ASD and elucidate mechanisms through which highly penetrant, multigene copy number variants contribute to disease risk.

Published
2020

17. Peripheral blood gene expression reveals an inflammatory transcriptomic signature in Friedreich’s ataxia patients

Author

Nachun, Daniel, Gao, Fuying, Isaacs, Charles, Strawser, Cassandra, Yang, Zhongan, Dokuru, Deepika, Van Berlo, Victoria, Sears, Renee, Farmer, Jennifer, Perlman, Susan, Lynch, David R, and Coppola, Giovanni

Subjects
Rare Diseases, Genetics, Human Genome, Neurodegenerative, Neurosciences, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Neurological, Adult, Biomarkers, Case-Control Studies, Female, Friedreich Ataxia, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Humans, Inflammation, Inflammation Mediators, Male, Middle Aged, Transcriptome, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Transcriptional changes in Friedreich's ataxia (FRDA), a rare and debilitating recessive Mendelian neurodegenerative disorder, have been studied in affected but inaccessible tissues-such as dorsal root ganglia, sensory neurons and cerebellum-in animal models or small patient series. However, transcriptional changes induced by FRDA in peripheral blood, a readily accessible tissue, have not been characterized in a large sample. We used differential expression, association with disability stage, network analysis and enrichment analysis to characterize the peripheral blood transcriptome and identify genes that were differentially expressed in FRDA patients (n = 418) compared with both heterozygous expansion carriers (n = 228) and controls (n = 93 739 individuals in total), or were associated with disease progression, resulting in a disease signature for FRDA. We identified a transcriptional signature strongly enriched for an inflammatory innate immune response. Future studies should seek to further characterize the role of peripheral inflammation in FRDA pathology and determine its relevance to overall disease progression.

Published
2018

18. Progranulin levels in blood in Alzheimer's disease and mild cognitive impairment

Author

Cooper, Yonatan A, Nachun, Daniel, Dokuru, Deepika, Yang, Zhongan, Karydas, Anna M, Serrero, Ginette, Yue, Binbin, Initiative, Alzheimer's Disease Neuroimaging, Boxer, Adam L, Miller, Bruce L, and Coppola, Giovanni

Subjects
Clinical and Health Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Neurodegenerative, Alzheimer's Disease, Genetics, Alzheimer's Disease Related Dementias (ADRD), Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Brain Disorders, Acquired Cognitive Impairment, Aetiology, 2.1 Biological and endogenous factors, Neurological, Alzheimer's Disease Neuroimaging Initiative, Clinical Sciences, Clinical and health psychology
Abstract

ObjectiveChanges in progranulin (GRN) expression have been hypothesized to alter risk for Alzheimer's disease (AD). We investigated the relationship between GRN expression in peripheral blood and clinical diagnosis of AD and mild cognitive impairment (MCI).MethodsPeripheral blood progranulin gene expression was measured, using microarrays from Alzheimer's (n = 186), MCI (n = 118), and control (n = 204) subjects from the University of California San Francisco Memory and Aging Center (UCSF-MAC) and two independent published series (AddNeuroMed and ADNI). GRN gene expression was correlated with clinical, demographic, and genetic data, including APOE haplotype and the GRN rs5848 single-nucleotide polymorphism. Finally, we assessed progranulin protein levels, using enzyme-linked immunosorbent assay, and methylation status using methylation microarrays.ResultsWe observed an increase in blood progranulin gene expression and a decrease in GRN promoter methylation in males (P = 0.007). Progranulin expression was 13% higher in AD and MCI patients compared with controls in the UCSF-MAC cohort (F2,505 = 10.41, P = 3.72*10-5). This finding was replicated in the AddNeuroMed (F2,271 = 17.9, P = 4.83*10-8) but not the ADNI series. The rs5848 SNP (T-allele) predicted decreased blood progranulin gene expression (P = 0.03). The APOE4 haplotype was positively associated with progranulin expression independent of diagnosis (P = 0.04). Finally, we did not identify differences in plasma progranulin protein levels or gene methylation between diagnostic categories.InterpretationProgranulin mRNA is elevated in peripheral blood of patients with AD and MCI and its expression is associated with numerous genetic and demographic factors. These data suggest a role in the pathogenesis of neurodegenerative dementias besides frontotemporal dementia.

Published
2018

22. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Author

Anand, Shankara, Gabriel, Stacey, Getz, Gad A., Graubert, Aaron, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Li, Xiao, MacArthur, Daniel G., Meier, Samuel R., Nedzel, Jared L., Nguyen, Duyen T., Segrè, Ayellet V., Todres, Ellen, Balliu, Brunilda, Bonazzola, Rodrigo, Brown, Andrew, Conrad, Donald F., Cotter, Daniel J., Cox, Nancy, Das, Sayantan, Dermitzakis, Emmanouil T., Einson, Jonah, Engelhardt, Barbara E., Eskin, Eleazar, Flynn, Elise D., Fresard, Laure, Gamazon, Eric R., Garrido-Martín, Diego, Gay, Nicole R., Guigó, Roderic, Hamel, Andrew R., He, Yuan, Hoffman, Paul J., Hormozdiari, Farhad, Hou, Lei, Jo, Brian, Kasela, Silva, Kashin, Seva, Kellis, Manolis, Kwong, Alan, Li, Xin, Liang, Yanyu, Mangul, Serghei, Mohammadi, Pejman, Muñoz-Aguirre, Manuel, Nobel, Andrew B., Oliva, Meritxell, Park, Yongjin, Parsana, Princy, Reverter, Ferran, Rouhana, John M., Sabatti, Chiara, Saha, Ashis, Stephens, Matthew, Stranger, Barbara E., Teran, Nicole A., Viñuela, Ana, Wang, Gao, Wright, Fred, Wucher, Valentin, Zou, Yuxin, Ferreira, Pedro G., Li, Gen, Melé, Marta, Yeger-Lotem, Esti, Bradbury, Debra, Krubit, Tanya, McLean, Jeffrey A., Qi, Liqun, Robinson, Karna, Roche, Nancy V., Smith, Anna M., Tabor, David E., Undale, Anita, Bridge, Jason, Brigham, Lori E., Foster, Barbara A., Gillard, Bryan M., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Karasik, Ellen, Kopen, Gene, Leinweber, William F., McDonald, Alisa, Moser, Michael T., Myer, Kevin, Ramsey, Kimberley D., Roe, Brian, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Jewell, Scott D., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Barcus, Mary E., Branton, Philip A., Sobin, Leslie, Barker, Laura K., Gardiner, Heather M., Mosavel, Maghboeba, Siminoff, Laura A., Flicek, Paul, Haeussler, Maximilian, Juettemann, Thomas, Kent, W. James, Lee, Christopher M., Powell, Conner C., Rosenbloom, Kate R., Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Abell, Nathan S., Akey, Joshua, Chen, Lin, Demanelis, Kathryn, Doherty, Jennifer A., Feinberg, Andrew P., Hansen, Kasper D., Hickey, Peter F., Jasmine, Farzana, Jiang, Lihua, Kaul, Rajinder, Kibriya, Muhammad G., Li, Jin Billy, Li, Qin, Lin, Shin, Linder, Sandra E., Pierce, Brandon L., Rizzardi, Lindsay F., Skol, Andrew D., Smith, Kevin S., Snyder, Michael, Stamatoyannopoulos, John, Tang, Hua, Wang, Meng, Carithers, Latarsha J., Guan, Ping, Koester, Susan E., Little, A. Roger, Moore, Helen M., Nierras, Concepcion R., Rao, Abhi K., Vaught, Jimmie B., Volpi, Simona, de Goede, Olivia M., Nachun, Daniel C., Ferraro, Nicole M., Gloudemans, Michael J., Rao, Abhiram S., Smail, Craig, Eulalio, Tiffany Y., Aguet, François, Ng, Bernard, Xu, Jishu, Barbeira, Alvaro N., Castel, Stephane E., Kim-Hellmuth, Sarah, Park, YoSon, Scott, Alexandra J., Strober, Benjamin J., Brown, Christopher D., Wen, Xiaoquan, Hall, Ira M., Battle, Alexis, Lappalainen, Tuuli, Im, Hae Kyung, Ardlie, Kristin G., Mostafavi, Sara, Quertermous, Thomas, Kirkegaard, Karla, and Montgomery, Stephen B.

Published
2021
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23. A Quantitative Proteome Map of the Human Body

Author

Aguet, François, Anand, Shankara, Ardlie, Kristin G., Gabriel, Stacey, Getz, Gad, Graubert, Aaron, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, MacArthur, Daniel G., Meier, Samuel R., Nedzel, Jared L., Nguyen, Duyen Y., Segrè, Ayellet V., Todres, Ellen, Balliu, Brunilda, Barbeira, Alvaro N., Battle, Alexis, Bonazzola, Rodrigo, Brown, Andrew, Brown, Christopher D., Castel, Stephane E., Conrad, Don, Cotter, Daniel J., Cox, Nancy, Das, Sayantan, de Goede, Olivia M., Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Eulalio, Tiffany Y., Ferraro, Nicole M., Flynn, Elise, Fresard, Laure, Gamazon, Eric R., Garrido-Martín, Diego, Gay, Nicole R., Guigó, Roderic, Hamel, Andrew R., He, Yuan, Hoffman, Paul J., Hormozdiari, Farhad, Hou, Lei, Im, Hae Kyung, Jo, Brian, Kasela, Silva, Kellis, Manolis, Kim-Hellmuth, Sarah, Kwong, Alan, Lappalainen, Tuuli, Li, Xin, Liang, Yanyu, Mangul, Serghei, Mohammadi, Pejman, Montgomery, Stephen B., Muñoz-Aguirre, Manuel, Nachun, Daniel C., Nobel, Andrew B., Oliva, Meritxell, Park, YoSon, Park, Yongjin, Parsana, Princy, Reverter, Ferran, Rouhana, John M., Sabatti, Chiara, Saha, Ashis, Skol, Andrew D., Stephens, Matthew, Stranger, Barbara E., Strober, Benjamin J., Teran, Nicole A., Viñuela, Ana, Wang, Gao, Wen, Xiaoquan, Wright, Fred, Wucher, Valentin, Zou, Yuxin, Ferreira, Pedro G., Li, Gen, Melé, Marta, Yeger-Lotem, Esti, Barcus, Mary E., Bradbury, Debra, Krubit, Tanya, McLean, Jeffrey A., Qi, Liqun, Robinson, Karna, Roche, Nancy V., Smith, Anna M., Sobin, Leslie, Tabor, David E., Undale, Anita, Bridge, Jason, Brigham, Lori E., Foster, Barbara A., Gillard, Bryan M., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Karasik, Ellen, Kopen, Gene, Leinweber, William F., McDonald, Alisa, Moser, Michael T., Myer, Kevin, Ramsey, Kimberley D., Roe, Brian, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Jewell, Scott D., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Branton, Philip A., Barker, Laura K., Gardiner, Heather M., Mosavel, Maghboeba, Siminoff, Laura A., Flicek, Paul, Haeussler, Maximilian, Juettemann, Thomas, Kent, W. James, Lee, Christopher M., Powell, Conner C., Rosenbloom, Kate R., Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Abell, Nathan S., Akey, Joshua, Chen, Lin, Demanelis, Kathryn, Doherty, Jennifer A., Feinberg, Andrew P., Hansen, Kasper D., Hickey, Peter F., Jasmine, Farzana, Kaul, Rajinder, Kibriya, Muhammad G., Li, Jin Billy, Li, Qin, Linder, Sandra E., Pierce, Brandon L., Rizzardi, Lindsay F., Smith, Kevin S., Stamatoyannopoulos, John, Tang, Hua, Carithers, Latarsha J., Guan, Ping, Koester, Susan E., Little, A. Roger, Moore, Helen M., Nierras, Concepcion R., Rao, Abhi K., Vaught, Jimmie B., Volpi, Simona, Jiang, Lihua, Wang, Meng, Lin, Shin, Jian, Ruiqi, Li, Xiao, Chan, Joanne, Dong, Guanlan, Fang, Huaying, Robinson, Aaron E., and Snyder, Michael P.

Published
2020
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24. Clonal haematopoiesis of indeterminate potential and atrial fibrillation: an east Asian cohort study

Author

Ahn, Hyo-Jeong, primary, An, Hong Yul, additional, Ryu, Gangpyo, additional, Lim, Jiwoo, additional, Sun, Choonghyun, additional, Song, Han, additional, Choi, Su-Yeon, additional, Lee, Heesun, additional, Maurer, Taylor, additional, Nachun, Daniel, additional, Kwon, Soonil, additional, Lee, So-Ryoung, additional, Lip, Gregory Y H, additional, Oh, Seil, additional, Jaiswal, Siddhartha, additional, Koh, Youngil, additional, and Choi, Eue-Keun, additional

Published
2024
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26. Transcriptomics and chromatin accessibility in multiple African population samples

Author

DeGorter, Marianne K, primary, Goddard, Page C, additional, Karakoc, Emre, additional, Kundu, Soumya, additional, Yan, Stephanie M, additional, Nachun, Daniel, additional, Abell, Nathan, additional, Aguirre, Matthew, additional, Carstensen, Tommy, additional, Chen, Ziwei, additional, Durrant, Matthew, additional, Dwaracherla, Vikranth R, additional, Feng, Karen, additional, Gloudemans, Michael J, additional, Hunter, Naiomi, additional, Moorthy, Mohana P S, additional, Pomilla, Cristina, additional, Rodrigues, Kameron B, additional, Smith, Courtney J, additional, Smith, Kevin S, additional, Ungar, Rachel A, additional, Balliu, Brunilda, additional, Fellay, Jacques, additional, Flicek, Paul, additional, McLaren, Paul J, additional, Henn, Brenna, additional, McCoy, Rajiv C, additional, Sugden, Lauren, additional, Kundaje, Anshul, additional, Sandhu, Manjinder S, additional, Gurdasani, Deepti, additional, and Montgomery, Stephen B, additional

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

Author

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

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

30. Peripheral inflammation in neurodegenerative diseases

Author

Nachun, Daniel

Subjects
Neurosciences, Genetics, Immunology, Inflammation, Innate Immunity, Neurodegeneration, Peripheral Blood
Abstract

This thesis constitutes an exhaustive analysis of peripheral blood gene expression across a diverse set of neurodegenerative disease. The first manuscript included in the thesis focuses on the analysis of peripheral blood gene expression in Friedreich’s ataxia, a rare pediatric onset neurodegenerative disease caused by an autosomal recessive repeat expansion in the FXN gene, where the genetic basis of the disease is fully understood. The second manuscript takes a similar approach but instead focuses on neurodegenerative disorders with complex genetics and later onset, in particular Alzheimer’s disease (AD), mild cognitive impairment (MCI), and five disorders in the frontotemporal dementia (FTD) spectrum: behavioral variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non-fluent variant primary progressive aphasia (nfvPPA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). The initial focus of the project was to find specific gene expression biomarker candidates to build a biomarker panel, and to develop predictive models for disease status or severity from gene expression in blood. It became clear as the thesis progressed that both of these goals were not feasible, because expression changes in individual genes were too subtle and noisy to make viable biomarkers, and machine learning models had no predictive power for disease status or severity. However, systems level of analysis of the peripheral blood transcriptome with weighted gene co-expression network analysis (WGCNA) revealed evidence of an increased innate immune inflammatory response in monocytes and neutrophils. This inflammatory response was found to overlap strongly with microglia-expressed genes, particularly those genes found to be affected in post-mortem AD brains. Because of this overlap with microglial genes, the genes in the inflammatory response in blood are also enriched for genetic risk for AD as determined by genome wide association studies (GWAS). The remarkable similarity of this inflammatory response across a wide array of neurodegenerative diseases warrants further investigation, particularly to determine how and why inflammatory signals enter peripheral blood from the central and peripheral nervous system in the diseases and whether this inflammation is pathological or protective and should be a target for future therapeutic interventions.

Published
2018

31. 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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32. PRC2 clock: a universal epigenetic biomarker of aging and rejuvenation

Author

Moqri, Mahdi, primary, Cipriano, Andrea, additional, Nachun, Daniel, additional, Murty, Tara, additional, de Sena Brandine, Guilherme, additional, Rasouli, Sajede, additional, Tarkhov, Andrei, additional, Aberg, Karolina A., additional, van den Oord, Edwin, additional, Zhou, Wanding, additional, Smith, Andrew, additional, Mackall, Crystal, additional, Gladyshev, Vadim, additional, Horvath, Steve, additional, Snyder, Michael P., additional, and Sebastiano, Vittorio, additional

Published
2022
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33. Additional file 1 of Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

Author

Gloudemans, Michael J., Balliu, Brunilda, Nachun, Daniel, Schnurr, Theresia M., Durrant, Matthew G., Ingelsson, Erik, Wabitsch, Martin, Quertermous, Thomas, Montgomery, Stephen B., Knowles, Joshua W., and Carcamo-Orive, Ivan

Abstract

Additional file 1: Document containing supplementary Figures S1-S7 and supplementary Tables S1-S9. Fig. S1. Selection of genome-wide significant loci and overlapping eQTL/sQTL features for colocalization testing. Fig. S2. Example LocusCompare plots for each quartile of the CLPP-mod score. Fig. S3. Characteristics of candidate and colocalized genes. Fig. S4. Three separate loci with multiple colocalized genes. Fig. S5. Effects of increased gene expression on cardiometabolic trait level / risk, according to the alignment of GWAS and eQTL directions at colocalized loci. Fig. S6. All uniquely colocalized genes that are differentially expressed (DE) under at least one perturbation condition. Fig. S7. Proximal interaction networks for each of the monogenic IR or T2D genes that directly interacts with at least one of the uniquely colocalized genes in perturbation conditions. Table S1. GWAS used for colocalization analysis. Table S2. GTEx QTL tissues used for colocalization analysis. Table S3. List of metabolic perturbations and their abbreviations. Table S4. Genetic studies-based IR/T2D genes used in PPI network analysis. Table S5. Monogenic IR/T2D genes used in PPI network analysis. Table S6. Number of candidate/colocalized genes and loci per GWAS. Table S7. Number of candidate/colocalized genes and loci per QTL type / tissue. Table S8. Fraction of tissue-specific, single-gene colocalizations in each tissue / disease combination. Table S9. Integrative summary table for all uniquely colocalized genes at loci with a WHR, TG, and/or HDL colocalization, but not an insulin sensitivity, T2D, fasting glucose, or fasting insulin colocalization.

Published
2022
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34. Clonal hematopoiesis of indeterminate potential, DNA methylation, and risk for coronary artery disease

Author

Uddin, Md Mesbah, primary, Nguyen, Ngoc Quynh, additional, Yu, Bing, additional, Brody, Jennifer, additional, Pampana, Akhil, additional, Nakao, Tetsushi, additional, Fornage, Myriam, additional, Bressler, Jan, additional, Sotoodehnia, Nona, additional, Weinstock, Josh, additional, Honigberg, Michael, additional, Nachun, Daniel, additional, Bhattacharya, Romit, additional, Griffin, Gabriel, additional, Chander, Varuna, additional, Gibbs, Richard, additional, Rotter, Jerome, additional, Liu, Chunyu, additional, Baccarelli, Andrea, additional, Chasman, Daniel, additional, Whitsel, Eric, additional, Kiel, Douglas, additional, Murabito, Joanne, additional, Boerwinkle, Eric, additional, Ebert, Benjamin, additional, JAISWAL, SIDDHARTHA, additional, Floyd, James, additional, Bick, Alexander, additional, Ballantyne, Christie, additional, Psaty, Bruce, additional, Natarajan, Pradeep, additional, and Conneely, Karen, additional

Published
2022
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35. Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

Author

Gloudemans, Michael J., primary, Balliu, Brunilda, additional, Nachun, Daniel, additional, Durrant, Matthew G., additional, Ingelsson, Erik, additional, Wabitsch, Martin, additional, Quertermous, Thomas, additional, Montgomery, Stephen B., additional, Knowles, Joshua W., additional, and Carcamo-Orive, Ivan, additional

Published
2022
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36. Transcriptomic profiling of whole blood in 22q11.2 reciprocal copy number variants reveals that cell proportion highly impacts gene expression

Author

Lin, Amy, primary, Forsyth, Jennifer K., additional, Hoftman, Gil D., additional, Kushan-Wells, Leila, additional, Jalbrzikowski, Maria, additional, Dokuru, Deepika, additional, Coppola, Giovanni, additional, Fiksinski, Ania, additional, Zinkstok, Janneke, additional, Vorstman, Jacob, additional, Nachun, Daniel, additional, and Bearden, Carrie E., additional

Published
2021
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37. Clonal Hematopoiesis is Associated with Reduced Risk of Alzheimer's Disease

Author

Bouzid, Hind, primary, Belk, Julia, additional, Jan, Max, additional, Qi, Yanyan, additional, Sarnowski, Chloé, additional, Wirth, Sara, additional, Ma, Lisa, additional, Chrostek, Matthew, additional, Ahmad, Herra, additional, Nachun, Daniel, additional, Yao, Winnie, additional, Beiser, Alexa, additional, Bick, Alexander G., additional, Bis, Joshua, additional, Fornage, Myriam, additional, Longstreth, W. T., additional, Lopez, Oscar, additional, Nataranjan, Pradeep, additional, Psaty, Bruce, additional, Satizabal, Claudia, additional, Weinstock, Joshua, additional, Larson, Eric, additional, Crane, Paul, additional, Keene, C. Dirk, additional, Seshadri, Sudha, additional, Satpathy, Ansuman T., additional, Montine, Thomas, additional, and Jaiswal, Siddhartha, additional

Published
2021
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38. Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

Author

Gloudemans, Michael J., primary, Balliu, Brunilda, additional, Nachun, Daniel, additional, Durrant, Matthew G., additional, Ingelsson, Erik, additional, Wabitsch, Martin, additional, Quertermous, Thomas, additional, Montgomery, Stephen B., additional, Knowles, Joshua W., additional, and Carcamo-Orive, Ivan, additional

Published
2021
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40. Transcriptomic profiling of whole blood in 22q11.2 reciprocal copy number variants reveals that cell proportion highly impacts gene expression

Author

Integrale & Algemene Kindergen Onderzoek, Ontwikkelingsstoornissen Med., Lin, Amy, Forsyth, Jennifer K, Hoftman, Gil D, Kushan-Wells, Leila, Jalbrzikowski, Maria, Dokuru, Deepika, Coppola, Giovanni, Fiksinski, Ania, Zinkstok, Janneke, Vorstman, Jacob, Nachun, Daniel, Bearden, Carrie E, Integrale & Algemene Kindergen Onderzoek, Ontwikkelingsstoornissen Med., Lin, Amy, Forsyth, Jennifer K, Hoftman, Gil D, Kushan-Wells, Leila, Jalbrzikowski, Maria, Dokuru, Deepika, Coppola, Giovanni, Fiksinski, Ania, Zinkstok, Janneke, Vorstman, Jacob, Nachun, Daniel, and Bearden, Carrie E

Published
2021

43. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Author

de Goede, Olivia M., primary, Nachun, Daniel C., additional, Ferraro, Nicole M., additional, Gloudemans, Michael J., additional, Rao, Abhiram S., additional, Smail, Craig, additional, Eulalio, Tiffany Y., additional, Aguet, François, additional, Ng, Bernard, additional, Xu, Jishu, additional, Barbeira, Alvaro N., additional, Castel, Stephane E., additional, Kim-Hellmuth, Sarah, additional, Park, YoSon, additional, Scott, Alexandra J., additional, Strober, Benjamin J., additional, Brown, Christopher D., additional, Wen, Xiaoquan, additional, Hall, Ira M., additional, Battle, Alexis, additional, Lappalainen, Tuuli, additional, Im, Hae Kyung, additional, Ardlie, Kristin G., additional, Mostafavi, Sara, additional, Quertermous, Thomas, additional, Kirkegaard, Karla, additional, Montgomery, Stephen B., additional, Anand, Shankara, additional, Gabriel, Stacey, additional, Getz, Gad A., additional, Graubert, Aaron, additional, Hadley, Kane, additional, Handsaker, Robert E., additional, Huang, Katherine H., additional, Li, Xiao, additional, MacArthur, Daniel G., additional, Meier, Samuel R., additional, Nedzel, Jared L., additional, Nguyen, Duyen T., additional, Segrè, Ayellet V., additional, Todres, Ellen, additional, Balliu, Brunilda, additional, Bonazzola, Rodrigo, additional, Brown, Andrew, additional, Conrad, Donald F., additional, Cotter, Daniel J., additional, Cox, Nancy, additional, Das, Sayantan, additional, Dermitzakis, Emmanouil T., additional, Einson, Jonah, additional, Engelhardt, Barbara E., additional, Eskin, Eleazar, additional, Flynn, Elise D., additional, Fresard, Laure, additional, Gamazon, Eric R., additional, Garrido-Martín, Diego, additional, Gay, Nicole R., additional, Guigó, Roderic, additional, Hamel, Andrew R., additional, He, Yuan, additional, Hoffman, Paul J., additional, Hormozdiari, Farhad, additional, Hou, Lei, additional, Jo, Brian, additional, Kasela, Silva, additional, Kashin, Seva, additional, Kellis, Manolis, additional, Kwong, Alan, additional, Li, Xin, additional, Liang, Yanyu, additional, Mangul, Serghei, additional, Mohammadi, Pejman, additional, Muñoz-Aguirre, Manuel, additional, Nobel, Andrew B., additional, Oliva, Meritxell, additional, Park, Yongjin, additional, Parsana, Princy, additional, Reverter, Ferran, additional, Rouhana, John M., additional, Sabatti, Chiara, additional, Saha, Ashis, additional, Stephens, Matthew, additional, Stranger, Barbara E., additional, Teran, Nicole A., additional, Viñuela, Ana, additional, Wang, Gao, additional, Wright, Fred, additional, Wucher, Valentin, additional, Zou, Yuxin, additional, Ferreira, Pedro G., additional, Li, Gen, additional, Melé, Marta, additional, Yeger-Lotem, Esti, additional, Bradbury, Debra, additional, Krubit, Tanya, additional, McLean, Jeffrey A., additional, Qi, Liqun, additional, Robinson, Karna, additional, Roche, Nancy V., additional, Smith, Anna M., additional, Tabor, David E., additional, Undale, Anita, additional, Bridge, Jason, additional, Brigham, Lori E., additional, Foster, Barbara A., additional, Gillard, Bryan M., additional, Hasz, Richard, additional, Hunter, Marcus, additional, Johns, Christopher, additional, Johnson, Mark, additional, Karasik, Ellen, additional, Kopen, Gene, additional, Leinweber, William F., additional, McDonald, Alisa, additional, Moser, Michael T., additional, Myer, Kevin, additional, Ramsey, Kimberley D., additional, Roe, Brian, additional, Shad, Saboor, additional, Thomas, Jeffrey A., additional, Walters, Gary, additional, Washington, Michael, additional, Wheeler, Joseph, additional, Jewell, Scott D., additional, Rohrer, Daniel C., additional, Valley, Dana R., additional, Davis, David A., additional, Mash, Deborah C., additional, Barcus, Mary E., additional, Branton, Philip A., additional, Sobin, Leslie, additional, Barker, Laura K., additional, Gardiner, Heather M., additional, Mosavel, Maghboeba, additional, Siminoff, Laura A., additional, Flicek, Paul, additional, Haeussler, Maximilian, additional, Juettemann, Thomas, additional, Kent, W. James, additional, Lee, Christopher M., additional, Powell, Conner C., additional, Rosenbloom, Kate R., additional, Ruffier, Magali, additional, Sheppard, Dan, additional, Taylor, Kieron, additional, Trevanion, Stephen J., additional, Zerbino, Daniel R., additional, Abell, Nathan S., additional, Akey, Joshua, additional, Chen, Lin, additional, Demanelis, Kathryn, additional, Doherty, Jennifer A., additional, Feinberg, Andrew P., additional, Hansen, Kasper D., additional, Hickey, Peter F., additional, Jasmine, Farzana, additional, Jiang, Lihua, additional, Kaul, Rajinder, additional, Kibriya, Muhammad G., additional, Li, Jin Billy, additional, Li, Qin, additional, Lin, Shin, additional, Linder, Sandra E., additional, Pierce, Brandon L., additional, Rizzardi, Lindsay F., additional, Skol, Andrew D., additional, Smith, Kevin S., additional, Snyder, Michael, additional, Stamatoyannopoulos, John, additional, Tang, Hua, additional, Wang, Meng, additional, Carithers, Latarsha J., additional, Guan, Ping, additional, Koester, Susan E., additional, Little, A. Roger, additional, Moore, Helen M., additional, Nierras, Concepcion R., additional, Rao, Abhi K., additional, Vaught, Jimmie B., additional, and Volpi, Simona, additional

Published
2021
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44. An integrated approach to identify environmental modulators of genetic risk factors for complex traits

Author

Balliu, Brunilda, primary, -Orive, Ivan Carcamo, additional, Gloudemans, Michael J., additional, Nachun, Daniel C., additional, Durrant, Matthew G., additional, Gazal, Steven, additional, Park, Chong Y., additional, Knowles, David A., additional, Wabitsch, Martin, additional, Quertermous, Thomas, additional, Knowles, Joshua W., additional, and Montgomery, Stephen B., additional

Published
2021
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46. A Quantitative Proteome Map of the Human Body

Author

Jiang, Lihua, primary, Wang, Meng, additional, Lin, Shin, additional, Jian, Ruiqi, additional, Li, Xiao, additional, Chan, Joanne, additional, Dong, Guanlan, additional, Fang, Huaying, additional, Robinson, Aaron E., additional, Snyder, Michael P., additional, Aguet, François, additional, Anand, Shankara, additional, Ardlie, Kristin G., additional, Gabriel, Stacey, additional, Getz, Gad, additional, Graubert, Aaron, additional, Hadley, Kane, additional, Handsaker, Robert E., additional, Huang, Katherine H., additional, Kashin, Seva, additional, MacArthur, Daniel G., additional, Meier, Samuel R., additional, Nedzel, Jared L., additional, Nguyen, Duyen Y., additional, Segrè, Ayellet V., additional, Todres, Ellen, additional, Balliu, Brunilda, additional, Barbeira, Alvaro N., additional, Battle, Alexis, additional, Bonazzola, Rodrigo, additional, Brown, Andrew, additional, Brown, Christopher D., additional, Castel, Stephane E., additional, Conrad, Don, additional, Cotter, Daniel J., additional, Cox, Nancy, additional, Das, Sayantan, additional, de Goede, Olivia M., additional, Dermitzakis, Emmanouil T., additional, Engelhardt, Barbara E., additional, Eskin, Eleazar, additional, Eulalio, Tiffany Y., additional, Ferraro, Nicole M., additional, Flynn, Elise, additional, Fresard, Laure, additional, Gamazon, Eric R., additional, Garrido-Martín, Diego, additional, Gay, Nicole R., additional, Guigó, Roderic, additional, Hamel, Andrew R., additional, He, Yuan, additional, Hoffman, Paul J., additional, Hormozdiari, Farhad, additional, Hou, Lei, additional, Im, Hae Kyung, additional, Jo, Brian, additional, Kasela, Silva, additional, Kellis, Manolis, additional, Kim-Hellmuth, Sarah, additional, Kwong, Alan, additional, Lappalainen, Tuuli, additional, Li, Xin, additional, Liang, Yanyu, additional, Mangul, Serghei, additional, Mohammadi, Pejman, additional, Montgomery, Stephen B., additional, Muñoz-Aguirre, Manuel, additional, Nachun, Daniel C., additional, Nobel, Andrew B., additional, Oliva, Meritxell, additional, Park, YoSon, additional, Park, Yongjin, additional, Parsana, Princy, additional, Reverter, Ferran, additional, Rouhana, John M., additional, Sabatti, Chiara, additional, Saha, Ashis, additional, Skol, Andrew D., additional, Stephens, Matthew, additional, Stranger, Barbara E., additional, Strober, Benjamin J., additional, Teran, Nicole A., additional, Viñuela, Ana, additional, Wang, Gao, additional, Wen, Xiaoquan, additional, Wright, Fred, additional, Wucher, Valentin, additional, Zou, Yuxin, additional, Ferreira, Pedro G., additional, Li, Gen, additional, Melé, Marta, additional, Yeger-Lotem, Esti, additional, Barcus, Mary E., additional, Bradbury, Debra, additional, Krubit, Tanya, additional, McLean, Jeffrey A., additional, Qi, Liqun, additional, Robinson, Karna, additional, Roche, Nancy V., additional, Smith, Anna M., additional, Sobin, Leslie, additional, Tabor, David E., additional, Undale, Anita, additional, Bridge, Jason, additional, Brigham, Lori E., additional, Foster, Barbara A., additional, Gillard, Bryan M., additional, Hasz, Richard, additional, Hunter, Marcus, additional, Johns, Christopher, additional, Johnson, Mark, additional, Karasik, Ellen, additional, Kopen, Gene, additional, Leinweber, William F., additional, McDonald, Alisa, additional, Moser, Michael T., additional, Myer, Kevin, additional, Ramsey, Kimberley D., additional, Roe, Brian, additional, Shad, Saboor, additional, Thomas, Jeffrey A., additional, Walters, Gary, additional, Washington, Michael, additional, Wheeler, Joseph, additional, Jewell, Scott D., additional, Rohrer, Daniel C., additional, Valley, Dana R., additional, Davis, David A., additional, Mash, Deborah C., additional, Branton, Philip A., additional, Barker, Laura K., additional, Gardiner, Heather M., additional, Mosavel, Maghboeba, additional, Siminoff, Laura A., additional, Flicek, Paul, additional, Haeussler, Maximilian, additional, Juettemann, Thomas, additional, Kent, W. James, additional, Lee, Christopher M., additional, Powell, Conner C., additional, Rosenbloom, Kate R., additional, Ruffier, Magali, additional, Sheppard, Dan, additional, Taylor, Kieron, additional, Trevanion, Stephen J., additional, Zerbino, Daniel R., additional, Abell, Nathan S., additional, Akey, Joshua, additional, Chen, Lin, additional, Demanelis, Kathryn, additional, Doherty, Jennifer A., additional, Feinberg, Andrew P., additional, Hansen, Kasper D., additional, Hickey, Peter F., additional, Jasmine, Farzana, additional, Kaul, Rajinder, additional, Kibriya, Muhammad G., additional, Li, Jin Billy, additional, Li, Qin, additional, Linder, Sandra E., additional, Pierce, Brandon L., additional, Rizzardi, Lindsay F., additional, Smith, Kevin S., additional, Stamatoyannopoulos, John, additional, Tang, Hua, additional, Carithers, Latarsha J., additional, Guan, Ping, additional, Koester, Susan E., additional, Little, A. Roger, additional, Moore, Helen M., additional, Nierras, Concepcion R., additional, Rao, Abhi K., additional, Vaught, Jimmie B., additional, and Volpi, Simona, additional

Published
2020
Full Text
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