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Schraiber JG, Edge MD, Pennell M. Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations. PLoS Biol 2024; 22:e3002847. [PMID: 39383205 DOI: 10.1371/journal.pbio.3002847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 09/17/2024] [Indexed: 10/11/2024] Open
Abstract
In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.
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Affiliation(s)
- Joshua G Schraiber
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
| | - Michael D Edge
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
| | - Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
- Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America
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2
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Li Z, Wang M, Zeng S, Wang Z, Ying Y, Chen Q, Zhang C, He W, Sheng C, Wang Y, Zhang Z, Xu C, Wang H. Investigating the Shared Genetic Architecture Between Leukocyte Telomere Length and Prostate Cancer. World J Mens Health 2024; 42:42.e84. [PMID: 39344121 DOI: 10.5534/wjmh.240062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/31/2024] [Accepted: 06/18/2024] [Indexed: 10/01/2024] Open
Abstract
PURPOSE Evidence of an association between leukocyte telomere length (LTL) and prostate cancer (PCa) is accumulating; however, their shared genetic basis remains unclear. MATERIALS AND METHODS Using summary statistics obtained from the genome-wide association study (GWAS), we quantified the global and local genetic correlations between two traits. Subsequently, we identified potential pleiotropic loci, common tissue-enriched regions, and risk gene loci while inferring assumed causal relationships. RESULTS Our study demonstrated a global genetic correlation between LTL and PCa (genetic correlation=0.066, p=0.017), which was further confirmed in local genomic regions. Cross-trait GWAS meta-analysis revealed 44 shared loci, including 10 novel pleiotropic single nucleotide polymorphisms appearing concurrently in significant local genetic correlation regions. Notably, two new loci (rs9419958; rs3730668) were additionally validated to co-localize. For the first time, we identified a significant shared genetic enrichment of both traits in the small intestine tissue at the terminal ileum, with functional genes in this region affecting both LTL and PCa. Concurrently, Mendelian randomization analysis indicated a positive causal relationship between LTL and PCa. CONCLUSIONS In conclusion, our study makes a significant contribution to the ongoing debate concerning the potential association between longer LTL and a higher risk of PCa. Additionally, we provide new evidence for the development of therapeutic targets for PCa and propose new directions for future risk prediction in this regard.
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Affiliation(s)
- Zhizhou Li
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Maoyu Wang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Shuxiong Zeng
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Ziwei Wang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yidie Ying
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Qing Chen
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chen Zhang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Wei He
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chaoyang Sheng
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yi Wang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Zhensheng Zhang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chuanliang Xu
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
| | - Huiqing Wang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
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3
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Timasheva Y, Lepik K, Liska O, Papp B, Kutalik Z. Widespread natural selection on metabolite levels in humans. Genome Res 2024; 34:1121-1129. [PMID: 39152035 PMCID: PMC11444169 DOI: 10.1101/gr.278756.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 08/08/2024] [Indexed: 08/19/2024]
Abstract
Natural selection acts ubiquitously on complex human traits, predominantly constraining the occurrence of extreme phenotypes (stabilizing selection). These constraints propagate to DNA sequence variants associated with traits under selection. The genetic signatures of such evolutionary events can thus be detected via combining effect size estimates from genetic association studies and the corresponding allele frequencies. Although this approach has been successfully applied to high-level traits, the prevalence and mode of selection acting on molecular traits remain poorly understood. Here, we estimate the action of natural selection on genetic variants associated with metabolite levels, an important layer of molecular traits. By leveraging summary statistics of published genome-wide association studies with large sample sizes, we find strong evidence of stabilizing selection for 15 out of 97 plasma metabolites, with nonessential amino acids displaying especially strong selection signatures. Mendelian randomization analysis reveals that metabolites under stronger stabilizing selection display larger effects on a range of clinically relevant complex traits, suggesting that maintaining a disease-free profile may be an important source of selective constraints on the metabolome. Metabolites under strong stabilizing selection in humans are also more conserved in their concentrations among diverse mammalian species, suggesting shared selective forces across micro- and macroevolutionary timescales. Overall, this study demonstrates that variation in metabolite levels among humans is frequently shaped by natural selection and this may act through their causal impact on disease susceptibility.
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Affiliation(s)
- Yanina Timasheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Center of Russian Academy of Sciences, 450054 Ufa, Russia
- Department of Medical Genetics, Bashkir State Medical University, 450008 Ufa, Russia
- Department of Computational Biology, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Kaido Lepik
- Department of Computational Biology, University of Lausanne, CH-1015 Lausanne, Switzerland
- Center for Primary Care and Public Health, University of Lausanne, CH-1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Orsolya Liska
- HCEMM-BRC Metabolic Systems Biology Lab, H-6726 Szeged, Hungary
- Synthetic and Systems Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, HUN-REN, H-6726 Szeged, Hungary
- Doctoral School of Biology, University of Szeged, H-6726 Szeged, Hungary
| | - Balázs Papp
- HCEMM-BRC Metabolic Systems Biology Lab, H-6726 Szeged, Hungary
- Synthetic and Systems Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, HUN-REN, H-6726 Szeged, Hungary
- National Laboratory for Health Security, Institute of Biochemistry, Biological Research Centre, HUN-REN, H-6726 Szeged, Hungary
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, CH-1015 Lausanne, Switzerland;
- Center for Primary Care and Public Health, University of Lausanne, CH-1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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4
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Stone K, Platig J, Quackenbush J, Fagny M. The Importance of Regulatory Network Structure for Complex Trait Heritability and Evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582063. [PMID: 38464142 PMCID: PMC10925220 DOI: 10.1101/2024.02.27.582063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Complex traits are determined by many loci-mostly regulatory elements-that, through combinatorial interactions, can affect multiple traits. Such high levels of epistasis and pleiotropy have been proposed in the omnigenic model and may explain why such a large part of complex trait heritability is usually missed by genome-wide association studies while raising questions about the possibility for such traits to evolve in response to environmental constraints. To explore the molecular bases of complex traits and understand how they can adapt, we systematically analyzed the distribution of SNP heritability for ten traits across 29 tissue-specific Expression Quantitative Trait Locus (eQTL) networks. We find that heritability is clustered in a small number of tissue-specific, functionally relevant SNP-gene modules and that the greatest heritability occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. We confirm that this structure has allowed complex traits to evolve in response to environmental constraints, with the local "hubs" being the preferential targets of past and ongoing directional selection. Together, these results provide a conceptual framework for understanding complex trait architecture and evolution.
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Affiliation(s)
- Katherine Stone
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - John Platig
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Maud Fagny
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Genetique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette 91190 France
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Tkachenko AA, Changalidis AI, Maksiutenko EM, Nasykhova YA, Barbitoff YA, Glotov AS. Replication of Known and Identification of Novel Associations in Biobank-Scale Datasets: A Survey Using UK Biobank and FinnGen. Genes (Basel) 2024; 15:931. [PMID: 39062709 PMCID: PMC11275374 DOI: 10.3390/genes15070931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 07/03/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
Over the last two decades, numerous genome-wide association studies (GWAS) have been performed to unveil the genetic architecture of human complex traits. Despite multiple efforts aimed at the trans-biobank integration of GWAS results, no systematic analysis of the variant-level properties affecting the replication of known associations (or identifying novel ones) in genome-wide meta-analysis has yet been performed using biobank-scale data. To address this issue, we performed a systematic comparison of GWAS summary statistics for 679 complex traits in the UK Biobank (UKB) and FinnGen (FG) cohorts. We identified 37,148 index variants with genome-wide associations with at least one trait in either cohort or in the meta-analysis, only 3528 (9.5%) of which were shared between UKB and FG. Nearly twice as many variants (6577) were replicated in another dataset at the significance level adjusted for the number of variants selected for replication. However, as many as 9230 loci failed to be replicated. Moreover, as many as 5813 loci were observed as significant associations only in meta-analysis results, highlighting the importance of trans-biobank meta-analysis efforts. We showed that variants that failed to replicate in UKB or FG tend to correspond to rare, less pleiotropic variants with lower effect sizes and lower LD score values. Genome-wide associations specific to meta-analysis were also enriched in low-effect variants; however, such variants tended to be more common and have more consistent frequencies between populations. Taken together, our results show a relatively high rate of non-replication of genome-wide associations in the studied cohorts and highlight both widely appreciated and less acknowledged properties of the associations affecting their identification and replication.
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Affiliation(s)
| | | | | | | | - Yury A. Barbitoff
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia; (A.A.T.); (A.I.C.); (E.M.M.); (Y.A.N.); (A.S.G.)
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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7
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 PMCID: PMC11456345 DOI: 10.1038/s41588-024-01682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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8
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Schraiber JG, Edge MD, Pennell M. Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.10.579721. [PMID: 38496530 PMCID: PMC10942266 DOI: 10.1101/2024.02.10.579721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.
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9
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Chen S, Liu S, Shi S, Yin H, Tang Y, Zhang J, Li W, Liu G, Qu K, Ding X, Wang Y, Liu J, Zhang S, Fang L, Yu Y. Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats. Mol Biol Evol 2024; 41:msae003. [PMID: 38266195 PMCID: PMC10834038 DOI: 10.1093/molbev/msae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024] Open
Abstract
The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.
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Affiliation(s)
- Siqian Chen
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Shuli Liu
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Shaolei Shi
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Hongwei Yin
- Agriculture Genome Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yongjie Tang
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jinning Zhang
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Wenlong Li
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Gang Liu
- National Animal Husbandry Service, Beijing 100125, China
| | - Kaixing Qu
- Academy of Science and Technology, Chuxiong Normal University, Chuxiong 675000, China
| | - Xiangdong Ding
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yachun Wang
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jianfeng Liu
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, Denmark
| | - Ying Yu
- National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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10
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Schnitzler GR, Kang H, Fang S, Angom RS, Lee-Kim VS, Ma XR, Zhou R, Zeng T, Guo K, Taylor MS, Vellarikkal SK, Barry AE, Sias-Garcia O, Bloemendal A, Munson G, Guckelberger P, Nguyen TH, Bergman DT, Hinshaw S, Cheng N, Cleary B, Aragam K, Lander ES, Finucane HK, Mukhopadhyay D, Gupta RM, Engreitz JM. Convergence of coronary artery disease genes onto endothelial cell programs. Nature 2024; 626:799-807. [PMID: 38326615 PMCID: PMC10921916 DOI: 10.1038/s41586-024-07022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/03/2024] [Indexed: 02/09/2024]
Abstract
Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.
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Affiliation(s)
- Gavin R Schnitzler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Helen Kang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Shi Fang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ramcharan S Angom
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Vivian S Lee-Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - X Rosa Ma
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Ronghao Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Tony Zeng
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Katherine Guo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Martin S Taylor
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Shamsudheen K Vellarikkal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aurelie E Barry
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Oscar Sias-Garcia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Glen Munson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Tung H Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Drew T Bergman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Stephen Hinshaw
- Department of Chemical and Systems Biology, ChEM-H, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nathan Cheng
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Faculty of Computing and Data Sciences, Departments of Biology and Biomedical Engineering, Biological Design Center, and Program in Bioinformatics, Boston University, Boston, MA, USA
| | - Krishna Aragam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Debabrata Mukhopadhyay
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Rajat M Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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11
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Miller AP, Gizer IR. Neurogenetic and multi-omic sources of overlap among sensation seeking, alcohol consumption, and alcohol use disorder. Addict Biol 2024; 29:e13365. [PMID: 38380706 PMCID: PMC10882188 DOI: 10.1111/adb.13365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/26/2023] [Accepted: 11/26/2023] [Indexed: 02/22/2024]
Abstract
Sensation seeking is bidirectionally associated with levels of alcohol consumption in both adult and adolescent samples, and shared neurobiological and genetic influences may in part explain these associations. Links between sensation seeking and alcohol use disorder (AUD) may primarily manifest via increased alcohol consumption rather than through direct effects on increasing problems and consequences. Here the overlap among sensation seeking, alcohol consumption, and AUD was examined using multivariate modelling approaches for genome-wide association study (GWAS) summary statistics in conjunction with neurobiologically informed analyses at multiple levels of investigation. Meta-analytic and genomic structural equation modelling (GenomicSEM) approaches were used to conduct GWAS of sensation seeking, alcohol consumption, and AUD. Resulting summary statistics were used in downstream analyses to examine shared brain tissue enrichment of heritability and genome-wide evidence of overlap (e.g., stratified GenomicSEM, RRHO, genetic correlations with neuroimaging phenotypes), and to identify genomic regions likely contributing to observed genetic overlap across traits (e.g., H-MAGMA and LAVA). Across approaches, results supported shared neurogenetic architecture between sensation seeking and alcohol consumption characterised by overlapping enrichment of genes expressed in midbrain and striatal tissues and variants associated with increased cortical surface area. Alcohol consumption and AUD evidenced overlap in relation to variants associated with decreased frontocortical thickness. Finally, genetic mediation models provided evidence of alcohol consumption mediating associations between sensation seeking and AUD. This study extends previous research by examining critical sources of neurogenetic and multi-omic overlap among sensation seeking, alcohol consumption, and AUD which may underlie observed phenotypic associations.
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Affiliation(s)
- Alex P. Miller
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Ian R. Gizer
- Department of Psychological SciencesUniversity of MissouriColumbiaMissouriUSA
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12
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Ravichandran P, Parsana P, Keener R, Hansen KD, Battle A. Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576447. [PMID: 38328080 PMCID: PMC10849507 DOI: 10.1101/2024.01.20.576447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Background Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks. Results We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples. Conclusion This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.
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Affiliation(s)
| | - Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kaspar D Hansen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA
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13
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Zhu X, Ma S, Wong WH. Genetic effects of sequence-conserved enhancer-like elements on human complex traits. Genome Biol 2024; 25:1. [PMID: 38167462 PMCID: PMC10759394 DOI: 10.1186/s13059-023-03142-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits. RESULTS Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes. CONCLUSIONS Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.
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Affiliation(s)
- Xiang Zhu
- Department of Statistics, The Pennsylvania State University, 326 Thomas Building, University Park, 16802, PA, USA.
- Huck Institutes of the Life Sciences, The Pennsylvania State University, 201 Huck Life Sciences Building, University Park, 16802, PA, USA.
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA.
| | - Shining Ma
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road MC5464, Stanford, 94305, CA, USA
| | - Wing Hung Wong
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA.
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road MC5464, Stanford, 94305, CA, USA.
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14
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Li J, Zhao T, Guan D, Pan Z, Bai Z, Teng J, Zhang Z, Zheng Z, Zeng J, Zhou H, Fang L, Cheng H. Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits. CELL GENOMICS 2023; 3:100390. [PMID: 37868039 PMCID: PMC10589632 DOI: 10.1016/j.xgen.2023.100390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/25/2023] [Accepted: 08/02/2023] [Indexed: 10/24/2023]
Abstract
Assessment of genomic conservation between humans and pigs at the functional level can improve the potential of pigs as a human biomedical model. To address this, we developed a deep learning-based approach to learn the genomic conservation at the functional level (DeepGCF) between species by integrating 386 and 374 functional profiles from humans and pigs, respectively. DeepGCF demonstrated better prediction performance compared with the previous method. In addition, the resulting DeepGCF score captures the functional conservation between humans and pigs by examining chromatin states, sequence ontologies, and regulatory variants. We identified a core set of genomic regions as functionally conserved that plays key roles in gene regulation and is enriched for the heritability of complex traits and diseases in humans. Our results highlight the importance of cross-species functional comparison in illustrating the genetic and evolutionary basis of complex phenotypes.
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Affiliation(s)
- Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA 95616, USA
| | - Tianjing Zhao
- Department of Animal Science, University of California, Davis, Davis, CA 95616, USA
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA 95616, USA
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA 95616, USA
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics (QGG), Aarhus University, 8000 Aarhus, Denmark
| | - Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA 95616, USA
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics (QGG), Aarhus University, 8000 Aarhus, Denmark
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA 95616, USA
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15
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Caglayan E, Konopka G. Decoding DNA sequence-driven evolution of the human brain epigenome at cellular resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557820. [PMID: 37745404 PMCID: PMC10515917 DOI: 10.1101/2023.09.14.557820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
DNA-based evolutionary comparisons of regulatory genomic elements enable insight into functional changes, overcoming tissue inaccessibility. Here, we harnessed adult and fetal cortex single-cell ATAC-seq datasets to uncover DNA substitutions specific to the human and human-ancestral lineages within apes. We found that fetal microglia identity is evolutionarily divergent in all lineages, whereas other cell types are conserved. Using multiomic datasets, we further identified genes linked to multiple lineage-divergent gene regulatory elements and implicated biological pathways associated with these divergent features. We also uncovered patterns of transcription factor binding site evolution across lineages and identified expansion of bHLH-PAS factor targets in human-hominin lineages, and MEF2 factor targets in the ape lineage. Finally, conserved features were more enriched in brain disease variants, whereas there was no distinct enrichment on the human lineage compared to its ancestral lineages. Our study identifies major evolutionary patterns in the human brain epigenome at cellular resolution.
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Affiliation(s)
- Emre Caglayan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA
- Peter O’Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA
- Peter O’Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX 75390, USA
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16
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Kun E, Javan EM, Smith O, Gulamali F, de la Fuente J, Flynn BI, Vajrala K, Trutner Z, Jayakumar P, Tucker-Drob EM, Sohail M, Singh T, Narasimhan VM. The genetic architecture and evolution of the human skeletal form. Science 2023; 381:eadf8009. [PMID: 37471560 PMCID: PMC11075689 DOI: 10.1126/science.adf8009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 06/20/2023] [Indexed: 07/22/2023]
Abstract
The human skeletal form underlies bipedalism, but the genetic basis of skeletal proportions (SPs) is not well characterized. We applied deep-learning models to 31,221 x-rays from the UK Biobank to extract a comprehensive set of SPs, which were associated with 145 independent loci genome-wide. Structural equation modeling suggested that limb proportions exhibited strong genetic sharing but were independent of width and torso proportions. Polygenic score analysis identified specific associations between osteoarthritis and hip and knee SPs. In contrast to other traits, SP loci were enriched in human accelerated regions and in regulatory elements of genes that are differentially expressed between humans and great apes. Combined, our work identifies specific genetic variants that affect the skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.
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Affiliation(s)
- Eucharist Kun
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Emily M. Javan
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Olivia Smith
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Faris Gulamali
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Javier de la Fuente
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Brianna I. Flynn
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Kushal Vajrala
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Zoe Trutner
- Department of Surgery and Perioperative Care, The University of Texas at Austin, Austin, TX, USA
| | - Prakash Jayakumar
- Department of Surgery and Perioperative Care, The University of Texas at Austin, Austin, TX, USA
| | | | - Mashaal Sohail
- Centro de Ciencias Genómicas (CCG), Universidad Nacional Autónoma de México (UNAM), 62209 Cuernavaca, Mexico
| | - Tarjinder Singh
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- The New York Genome Center, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University, New York, NY, USA
| | - Vagheesh M. Narasimhan
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
- Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX, USA
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17
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Clifton NE, Schulmann A, Holmans PA, O'Donovan MC, Vawter MP. The relationship between case-control differential gene expression from brain tissue and genetic associations in schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2023; 192:85-92. [PMID: 36652379 PMCID: PMC10257740 DOI: 10.1002/ajmg.b.32931] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/23/2022] [Accepted: 01/05/2023] [Indexed: 01/19/2023]
Abstract
Large numbers of genetic loci have been identified that are known to contain common risk alleles for schizophrenia, but linking associated alleles to specific risk genes remains challenging. Given that most alleles that influence liability to schizophrenia are thought to do so by altered gene expression, intuitively, case-control differential gene expression studies should highlight genes with a higher probability of being associated with schizophrenia and could help identify the most likely causal genes within associated loci. Here, we test this hypothesis by comparing transcriptome analysis of the dorsolateral prefrontal cortex from 563 schizophrenia cases and 802 controls with genome-wide association study (GWAS) data from the third wave study of the Psychiatric Genomics Consortium. Genes differentially expressed in schizophrenia were not enriched for common allelic association statistics compared with other brain-expressed genes, nor were they enriched for genes within associated loci previously reported to be prioritized by genetic fine-mapping. Genes prioritized by Summary-based Mendelian Randomization were underexpressed in cases compared to other genes in the same GWAS loci. However, the overall strength and direction of expression change predicted by SMR were not related to that observed in the differential expression data. Overall, this study does not support the hypothesis that genes identified as differentially expressed from RNA sequencing of bulk brain tissue are enriched for those that show evidence for genetic associations. Such data have limited utility for prioritizing genes in currently associated loci in schizophrenia.
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Affiliation(s)
- Nicholas E Clifton
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Anton Schulmann
- Functional Genomics Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, California, USA
| | - Peter A Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Marquis P Vawter
- Functional Genomics Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, California, USA
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18
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Lu-Chen W, Khurshid S, Gunn S, Trinquart L, Lunetta KL, Xu H, Benjamin EJ, Ellinor PT, Anderson CD, Lubitz SA. Clinical and Genetic Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke. Stroke 2023; 54:1777-1785. [PMID: 37363945 PMCID: PMC10313140 DOI: 10.1161/strokeaha.122.041533] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/05/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Stroke is a leading cause of death and disability worldwide. Atrial fibrillation (AF) is a common cause of stroke but may not be detectable at the time of stroke. We hypothesized that an AF polygenic risk score (PRS) can discriminate between cardioembolic stroke and noncardioembolic strokes. METHODS We evaluated AF and stroke risk in 26 145 individuals of European descent from the Stroke Genetics Network case-control study. AF genetic risk was estimated using 3 recently developed PRS methods (LDpred-funct-inf, sBayesR, and PRS-CS) and 2 previously validated PRSs. We performed logistic regression of each AF PRS on AF status and separately cardioembolic stroke, adjusting for clinical risk score (CRS), imputation group, and principal components. We calculated model discrimination of AF and cardioembolic stroke using the concordance statistic (c-statistic) and compared c-statistics using 2000-iteration bootstrapping. We also assessed reclassification of cardioembolic stroke with the addition of PRS to either CRS or a modified CHA2DS2-VASc score alone. RESULTS Each AF PRS was significantly associated with AF and with cardioembolic stroke after adjustment for CRS. Addition of each AF PRS significantly improved discrimination as compared with CRS alone (P<0.01). When combined with the CRS, both PRS-CS and LDpred scores discriminated both AF and cardioembolic stroke (c-statistic 0.84 for AF; 0.74 for cardioembolic stroke) better than 3 other PRS scores (P<0.01). Using PRS-CS PRS and CRS in combination resulted in more appropriate reclassification of stroke events as compared with CRS alone (event reclassification [net reclassification indices]+=14% [95% CI, 10%-18%]; nonevent reclassification [net reclassification indices]-=17% [95% CI, 15%-0.19%]) or the modified CHA2DS2-VASc score (net reclassification indices+=11% [95% CI, 7%-15%]; net reclassification indices-=14% [95% CI, 12%-16%]) alone. CONCLUSIONS Addition of polygenic risk of AF to clinical risk factors modestly improves the discrimination of cardioembolic from noncardioembolic strokes, as well as reclassification of stroke subtype. Polygenic risk of AF may be a useful biomarker for identifying strokes caused by AF.
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Affiliation(s)
- Weng Lu-Chen
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sophia Gunn
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - Huichun Xu
- Department of Medicine, University of Maryland School of Medicine, Maryland, USA
| | | | - Emelia J. Benjamin
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine Boston, Massachusetts, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christopher D. Anderson
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Steven A. Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
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19
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Miller AP, Gizer IR. Neurogenetic and multi-omic sources of overlap among sensation seeking, alcohol consumption, and alcohol use disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.30.23290733. [PMID: 37333128 PMCID: PMC10274973 DOI: 10.1101/2023.05.30.23290733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Sensation seeking is bidirectionally associated with levels of alcohol consumption in both adult and adolescent samples and shared neurobiological and genetic influences may in part explain this association. Links between sensation seeking and alcohol use disorder (AUD) may primarily manifest via increased alcohol consumption rather than through direct effects on increasing problems and consequences. Here the overlap between sensation seeking, alcohol consumption, and AUD was examined using multivariate modeling approaches for genome-wide association study (GWAS) summary statistics in conjunction with neurobiologically-informed analyses at multiple levels of investigation. Meta-analytic and genomic structural equation modeling (GenomicSEM) approaches were used to conduct GWAS of sensation seeking, alcohol consumption, and AUD. Resulting summary statistics were used in downstream analyses to examine shared brain tissue enrichment of heritability and genome-wide evidence of overlap (e.g., stratified GenomicSEM, RRHO, genetic correlations with neuroimaging phenotypes) and to identify genomic regions likely contributing to observed genetic overlap across traits (e.g., HMAGMA, LAVA). Across approaches, results supported shared neurogenetic architecture between sensation seeking and alcohol consumption characterized by overlapping enrichment of genes expressed in midbrain and striatal tissues and variants associated with increased cortical surface area. Alcohol consumption and AUD evidenced overlap in relation to variants associated with decreased frontocortical thickness. Finally, genetic mediation models provided evidence of alcohol consumption mediating associations between sensation seeking and AUD. This study extends previous research by examining critical sources of neurogenetic and multi-omic overlap among sensation seeking, alcohol consumption, and AUD which may underlie observed phenotypic associations.
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Affiliation(s)
- Alex P. Miller
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Ian R. Gizer
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
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20
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Chen Z, Reynolds RH, Pardiñas AF, Gagliano Taliun SA, van Rheenen W, Lin K, Shatunov A, Gustavsson EK, Fogh I, Jones AR, Robberecht W, Corcia P, Chiò A, Shaw PJ, Morrison KE, Veldink JH, van den Berg LH, Shaw CE, Powell JF, Silani V, Hardy JA, Houlden H, Owen MJ, Turner MR, Ryten M, Al-Chalabi A. The contribution of Neanderthal introgression and natural selection to neurodegenerative diseases. Neurobiol Dis 2023; 180:106082. [PMID: 36925053 DOI: 10.1016/j.nbd.2023.106082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023] Open
Abstract
Humans are thought to be more susceptible to neurodegeneration than equivalently-aged primates. It is not known whether this vulnerability is specific to anatomically-modern humans or shared with other hominids. The contribution of introgressed Neanderthal DNA to neurodegenerative disorders remains uncertain. It is also unclear how common variants associated with neurodegenerative disease risk are maintained by natural selection in the population despite their deleterious effects. In this study, we aimed to quantify the genome-wide contribution of Neanderthal introgression and positive selection to the heritability of complex neurodegenerative disorders to address these questions. We used stratified-linkage disequilibrium score regression to investigate the relationship between five SNP-based signatures of natural selection, reflecting different timepoints of evolution, and genome-wide associated variants of the three most prevalent neurodegenerative disorders: Alzheimer's disease, amyotrophic lateral sclerosis and Parkinson's disease. We found no evidence for enrichment of positively-selected SNPs in the heritability of Alzheimer's disease, amyotrophic lateral sclerosis and Parkinson's disease, suggesting that common deleterious disease variants are unlikely to be maintained by positive selection. There was no enrichment of Neanderthal introgression in the SNP-heritability of these disorders, suggesting that Neanderthal admixture is unlikely to have contributed to disease risk. These findings provide insight into the origins of neurodegenerative disorders within the evolution of Homo sapiens and addresses a long-standing debate, showing that Neanderthal admixture is unlikely to have contributed to common genetic risk of neurodegeneration in anatomically-modern humans.
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Affiliation(s)
- Zhongbo Chen
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London (UCL), London, UK; Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, UCL, London, UK; NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL, London, UK.
| | - Regina H Reynolds
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, UCL, London, UK; NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL, London, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Sarah A Gagliano Taliun
- Department of Medicine & Department of Neurosciences, Université de Montréal, Montréal, Québec, Canada; Montréal Heart Institute, Montréal, Québec, Canada
| | - Wouter van Rheenen
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, the Netherlands
| | - Kuang Lin
- Nuffield Department of Population Health, Oxford University, Oxford, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Emil K Gustavsson
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, UCL, London, UK; NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL, London, UK
| | - Isabella Fogh
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ashley R Jones
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Wim Robberecht
- Department of Neurology, University Hospital Leuven, Leuven, Belgium; Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease, Leuven, Belgium; Vesalius Research Center, Laboratory of Neurobiology, Leuven, Belgium
| | - Philippe Corcia
- ALS Center, Department of Neurology, CHRU Bretonneau, Tours, France
| | - Adriano Chiò
- Rita Levi Montalcini Department of Neuroscience, ALS Centre, University of Torino, Turin, Italy; Azienda Ospedaliera Universitaria Città della Salute e della Scienza, Torino, Italy
| | - Pamela J Shaw
- Academic Neurology Unit, Department of Neuroscience, Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, UK
| | - Karen E Morrison
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Jan H Veldink
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, the Netherlands
| | - Leonard H van den Berg
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, the Netherlands
| | - Christopher E Shaw
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John F Powell
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Vincenzo Silani
- Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milano, Italy; Department of Pathophysiology and Transplantation, Dino Ferrari Center, Università degli Studi di Milano, 20122 Milano, Italy
| | - John A Hardy
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London (UCL), London, UK; Reta Lila Weston Institute, Queen Square Institute of Neurology, UCL, London, UK; UK Dementia Research Institute, Queen Square Institute of Neurology, UCL, London, UK; NIHR University College London Hospitals Biomedical Research Centre, London, UK; Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, SAR, China
| | - Henry Houlden
- Department of Neuromuscular Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Mina Ryten
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, UCL, London, UK; NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL, London, UK
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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21
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Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, Genereux DP, Dong MX, Bianchi M, Andrews G, Sakthikumar S, Nordin J, Roy A, Christmas MJ, Marinescu VD, Wang C, Wallerman O, Xue J, Yao S, Sun Q, Szatkiewicz J, Wen J, Huckins LM, Lawler A, Keough KC, Zheng Z, Zeng J, Wray NR, Li Y, Johnson J, Chen J, Paten B, Reilly SK, Hughes GM, Weng Z, Pollard KS, Pfenning AR, Forsberg-Nilsson K, Karlsson EK, Lindblad-Toh K. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 2023; 380:eabn2937. [PMID: 37104612 PMCID: PMC10259825 DOI: 10.1126/science.abn2937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/09/2023] [Indexed: 04/29/2023]
Abstract
Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.
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Affiliation(s)
- Patrick F. Sullivan
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Jennifer R. S. Meadows
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - BaDoi N. Phan
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Xue Li
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Diane P. Genereux
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Michael X. Dong
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Matteo Bianchi
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Gregory Andrews
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Sharadha Sakthikumar
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Jessika Nordin
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Ananya Roy
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Matthew J. Christmas
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Voichita D. Marinescu
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Chao Wang
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Ola Wallerman
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - James Xue
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Center for System Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Quan Sun
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jin Szatkiewicz
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Laura M. Huckins
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alyssa Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kathleen C. Keough
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Yun Li
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jessica Johnson
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | | | - Benedict Paten
- UC Santa Cruz Genomics Institute, Santa Cruz, CA 95064, USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Graham M. Hughes
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Katherine S. Pollard
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Andreas R. Pfenning
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Karin Forsberg-Nilsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
- Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK
| | - Elinor K. Karlsson
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kerstin Lindblad-Toh
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
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22
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Wu BS, Ge YJ, Zhang W, Chen SD, Xiang ST, Zhang YR, Ou YN, Jiang YC, Tan L, Cheng W, Suckling J, Feng JF, Yu JT, Mao Y. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. Neuroimage 2023; 269:119928. [PMID: 36740028 DOI: 10.1016/j.neuroimage.2023.119928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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23
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Ou YN, Wu BS, Ge YJ, Zhang Y, Jiang YC, Kuo K, Yang L, Tan L, Feng JF, Cheng W, Yu JT. The genetic architecture of human amygdala volumes and their overlap with common brain disorders. Transl Psychiatry 2023; 13:90. [PMID: 36906575 PMCID: PMC10008562 DOI: 10.1038/s41398-023-02387-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/13/2023] Open
Abstract
The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the SNP, locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in Adolescent Brain Cognitive Development (ABCD) cohort. The multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated (P < 5 × 10-8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.
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Affiliation(s)
- Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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24
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Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, Genereux DP, Dong MX, Bianchi M, Andrews G, Sakthikumar S, Nordin J, Roy A, Christmas MJ, Marinescu VD, Wallerman O, Xue JR, Li Y, Yao S, Sun Q, Szatkiewicz J, Wen J, Huckins LM, Lawler AJ, Keough KC, Zheng Z, Zeng J, Wray NR, Johnson J, Chen J, Paten B, Reilly SK, Hughes GM, Weng Z, Pollard KS, Pfenning AR, Forsberg-Nilsson K, Karlsson EK, Lindblad-Toh K. Leveraging Base Pair Mammalian Constraint to Understand Genetic Variation and Human Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.531987. [PMID: 36945512 PMCID: PMC10028973 DOI: 10.1101/2023.03.10.531987] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
Although thousands of genomic regions have been associated with heritable human diseases, attempts to elucidate biological mechanisms are impeded by a general inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function that is agnostic to cell type or disease mechanism. Here, single base phyloP scores from the whole genome alignment of 240 placental mammals identified 3.5% of the human genome as significantly constrained, and likely functional. We compared these scores to large-scale genome annotation, genome-wide association studies (GWAS), copy number variation, clinical genetics findings, and cancer data sets. Evolutionarily constrained positions are enriched for variants explaining common disease heritability (more than any other functional annotation). Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.
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Affiliation(s)
- Patrick F. Sullivan
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Stockholm, Sweden
| | - Jennifer R. S. Meadows
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Steven Gazal
- Keck School of Medicine, University of Southern California; Los Angeles, CA 90033, USA
| | - BaDoi N. Phan
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University; Pittsburgh, PA 15213, USA
- Medical Scientist Training Program, University of Pittsburgh School of Medicine; Pittsburgh, PA 15261, USA
- Neuroscience Institute, Carnegie Mellon University; Pittsburgh, PA 15213, USA
| | - Xue Li
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Morningside Graduate School of Biomedical Sciences, UMass Chan Medical School; Worcester, MA 01605, USA
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
| | | | - Michael X. Dong
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Matteo Bianchi
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Gregory Andrews
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
| | - Sharadha Sakthikumar
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
| | - Jessika Nordin
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University; Uppsala, 751 85, Sweden
| | - Ananya Roy
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University; Uppsala, 751 85, Sweden
| | - Matthew J. Christmas
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Voichita D. Marinescu
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Ola Wallerman
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - James R. Xue
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Department of Organismic and Evolutionary Biology, Harvard University; Cambridge, MA 02138, USA
| | - Yun Li
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Stockholm, Sweden
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, USA
| | - Jin Szatkiewicz
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
| | - Laura M. Huckins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Alyssa J. Lawler
- Neuroscience Institute, Carnegie Mellon University; Pittsburgh, PA 15213, USA
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University; Pittsburgh, PA 15213, USA
| | - Kathleen C. Keough
- Department of Epidemiology & Biostatistics, University of California San Francisco; San Francisco, CA 94158, USA
- Fauna Bio Incorporated; Emeryville, CA 94608, USA
- Gladstone Institutes; San Francisco, CA 94158, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland; Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland; Brisbane, Queensland, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland; Brisbane, Queensland, Australia
- Queensland Brain Institute, University of Queensland; Brisbane, Queensland, Australia
| | - Jessica Johnson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, USA
| | | | - Benedict Paten
- Genomics Institute, University of California Santa Cruz; Santa Cruz, CA 95064, USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine; New Haven, CT 06510, USA
| | - Graham M. Hughes
- School of Biology and Environmental Science, University College Dublin; Belfield, Dublin 4, Ireland
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
| | - Katherine S. Pollard
- Department of Epidemiology & Biostatistics, University of California San Francisco; San Francisco, CA 94158, USA
- Gladstone Institutes; San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub; San Francisco, CA 94158, USA
| | - Andreas R. Pfenning
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University; Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University; Pittsburgh, PA 15213, USA
| | - Karin Forsberg-Nilsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University; Uppsala, 751 85, Sweden
- Biodiscovery Institute, University of Nottingham; Nottingham, UK
| | - Elinor K. Karlsson
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
- Program in Molecular Medicine, UMass Chan Medical School; Worcester, MA 01605, USA
| | - Kerstin Lindblad-Toh
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
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25
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Agarwal V, Inoue F, Schubach M, Martin BK, Dash PM, Zhang Z, Sohota A, Noble WS, Yardimci GG, Kircher M, Shendure J, Ahituv N. Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.05.531189. [PMID: 36945371 PMCID: PMC10028905 DOI: 10.1101/2023.03.05.531189] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
The human genome contains millions of candidate cis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific 'on switches' providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.
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Affiliation(s)
- Vikram Agarwal
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- mRNA Center of Excellence, Sanofi Pasteur Inc., Waltham, MA 02451, USA
| | - Fumitaka Inoue
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Max Schubach
- Berlin Institute of Health of Health at Charité - Universitätsmedizin Berlin, 10178, Berlin, Germany
| | - Beth K. Martin
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Pyaree Mohan Dash
- Berlin Institute of Health of Health at Charité - Universitätsmedizin Berlin, 10178, Berlin, Germany
| | - Zicong Zhang
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Ajuni Sohota
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Galip Gürkan Yardimci
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA
| | - Martin Kircher
- Berlin Institute of Health of Health at Charité - Universitätsmedizin Berlin, 10178, Berlin, Germany
- Institute of Human Genetics, University Medical Center Schleswig-Holstein, University of Lübeck, Lübeck, Germany
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, Seattle, WA 98195, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA
- Allen Center for Cell Lineage Tracing, University of Washington, Seattle, WA 98195, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
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26
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Current advances in primate genomics: novel approaches for understanding evolution and disease. Nat Rev Genet 2023; 24:314-331. [PMID: 36599936 DOI: 10.1038/s41576-022-00554-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2022] [Indexed: 01/05/2023]
Abstract
Primate genomics holds the key to understanding fundamental aspects of human evolution and disease. However, genetic diversity and functional genomics data sets are currently available for only a few of the more than 500 extant primate species. Concerted efforts are under way to characterize primate genomes, genetic polymorphism and divergence, and functional landscapes across the primate phylogeny. The resulting data sets will enable the connection of genotypes to phenotypes and provide new insight into aspects of the genetics of primate traits, including human diseases. In this Review, we describe the existing genome assemblies as well as genetic variation and functional genomic data sets. We highlight some of the challenges with sample acquisition. Finally, we explore how technological advances in single-cell functional genomics and induced pluripotent stem cell-derived organoids will facilitate our understanding of the molecular foundations of primate biology.
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27
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Kun E, Javan EM, Smith O, Gulamali F, de la Fuente J, Flynn BI, Vajrala K, Trutner Z, Jayakumar P, Tucker-Drob EM, Sohail M, Singh T, Narasimhan VM. The genetic architecture of the human skeletal form. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.03.521284. [PMID: 36712136 PMCID: PMC9881884 DOI: 10.1101/2023.01.03.521284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The human skeletal form underlies our ability to walk on two legs, but unlike standing height, the genetic basis of limb lengths and skeletal proportions is less well understood. Here we applied a deep learning model to 31,221 whole body dual-energy X-ray absorptiometry (DXA) images from the UK Biobank (UKB) to extract 23 different image-derived phenotypes (IDPs) that include all long bone lengths as well as hip and shoulder width, which we analyzed while controlling for height. All skeletal proportions are highly heritable (∼40-50%), and genome-wide association studies (GWAS) of these traits identified 179 independent loci, of which 102 loci were not associated with height. These loci are enriched in genes regulating skeletal development as well as associated with rare human skeletal diseases and abnormal mouse skeletal phenotypes. Genetic correlation and genomic structural equation modeling indicated that limb proportions exhibited strong genetic sharing but were genetically independent of width and torso proportions. Phenotypic and polygenic risk score analyses identified specific associations between osteoarthritis (OA) of the hip and knee, the leading causes of adult disability in the United States, and skeletal proportions of the corresponding regions. We also found genomic evidence of evolutionary change in arm-to-leg and hip-width proportions in humans consistent with striking anatomical changes in these skeletal proportions in the hominin fossil record. In contrast to cardiovascular, auto-immune, metabolic, and other categories of traits, loci associated with these skeletal proportions are significantly enriched in human accelerated regions (HARs), and regulatory elements of genes differentially expressed through development between humans and the great apes. Taken together, our work validates the use of deep learning models on DXA images to identify novel and specific genetic variants affecting the human skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.
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Affiliation(s)
- Eucharist Kun
- Department of Integrative Biology, The University of Texas at Austin
| | - Emily M Javan
- Department of Integrative Biology, The University of Texas at Austin
| | - Olivia Smith
- Department of Integrative Biology, The University of Texas at Austin
| | | | | | - Brianna I Flynn
- Department of Integrative Biology, The University of Texas at Austin
| | - Kushal Vajrala
- Department of Integrative Biology, The University of Texas at Austin
| | - Zoe Trutner
- Department of Surgery and Perioperative Care, The University of Texas at Austin
| | - Prakash Jayakumar
- Department of Surgery and Perioperative Care, The University of Texas at Austin
| | | | - Mashaal Sohail
- Centro de Ciencias Genómicas (CCG), Universidad Nacional Autónoma de México (UNAM)
| | - Tarjinder Singh
- The Department of Psychiatry at Columbia University Irving Medical Center
- The New York Genome Center
- Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin
- Department of Statistics and Data Science, The University of Texas at Austin
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28
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Fong SL, Capra JA. Function and Constraint in Enhancer Sequences with Multiple Evolutionary Origins. Genome Biol Evol 2022; 14:evac159. [PMID: 36314566 PMCID: PMC9673499 DOI: 10.1093/gbe/evac159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/22/2022] [Indexed: 11/04/2022] Open
Abstract
Thousands of human gene regulatory enhancers are composed of sequences with multiple evolutionary origins. These evolutionarily "complex" enhancers consist of older "core" sequences and younger "derived" sequences. However, the functional relationship between the sequences of different evolutionary origins within complex enhancers is poorly understood. We evaluated the function, selective pressures, and sequence variation across core and derived components of human complex enhancers. We find that both components are older than expected from the genomic background, and complex enhancers are enriched for core and derived sequences of similar evolutionary ages. Both components show strong evidence of biochemical activity in massively parallel report assays. However, core and derived sequences have distinct transcription factor (TF)-binding preferences that are largely similar across evolutionary origins. As expected, given these signatures of function, both core and derived sequences have substantial evidence of purifying selection. Nonetheless, derived sequences exhibit weaker purifying selection than adjacent cores. Derived sequences also tolerate more common genetic variation and are enriched compared with cores for expression quantitative trait loci associated with gene expression variability in human populations. In conclusion, both core and derived sequences have strong evidence of gene regulatory function, but derived sequences have distinct constraint profiles, TF-binding preferences, and tolerance to variation compared with cores. We propose that the step-wise integration of younger derived with older core sequences has generated regulatory substrates with robust activity and the potential for functional variation. Our analyses demonstrate that synthesizing study of enhancer evolution and function can aid interpretation of regulatory sequence activity and functional variation across human populations.
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Affiliation(s)
- Sarah L Fong
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee
| | - John A Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco
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29
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Niarchou M, Gustavson DE, Sathirapongsasuti JF, Anglada-Tort M, Eising E, Bell E, McArthur E, Straub P, McAuley JD, Capra JA, Ullén F, Creanza N, Mosing MA, Hinds DA, Davis LK, Jacoby N, Gordon RL. Genome-wide association study of musical beat synchronization demonstrates high polygenicity. Nat Hum Behav 2022; 6:1292-1309. [PMID: 35710621 PMCID: PMC9489530 DOI: 10.1038/s41562-022-01359-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 04/21/2022] [Indexed: 02/02/2023]
Abstract
Moving in synchrony to the beat is a fundamental component of musicality. Here we conducted a genome-wide association study to identify common genetic variants associated with beat synchronization in 606,825 individuals. Beat synchronization exhibited a highly polygenic architecture, with 69 loci reaching genome-wide significance (P < 5 × 10-8) and single-nucleotide-polymorphism-based heritability (on the liability scale) of 13%-16%. Heritability was enriched for genes expressed in brain tissues and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central-nervous-system-expressed genes linked to the genetic basis of the trait. We performed validations of the self-report phenotype (through separate experiments) and of the genome-wide association study (polygenic scores for beat synchronization were associated with patients algorithmically classified as musicians in medical records of a separate biobank). Genetic correlations with breathing function, motor function, processing speed and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.
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Affiliation(s)
- Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Daniel E Gustavson
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Manuel Anglada-Tort
- Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Else Eising
- Department of Language and Genetics, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Eamonn Bell
- Department of Music, Columbia University, New York, NY, USA
- Department of Computer Science, Durham University, Durham, UK
| | - Evonne McArthur
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Straub
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - J Devin McAuley
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - John A Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institutet, Solna, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Nicole Creanza
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN, USA
| | - Miriam A Mosing
- Department of Neuroscience, Karolinska Institutet, Solna, Sweden
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA.
| | - Nori Jacoby
- Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Reyna L Gordon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Otolaryngology-Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Psychology, Vanderbilt University, Nashville, TN, USA.
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA.
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30
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Young M, Richard D, Grabowski M, Auerbach BM, de Bakker BS, Hagoort J, Muthuirulan P, Kharkar V, Kurki HK, Betti L, Birkenstock L, Lewton KL, Capellini TD. The developmental impacts of natural selection on human pelvic morphology. SCIENCE ADVANCES 2022; 8:eabq4884. [PMID: 35977020 PMCID: PMC9385149 DOI: 10.1126/sciadv.abq4884] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Evolutionary responses to selection for bipedalism and childbirth have shaped the human pelvis, a structure that differs substantially from that in apes. Morphology related to these factors is present by birth, yet the developmental-genetic mechanisms governing pelvic shape remain largely unknown. Here, we pinpoint and characterize a key gestational window when human-specific pelvic morphology becomes recognizable, as the ilium and the entire pelvis acquire traits essential for human walking and birth. We next use functional genomics to molecularly characterize chondrocytes from different pelvic subelements during this window to reveal their developmental-genetic architectures. We then find notable evidence of ancient selection and genetic constraint on regulatory sequences involved in ilium expansion and growth, findings complemented by our phenotypic analyses showing that variation in iliac traits is reduced in humans compared to African apes. Our datasets provide important resources for musculoskeletal biology and begin to elucidate developmental mechanisms that shape human-specific morphology.
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Affiliation(s)
- Mariel Young
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Daniel Richard
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Mark Grabowski
- Research Centre in Evolutionary Anthropology and Palaeoecology, Liverpool John Moores University, Liverpool L3 3AF, UK
- Department of Biosciences, Centre for Ecological and Evolutionary Synthesis (CEES), University of Oslo, Oslo, Norway
| | - Benjamin M. Auerbach
- Department of Anthropology, The University of Tennessee, Knoxville, TN, USA
- Department of Ecology and Evolutionary Biology, The University of Tennessee, Knoxville, TN, USA
| | - Bernadette S. de Bakker
- Department of Obstetrics and Gynecology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Jaco Hagoort
- Department of Medical Biology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, Netherlands
| | | | - Vismaya Kharkar
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Helen K. Kurki
- Department of Anthropology, University of Victoria, STN CSC, Victoria, BC V8W 2Y2, Canada
| | - Lia Betti
- School of Life and Health Sciences, University of Roehampton, London SW15 4JD, UK
| | | | - Kristi L. Lewton
- Department of Integrative Anatomical Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Terence D. Capellini
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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31
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Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/03/2021] [Accepted: 05/27/2022] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
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Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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32
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Grotzinger AD, Mallard TT, Akingbuwa WA, Ip HF, Adams MJ, Lewis CM, McIntosh AM, Grove J, Dalsgaard S, Lesch KP, Strom N, Meier SM, Mattheisen M, Børglum AD, Mors O, Breen G, Lee PH, Kendler KS, Smoller JW, Tucker-Drob EM, Nivard MG. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat Genet 2022; 54:548-559. [PMID: 35513722 PMCID: PMC9117465 DOI: 10.1038/s41588-022-01057-4] [Citation(s) in RCA: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 03/21/2022] [Indexed: 12/30/2022]
Abstract
We interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. We identify four broad factors (neurodevelopmental, compulsive, psychotic and internalizing) that underlie genetic correlations among the disorders and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce stratified genomic structural equation modeling, which we use to identify gene sets that disproportionately contribute to genetic risk sharing. This includes protein-truncating variant-intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for genetic overlap across disorders with psychotic features. Multivariate association analyses detect 152 (20 new) independent loci that act on the individual factors and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate-to-high genetic correlations across all 11 disorders, we find little utility of a single dimension of genetic risk across psychiatric disorders either at the level of biobehavioral correlates or at the level of individual variants.
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Affiliation(s)
- Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA.
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA.
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Wonuola A Akingbuwa
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, the Netherlands
| | - Hill F Ip
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | | | - Jakob Grove
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Søren Dalsgaard
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Klaus-Peter Lesch
- Section of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
- Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Nora Strom
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Sandra M Meier
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Manuel Mattheisen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- iSEQ Center, Aarhus University, Aarhus, Denmark
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada
| | - Anders D Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ole Mors
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | - Phil H Lee
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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33
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Siewert-Rocks KM, Kim SS, Yao DW, Shi H, Price AL. Leveraging gene co-regulation to identify gene sets enriched for disease heritability. Am J Hum Genet 2022; 109:393-404. [PMID: 35108496 PMCID: PMC8948163 DOI: 10.1016/j.ajhg.2022.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 01/04/2022] [Indexed: 12/15/2022] Open
Abstract
Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWASs) detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by predicted expression. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; a gene set is enriched for heritability if genes with high co-regulation to the set have higher TWAS chi-square statistics than genes with low co-regulation to the set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well calibrated and well powered. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched sets, recapitulating known biology. For Alzheimer disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify enriched gene sets.
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Affiliation(s)
- Katherine M Siewert-Rocks
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Douglas W Yao
- Program in Systems, Synthetic, and Quantitative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
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34
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Makowski C, van der Meer D, Dong W, Wang H, Wu Y, Zou J, Liu C, Rosenthal SB, Hagler DJ, Fan CC, Kremen WS, Andreassen OA, Jernigan TL, Dale AM, Zhang K, Visscher PM, Yang J, Chen CH. Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. Science 2022; 375:522-528. [PMID: 35113692 DOI: 10.1126/science.abe8457] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca's area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.
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Affiliation(s)
- Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Hao Wang
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Yan Wu
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Jingjing Zou
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA
| | - Cin Liu
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Sara B Rosenthal
- Center for Computational Biology and Bioinformatics, University of California, San Diego, CA, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - William S Kremen
- Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, CA, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, CA, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA.,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
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35
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Hautakangas H, Winsvold BS, Ruotsalainen SE, Bjornsdottir G, Harder AVE, Kogelman LJA, Thomas LF, Noordam R, Benner C, Gormley P, Artto V, Banasik K, Bjornsdottir A, Boomsma DI, Brumpton BM, Burgdorf KS, Buring JE, Chalmer MA, de Boer I, Dichgans M, Erikstrup C, Färkkilä M, Garbrielsen ME, Ghanbari M, Hagen K, Häppölä P, Hottenga JJ, Hrafnsdottir MG, Hveem K, Johnsen MB, Kähönen M, Kristoffersen ES, Kurth T, Lehtimäki T, Lighart L, Magnusson SH, Malik R, Pedersen OB, Pelzer N, Penninx BWJH, Ran C, Ridker PM, Rosendaal FR, Sigurdardottir GR, Skogholt AH, Sveinsson OA, Thorgeirsson TE, Ullum H, Vijfhuizen LS, Widén E, van Dijk KW, Aromaa A, Belin AC, Freilinger T, Ikram MA, Järvelin MR, Raitakari OT, Terwindt GM, Kallela M, Wessman M, Olesen J, Chasman DI, Nyholt DR, Stefánsson H, Stefansson K, van den Maagdenberg AMJM, Hansen TF, Ripatti S, Zwart JA, Palotie A, Pirinen M. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat Genet 2022; 54:152-160. [PMID: 35115687 PMCID: PMC8837554 DOI: 10.1038/s41588-021-00990-0] [Citation(s) in RCA: 153] [Impact Index Per Article: 76.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022]
Abstract
Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that seem specific for migraine without aura (near SPINK2 and near FECH) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.
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Affiliation(s)
- Heidi Hautakangas
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Bendik S Winsvold
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | | | - Aster V E Harder
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Lisette J A Kogelman
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Christian Benner
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | | | - Ville Artto
- Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Karina Banasik
- Novo Nordic Foundation Center for Protein Research, Copenhagen University, Copenhagen, Denmark
| | | | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Ben M Brumpton
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Julie E Buring
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mona Ameri Chalmer
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Irene de Boer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (Synergy), Munich, Germany
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Markus Färkkilä
- Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Maiken Elvestad Garbrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Knut Hagen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Clinical Research Unit Central Norway, St. Olavs University Hospital, Trondheim, Norway
| | - Paavo Häppölä
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Jouke-Jan Hottenga
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | | | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Bakke Johnsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Espen S Kristoffersen
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Department of General Practice, Institute of Health and Society, University of Oslo, Oslo, Norway
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Tobias Kurth
- Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lannie Lighart
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | | | - Rainer Malik
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Ole Birger Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Nadine Pelzer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, the Netherlands
| | - Caroline Ran
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | | | - Henrik Ullum
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Arpo Aromaa
- National Public Health Institute (Finnish Institute for Health and Welfare - THL), Helsinki, Finland
| | | | - Tobias Freilinger
- Klinikum Passau, Department of Neurology, Passau, Germany
- Centre of Neurology, Hertie Institute for Clinical Brain Research, Tuebingen, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Mikko Kallela
- Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Maija Wessman
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Jes Olesen
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Dale R Nyholt
- School of Biomedical Sciences and Centre for Genomics and Personalised Health, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | | | | | - Arn M J M van den Maagdenberg
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Thomas Folkmann Hansen
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
- Novo Nordic Foundation Center for Protein Research, Copenhagen University, Copenhagen, Denmark
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - John-Anker Zwart
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
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36
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Hutchinson A, Reales G, Willis T, Wallace C. Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR. PLoS Genet 2021; 17:e1009853. [PMID: 34669738 PMCID: PMC8559959 DOI: 10.1371/journal.pgen.1009853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/01/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Willis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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37
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MacPhillamy C, Pitchford WS, Alinejad-Rokny H, Low WY. Opportunity to improve livestock traits using 3D genomics. Anim Genet 2021; 52:785-798. [PMID: 34494283 DOI: 10.1111/age.13135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2021] [Indexed: 11/30/2022]
Abstract
The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genome-wide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these high-throughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.
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Affiliation(s)
- C MacPhillamy
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy Campus, Mudla Wirra Rd, Roseworthy, SA, 5371, Australia
| | - W S Pitchford
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy Campus, Mudla Wirra Rd, Roseworthy, SA, 5371, Australia
| | - H Alinejad-Rokny
- Biological & Medical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia.,School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia
| | - W Y Low
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy Campus, Mudla Wirra Rd, Roseworthy, SA, 5371, Australia
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38
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Yengo L, Yang J, Keller MC, Goddard ME, Wray NR, Visscher PM. Genomic partitioning of inbreeding depression in humans. Am J Hum Genet 2021; 108:1488-1501. [PMID: 34214457 DOI: 10.1016/j.ajhg.2021.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 06/01/2021] [Indexed: 02/05/2023] Open
Abstract
Across species, offspring of related individuals often exhibit significant reduction in fitness-related traits, known as inbreeding depression (ID), yet the genetic and molecular basis for ID remains elusive. Here, we develop a method to quantify enrichment of ID within specific genomic annotations and apply it to human data. We analyzed the phenomes and genomes of ∼350,000 unrelated participants of the UK Biobank and found, on average of over 11 traits, significant enrichment of ID within genomic regions with high recombination rates (>21-fold; p < 10-5), with conserved function across species (>19-fold; p < 10-4), and within regulatory elements such as DNase I hypersensitive sites (∼5-fold; p = 8.9 × 10-7). We also quantified enrichment of ID within trait-associated regions and found suggestive evidence that genomic regions contributing to additive genetic variance in the population are enriched for ID signal. We find strong correlations between functional enrichment of SNP-based heritability and that of ID (r = 0.8, standard error: 0.1). These findings provide empirical evidence that ID is most likely due to many partially recessive deleterious alleles in low linkage disequilibrium regions of the genome. Our study suggests that functional characterization of ID may further elucidate the genetic architectures and biological mechanisms underlying complex traits and diseases.
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McArthur E, Rinker DC, Capra JA. Quantifying the contribution of Neanderthal introgression to the heritability of complex traits. Nat Commun 2021; 12:4481. [PMID: 34294692 PMCID: PMC8298587 DOI: 10.1038/s41467-021-24582-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 06/24/2021] [Indexed: 11/15/2022] Open
Abstract
Eurasians have ~2% Neanderthal ancestry, but we lack a comprehensive understanding of the genome-wide influence of Neanderthal introgression on modern human diseases and traits. Here, we quantify the contribution of introgressed alleles to the heritability of more than 400 diverse traits. We show that genomic regions in which detectable Neanderthal ancestry remains are depleted of heritability for all traits considered, except those related to skin and hair. Introgressed variants themselves are also depleted for contributions to the heritability of most traits. However, introgressed variants shared across multiple Neanderthal populations are enriched for heritability and have consistent directions of effect on several traits with potential relevance to human adaptation to non-African environments, including hair and skin traits, autoimmunity, chronotype, bone density, lung capacity, and menopause age. Integrating our results, we propose a model in which selection against introgressed functional variation was the dominant trend (especially for cognitive traits); however, for a few traits, introgressed variants provided beneficial variation via uni-directional (e.g., lightening skin color) or bi-directional (e.g., modulating immune response) effects. We lack a comprehensive understanding of how Neanderthal ancestry influences human traits. This study finds that regions with Neanderthal ancestry are broadly depleted of trait-associated variation; yet, introgressed variants likely contributed to human adaptation in a few traits, like skin color and immune response modulation.
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Affiliation(s)
- Evonne McArthur
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | - David C Rinker
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, 37235, USA
| | - John A Capra
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37235, USA. .,Department of Biological Sciences, Vanderbilt University, Nashville, TN, 37235, USA. .,Bakar Computational Health Sciences Institute and Department of Epidemiology and Statistics, University of California San Francisco, San Francisco, CA, 94107, USA.
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40
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Zabad S, Ragsdale AP, Sun R, Li Y, Gravel S. Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment. Genet Epidemiol 2021; 45:621-632. [PMID: 34157784 DOI: 10.1002/gepi.22388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 04/10/2021] [Accepted: 05/04/2021] [Indexed: 11/05/2022]
Abstract
Linkage-Disequilibrium Score Regression (LDSC) is a popular framework for analyzing Genome-wide Association Studies (GWAS) summary statistics that allows for estimating single nucleotide polymorphism heritability, confounding, and functional enrichment of genetic variants with different annotations. Recent work has highlighted the influence of implicit and explicit assumptions of the model on the biological interpretation of the results. In this study, we explored a formulation of LDSC that replaces the r 2 measure of LD with a recently proposed unbiased estimator of the D 2 statistic. In addition to modest statistical difference across estimators, this derivation highlighted implicit and unrealistic assumptions about the relationship between allele frequency, effect size, and annotation status. We carry out a systematic comparison of alternative LDSC formulations by applying them to summary statistics from 47 GWAS traits. Our results show that commonly used models likely underestimate functional enrichment. These results highlight the importance of calibrating the LDSC model to achieve a more robust understanding of polygenic traits.
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Affiliation(s)
- Shadi Zabad
- Department of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Aaron P Ragsdale
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Rosie Sun
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Yue Li
- Department of Computer Science, McGill University, Montreal, Quebec, Canada.,Quantitative Life Science, McGill University, Montreal, Quebec, Canada
| | - Simon Gravel
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Quantitative Life Science, McGill University, Montreal, Quebec, Canada
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41
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Richard D, Capellini TD. Shifting epigenetic contexts influence regulatory variation and disease risk. Aging (Albany NY) 2021; 13:15699-15749. [PMID: 34138751 PMCID: PMC8266365 DOI: 10.18632/aging.203194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/01/2021] [Indexed: 11/25/2022]
Abstract
Epigenetic shifts are a hallmark of aging that impact transcriptional networks at regulatory level. These shifts may modify the effects of genetic regulatory variants during aging and contribute to disease pathomechanism. However, these shifts occur on the backdrop of epigenetic changes experienced throughout an individual's development into adulthood; thus, the phenotypic, and ultimately fitness, effects of regulatory variants subject to developmental- versus aging-related epigenetic shifts may differ considerably. Natural selection therefore may act differently on variants depending on their changing epigenetic context, which we propose as a novel lens through which to consider regulatory sequence evolution and phenotypic effects. Here, we define genomic regions subjected to altered chromatin accessibility as tissues transition from their fetal to adult forms, and subsequently from early to late adulthood. Based on these epigenomic datasets, we examine patterns of evolutionary constraint and potential functional impacts of sequence variation (e.g., genetic disease risk associations). We find that while the signals observed with developmental epigenetic changes are consistent with stronger fitness consequences (i.e., negative selection pressures), they tend to have weaker effects on genetic risk associations for aging-related diseases. Conversely, we see stronger effects of variants with increased local accessibility in adult tissues, strongest in young adult when compared to old. We propose a model for how epigenetic status of a region may influence the effects of evolutionary relevant sequence variation, and suggest that such a perspective on gene regulatory networks may elucidate our understanding of aging biology.
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Affiliation(s)
- Daniel Richard
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Terence D Capellini
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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42
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Fong SL, Capra JA. Modeling the evolutionary architectures of transcribed human enhancer sequences reveals distinct origins, functions, and associations with human-trait variation. Mol Biol Evol 2021; 38:3681-3696. [PMID: 33973014 PMCID: PMC8382917 DOI: 10.1093/molbev/msab138] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Despite the importance of gene regulatory enhancers in human biology and evolution, we lack a comprehensive model of enhancer evolution and function. This substantially limits our understanding of the genetic basis of species divergence and our ability to interpret the effects of noncoding variants on human traits. To explore enhancer sequence evolution and its relationship to regulatory function, we traced the evolutionary origins of transcribed human enhancer sequences with activity across diverse tissues and cellular contexts from the FANTOM5 consortium. The transcribed enhancers are enriched for sequences of a single evolutionary age (“simple” evolutionary architectures) compared with enhancers that are composites of sequences of multiple evolutionary ages (“complex” evolutionary architectures), likely indicating constraint against genomic rearrangements. Complex enhancers are older, more pleiotropic, and more active across species than simple enhancers. Genetic variants within complex enhancers are also less likely to associate with human traits and biochemical activity. Transposable-element-derived sequences (TEDS) have made diverse contributions to enhancers of both architectures; the majority of TEDS are found in enhancers with simple architectures, while a minority have remodeled older sequences to create complex architectures. Finally, we compare the evolutionary architectures of transcribed enhancers with histone-mark-defined enhancers. Our results reveal that most human transcribed enhancers are ancient sequences of a single age, and thus the evolution of most human enhancers was not driven by increases in evolutionary complexity over time. Our analyses further suggest that considering enhancer evolutionary histories provides context that can aid interpretation of the effects of variants on enhancer function. Based on these results, we propose a framework for analyzing enhancer evolutionary architecture.
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Affiliation(s)
- Sarah L Fong
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - John A Capra
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.,Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA.,Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
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43
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Abstract
DNA methylation is a critical regulatory mechanism implicated in development, learning, memory, and disease in the human brain. Here we have elucidated DNA methylation changes during recent human brain evolution. We demonstrate dynamic evolutionary trajectories of DNA methylation in cell-type and cytosine-context specific manner. Specifically, DNA methylation in non-CG context, namely CH methylation, has increased (hypermethylation) in neuronal gene bodies during human brain evolution, contributing to human-specific down-regulation of genes and co-expression modules. The effects of CH hypermethylation is particularly pronounced in early development and neuronal subtypes. In contrast, DNA methylation in CG context shows pronounced reduction (hypomethylation) in human brains, notably in cis-regulatory regions, leading to upregulation of downstream genes. We show that the majority of differential CG methylation between neurons and oligodendrocytes originated before the divergence of hominoids and catarrhine monkeys, and harbors strong signal for genetic risk for schizophrenia. Remarkably, a substantial portion of differential CG methylation between neurons and oligodendrocytes emerged in the human lineage since the divergence from the chimpanzee lineage and carries significant genetic risk for schizophrenia. Therefore, recent epigenetic evolution of human cortex has shaped the cellular regulatory landscape and contributed to the increased vulnerability to neuropsychiatric diseases.
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44
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McArthur E, Capra JA. Topologically associating domain boundaries that are stable across diverse cell types are evolutionarily constrained and enriched for heritability. Am J Hum Genet 2021; 108:269-283. [PMID: 33545030 PMCID: PMC7895846 DOI: 10.1016/j.ajhg.2021.01.001] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 12/29/2020] [Indexed: 12/22/2022] Open
Abstract
Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs-TAD boundaries-contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD-boundary disruption have been implicated in rare-disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common-trait-associated variants. Here, we investigate an attribute of 3D genome architecture-the stability of TAD boundaries across cell types-and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWASs), we investigate the differences in disease association and evolutionary pressure on variation in TADs versus TAD boundaries. We demonstrate that genetic variation in TAD boundaries contributes more to complex-trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial variation. Compared to boundaries unique to a specific cell type, boundaries stable across cell types are further enriched for complex-trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genome's functional landscape and enabling variant interpretation that takes 3D structure into account.
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Affiliation(s)
- Evonne McArthur
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - John A Capra
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37235, USA; Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, 94158; Bakar Institute for Computational Health Sciences, University of California, San Francisco, CA, 94158.
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45
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Wendt FR, Pathak GA, Overstreet C, Tylee DS, Gelernter J, Atkinson EG, Polimanti R. Characterizing the effect of background selection on the polygenicity of brain-related traits. Genomics 2021; 113:111-119. [PMID: 33278486 PMCID: PMC7855394 DOI: 10.1016/j.ygeno.2020.11.032] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/20/2020] [Accepted: 11/30/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have demonstrated that psychopathology phenotypes are affected by many risk alleles with small effect (polygenicity). It is unclear how ubiquitously evolutionary pressures influence the genetic architecture of these traits. METHODS We partitioned SNP heritability to assess the contribution of background (BGS) and positive selection, Neanderthal local ancestry, functional significance, and genotype networks in 75 brain-related traits (8411 ≤ N ≤ 1,131,181, mean N = 205,289). We applied binary annotations by dichotomizing each measure based on top 2%, 1%, and 0.5% of all scores genome-wide. Effect size distribution features were calculated using GENESIS. We tested the relationship between effect size distribution descriptive statistics and natural selection. In a subset of traits, we explore the inclusion of diagnostic heterogeneity (e.g., number of diagnostic combinations and total symptoms) in the tested relationship. RESULTS SNP-heritability was enriched (false discovery rate q < 0.05) for loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and loss-of-function (LoF)-intolerant regions (67 phenotypes). These effects were strongest in GWAS of schizophrenia (1.90-fold BGS, 1.16-fold genic, and 1.92-fold LoF), educational attainment (1.86-fold BGS, 1.12-fold genic, and 1.79-fold LoF), and cognitive performance (2.29-fold BGS, 1.12-fold genic, and 1.79-fold LoF). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ2 parameter) in 75 brain-related traits (β = 4.39 × 10-5, p = 1.43 × 10-5, model r2 = 0.548). Considering the number of DSM-5 diagnostic combinations per psychiatric disorder improved model fit (σ2 ~ BTop2% × Genic × diagnostic combinations; model r2 = 0.661). CONCLUSIONS Brain-related phenotypes with larger variance in risk locus effect sizes are associated with loci under BGS. We show exploratory results suggesting that diagnostic complexity may also contribute to the increased polygenicity of psychiatric disorders.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA
| | - Cassie Overstreet
- National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA CT Healthcare System and Department of Psychiatry, Yale University School of Medicine, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA; Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Elizabeth G Atkinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA.
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46
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Weissbrod O, Hormozdiari F, Benner C, Cui R, Ulirsch J, Gazal S, Schoech AP, van de Geijn B, Reshef Y, Márquez-Luna C, O'Connor L, Pirinen M, Finucane HK, Price AL. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat Genet 2020; 52:1355-1363. [PMID: 33199916 PMCID: PMC7710571 DOI: 10.1038/s41588-020-00735-5] [Citation(s) in RCA: 162] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 10/02/2020] [Indexed: 01/16/2023]
Abstract
Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.
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Affiliation(s)
- Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christian Benner
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Ran Cui
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacob Ulirsch
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Cambridge, MA, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yakir Reshef
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Carla Márquez-Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Luke O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Hilary K Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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47
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Cooper-Knock J, Zhang S, Kenna KP, Moll T, Franklin JP, Allen S, Nezhad HG, Iacoangeli A, Yacovzada NY, Eitan C, Hornstein E, Elhaik E, Celadova P, Bose D, Farhan S, Fishilevich S, Lancet D, Morrison KE, Shaw CE, Al-Chalabi A, Veldink JH, Kirby J, Snyder MP, Shaw PJ. Rare Variant Burden Analysis within Enhancers Identifies CAV1 as an ALS Risk Gene. Cell Rep 2020; 33:108456. [PMID: 33264630 PMCID: PMC7710676 DOI: 10.1016/j.celrep.2020.108456] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 09/15/2020] [Accepted: 11/09/2020] [Indexed: 02/01/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease. CAV1 and CAV2 organize membrane lipid rafts (MLRs) important for cell signaling and neuronal survival, and overexpression of CAV1 ameliorates ALS phenotypes in vivo. Genome-wide association studies localize a large proportion of ALS risk variants within the non-coding genome, but further characterization has been limited by lack of appropriate tools. By designing and applying a pipeline to identify pathogenic genetic variation within enhancer elements responsible for regulating gene expression, we identify disease-associated variation within CAV1/CAV2 enhancers, which replicate in an independent cohort. Discovered enhancer mutations reduce CAV1/CAV2 expression and disrupt MLRs in patient-derived cells, and CRISPR-Cas9 perturbation proximate to a patient mutation is sufficient to reduce CAV1/CAV2 expression in neurons. Additional enrichment of ALS-associated mutations within CAV1 exons positions CAV1 as an ALS risk gene. We propose CAV1/CAV2 overexpression as a personalized medicine target for ALS.
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Affiliation(s)
- Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK.
| | - Sai Zhang
- Stanford Center for Genomics and Personalized Medicine, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kevin P Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - John P Franklin
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Samantha Allen
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Helia Ghahremani Nezhad
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nancy Y Yacovzada
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Chen Eitan
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Hornstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Elhaik
- Department of Biology, Lund University, Lund, Sweden
| | - Petra Celadova
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, UK
| | - Daniel Bose
- Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, UK
| | - Sali Farhan
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Simon Fishilevich
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Doron Lancet
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | - Christopher E Shaw
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Janine Kirby
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Michael P Snyder
- Stanford Center for Genomics and Personalized Medicine, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK.
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48
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Liu S, Yu Y, Zhang S, Cole JB, Tenesa A, Wang T, McDaneld TG, Ma L, Liu GE, Fang L. Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human. BMC Biol 2020; 18:80. [PMID: 32620158 PMCID: PMC7334855 DOI: 10.1186/s12915-020-00792-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 05/12/2020] [Indexed: 02/01/2023] Open
Abstract
Background Lack of comprehensive functional annotations across a wide range of tissues and cell types severely hinders the biological interpretations of phenotypic variation, adaptive evolution, and domestication in livestock. Here we used a combination of comparative epigenomics, genome-wide association study (GWAS), and selection signature analysis, to shed light on potential adaptive evolution in cattle. Results We cross-mapped 8 histone marks of 1300 samples from human to cattle, covering 178 unique tissues/cell types. By uniformly analyzing 723 RNA-seq and 40 whole genome bisulfite sequencing (WGBS) datasets in cattle, we validated that cross-mapped histone marks captured tissue-specific expression and methylation, reflecting tissue-relevant biology. Through integrating cross-mapped tissue-specific histone marks with large-scale GWAS and selection signature results, we for the first time detected relevant tissues and cell types for 45 economically important traits and artificial selection in cattle. For instance, immune tissues are significantly associated with health and reproduction traits, multiple tissues for milk production and body conformation traits (reflecting their highly polygenic architecture), and thyroid for the different selection between beef and dairy cattle. Similarly, we detected relevant tissues for 58 complex traits and diseases in humans and observed that immune and fertility traits in humans significantly correlated with those in cattle in terms of relevant tissues, which facilitated the identification of causal genes for such traits. For instance, PIK3CG, a gene highly specifically expressed in mononuclear cells, was significantly associated with both age-at-menopause in human and daughter-still-birth in cattle. ICAM, a T cell-specific gene, was significantly associated with both allergic diseases in human and metritis in cattle. Conclusion Collectively, our results highlighted that comparative epigenomics in conjunction with GWAS and selection signature analyses could provide biological insights into the phenotypic variation and adaptive evolution. Cattle may serve as a model for human complex traits, by providing additional information beyond laboratory model organisms, particularly when more novel phenotypes become available in the near future.
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Affiliation(s)
- Shuli Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA.,College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA
| | - Albert Tenesa
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.,The Roslin Institute, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Ting Wang
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Tara G McDaneld
- US Meat Animal Research Center, Agricultural Research Service, USDA, Clay Center, NE, 68933, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA.
| | - Lingzhao Fang
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA. .,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. .,Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
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Alpay BA, Demetci P, Istrail S, Aguiar D. Combinatorial and statistical prediction of gene expression from haplotype sequence. Bioinformatics 2020; 36:i194-i202. [PMID: 32657373 PMCID: PMC7355230 DOI: 10.1093/bioinformatics/btaa318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) have discovered thousands of significant genetic effects on disease phenotypes. By considering gene expression as the intermediary between genotype and disease phenotype, expression quantitative trait loci studies have interpreted many of these variants by their regulatory effects on gene expression. However, there remains a considerable gap between genotype-to-gene expression association and genotype-to-gene expression prediction. Accurate prediction of gene expression enables gene-based association studies to be performed post hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for subsequent experimental investigation. RESULTS In this work, we develop gene expression prediction methods that relax the independence and additivity assumptions between genetic markers. First, we consider gene expression prediction from a regression perspective and develop the HAPLEXR algorithm which combines haplotype clusterings with allelic dosages. Second, we introduce the new gene expression classification problem, which focuses on identifying expression groups rather than continuous measurements; we formalize the selection of an appropriate number of expression groups using the principle of maximum entropy. Third, we develop the HAPLEXD algorithm that models haplotype sharing with a modified suffix tree data structure and computes expression groups by spectral clustering. In both models, we penalize model complexity by prioritizing genetic clusters that indicate significant effects on expression. We compare HAPLEXR and HAPLEXD with three state-of-the-art expression prediction methods and two novel logistic regression approaches across five GTEx v8 tissues. HAPLEXD exhibits significantly higher classification accuracy overall; HAPLEXR shows higher prediction accuracy on approximately half of the genes tested and the largest number of best predicted genes (r2>0.1) among all methods. We show that variant and haplotype features selected by HAPLEXR are smaller in size than competing methods (and thus more interpretable) and are significantly enriched in functional annotations related to gene regulation. These results demonstrate the importance of explicitly modeling non-dosage dependent and intragenic epistatic effects when predicting expression. AVAILABILITY AND IMPLEMENTATION Source code and binaries are freely available at https://github.com/rapturous/HAPLEX. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Berk A Alpay
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Pinar Demetci
- Department of Computer Science and Center for Computational Biology, Brown University, Providence, RI 02912, USA
| | - Sorin Istrail
- Department of Computer Science and Center for Computational Biology, Brown University, Providence, RI 02912, USA
| | - Derek Aguiar
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
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Wendt FR, Pathak GA, Tylee DS, Goswami A, Polimanti R. Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. ACTA ACUST UNITED AC 2020; 4:2470547020924844. [PMID: 32518889 PMCID: PMC7254587 DOI: 10.1177/2470547020924844] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 04/17/2020] [Indexed: 12/15/2022]
Abstract
Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Aranyak Goswami
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
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