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Starr AL, Nishimura T, Igarashi KJ, Funamoto C, Nakauchi H, Fraser HB. Disentangling cell-intrinsic and extrinsic factors underlying evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592777. [PMID: 38798687 PMCID: PMC11118348 DOI: 10.1101/2024.05.06.592777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
A key goal of developmental biology is to determine the extent to which cells and organs develop autonomously, as opposed to requiring interactions with other cells or environmental factors. Chimeras have played a foundational role in this by enabling qualitative classification of cell-intrinsically vs. extrinsically driven processes. Here, we extend this framework to precisely decompose evolutionary divergence in any quantitative trait into cell-intrinsic, extrinsic, and intrinsic-extrinsic interaction components. Applying this framework to thousands of gene expression levels in reciprocal rat-mouse chimeras, we found that the majority of their divergence is attributable to cell-intrinsic factors, though extrinsic factors also play an integral role. For example, a rat-like extracellular environment extrinsically up-regulates the expression of a key transcriptional regulator of the endoplasmic reticulum (ER) stress response in some but not all cell types, which in turn strongly predicts extrinsic up-regulation of its target genes and of the ER stress response pathway as a whole. This effect is also seen at the protein level, suggesting propagation through multiple regulatory levels. Applying our framework to a cellular trait, neuronal differentiation, revealed a complex interaction of intrinsic and extrinsic factors. Finally, we show that imprinted genes are dramatically mis-expressed in species-mismatched environments, suggesting that mismatch between rapidly evolving intrinsic and extrinsic mechanisms controlling gene imprinting may contribute to barriers to interspecies chimerism. Overall, our conceptual framework opens new avenues to investigate the mechanistic basis of developmental processes and evolutionary divergence across myriad quantitative traits in any multicellular organism.
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Affiliation(s)
| | - Toshiya Nishimura
- Institute for Stem Cell Biology and Regenerative Medicine, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- WPI Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Osaka, 565-0871, Japan (current address for T.N.)
- Division of Stem Cell and Organoid Medicine, Department of Genome Biology, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan
| | - Kyomi J. Igarashi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Chihiro Funamoto
- Division of Stem Cell and Organoid Medicine, Department of Genome Biology, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan
| | - Hiromitsu Nakauchi
- Institute for Stem Cell Biology and Regenerative Medicine, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Division of Stem Cell Therapy, Distinguished Professor Unit, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
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2
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Renganaath K, Albert FW. Trans-eQTL hotspots shape complex traits by modulating cellular states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.14.567054. [PMID: 38014174 PMCID: PMC10680915 DOI: 10.1101/2023.11.14.567054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.
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Affiliation(s)
- Kaushik Renganaath
- Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN 55455, USA
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3
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Caudal É, Loegler V, Dutreux F, Vakirlis N, Teyssonnière É, Caradec C, Friedrich A, Hou J, Schacherer J. Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast. Nat Genet 2024; 56:1278-1287. [PMID: 38778243 PMCID: PMC11176082 DOI: 10.1038/s41588-024-01769-9] [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: 02/14/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.
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Affiliation(s)
- Élodie Caudal
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Victor Loegler
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Fabien Dutreux
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | | | | | - Claudia Caradec
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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4
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Benjamin KJM, Chen Q, Eagles NJ, Huuki-Myers LA, Collado-Torres L, Stolz JM, Pertea G, Shin JH, Paquola ACM, Hyde TM, Kleinman JE, Jaffe AE, Han S, Weinberger DR. Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry. Nat Neurosci 2024; 27:1064-1074. [PMID: 38769152 PMCID: PMC11156587 DOI: 10.1038/s41593-024-01636-0] [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: 05/26/2023] [Accepted: 03/29/2024] [Indexed: 05/22/2024]
Abstract
Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | | | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Neumora Therapeutics, Watertown, MA, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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5
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Oh W, Jung J, Joo JWJ. MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization. BMC Bioinformatics 2024; 25:192. [PMID: 38750431 PMCID: PMC11094870 DOI: 10.1186/s12859-024-05808-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization. RESULTS MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. CONCLUSION These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.
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Affiliation(s)
- Wonseok Oh
- Department of Industrial Pharmacy, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Junghyun Jung
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA
| | - Jong Wha J Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.
- Division of AI Software Convergence, Dongguk University-Seoul, Seoul, 04620, South Korea.
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6
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Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. Proc Natl Acad Sci U S A 2024; 121:e2319211121. [PMID: 38696467 PMCID: PMC11087752 DOI: 10.1073/pnas.2319211121] [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: 11/02/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
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Affiliation(s)
- Elie Marcel Teyssonnière
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Pauline Trébulle
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
| | - Julia Muenzner
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Victor Loegler
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Daniela Ludwig
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Fatma Amari
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Anne Friedrich
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Jing Hou
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Markus Ralser
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Max Planck Institute for Molecular Genetics, Berlin14195, Germany
| | - Joseph Schacherer
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
- Institut Universitaire de France, Paris75000, France
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7
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Wang L, Babushkin N, Liu Z, Liu X. Trans-eQTL mapping in gene sets identifies network effects of genetic variants. CELL GENOMICS 2024; 4:100538. [PMID: 38565144 PMCID: PMC11019359 DOI: 10.1016/j.xgen.2024.100538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
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Affiliation(s)
- Lili Wang
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Nikita Babushkin
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Xuanyao Liu
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
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8
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. Mol Syst Biol 2024; 20:362-373. [PMID: 38355920 PMCID: PMC10987670 DOI: 10.1038/s44320-024-00021-0] [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: 09/15/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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9
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Lu Y, Oliva M, Pierce BL, Liu J, Chen LS. Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits. Nat Commun 2024; 15:2383. [PMID: 38493154 PMCID: PMC10944527 DOI: 10.1038/s41467-024-46675-0] [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: 10/06/2022] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.
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Affiliation(s)
- Yihao Lu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Meritxell Oliva
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Jin Liu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
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10
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. G3 (BETHESDA, MD.) 2024; 14:jkae015. [PMID: 38262701 PMCID: PMC11021028 DOI: 10.1093/g3journal/jkae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/25/2024]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- School of Biological Sciences, The University of Oklahoma, 730 Van Vleet Oval, Norman, OK 73019, USA
| | - Stuart J Macdonald
- Molecular Biosciences, University of Kansas, 1200 Sunnyside Ave, Lawrence, KS 66045, USA
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11
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Balogun EJ, Ness RW. The Effects of De Novo Mutation on Gene Expression and the Consequences for Fitness in Chlamydomonas reinhardtii. Mol Biol Evol 2024; 41:msae035. [PMID: 38366781 PMCID: PMC10910851 DOI: 10.1093/molbev/msae035] [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: 09/14/2023] [Revised: 02/01/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Mutation is the ultimate source of genetic variation, the bedrock of evolution. Yet, predicting the consequences of new mutations remains a challenge in biology. Gene expression provides a potential link between a genotype and its phenotype. But the variation in gene expression created by de novo mutation and the fitness consequences of mutational changes to expression remain relatively unexplored. Here, we investigate the effects of >2,600 de novo mutations on gene expression across the transcriptome of 28 mutation accumulation lines derived from 2 independent wild-type genotypes of the green algae Chlamydomonas reinhardtii. We observed that the amount of genetic variance in gene expression created by mutation (Vm) was similar to the variance that mutation generates in typical polygenic phenotypic traits and approximately 15-fold the variance seen in the limited species where Vm in gene expression has been estimated. Despite the clear effect of mutation on expression, we did not observe a simple additive effect of mutation on expression change, with no linear correlation between the total expression change and mutation count of individual MA lines. We therefore inferred the distribution of expression effects of new mutations to connect the number of mutations to the number of differentially expressed genes (DEGs). Our inferred DEE is highly L-shaped with 95% of mutations causing 0-1 DEG while the remaining 5% are spread over a long tail of large effect mutations that cause multiple genes to change expression. The distribution is consistent with many cis-acting mutation targets that affect the expression of only 1 gene and a large target of trans-acting targets that have the potential to affect tens or hundreds of genes. Further evidence for cis-acting mutations can be seen in the overabundance of mutations in or near differentially expressed genes. Supporting evidence for trans-acting mutations comes from a 15:1 ratio of DEGs to mutations and the clusters of DEGs in the co-expression network, indicative of shared regulatory architecture. Lastly, we show that there is a negative correlation with the extent of expression divergence from the ancestor and fitness, providing direct evidence of the deleterious effects of perturbing gene expression.
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Affiliation(s)
- Eniolaye J Balogun
- Department of Biology, William G. Davis Building, University of Toronto, Mississauga L5L-1C6, Canada
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto M5S-3B2, Canada
| | - Rob W Ness
- Department of Biology, William G. Davis Building, University of Toronto, Mississauga L5L-1C6, Canada
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12
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O'Brien CL, Summers KM, Martin NM, Carter-Cusack D, Yang Y, Barua R, Dixit OVA, Hume DA, Pavli P. The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease. Hum Genet 2024; 143:233-261. [PMID: 38421405 PMCID: PMC11043138 DOI: 10.1007/s00439-024-02642-9] [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: 08/25/2023] [Accepted: 01/14/2024] [Indexed: 03/02/2024]
Abstract
The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.
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Affiliation(s)
- Claire L O'Brien
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Kim M Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Natalia M Martin
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Dylan Carter-Cusack
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Yuanhao Yang
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Rasel Barua
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Ojas V A Dixit
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
| | - Paul Pavli
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia.
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia.
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13
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Lappalainen T, Li YI, Ramachandran S, Gusev A. Genetic and molecular architecture of complex traits. Cell 2024; 187:1059-1075. [PMID: 38428388 DOI: 10.1016/j.cell.2024.01.023] [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: 10/03/2023] [Revised: 12/20/2023] [Accepted: 01/16/2024] [Indexed: 03/03/2024]
Abstract
Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.
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Affiliation(s)
- Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Yang I Li
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sohini Ramachandran
- Ecology, Evolution and Organismal Biology, Center for Computational Molecular Biology, and the Data Science Institute, Brown University, Providence, RI 029129, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
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14
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Durkin SM, Ballinger MA, Nachman MW. Tissue-specific and cis-regulatory changes underlie parallel, adaptive gene expression evolution in house mice. PLoS Genet 2024; 20:e1010892. [PMID: 38306396 PMCID: PMC10866503 DOI: 10.1371/journal.pgen.1010892] [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: 07/31/2023] [Revised: 02/14/2024] [Accepted: 01/22/2024] [Indexed: 02/04/2024] Open
Abstract
Changes in gene regulation have long been appreciated as a driving force of adaptive evolution, however the relative contributions of cis- and trans-acting changes to gene regulation over short evolutionary timescales remain unclear. Instances of recent, parallel phenotypic evolution provide an opportunity to assess whether parallel patterns are seen at the level of gene expression, and to assess the relative contribution of cis- and trans- changes to gene regulation in the early stages of divergence. Here, we studied gene expression in liver and brown adipose tissue in two wild-derived strains of house mice that independently adapted to cold, northern environments, and we compared them to a strain of house mice from a warm, tropical environment. To investigate gene regulatory evolution, we studied expression in parents and allele-specific expression in F1 hybrids of crosses between warm-adapted and cold-adapted strains. First, we found that the different cold-adapted mice showed both unique and shared changes in expression, but that the proportion of shared changes (i.e. parallelism) was greater than expected by chance. Second, we discovered that expression evolution occurred largely at tissue-specific and cis-regulated genes, and that these genes were over-represented in parallel cases of evolution. Finally, we integrated the expression data with scans for selection in natural populations and found substantial parallelism in the two northern populations for genes under selection. Furthermore, selection outliers were associated with cis-regulated genes more than expected by chance; cis-regulated genes under selection influenced phenotypes such as body size, immune functioning, and activity level. These results demonstrate that parallel patterns of gene expression in mice that have independently adapted to cold environments are driven largely by tissue-specific and cis-regulatory changes, providing insight into the mechanisms of adaptive gene regulatory evolution at the earliest stages of divergence.
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Affiliation(s)
- Sylvia M. Durkin
- Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Mallory A. Ballinger
- Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Michael W. Nachman
- Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America
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15
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LaPierre N, Pimentel H. Accounting for isoform expression increases power to identify genetic regulation of gene expression. PLoS Comput Biol 2024; 20:e1011857. [PMID: 38346082 PMCID: PMC10890775 DOI: 10.1371/journal.pcbi.1011857] [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: 09/29/2023] [Revised: 02/23/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
A core problem in genetics is molecular quantitative trait locus (QTL) mapping, in which genetic variants associated with changes in the molecular phenotypes are identified. One of the most-studied molecular QTL mapping problems is expression QTL (eQTL) mapping, in which the molecular phenotype is gene expression. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. Here, we examine a broader question: which genes have at least one isoform whose expression level is regulated by genetic variants? In this study, we propose and evaluate several approaches to answering this question, demonstrating that "isoform-aware" methods-those that account for the expression levels of individual isoforms-have substantially greater power to answer this question than standard "gene-level" eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.
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Affiliation(s)
- Nathan LaPierre
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of Chicago, Illinois, United States of America
| | - Harold Pimentel
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
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16
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. CELL GENOMICS 2024; 4:100459. [PMID: 38190102 PMCID: PMC10794783 DOI: 10.1016/j.xgen.2023.100459] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/19/2023] [Accepted: 11/09/2023] [Indexed: 01/09/2024]
Abstract
Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France; Institut Universitaire de France (IUF), Paris, France.
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France.
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17
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Pereira V, Kuzmin E. Trans-regulatory variant network contributes to missing heritability. CELL GENOMICS 2024; 4:100470. [PMID: 38216282 PMCID: PMC10794830 DOI: 10.1016/j.xgen.2023.100470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 01/14/2024]
Abstract
In a recent Cell Genomics article, Tsouris et al.1 analyze the transcriptomes of a large diallel panel of hybrids from Saccharomyces cerevisiae natural isolates to study cis- and trans-regulatory changes underlying gene expression variation. Vanessa Pereira and Elena Kuzmin discuss the authors' findings and the wider context in missing heritability research in this preview.
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Affiliation(s)
- Vanessa Pereira
- Department of Biology, Concordia University, Montreal, Canada; Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada
| | - Elena Kuzmin
- Department of Biology, Concordia University, Montreal, Canada; Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada; Department of Human Genetics, Rosalind & Morris Goodman Cancer Institute, McGill University, Montreal, Canada.
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18
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Noah Alexander
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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19
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Abraham LN, Croll D. Genome-wide expression QTL mapping reveals the highly dynamic regulatory landscape of a major wheat pathogen. BMC Biol 2023; 21:263. [PMID: 37981685 PMCID: PMC10658818 DOI: 10.1186/s12915-023-01763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/07/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND In agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood. RESULTS We used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture. CONCLUSIONS Overall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change.
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Affiliation(s)
- Leen Nanchira Abraham
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, 2000, Neuchâtel, Switzerland
- Present address: Institute of Plant Sciences, University of Cologne, Cologne, Germany
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, 2000, Neuchâtel, Switzerland.
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20
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Dhindsa RS, Burren OS, Sun BB, Prins BP, Matelska D, Wheeler E, Mitchell J, Oerton E, Hristova VA, Smith KR, Carss K, Wasilewski S, Harper AR, Paul DS, Fabre MA, Runz H, Viollet C, Challis B, Platt A, Vitsios D, Ashley EA, Whelan CD, Pangalos MN, Wang Q, Petrovski S. Rare variant associations with plasma protein levels in the UK Biobank. Nature 2023; 622:339-347. [PMID: 37794183 PMCID: PMC10567546 DOI: 10.1038/s41586-023-06547-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 08/15/2023] [Indexed: 10/06/2023]
Abstract
Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.
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Affiliation(s)
- Ryan S Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, US.
| | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benjamin B Sun
- Translational Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Bram P Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dorota Matelska
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Eleanor Wheeler
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jonathan Mitchell
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Erin Oerton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ventzislava A Hristova
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, US
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Sebastian Wasilewski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andrew R Harper
- Clinical Development, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Margarete A Fabre
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Heiko Runz
- Translational Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Coralie Viollet
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benjamin Challis
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Adam Platt
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Euan A Ashley
- Division of Cardiology, Department of Medicine, Stanford University, Palo Alto, CA, USA
| | | | | | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, US
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
- Department of Medicine, Austin Health, University of Melbourne, Melbourne, Victoria, Australia.
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21
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Teyssonnière E, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558197. [PMID: 37781592 PMCID: PMC10541136 DOI: 10.1101/2023.09.18.558197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship. Highlights At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.
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22
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Vande Zande P, Zhou X, Selmecki A. The Dynamic Fungal Genome: Polyploidy, Aneuploidy and Copy Number Variation in Response to Stress. Annu Rev Microbiol 2023; 77:341-361. [PMID: 37307856 PMCID: PMC10599402 DOI: 10.1146/annurev-micro-041320-112443] [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] [Indexed: 06/14/2023]
Abstract
Fungal species have dynamic genomes and often exhibit genomic plasticity in response to stress. This genome plasticity often comes with phenotypic consequences that affect fitness and resistance to stress. Fungal pathogens exhibit genome plasticity in both clinical and agricultural settings and often during adaptation to antifungal drugs, posing significant challenges to human health. Therefore, it is important to understand the rates, mechanisms, and impact of large genomic changes. This review addresses the prevalence of polyploidy, aneuploidy, and copy number variation across diverse fungal species, with special attention to prominent fungal pathogens and model species. We also explore the relationship between environmental stress and rates of genomic changes and highlight the mechanisms underlying genotypic and phenotypic changes. A comprehensive understanding of these dynamic fungal genomes is needed to identify novel solutions for the increase in antifungal drug resistance.
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Affiliation(s)
- Pétra Vande Zande
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota, USA;
| | - Xin Zhou
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota, USA;
| | - Anna Selmecki
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota, USA;
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23
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Ferguson R, Chat V, Morales L, Simpson D, Monson KR, Cohen E, Zusin S, Madonna G, Capone M, Simeone E, Pavlick A, Luke JJ, Gajewski TF, Osman I, Ascierto P, Weber J, Kirchhoff T. Germline immunomodulatory expression quantitative trait loci (ieQTLs) associated with immune-related toxicity from checkpoint inhibition. Eur J Cancer 2023; 189:112923. [PMID: 37301715 PMCID: PMC11000635 DOI: 10.1016/j.ejca.2023.05.011] [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: 03/13/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Immune checkpoint inhibition (ICI) has improved clinical outcomes for metastatic melanoma patients; however, 65-80% of patients treated with ICI experience immune-related adverse events (irAEs). Given the plausible link of irAEs with underlying host immunity, we explored whether germline genetic variants controlling the expression of 42 immunomodulatory genes were associated with the risk of irAEs in melanoma patients treated with the single-agent anti-CTLA-4 antibody ipilimumab (IPI). METHODS We identified 42 immunomodulatory expression quantitative trait loci (ieQTLs) most significantly associated with the expression of 382 immune-related genes. These germline variants were genotyped in IPI-treated melanoma patients, collected as part of a multi-institutional collaboration. We tested the association of ieQTLs with irAEs in a discovery cohort of 95 patients, followed by validation in an additional 97 patients. RESULTS We found that the alternate allele of rs7036417, a variant linked to increased expression of SYK, was strongly associated with an increased risk of grade 3-4 toxicity [odds ratio (OR) = 7.46; 95% confidence interval (CI) = 2.65-21.03; p = 1.43E-04]. This variant was not associated with response (OR = 0.90; 95% CI = 0.37-2.21; p = 0.82). CONCLUSION We report that rs7036417 is associated with increased risk of severe irAEs, independent of IPI efficacy. SYK plays an important role in B-cell/T-cell expansion, and increased pSYK has been reported in patients with autoimmune disease. The association between rs7036417 and IPI irAEs in our data suggests a role of SYK overexpression in irAE development. These findings support the hypothesis that inherited variation in immune-related pathways modulates ICI toxicity and suggests SYK as a possible future target for therapies to reduce irAEs.
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Affiliation(s)
- Robert Ferguson
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Vylyny Chat
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Leah Morales
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Danny Simpson
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Kelsey R Monson
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Elisheva Cohen
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Sarah Zusin
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Gabriele Madonna
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Mariaelena Capone
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Ester Simeone
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Anna Pavlick
- Division of Hematology & Medical Oncology, the Cutaneous Oncology Program, Weill Cornell Medicine and New York-Presbyterian, New York, USA
| | - Jason J Luke
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15213, USA; UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Thomas F Gajewski
- Department of Pathology, University of Chicago, Chicago, IL, USA; Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA; Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Iman Osman
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA; Department of Medicine, New York University-Grossman School of Medicine, New York, NY, USA; Ronald O. Perelman Department of Dermatology, New York University-Grossman School of Medicine, New York, NY, USA
| | - Paolo Ascierto
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Jeffrey Weber
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA; Department of Medicine, New York University-Grossman School of Medicine, New York, NY, USA
| | - Tomas Kirchhoff
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA.
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550015. [PMID: 37503053 PMCID: PMC10370210 DOI: 10.1101/2023.07.21.550015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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25
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550013. [PMID: 37546809 PMCID: PMC10401925 DOI: 10.1101/2023.07.21.550013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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26
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Littman R, Cheng M, Wang N, Peng C, Yang X. SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics. iScience 2023; 26:107124. [PMID: 37434694 PMCID: PMC10331489 DOI: 10.1016/j.isci.2023.107124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/31/2023] [Accepted: 06/09/2023] [Indexed: 07/13/2023] Open
Abstract
Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.
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Affiliation(s)
- Russell Littman
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Michael Cheng
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ning Wang
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
| | - Chao Peng
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Xia Yang
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences (QCBio), Los Angeles, CA, USA
- Molecular Biology Institute (MBI), Los Angeles, CA, USA
- Brain Research Institute (BRI), Los Angeles, CA, USA
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27
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548746. [PMID: 37503205 PMCID: PMC10370140 DOI: 10.1101/2023.07.12.548746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper quickly results in cell and tissue damage that can range in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes also respond to other non-essential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the response to heavy metal stress. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource (DSPR) using a combination of differential expression analysis and expression quantitative trait locus (eQTL) mapping. Differential expression analysis revealed clear patterns of tissue-specific expression, primarily driven by a more pronounced gene expression response in gut tissue. eQTL mapping of gene expression under control and copper conditions as well as for the change in gene expression following copper exposure (copper response eQTL) revealed hundreds of genes with tissue-specific local cis-eQTL and many distant trans-eQTL. eQTL associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited genotype by environment effects on gene expression under copper stress, illuminating several tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight many candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- 1200 Sunnyside Ave, University of Kansas, Molecular Biosciences, Lawrence, KS 66045, USA
- 730 Van Vleet Oval, University of Oklahoma, Biology, Norman, OK 73019, USA
| | - Stuart J Macdonald
- 1200 Sunnyside Ave, University of Kansas, Molecular Biosciences, Lawrence, KS 66045, USA
- 1200 Sunnyside Ave, University of Kansas, Center for Computational Biology, Lawrence, KS 66045, USA
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28
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Snyman M, Xu S. The effects of mutations on gene expression and alternative splicing. Proc Biol Sci 2023; 290:20230565. [PMID: 37403507 PMCID: PMC10320348 DOI: 10.1098/rspb.2023.0565] [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: 03/09/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023] Open
Abstract
Understanding the relationship between mutations and their genomic and phenotypic consequences has been a longstanding goal of evolutionary biology. However, few studies have investigated the impact of mutations on gene expression and alternative splicing on the genome-wide scale. In this study, we aim to bridge this knowledge gap by utilizing whole-genome sequencing data and RNA sequencing data from 16 obligately parthenogenetic Daphnia mutant lines to investigate the effects of ethyl methanesulfonate-induced mutations on gene expression and alternative splicing. Using rigorous analyses of mutations, expression changes and alternative splicing, we show that trans-effects are the major contributor to the variance in gene expression and alternative splicing between the wild-type and mutant lines, whereas cis mutations only affected a limited number of genes and do not always alter gene expression. Moreover, we show that there is a significant association between differentially expressed genes and exonic mutations, indicating that exonic mutations are an important driver of altered gene expression.
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Affiliation(s)
- Marelize Snyman
- Department of Biology, University of Texas at Arlington, Arlington, TX 76019, USA
| | - Sen Xu
- Department of Biology, University of Texas at Arlington, Arlington, TX 76019, USA
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29
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Jallet A, Friedrich A, Schacherer J. Impact of the acquired subgenome on the transcriptional landscape in Brettanomyces bruxellensis allopolyploids. G3 (BETHESDA, MD.) 2023; 13:jkad115. [PMID: 37226280 PMCID: PMC10320193 DOI: 10.1093/g3journal/jkad115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/21/2023] [Accepted: 05/18/2023] [Indexed: 05/26/2023]
Abstract
Gene expression variation can provide an overview of the changes in regulatory networks that underlie phenotypic diversity. Certain evolutionary trajectories such as polyploidization events can have an impact on the transcriptional landscape. Interestingly, the evolution of the yeast species Brettanomyces bruxellensis has been punctuated by diverse allopolyploidization events leading to the coexistence of a primary diploid genome associated with various haploid acquired genomes. To assess the impact of these events on gene expression, we generated and compared the transcriptomes of a set of 87 B. bruxellensis isolates, selected as being representative of the genomic diversity of this species. Our analysis revealed that acquired subgenomes strongly impact the transcriptional patterns and allow discrimination of allopolyploid populations. In addition, clear transcriptional signatures related to specific populations have been revealed. The transcriptional variations observed are related to some specific biological processes such as transmembrane transport and amino acids metabolism. Moreover, we also found that the acquired subgenome causes the overexpression of some genes involved in the production of flavor-impacting secondary metabolites, especially in isolates of the beer population.
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Affiliation(s)
- Arthur Jallet
- CNRS, GMGM UMR 7156, Université de Strasbourg, 67000 Strasbourg, France
| | - Anne Friedrich
- CNRS, GMGM UMR 7156, Université de Strasbourg, 67000 Strasbourg, France
| | - Joseph Schacherer
- CNRS, GMGM UMR 7156, Université de Strasbourg, 67000 Strasbourg, France
- Institut Universitaire de France (IUF), 75005 Paris, France
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30
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Marrella MA, Biase FH. Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing. J Anim Sci Biotechnol 2023; 14:62. [PMID: 37143150 PMCID: PMC10161580 DOI: 10.1186/s40104-023-00861-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/05/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. RESULTS In this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 35 significant eQTLs (P < 5 × 10-8) following the ANOVA model and 39 significant eQTLs (P < 5 × 10-8) following the additive model. Using a differential gene expression framework, we identified 930 and six significant eQTLs (P < 5 × 10-8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. CONCLUSIONS Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed. Our proposed approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.
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Affiliation(s)
- Mackenzie A Marrella
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Fernando H Biase
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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31
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Collins MA, Avery R, Albert FW. Substrate-specific effects of natural genetic variation on proteasome activity. PLoS Genet 2023; 19:e1010734. [PMID: 37126494 PMCID: PMC10174532 DOI: 10.1371/journal.pgen.1010734] [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: 06/14/2022] [Revised: 05/11/2023] [Accepted: 04/04/2023] [Indexed: 05/02/2023] Open
Abstract
Protein degradation is an essential biological process that regulates protein abundance and removes misfolded and damaged proteins from cells. In eukaryotes, most protein degradation occurs through the stepwise actions of two functionally distinct entities, the ubiquitin system and the proteasome. Ubiquitin system enzymes attach ubiquitin to cellular proteins, targeting them for degradation. The proteasome then selectively binds and degrades ubiquitinated substrate proteins. Genetic variation in ubiquitin system genes creates heritable differences in the degradation of their substrates. However, the challenges of measuring the degradative activity of the proteasome independently of the ubiquitin system in large samples have limited our understanding of genetic influences on the proteasome. Here, using the yeast Saccharomyces cerevisiae, we built and characterized reporters that provide high-throughput, ubiquitin system-independent measurements of proteasome activity. Using single-cell measurements of proteasome activity from millions of genetically diverse yeast cells, we mapped 15 loci across the genome that influence proteasomal protein degradation. Twelve of these 15 loci exerted specific effects on the degradation of two distinct proteasome substrates, revealing a high degree of substrate-specificity in the genetics of proteasome activity. Using CRISPR-Cas9-based allelic engineering, we resolved a locus to a causal variant in the promoter of RPT6, a gene that encodes a subunit of the proteasome's 19S regulatory particle. The variant increases RPT6 expression, which we show results in increased proteasome activity. Our results reveal the complex genetic architecture of proteasome activity and suggest that genetic influences on the proteasome may be an important source of variation in the many cellular and organismal traits shaped by protein degradation.
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Affiliation(s)
- Mahlon A. Collins
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Randi Avery
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Frank W. Albert
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
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32
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Sun G, Yu H, Wang P, Lopez-Guerrero M, Mural RV, Mizero ON, Grzybowski M, Song B, van Dijk K, Schachtman DP, Zhang C, Schnable JC. A role for heritable transcriptomic variation in maize adaptation to temperate environments. Genome Biol 2023; 24:55. [PMID: 36964601 PMCID: PMC10037803 DOI: 10.1186/s13059-023-02891-3] [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: 01/28/2022] [Accepted: 03/06/2023] [Indexed: 03/26/2023] Open
Abstract
Background Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. Result We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Conclusion Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions. Supplementary information The online version contains supplementary material available at 10.1186/s13059-023-02891-3.
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Affiliation(s)
- Guangchao Sun
- grid.24434.350000 0004 1937 0060Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Huihui Yu
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, USA
| | - Peng Wang
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Martha Lopez-Guerrero
- grid.24434.350000 0004 1937 0060Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Ravi V. Mural
- grid.24434.350000 0004 1937 0060Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Olivier N. Mizero
- grid.24434.350000 0004 1937 0060Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Marcin Grzybowski
- grid.24434.350000 0004 1937 0060Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Baoxing Song
- grid.5386.8000000041936877XInstitute for Genomic Diversity, Cornell University, Ithaca, USA
| | - Karin van Dijk
- grid.24434.350000 0004 1937 0060Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Daniel P. Schachtman
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Chi Zhang
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, USA
| | - James C. Schnable
- grid.24434.350000 0004 1937 0060Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- grid.24434.350000 0004 1937 0060Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
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33
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Sterken MG, Nijveen H, van Zanten M, Jiménez-Gómez JM, Geshnizjani N, Willems LAJ, Rienstra J, Hilhorst HWM, Ligterink W, Snoek BL. Plasticity of maternal environment-dependent expression-QTLs of tomato seeds. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:28. [PMID: 36810666 PMCID: PMC9944408 DOI: 10.1007/s00122-023-04322-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 10/27/2022] [Indexed: 06/18/2023]
Abstract
Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depend on seed quality and on environmental factors such as nutrient availability. In tomato (Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments. In this study, we applied RNA-sequencing to construct a linkage map and measure gene expression of seeds of a tomato recombinant inbred line (RIL) population derived from a cross between S. lycopersicum (cv. Moneymaker) and S. pimpinellifolium (G1.1554). The seeds matured on plants cultivated under different nutritional environments, i.e., on high phosphorus or low nitrogen. The obtained single-nucleotide polymorphisms (SNPs) were subsequently used to construct a genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.
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Affiliation(s)
- Mark G. Sterken
- Laboratory of Nematology, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Harm Nijveen
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, 6708 PB Wageningen, The Netherlands
- Laboratory of Bioinformatics, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Martijn van Zanten
- Plant Stress Resilience, Institute of Environmental Biology, Utrecht University, 3584 CH Utrecht, The Netherlands
| | - Jose M. Jiménez-Gómez
- Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris-Saclay, 78000 Versailles, France
| | - Nafiseh Geshnizjani
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Leo A. J. Willems
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Juriaan Rienstra
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Henk W. M. Hilhorst
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Wilco Ligterink
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Basten L. Snoek
- Laboratory of Nematology, Wageningen University, 6708 PB Wageningen, The Netherlands
- Theoretical Biology and Bioinformatics, Institute of Biodynamics and Biocomplexity, Utrecht University, 3584 CH Utrecht, The Netherlands
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34
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Mechanisms of regulatory evolution in yeast. Curr Opin Genet Dev 2022; 77:101998. [PMID: 36220001 PMCID: PMC10117219 DOI: 10.1016/j.gde.2022.101998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 02/06/2023]
Abstract
Studies of regulatory variation in yeast - at the level of new mutations, polymorphisms within a species, and divergence between species - have provided great insight into the molecular and evolutionary processes responsible for the evolution of gene expression in eukaryotes. The increasing ease with which yeast genomes can be manipulated and expression quantified in a high-throughput manner has recently accelerated mechanistic studies of cis- and trans-regulatory variation at multiple evolutionary timescales. These studies have, for example, identified differences in the properties of cis- and trans-acting mutations that affect their evolutionary fate, experimentally characterized the molecular mechanisms through which cis- and trans-regulatory variants act, and illustrated how regulatory networks can diverge between species with or without changes in gene expression.
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35
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Macdonald SJ, Long AD. Discovery of malathion resistance QTL in Drosophila melanogaster using a bulked phenotyping approach. G3 (BETHESDA, MD.) 2022; 12:jkac279. [PMID: 36250804 PMCID: PMC9713458 DOI: 10.1093/g3journal/jkac279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022]
Abstract
Drosophila melanogaster has proved an effective system with which to understand the evolutionary genetics and molecular mechanisms of insecticide resistance. Insecticide use has left signatures of selection in the fly genome, and both functional and quantitative genetic studies in the system have identified genes and variants associated with resistance. Here, we use D. melanogaster and leverage a bulk phenotyping and pooled sequencing "extreme quantitative trait loci" approach to genetically dissect variation in resistance to malathion, an organophosphate insecticide. We resolve 2 quantitative trait loci, one of which implicates allelic variation at the cytochrome P450 gene Cyp6g1, a strong candidate based on previous work. The second shows no overlap with hits from a previous genome-wide association study for malathion resistance, recapitulating other studies showing that different strategies for complex trait dissection in flies can yield apparently different architectures. Notably, we see no genetic signal at the Ace gene. Ace encodes the target of organophosphate insecticide inhibition, and genome-wide association studies have identified strong Ace-linked associations with resistance in flies. The absence of quantitative trait locus implicating Ace here is most likely because our mapping population does not segregate for several of the known functional polymorphisms impacting resistance at Ace, perhaps because our population is derived from flies collected prior to the widespread use of organophosphate insecticides. Our fundamental approach can be an efficient, powerful strategy to dissect genetic variation in resistance traits. Nonetheless, studies seeking to interrogate contemporary insecticide resistance variation may benefit from deriving mapping populations from more recently collected strains.
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Affiliation(s)
- Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66046, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA
| | - Anthony D Long
- Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, CA 92697, USA
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36
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Salzberg LI, Martos AAR, Lombardi L, Jermiin LS, Blanco A, Byrne KP, Wolfe KH. A widespread inversion polymorphism conserved among Saccharomyces species is caused by recurrent homogenization of a sporulation gene family. PLoS Genet 2022; 18:e1010525. [PMID: 36441813 PMCID: PMC9731477 DOI: 10.1371/journal.pgen.1010525] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/08/2022] [Accepted: 11/12/2022] [Indexed: 11/29/2022] Open
Abstract
Saccharomyces genomes are highly collinear and show relatively little structural variation, both within and between species of this yeast genus. We investigated the only common inversion polymorphism known in S. cerevisiae, which affects a 24-kb 'flip/flop' region containing 15 genes near the centromere of chromosome XIV. The region exists in two orientations, called reference (REF) and inverted (INV). Meiotic recombination in this region is suppressed in crosses between REF and INV orientation strains such as the BY x RM cross. We find that the inversion polymorphism is at least 17 million years old because it is conserved across the genus Saccharomyces. However, the REF and INV isomers are not ancient alleles but are continually being re-created by re-inversion of the region within each species. Inversion occurs due to continual homogenization of two almost identical 4-kb sequences that form an inverted repeat (IR) at the ends of the flip/flop region. The IR consists of two pairs of genes that are specifically and strongly expressed during the late stages of sporulation. We show that one of these gene pairs, YNL018C/YNL034W, codes for a protein that is essential for spore formation. YNL018C and YNL034W are the founder members of a gene family, Centroid, whose members in other Saccharomycetaceae species evolve fast, duplicate frequently, and are preferentially located close to centromeres. We tested the hypothesis that Centroid genes are a meiotic drive system, but found no support for this idea.
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Affiliation(s)
- Letal I. Salzberg
- Conway Institute, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Alexandre A. R. Martos
- Conway Institute, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Lisa Lombardi
- Conway Institute, University College Dublin, Dublin, Ireland
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Lars S. Jermiin
- School of Medicine, University College Dublin, Dublin, Ireland
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- Earth Institute, University College Dublin, Dublin, Ireland
- Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Alfonso Blanco
- Conway Institute, University College Dublin, Dublin, Ireland
| | - Kevin P. Byrne
- Conway Institute, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Kenneth H. Wolfe
- Conway Institute, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
- * E-mail:
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37
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Hawe JS, Saha A, Waldenberger M, Kunze S, Wahl S, Müller-Nurasyid M, Prokisch H, Grallert H, Herder C, Peters A, Strauch K, Theis FJ, Gieger C, Chambers J, Battle A, Heinig M. Network reconstruction for trans acting genetic loci using multi-omics data and prior information. Genome Med 2022; 14:125. [PMID: 36344995 PMCID: PMC9641770 DOI: 10.1186/s13073-022-01124-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.
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Affiliation(s)
- Johann S Hawe
- Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Heart Centre Munich, Department of Cardiology, Technical University Munich, Munich, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany
| | - Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Sonja Kunze
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,IBE, Faculty of Medicine, LMU Munich, 81377, Munich, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.,Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Holger Prokisch
- Institute of Human Genetics, School of Medicine, Technische Universität München, Munich, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Fabian J Theis
- Department of Informatics, Technical University of Munich, Garching, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - John Chambers
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Lee Kong Chian School of Medicine, Nanyang Technological University, 308232, Singapore, Singapore
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Matthias Heinig
- Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany. .,Department of Informatics, Technical University of Munich, Garching, Germany. .,Munich Heart Association, Partner Site Munich, DZHK (German Centre for Cardiovascular Research), 10785, Berlin, Germany.
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38
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Schubert OT, Bloom JS, Sadhu MJ, Kruglyak L. Genome-wide base editor screen identifies regulators of protein abundance in yeast. eLife 2022; 11:e79525. [PMID: 36326816 PMCID: PMC9633064 DOI: 10.7554/elife.79525] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.
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Affiliation(s)
- Olga T Schubert
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
- Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH)ZürichSwitzerland
- Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag)DübendorfSwitzerland
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
| | - Meru J Sadhu
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
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39
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Fan Z, Tieman DM, Knapp SJ, Zerbe P, Famula R, Barbey CR, Folta KM, Amadeu RR, Lee M, Oh Y, Lee S, Whitaker VM. A multi-omics framework reveals strawberry flavor genes and their regulatory elements. THE NEW PHYTOLOGIST 2022; 236:1089-1107. [PMID: 35916073 PMCID: PMC9805237 DOI: 10.1111/nph.18416] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
Flavor is essential to consumer preference of foods and is an increasing focus of plant breeding programs. In fruit crops, identifying genes underlying volatile organic compounds has great promise to accelerate flavor improvement, but polyploidy and heterozygosity in many species have slowed progress. Here we use octoploid cultivated strawberry to demonstrate how genomic heterozygosity, transcriptomic intricacy and fruit metabolomic diversity can be treated as strengths and leveraged to uncover fruit flavor genes and their regulatory elements. Multi-omics datasets were generated including an expression quantitative trait loci map with 196 diverse breeding lines, haplotype-phased genomes of a highly-flavored breeding selection, a genome-wide structural variant map using five haplotypes, and volatile genome-wide association study (GWAS) with > 300 individuals. Overlaying regulatory elements, structural variants and GWAS-linked allele-specific expression of numerous genes to variation in volatile compounds important to flavor. In one example, the functional role of anthranilate synthase alpha subunit 1 in methyl anthranilate biosynthesis was supported via fruit transient gene expression assays. These results demonstrate a framework for flavor gene discovery in fruit crops and a pathway to molecular breeding of cultivars with complex and desirable flavor.
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Affiliation(s)
- Zhen Fan
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Denise M. Tieman
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Steven J. Knapp
- Department of Plant SciencesUniversity of CaliforniaDavisDavisCA95616USA
| | - Philipp Zerbe
- Department of Plant BiologyUniversity of California DavisDavisCA95616USA
| | - Randi Famula
- Department of Plant SciencesUniversity of CaliforniaDavisDavisCA95616USA
| | - Christopher R. Barbey
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Kevin M. Folta
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Rodrigo R. Amadeu
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Manbo Lee
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Youngjae Oh
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Seonghee Lee
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Vance M. Whitaker
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
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40
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Bankier S, Michoel T. eQTLs as causal instruments for the reconstruction of hormone linked gene networks. Front Endocrinol (Lausanne) 2022; 13:949061. [PMID: 36060942 PMCID: PMC9428692 DOI: 10.3389/fendo.2022.949061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.
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Affiliation(s)
- Sean Bankier
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
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41
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Hine E, Runcie DE, Allen SL, Wang Y, Chenoweth SF, Blows MW, McGuigan K. Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species. Genetics 2022; 222:6663993. [PMID: 35961029 PMCID: PMC9526065 DOI: 10.1093/genetics/iyac122] [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: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 11/29/2022] Open
Abstract
The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.
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Affiliation(s)
- Emma Hine
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA
| | - Scott L Allen
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Yiguan Wang
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia.,Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - Stephen F Chenoweth
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Mark W Blows
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Katrina McGuigan
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
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42
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Brettner L, Ho WC, Schmidlin K, Apodaca S, Eder R, Geiler-Samerotte K. Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes. Curr Opin Genet Dev 2022; 75:101951. [PMID: 35797741 DOI: 10.1016/j.gde.2022.101951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/01/2022] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.
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43
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Wade AR, Duruflé H, Sanchez L, Segura V. eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction. BMC Genomics 2022; 23:476. [PMID: 35764918 PMCID: PMC9238188 DOI: 10.1186/s12864-022-08690-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 06/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Multi-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability. Results Our study used 241 poplar genotypes, phenotyped in two common gardens, with xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic (SNP), and transcriptomic datasets. Prediction models for each trait were built separately for SNPs and transcripts, and compared to a third model integrated by concatenation of both omics. The advantage of integration varied across traits and, to understand such differences, an eQTL analysis was performed to characterize the interplay between the genome and transcriptome and classify the predicting features into cis or trans relationships. A strong, significant negative correlation was found between the change in predictability and the change in predictor ranking for trans eQTLs for traits evaluated in the site of transcriptomic sampling. Conclusions Consequently, beneficial integration happens when the redundancy of predictors is decreased, likely leaving the stage to other less prominent but complementary predictors. An additional gene ontology (GO) enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising method to explore data integration. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08690-7.
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The impact of species-wide gene expression variation on Caenorhabditis elegans complex traits. Nat Commun 2022; 13:3462. [PMID: 35710766 PMCID: PMC9203580 DOI: 10.1038/s41467-022-31208-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/08/2022] [Indexed: 12/15/2022] Open
Abstract
Phenotypic variation in organism-level traits has been studied in Caenorhabditis elegans wild strains, but the impacts of differences in gene expression and the underlying regulatory mechanisms are largely unknown. Here, we use natural variation in gene expression to connect genetic variants to differences in organismal-level traits, including drug and toxicant responses. We perform transcriptomic analyses on 207 genetically distinct C. elegans wild strains to study natural regulatory variation of gene expression. Using this massive dataset, we perform genome-wide association mappings to investigate the genetic basis underlying gene expression variation and reveal complex genetic architectures. We find a large collection of hotspots enriched for expression quantitative trait loci across the genome. We further use mediation analysis to understand how gene expression variation could underlie organism-level phenotypic variation for a variety of complex traits. These results reveal the natural diversity in gene expression and possible regulatory mechanisms in this keystone model organism, highlighting the promise of using gene expression variation to understand how phenotypic diversity is generated.
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45
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Wu D, Li X, Tanaka R, Wood JC, Tibbs-Cortes LE, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Diepenbrock CH, Li X, Deason NT, Schoenbaum GR, Yu J, Buell CR, DellaPenna D, Gore MA. Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics 2022; 221:6603118. [PMID: 35666198 PMCID: PMC9339294 DOI: 10.1093/genetics/iyac091] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/01/2022] [Indexed: 11/20/2022] Open
Abstract
Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.
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Affiliation(s)
| | | | | | - Joshua C Wood
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Nolan Bornowski
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - John P Hamilton
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Brieanne Vaillancourt
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Xianran Li
- United States Department of Agriculture, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Nicholas T Deason
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - C Robin Buell
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Dean DellaPenna
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Michael A Gore
- Corresponding author: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
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Gerdes Gyuricza I, Chick JM, Keele GR, Deighan AG, Munger SC, Korstanje R, Gygi SP, Churchill GA. Genome-wide transcript and protein analysis highlights the role of protein homeostasis in the aging mouse heart. Genome Res 2022; 32:838-852. [PMID: 35277432 PMCID: PMC9104701 DOI: 10.1101/gr.275672.121] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 03/09/2022] [Indexed: 11/25/2022]
Abstract
Investigation of the molecular mechanisms of aging in the human heart is challenging because of confounding factors, such as diet and medications, as well as limited access to tissues from healthy aging individuals. The laboratory mouse provides an ideal model to study aging in healthy individuals in a controlled environment. However, previous mouse studies have examined only a narrow range of the genetic variation that shapes individual differences during aging. Here, we analyze transcriptome and proteome data from 185 genetically diverse male and female mice at ages 6, 12, and 18 mo to characterize molecular changes that occur in the aging heart. Transcripts and proteins reveal activation of pathways related to exocytosis and cellular transport with age, whereas processes involved in protein folding decrease with age. Additional changes are apparent only in the protein data including reduced fatty acid oxidation and increased autophagy. For proteins that form complexes, we see a decline in correlation between their component subunits with age, suggesting age-related loss of stoichiometry. The most affected complexes are themselves involved in protein homeostasis, which potentially contributes to a cycle of progressive breakdown in protein quality control with age. Our findings highlight the important role of post-transcriptional regulation in aging. In addition, we identify genetic loci that modulate age-related changes in protein homeostasis, suggesting that genetic variation can alter the molecular aging process.
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Affiliation(s)
| | - Joel M Chick
- Vividion Therapeutics, San Diego, California 92121, USA
| | | | | | | | - Ron Korstanje
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | - Steven P Gygi
- Harvard Medical School, Boston, Massachusetts 02115, USA
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Grossbach J, Gillet L, Clément‐Ziza M, Schmalohr CL, Schubert OT, Schütter M, Mawer JSP, Barnes CA, Bludau I, Weith M, Tessarz P, Graef M, Aebersold R, Beyer A. The impact of genomic variation on protein phosphorylation states and regulatory networks. Mol Syst Biol 2022; 18:e10712. [PMID: 35574625 PMCID: PMC9109056 DOI: 10.15252/msb.202110712] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well-defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.
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Affiliation(s)
- Jan Grossbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Ludovic Gillet
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Mathieu Clément‐Ziza
- Center for Molecular Medicine Cologne (CMMC)Medical Faculty, University of CologneCologneGermany
- Lesaffre InternationalMarcq‐en‐BarœulFrance
| | - Corinna L Schmalohr
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Olga T Schubert
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesCAUSA
| | | | | | | | - Isabell Bludau
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Peter Tessarz
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
- Max Planck Institute for Biology of AgeingCologneGermany
| | - Martin Graef
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
- Max Planck Institute for Biology of AgeingCologneGermany
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Faculty of ScienceUniversity of ZurichZurichSwitzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
- Center for Molecular Medicine Cologne (CMMC)Medical Faculty, University of CologneCologneGermany
- Institute for GeneticsFaculty of Mathematics and Natural SciencesUniversity of CologneCologneGermany
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48
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Macdonald SJ, Cloud-Richardson KM, Sims-West DJ, Long AD. Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing. Genetics 2022; 220:iyab238. [PMID: 35100395 PMCID: PMC8893256 DOI: 10.1093/genetics/iyab238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/19/2021] [Indexed: 01/22/2024] Open
Abstract
Despite the value of recombinant inbred lines for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to recombinant inbred lines for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here, we describe such an extreme quantitative trait locus, or extreme quantitative trait loci, mapping strategy that builds on an existing multiparental population, the Drosophila Synthetic Population Resource, and involves phenotyping and genotyping a population derived by mixing hundreds of Drosophila Synthetic Population Resource recombinant inbred lines. Simulations demonstrate that challenging, yet experimentally tractable extreme quantitative trait loci designs (≥4 replicates, ≥5,000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional recombinant inbred line-based quantitative trait loci mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated extreme quantitative trait loci experiment that identifies 7 quantitative trait loci for caffeine resistance. Two mapped extreme quantitative trait loci factors replicate loci previously identified in recombinant inbred lines, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping extreme quantitative trait loci design has considerable advantages.
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Affiliation(s)
- Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA
| | | | - Dylan J Sims-West
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
| | - Anthony D Long
- Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, CA 92697, USA
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49
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Malik MA, Michoel T. Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders. G3 (BETHESDA, MD.) 2022; 12:6447512. [PMID: 34864982 PMCID: PMC9210293 DOI: 10.1093/g3journal/jkab410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/11/2021] [Indexed: 11/15/2022]
Abstract
Random effects models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in random effects models is a challenge that has so far relied on numerical gradient-based optimizers to maximize the likelihood function. This is unsatisfactory because the resulting solution is poorly characterized and the efficiency of the method may be suboptimal. Here, we prove analytically that maximum-likelihood latent variables can always be chosen orthogonal to the known confounding factors, in other words, that maximum-likelihood latent variables explain sample covariances not already explained by known factors. Based on this result, we propose a restricted maximum-likelihood (REML) method that estimates the latent variables by maximizing the likelihood on the restricted subspace orthogonal to the known confounding factors and show that this reduces to probabilistic principal component analysis on that subspace. The method then estimates the variance-covariance parameters by maximizing the remaining terms in the likelihood function given the latent variables, using a newly derived analytic solution for this problem. Compared to gradient-based optimizers, our method attains greater or equal likelihood values, can be computed using standard matrix operations, results in latent factors that do not overlap with any known factors, and has a runtime reduced by several orders of magnitude. Hence, the REML method facilitates the application of random effects modeling strategies for learning latent variance components to much larger gene expression datasets than possible with current methods.
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Affiliation(s)
- Muhammad Ammar Malik
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5020, Norway
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5020, Norway
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50
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Lutz S, Van Dyke K, Feraru MA, Albert FW. Multiple epistatic DNA variants in a single gene affect gene expression in trans. Genetics 2022; 220:iyab208. [PMID: 34791209 PMCID: PMC8733636 DOI: 10.1093/genetics/iyab208] [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: 09/10/2021] [Accepted: 11/09/2021] [Indexed: 01/08/2023] Open
Abstract
DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by nonadditive, epistatic interactions. Two variants at the 5'-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.
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Affiliation(s)
- Sheila Lutz
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Krisna Van Dyke
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matthew A Feraru
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
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