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Liu L, Ren D, Li K, Ji L, Feng M, Li Z, Meng L, He G, Shi Y. Unraveling schizophrenia's genetic complexity through advanced causal inference and chromatin 3D conformation. Schizophr Res 2024; 270:476-485. [PMID: 38996525 DOI: 10.1016/j.schres.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024]
Abstract
Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes-KCNN3, KCNH1, and KCND3-that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method's applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI.
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Affiliation(s)
- Liangjie Liu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Decheng Ren
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Keyi Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Lei Ji
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Mofan Feng
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zhuoheng Li
- Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109, USA
| | - Luming Meng
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou 510630, China
| | - Guang He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China; Research Institute for Doping Control, Shanghai University of Sport, Shanghai 200438, China.
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Schmidlin K, Ogbunugafor CB, Geiler-Samerotte K. Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593194. [PMID: 38766025 PMCID: PMC11100745 DOI: 10.1101/2024.05.08.593194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are commonplace within the fields of quantitative and evolutionary genetics, "environment-by-environment interaction" (ExE) is a term used less often. In this study, we find that environment-by-environment interactions are a meaningful driver of phenotypes, and that they differ across different genotypes (suggestive of ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. We show that the effectiveness of a drug combination, relative to single drugs, often varies across different drug resistant mutants. Even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that better predicts the direction and magnitude of ExE interactions for some mutants. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes, but also for broader efforts to understand the cell biology underlying these interactions and to resolve the source of phenotypic variance across populations. The relevance of ExExG interactions have been largely omitted from canon in evolutionary and population genetics, but these fields and others stand to benefit from perspectives that highlight how interactions between external forces craft the complex behavior of living systems.
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Affiliation(s)
- Kara Schmidlin
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, 85287
- School of Life Sciences, Arizona State University, Tempe AZ, 85287
| | - C. Brandon Ogbunugafor
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT,06511
- Santa Fe Institute, Santa Fe, NM, 87501
| | - Kerry Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, 85287
- School of Life Sciences, Arizona State University, Tempe AZ, 85287
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3
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Prokkola JM, Chew KK, Anttila K, Maamela KS, Yildiz A, Åsheim ER, Primmer CR, Aykanat T. Tissue-specific metabolic enzyme levels covary with whole-animal metabolic rates and life-history loci via epistatic effects. Philos Trans R Soc Lond B Biol Sci 2024; 379:20220482. [PMID: 38186275 PMCID: PMC10772610 DOI: 10.1098/rstb.2022.0482] [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: 06/16/2023] [Accepted: 12/03/2023] [Indexed: 01/09/2024] Open
Abstract
Metabolic rates, including standard (SMR) and maximum (MMR) metabolic rate have often been linked with life-history strategies. Variation in context- and tissue-level metabolism underlying SMR and MMR may thus provide a physiological basis for life-history variation. This raises a hypothesis that tissue-specific metabolism covaries with whole-animal metabolic rates and is genetically linked to life history. In Atlantic salmon (Salmo salar), variation in two loci, vgll3 and six6, affects life history via age-at-maturity as well as MMR. Here, using individuals with known SMR and MMR with different vgll3 and six6 genotype combinations, we measured proxies of mitochondrial density and anaerobic metabolism, i.e. maximal activities of the mitochondrial citrate synthase (CS) and lactate dehydrogenase (LDH) enzymes, in four tissues (heart, intestine, liver, white muscle) across low- and high-food regimes. We found enzymatic activities were related to metabolic rates, mainly SMR, in the intestine and heart. Individual loci were not associated with the enzymatic activities, but we found epistatic effects and genotype-by-environment interactions in CS activity in the heart and epistasis in LDH activity in the intestine. These effects suggest that mitochondrial density and anaerobic capacity in the heart and intestine may partly mediate variation in metabolic rates and life history via age-at-maturity. This article is part of the theme issue 'The evolutionary significance of variation in metabolic rates'.
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Affiliation(s)
- Jenni M. Prokkola
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, PO Box 56, 00014 Helsinki, Finland
- Natural Resources Institute Finland (Luke), Paavo Havaksen tie 3, 90570 Oulu, Finland
- Lammi Biological Station, University of Helsinki, Pääjärventie 320, 16900 Lammi, Finland
| | - Kuan Kiat Chew
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, PO Box 56, 00014 Helsinki, Finland
| | - Katja Anttila
- Department of Biology, University of Turku, 20014 Turku, Finland
| | - Katja S. Maamela
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, PO Box 56, 00014 Helsinki, Finland
- Lammi Biological Station, University of Helsinki, Pääjärventie 320, 16900 Lammi, Finland
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, 00014 Helsinki, Finland
| | - Atakan Yildiz
- Biotechnology Institute, Ankara University, Ankara 06135, Turkey
| | - Eirik R. Åsheim
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, PO Box 56, 00014 Helsinki, Finland
- Lammi Biological Station, University of Helsinki, Pääjärventie 320, 16900 Lammi, Finland
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, 00014 Helsinki, Finland
| | - Craig R. Primmer
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, PO Box 56, 00014 Helsinki, Finland
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, 00014 Helsinki, Finland
| | - Tutku Aykanat
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, PO Box 56, 00014 Helsinki, Finland
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Periyasamy S, Youssef P, John S, Thara R, Mowry BJ. Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes. Front Genet 2024; 14:1301150. [PMID: 38259618 PMCID: PMC10800577 DOI: 10.3389/fgene.2023.1301150] [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: 09/24/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.
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Affiliation(s)
- Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Pierre Youssef
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sujit John
- Schizophrenia Research Foundation, Chennai, Tamil Nadu, India
| | | | - Bryan J. Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
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Sigurðardóttir H, Boije H, Albertsdóttir E, Kristjansson T, Rhodin M, Lindgren G, Eriksson S. The genetics of gaits in Icelandic horses goes beyond DMRT3, with RELN and STAU2 identified as two new candidate genes. Genet Sel Evol 2023; 55:89. [PMID: 38082412 PMCID: PMC10712087 DOI: 10.1186/s12711-023-00863-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND In domesticated animals, many important traits are complex and regulated by a large number of genes, genetic interactions, and environmental influences. The ability of Icelandic horses to perform the gait 'pace' is largely influenced by a single mutation in the DMRT3 gene, but genetic modifiers likely exist. The aim of this study was to identify novel genetic factors that influence pacing ability and quality of the gait through a genome-wide association study (GWAS) and correlate new findings to previously identified quantitative trait loci (QTL) and mutations. RESULTS Three hundred and seventy-two Icelandic horses were genotyped with the 670 K+ Axiom Equine Genotyping Array, of which 362 had gait scores from breeding field tests. A GWAS revealed several SNPs on Equus caballus chromosomes (ECA) 4, 9, and 20 that were associated (p < 1.0 × 10-5) with the breeding field test score for pace. The two novel QTL on ECA4 and 9 were located within the RELN and STAU2 genes, respectively, which have previously been associated with locomotor behavior in mice. Haplotypes were identified and the most frequent one for each of these two QTL had a large favorable effect on pace score. The second most frequent haplotype for the RELN gene was positively correlated with scores for tölt, trot, gallop, and canter. Similarly, the second most frequent haplotype for the STAU2 gene had favorable effects on scores for trot and gallop. Different genotype ratios of the haplotypes in the RELN and STAU2 genes were also observed in groups of horses with different levels of pacing ability. Furthermore, interactions (p < 0.05) were detected for the QTL in the RELN and STAU2 genes with the DMRT3 gene. The novel QTL on ECA4, 9, and 20, along with the effects of the DMRT3 variant, were estimated to account jointly for 27.4% of the phenotypic variance of the gait pace. CONCLUSIONS Our findings provide valuable information about the genetic architecture of pace beyond the contribution of the DMRT3 gene and indicate genetic interactions that contribute to the complexity of this trait. Further investigation is needed to fully understand the underlying genetic factors and interactions.
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Affiliation(s)
- Heiðrún Sigurðardóttir
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, P.O. Box 7023, 75007, Uppsala, Sweden.
- Faculty of Agricultural Sciences, Agricultural University of Iceland, Borgarbyggð, 311, Hvanneyri, Iceland.
| | - Henrik Boije
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Elsa Albertsdóttir
- The Icelandic Agricultural Advisory Centre, Hagatorgi 1, 107, Reykjavik, Iceland
| | - Thorvaldur Kristjansson
- Faculty of Agricultural Sciences, Agricultural University of Iceland, Borgarbyggð, 311, Hvanneyri, Iceland
| | - Marie Rhodin
- Department of Anatomy, Physiology, and Biochemistry, Swedish University of Agricultural Sciences, P.O. Box 7011, 75007, Uppsala, Sweden
| | - Gabriella Lindgren
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, P.O. Box 7023, 75007, Uppsala, Sweden
- Department of Biosystems, Center for Animal Breeding and Genetics, KU Leuven, Kasteelpark Arenberg 30, 3001, Leuven, Belgium
| | - Susanne Eriksson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, P.O. Box 7023, 75007, Uppsala, Sweden
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Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [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: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
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Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
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Aguirre L, Hendelman A, Hutton SF, McCandlish DM, Lippman ZB. Idiosyncratic and dose-dependent epistasis drives variation in tomato fruit size. Science 2023; 382:315-320. [PMID: 37856609 PMCID: PMC10602613 DOI: 10.1126/science.adi5222] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/06/2023] [Indexed: 10/21/2023]
Abstract
Epistasis between genes is traditionally studied with mutations that eliminate protein activity, but most natural genetic variation is in cis-regulatory DNA and influences gene expression and function quantitatively. In this study, we used natural and engineered cis-regulatory alleles in a plant stem-cell circuit to systematically evaluate epistatic relationships controlling tomato fruit size. Combining a promoter allelic series with two other loci, we collected over 30,000 phenotypic data points from 46 genotypes to quantify how allele strength transforms epistasis. We revealed a saturating dose-dependent relationship but also allele-specific idiosyncratic interactions, including between alleles driving a step change in fruit size during domestication. Our approach and findings expose an underexplored dimension of epistasis, in which cis-regulatory allelic diversity within gene regulatory networks elicits nonlinear, unpredictable interactions that shape phenotypes.
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Affiliation(s)
- Lyndsey Aguirre
- Cold Spring Harbor Laboratory, School of Biological Sciences, Cold Spring Harbor, NY, USA
| | - Anat Hendelman
- Cold Spring Harbor Laboratory; Cold Spring Harbor, NY, USA
| | - Samuel F. Hutton
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, USA
| | | | - Zachary B. Lippman
- Cold Spring Harbor Laboratory, School of Biological Sciences, Cold Spring Harbor, NY, USA
- Cold Spring Harbor Laboratory; Cold Spring Harbor, NY, USA
- Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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8
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Kovuri P, Yadav A, Sinha H. Role of genetic architecture in phenotypic plasticity. Trends Genet 2023; 39:703-714. [PMID: 37173192 DOI: 10.1016/j.tig.2023.04.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023]
Abstract
Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.
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Affiliation(s)
- Purnima Kovuri
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India; Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai, India; Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India; Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai, India; Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.
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9
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Melchinger AE, Fernando R, Stricker C, Schön CC, Auinger HJ. Genomic prediction in hybrid breeding: I. Optimizing the training set design. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:176. [PMID: 37532821 PMCID: PMC10397156 DOI: 10.1007/s00122-023-04413-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/23/2023] [Indexed: 08/04/2023]
Abstract
Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids. Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (nTS) and crosses per parent (c) has received little attention. Our objective was to examine prediction accuracy ([Formula: see text]) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of nTS and c. In the theory, we developed estimates for [Formula: see text] of GBLUPs for hybrids: (i)[Formula: see text] based on the expected prediction accuracy, and (ii) [Formula: see text] based on [Formula: see text] of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (τSCA = 1%, 6%, 22%) of SCA variance in σG2 and heritability (h2 = 0.4, 0.8). Values of [Formula: see text] and [Formula: see text] closely agreed with [Formula: see text] for hybrids. For given size NTS = nTS × c of TS, [Formula: see text] of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest [Formula: see text] with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.
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Affiliation(s)
- Albrecht E Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Rohan Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Christian Stricker
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Hans-Jürgen Auinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
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10
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Fausett SR, Sandjak A, Billard B, Braendle C. Higher-order epistasis shapes natural variation in germ stem cell niche activity. Nat Commun 2023; 14:2824. [PMID: 37198172 DOI: 10.1038/s41467-023-38527-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/05/2023] [Indexed: 05/19/2023] Open
Abstract
To study how natural allelic variation explains quantitative developmental system variation, we characterized natural differences in germ stem cell niche activity, measured as progenitor zone (PZ) size, between two Caenorhabditis elegans isolates. Linkage mapping yielded candidate loci on chromosomes II and V, and we found that the isolate with a smaller PZ size harbours a 148 bp promoter deletion in the Notch ligand, lag-2/Delta, a central signal promoting germ stem cell fate. As predicted, introducing this deletion into the isolate with a large PZ resulted in a smaller PZ size. Unexpectedly, restoring the deleted ancestral sequence in the isolate with a smaller PZ did not increase-but instead further reduced-PZ size. These seemingly contradictory phenotypic effects are explained by epistatic interactions between the lag-2/Delta promoter, the chromosome II locus, and additional background loci. These results provide first insights into the quantitative genetic architecture regulating an animal stem cell system.
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Affiliation(s)
- Sarah R Fausett
- Université Côte d'Azur, CNRS, Inserm, IBV, Nice, France.
- Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USA.
| | - Asma Sandjak
- Université Côte d'Azur, CNRS, Inserm, IBV, Nice, France
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11
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Ang RML, Chen SAA, Kern AF, Xie Y, Fraser HB. Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing. CELL GENOMICS 2023; 3:100260. [PMID: 37082144 PMCID: PMC10112194 DOI: 10.1016/j.xgen.2023.100260] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/27/2022] [Accepted: 01/06/2023] [Indexed: 04/22/2023]
Abstract
The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.
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Affiliation(s)
- Roy Moh Lik Ang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Yihua Xie
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Corresponding author
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12
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Li Q, Zhao L, Zeng Y, Kuang Y, Guan Y, Chen B, Xu S, Tang B, Wu L, Mao X, Sun X, Shi J, Xu P, Diao F, Xue S, Bao S, Meng Q, Yuan P, Wang W, Ma N, Song D, Xu B, Dong J, Mu J, Zhang Z, Fan H, Gu H, Li Q, He L, Jin L, Wang L, Sang Q. Large-scale analysis of de novo mutations identifies risk genes for female infertility characterized by oocyte and early embryo defects. Genome Biol 2023; 24:68. [PMID: 37024973 PMCID: PMC10080761 DOI: 10.1186/s13059-023-02894-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 03/01/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Oocyte maturation arrest and early embryonic arrest are important reproductive phenotypes resulting in female infertility and cause the recurrent failure of assisted reproductive technology (ART). However, the genetic etiologies of these female infertility-related phenotypes are poorly understood. Previous studies have mainly focused on inherited mutations based on large pedigrees or consanguineous patients. However, the role of de novo mutations (DNMs) in these phenotypes remains to be elucidated. RESULTS To decipher the role of DNMs in ART failure and female infertility with oocyte and embryo defects, we explore the landscape of DNMs in 473 infertile parent-child trios and identify a set of 481 confident DNMs distributed in 474 genes. Gene ontology analysis reveals that the identified genes with DNMs are enriched in signaling pathways associated with female reproductive processes such as meiosis, embryonic development, and reproductive structure development. We perform functional assays on the effects of DNMs in a representative gene Tubulin Alpha 4a (TUBA4A), which shows the most significant enrichment of DNMs in the infertile parent-child trios. DNMs in TUBA4A disrupt the normal assembly of the microtubule network in HeLa cells, and microinjection of DNM TUBA4A cRNAs causes abnormalities in mouse oocyte maturation or embryo development, suggesting the pathogenic role of these DNMs in TUBA4A. CONCLUSIONS Our findings suggest novel genetic insights that DNMs contribute to female infertility with oocyte and embryo defects. This study also provides potential genetic markers and facilitates the genetic diagnosis of recurrent ART failure and female infertility.
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Affiliation(s)
- Qun Li
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
- Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Lin Zhao
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Yang Zeng
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Yanping Kuang
- Reproductive Medicine Center, Shanghai Ninth Hospital, Shanghai Jiao Tong University, Shanghai, 200011, China
| | - Yichun Guan
- Department of Reproductive Medicine, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Biaobang Chen
- NHC Key Lab of Reproduction Regulation (Shanghai Institute for Biomedical and Pharmaceutical Technologies), Fudan University, Shanghai, 200032, China
| | - Shiru Xu
- Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, 518001, Guangdong, China
| | - Bin Tang
- Reproductive Medicine Center, The First People's Hospital of Changde City, Changde, 415000, China
| | - Ling Wu
- Reproductive Medicine Center, Shanghai Ninth Hospital, Shanghai Jiao Tong University, Shanghai, 200011, China
| | - Xiaoyan Mao
- Reproductive Medicine Center, Shanghai Ninth Hospital, Shanghai Jiao Tong University, Shanghai, 200011, China
| | - Xiaoxi Sun
- Shanghai Ji Ai Genetics and IVF Institute, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Juanzi Shi
- Reproductive Medicine Center, Northwest Women's and Children's Hospital, Xi'an, 710000, China
| | - Peng Xu
- Hainan Jinghua Hejing Hospital for Reproductive Medicine, Haikou, 570125, China
| | - Feiyang Diao
- Reproductive Medicine Center, Jiangsu Province Hospital, Nanjing, 210036, China
| | - Songguo Xue
- Reproductive Medicine Center, School of Medicine, Shanghai East Hospital, Tongji University, Shanghai, China
| | - Shihua Bao
- Department of Reproductive Immunology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 201204, China
| | - Qingxia Meng
- Center for Reproduction and Genetics, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215000, China
| | - Ping Yuan
- IVF Center, Department of Obstetrics and Gynecology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Wenjun Wang
- IVF Center, Department of Obstetrics and Gynecology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Ning Ma
- Reproductive Medical Center, Maternal and Child Health Care Hospital of Hainan Province, Haikou, 570206, Hainan Province, China
| | - Di Song
- Naval Medical University, Changhai Hospital, Shanghai, China
| | - Bei Xu
- Reproductive Medicine Centre, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Jie Dong
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Jian Mu
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Zhihua Zhang
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Huizhen Fan
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Hao Gu
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Qiaoli Li
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China
| | - Lin He
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Lei Wang
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China.
| | - Qing Sang
- Institute of Pediatrics, Children's Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, the Institutes of Biomedical Sciences, the State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China.
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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14
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Loss-of-function mutation survey revealed that genes with background-dependent fitness are rare and functionally related in yeast. Proc Natl Acad Sci U S A 2022; 119:e2204206119. [PMID: 36067306 PMCID: PMC9478683 DOI: 10.1073/pnas.2204206119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
In different individuals, the same mutation can lead to different phenotypes due to genetic background effects. This is commonly observed in various systems, including many human diseases. While isolated examples of such background effects have been observed, a systematic view across a large number of individuals is still lacking. Here, we surveyed genetic background effects associated with gene loss-of-function mutations across a population of natural isolates of the yeast Saccharomyces cerevisiae. We found that ∼15% of genes can display a background-dependent fitness change. Genes related to mitochondrial functions are significantly overrepresented, and showed reversed patterns of fitness gain or loss with genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport, suggesting a potential functional rewiring. In natural populations, the same mutation can lead to different phenotypic outcomes due to the genetic variation that exists among individuals. Such genetic background effects are commonly observed, including in the context of many human diseases. However, systematic characterization of these effects at the species level is still lacking to date. Here, we sought to comprehensively survey background-dependent traits associated with gene loss-of-function (LoF) mutations in 39 natural isolates of Saccharomyces cerevisiae using a transposon saturation strategy. By analyzing the modeled fitness variability of a total of 4,469 genes, we found that 15% of them, when impacted by a LoF mutation, exhibited a significant gain- or loss-of-fitness phenotype in certain natural isolates compared with the reference strain S288C. Out of these 632 genes with predicted background-dependent fitness effects, around 2/3 impact multiple backgrounds with a gradient of predicted fitness change while 1/3 are specific to a single genetic background. Genes related to mitochondrial function are significantly overrepresented in the set of genes showing a continuous variation and display a potential functional rewiring with other genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport. Such rewiring effects are likely modulated by both the genetic background and the environment. While background-specific cases are rare and span diverse cellular processes, they can be functionally related at the individual level. All genes with background-dependent fitness effects tend to have an intermediate connectivity in the global genetic interaction network and have shown relaxed selection pressure at the population level, highlighting their potential evolutionary characteristics.
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15
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Alves AAC, da Costa RM, Fonseca LFS, Carvalheiro R, Ventura RV, Rosa GJDM, Albuquerque LG. A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle. Front Genet 2022; 13:834724. [PMID: 35692843 PMCID: PMC9178659 DOI: 10.3389/fgene.2022.834724] [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: 12/13/2021] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
This study aimed to perform a genome-wide association analysis (GWAS) using the Random Forest (RF) approach for scanning candidate genes for age at first calving (AFC) in Nellore cattle. Additionally, potential epistatic effects were investigated using linear mixed models with pairwise interactions between all markers with high importance scores within the tree ensemble non-linear structure. Data from Nellore cattle were used, including records of animals born between 1984 and 2015 and raised in commercial herds located in different regions of Brazil. The estimated breeding values (EBV) were computed and used as the response variable in the genomic analyses. After quality control, the remaining number of animals and SNPs considered were 3,174 and 360,130, respectively. Five independent RF analyses were carried out, considering different initialization seeds. The importance score of each SNP was averaged across the independent RF analyses to rank the markers according to their predictive relevance. A total of 117 SNPs associated with AFC were identified, which spanned 10 autosomes (2, 3, 5, 10, 11, 17, 18, 21, 24, and 25). In total, 23 non-overlapping genomic regions embedded 262 candidate genes for AFC. Enrichment analysis and previous evidence in the literature revealed that many candidate genes annotated close to the lead SNPs have key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cell maturation, and pheromone recognition. Furthermore, some genomic regions previously associated with fertility and growth traits in Nellore cattle were also detected in the present study, reinforcing the effectiveness of RF for pre-screening candidate regions associated with complex traits. Complementary analyses revealed that many SNPs top-ranked in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes, remarkably in the BTAs 3, 5, 11, and 21. The reported results are expected to enhance the understanding of genetic mechanisms involved in the biological regulation of AFC in this cattle breed.
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Affiliation(s)
- Anderson Antonio Carvalho Alves
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, Brazil
| | - Rebeka Magalhães da Costa
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, Brazil
| | - Larissa Fernanda Simielli Fonseca
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, Brazil
| | - Roberto Carvalheiro
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, Brazil.,National Council for Scientific and Technological Development (CNPq), Brasília, Brazil
| | - Ricardo Vieira Ventura
- Department of Animal Nutrition and Production, School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga, Brazil
| | | | - Lucia Galvão Albuquerque
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, Brazil.,National Council for Scientific and Technological Development (CNPq), Brasília, Brazil
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16
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Matthews LJ, Turkheimer E. Three legs of the missing heritability problem. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2022; 93:183-191. [PMID: 35533541 PMCID: PMC9172633 DOI: 10.1016/j.shpsa.2022.04.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/07/2022] [Accepted: 04/20/2022] [Indexed: 05/31/2023]
Abstract
The so-called 'missing heritability problem' is often characterized by behavior geneticists as a numerical discrepancy between alternative kinds of heritability. For example, while 'traditional heritability' derived from twin and family studies indicates that approximately ∼50% of variation in intelligence is attributable to genetics, 'SNP heritability' derived from genome-wide association studies indicates that only ∼10% of variation in intelligence is attributable to genetics. This 40% gap in variance accounted for by alternative kinds of heritability is frequently referred to as what's "missing." Philosophers have picked up on this reading, suggesting that "dissolving" the missing heritability problem is merely a matter of closing the numerical gap between traditional and molecular kinds of heritability. We argue that this framing of the problem undervalues the severity of the many challenges to scientific understanding of the "heritability" of human behavior. On our view, resolving the numerical discrepancies between alternative kinds of heritability will do little to advance scientific explanation and understanding of behavior genetics. Thus, we propose a new conceptual framework of the missing heritability problem that comprises three independent methodological and explanatory challenges: the numerical gap, the prediction gap, and the mechanism gap.
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17
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Posada-Reyes AB, Balderas-Martínez YI, Ávila-Ríos S, Vinuesa P, Fonseca-Coronado S. An Epistatic Network Describes oppA and glgB as Relevant Genes for Mycobacterium tuberculosis. Front Mol Biosci 2022; 9:856212. [PMID: 35712352 PMCID: PMC9194097 DOI: 10.3389/fmolb.2022.856212] [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: 01/16/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022] Open
Abstract
Mycobacterium tuberculosis is an acid-fast bacterium that causes tuberculosis worldwide. The role of epistatic interactions among different loci of the M. tuberculosis genome under selective pressure may be crucial for understanding the disease and the molecular basis of antibiotic resistance acquisition. Here, we analyzed polymorphic loci interactions by applying a model-free method for epistasis detection, SpydrPick, on a pan–genome-wide alignment created from a set of 254 complete reference genomes. By means of the analysis of an epistatic network created with the detected epistatic interactions, we found that glgB (α-1,4-glucan branching enzyme) and oppA (oligopeptide-binding protein) are putative targets of co-selection in M. tuberculosis as they were associated in the network with M. tuberculosis genes related to virulence, pathogenesis, transport system modulators of the immune response, and antibiotic resistance. In addition, our work unveiled potential pharmacological applications for genotypic antibiotic resistance inherent to the mutations of glgB and oppA as they epistatically interact with fprA and embC, two genes recently included as antibiotic-resistant genes in the catalog of the World Health Organization. Our findings showed that this approach allows the identification of relevant epistatic interactions that may lead to a better understanding of M. tuberculosis by deciphering the complex interactions of molecules involved in its metabolism, virulence, and pathogenesis and that may be applied to different bacterial populations.
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Affiliation(s)
- Ali-Berenice Posada-Reyes
- Posgrado en Ciencias Biológicas, UNAM, Mexico, Mexico
- Facultad de Estudios Superiores Cuautitlán, UNAM, Estado de Mexico, Mexico
- *Correspondence: Ali-Berenice Posada-Reyes, ; Salvador Fonseca-Coronado,
| | | | - Santiago Ávila-Ríos
- Instituto Nacional de Enfermedades Respiratorias “Ismael Cosio Villegas”, Ciudad de Mexico, Mexico
| | - Pablo Vinuesa
- Centro de Ciencias Genómicas, UNAM, Cuernavaca, Mexico
| | - Salvador Fonseca-Coronado
- Facultad de Estudios Superiores Cuautitlán, UNAM, Estado de Mexico, Mexico
- *Correspondence: Ali-Berenice Posada-Reyes, ; Salvador Fonseca-Coronado,
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18
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Stolyarova AV, Neretina TV, Zvyagina EA, Fedotova AV, Kondrashov A, Bazykin GA. Complex fitness landscape shapes variation in a hyperpolymorphic species. eLife 2022; 11:76073. [PMID: 35532122 PMCID: PMC9187340 DOI: 10.7554/elife.76073] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
It is natural to assume that patterns of genetic variation in hyperpolymorphic species can reveal large-scale properties of the fitness landscape that are hard to detect by studying species with ordinary levels of genetic variation. Here, we study such patterns in a fungus Schizophyllum commune, the most polymorphic species known. Throughout the genome, short-range linkage disequilibrium (LD) caused by attraction of minor alleles is higher between pairs of nonsynonymous than of synonymous variants. This effect is especially pronounced for pairs of sites that are located within the same gene, especially if a large fraction of the gene is covered by haploblocks, genome segments where the gene pool consists of two highly divergent haplotypes, which is a signature of balancing selection. Haploblocks are usually shorter than 1000 nucleotides, and collectively cover about 10% of the S. commune genome. LD tends to be substantially higher for pairs of nonsynonymous variants encoding amino acids that interact within the protein. There is a substantial correlation between LDs at the same pairs of nonsynonymous mutations in the USA and the Russian populations. These patterns indicate that selection in S. commune involves positive epistasis due to compensatory interactions between nonsynonymous alleles. When less polymorphic species are studied, analogous patterns can be detected only through interspecific comparisons. Changes to DNA known as mutations may alter how the proteins and other components of a cell work, and thus play an important role in allowing living things to evolve new traits and abilities over many generations. Whether a mutation is beneficial or harmful may differ depending on the genetic background of the individual – that is, depending on other mutations present in other positions within the same gene – due to a phenomenon called epistasis. Epistasis is known to affect how various species accumulate differences in their DNA compared to each other over time. For example, a mutation that is rare in humans and known to cause disease may be widespread in other primates because its negative effect is canceled out by another mutation that is standard for these species but absent in humans. However, it remains unclear whether epistasis plays a significant part in shaping genetic differences between individuals of the same species. A type of fungus known as Schizophyllum commune lives on rotting wood and is found across the world. It is one of the most genetically diverse species currently known, so there is a higher chance of pairs of compensatory mutations occurring and persisting for a long time in S. commune than in most other species, providing a unique opportunity to study epistasis. Here, Stolyarova et al. studied two distinct populations of S. commune, one from the USA and one from Russia. The team found that – unlike in humans, flies and other less genetically diverse species – epistasis maintains combinations of mutations in S. commune that individually would be harmful to the fungus but together compensate for each other. For example, pairs of mutations affecting specific molecules known as amino acids – the building blocks of proteins – that physically interact with each other tended to be found together in the same individuals. One potential downside of having pairs of compensatory mutations in the genome is that when the organism reproduces, the process of making sex cells may split up these pairs so that harmful mutations are inherited without their partner mutations. Thus, epistasis may have helped shape the way S. commune and other genetically diverse species have evolved.
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Affiliation(s)
| | - Tatiana V Neretina
- Biological Faculty, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Elena A Zvyagina
- Biological Faculty, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Anna V Fedotova
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Alexey Kondrashov
- Department of Ecology and Evolutionary Biology, University of Michigan-Ann Arbor, Ann Arbor, United States
| | - Georgii A Bazykin
- Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Moscow, Russian Federation
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19
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Perez BC, Bink MCAM, Svenson KL, Churchill GA, Calus MPL. Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice. G3 (BETHESDA, MD.) 2022; 12:6528848. [PMID: 35166767 PMCID: PMC8982369 DOI: 10.1093/g3journal/jkac039] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/29/2022] [Indexed: 12/14/2022]
Abstract
We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.
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Affiliation(s)
- Bruno C Perez
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | - Marco C A M Bink
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | | | | | - Mario P L Calus
- Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, The Netherlands
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20
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Santangelo JS, Ness RW, Cohan B, Fitzpatrick CR, Innes SG, Koch S, Miles LS, Munim S, Peres-Neto PR, Prashad C, Tong AT, Aguirre WE, Akinwole PO, Alberti M, Álvarez J, Anderson JT, Anderson JJ, Ando Y, Andrew NR, Angeoletto F, Anstett DN, Anstett J, Aoki-Gonçalves F, Arietta AZA, Arroyo MTK, Austen EJ, Baena-Díaz F, Barker CA, Baylis HA, Beliz JM, Benitez-Mora A, Bickford D, Biedebach G, Blackburn GS, Boehm MMA, Bonser SP, Bonte D, Bragger JR, Branquinho C, Brans KI, Bresciano JC, Brom PD, Bucharova A, Burt B, Cahill JF, Campbell KD, Carlen EJ, Carmona D, Castellanos MC, Centenaro G, Chalen I, Chaves JA, Chávez-Pesqueira M, Chen XY, Chilton AM, Chomiak KM, Cisneros-Heredia DF, Cisse IK, Classen AT, Comerford MS, Fradinger CC, Corney H, Crawford AJ, Crawford KM, Dahirel M, David S, De Haan R, Deacon NJ, Dean C, Del-Val E, Deligiannis EK, Denney D, Dettlaff MA, DiLeo MF, Ding YY, Domínguez-López ME, Dominoni DM, Draud SL, Dyson K, Ellers J, Espinosa CI, Essi L, Falahati-Anbaran M, Falcão JCF, Fargo HT, Fellowes MDE, Fitzpatrick RM, Flaherty LE, Flood PJ, Flores MF, Fornoni J, Foster AG, Frost CJ, Fuentes TL, Fulkerson JR, Gagnon E, Garbsch F, Garroway CJ, Gerstein AC, Giasson MM, Girdler EB, Gkelis S, Godsoe W, Golemiec AM, Golemiec M, González-Lagos C, Gorton AJ, Gotanda KM, Granath G, Greiner S, Griffiths JS, Grilo F, Gundel PE, Hamilton B, Hardin JM, He T, Heard SB, Henriques AF, Hernández-Poveda M, Hetherington-Rauth MC, Hill SJ, Hochuli DF, Hodgins KA, Hood GR, Hopkins GR, Hovanes KA, Howard AR, Hubbard SC, Ibarra-Cerdeña CN, Iñiguez-Armijos C, Jara-Arancio P, Jarrett BJM, Jeannot M, Jiménez-Lobato V, Johnson M, Johnson O, Johnson PP, Johnson R, Josephson MP, Jung MC, Just MG, Kahilainen A, Kailing OS, Kariñho-Betancourt E, Karousou R, Kirn LA, Kirschbaum A, Laine AL, LaMontagne JM, Lampei C, Lara C, Larson EL, Lázaro-Lobo A, Le JH, Leandro DS, Lee C, Lei Y, León CA, Lequerica Tamara ME, Levesque DC, Liao WJ, Ljubotina M, Locke H, Lockett MT, Longo TC, Lundholm JT, MacGillavry T, Mackin CR, Mahmoud AR, Manju IA, Mariën J, Martínez DN, Martínez-Bartolomé M, Meineke EK, Mendoza-Arroyo W, Merritt TJS, Merritt LEL, Migiani G, Minor ES, Mitchell N, Mohammadi Bazargani M, Moles AT, Monk JD, Moore CM, Morales-Morales PA, Moyers BT, Muñoz-Rojas M, Munshi-South J, Murphy SM, Murúa MM, Neila M, Nikolaidis O, Njunjić I, Nosko P, Núñez-Farfán J, Ohgushi T, Olsen KM, Opedal ØH, Ornelas C, Parachnowitsch AL, Paratore AS, Parody-Merino AM, Paule J, Paulo OS, Pena JC, Pfeiffer VW, Pinho P, Piot A, Porth IM, Poulos N, Puentes A, Qu J, Quintero-Vallejo E, Raciti SM, Raeymaekers JAM, Raveala KM, Rennison DJ, Ribeiro MC, Richardson JL, Rivas-Torres G, Rivera BJ, Roddy AB, Rodriguez-Muñoz E, Román JR, Rossi LS, Rowntree JK, Ryan TJ, Salinas S, Sanders NJ, Santiago-Rosario LY, Savage AM, Scheepens JF, Schilthuizen M, Schneider AC, Scholier T, Scott JL, Shaheed SA, Shefferson RP, Shepard CA, Shykoff JA, Silveira G, Smith AD, Solis-Gabriel L, Soro A, Spellman KV, Whitney KS, Starke-Ottich I, Stephan JG, Stephens JD, Szulc J, Szulkin M, Tack AJM, Tamburrino Í, Tate TD, Tergemina E, Theodorou P, Thompson KA, Threlfall CG, Tinghitella RM, Toledo-Chelala L, Tong X, Uroy L, Utsumi S, Vandegehuchte ML, VanWallendael A, Vidal PM, Wadgymar SM, Wang AY, Wang N, Warbrick ML, Whitney KD, Wiesmeier M, Wiles JT, Wu J, Xirocostas ZA, Yan Z, Yao J, Yoder JB, Yoshida O, Zhang J, Zhao Z, Ziter CD, Zuellig MP, Zufall RA, Zurita JE, Zytynska SE, Johnson MTJ. Global urban environmental change drives adaptation in white clover. Science 2022; 375:1275-1281. [PMID: 35298255 DOI: 10.1126/science.abk0989] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale.
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Affiliation(s)
- James S Santangelo
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Centre for Urban Environments, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Rob W Ness
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Centre for Urban Environments, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Beata Cohan
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | | | - Simon G Innes
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Department of Biology, University of Louisiana, Lafayette, LA, USA
| | - Sophie Koch
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Lindsay S Miles
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Centre for Urban Environments, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Samreen Munim
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Department of Biology, Queen's University, Kingston, ON, Canada
| | | | - Cindy Prashad
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Alex T Tong
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Windsor E Aguirre
- Department of Biological Sciences, DePaul University, Chicago, IL, USA
| | | | - Marina Alberti
- Department of Urban Design and Planning, University of Washington, Seattle, WA, USA
| | - Jackie Álvarez
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Jill T Anderson
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Joseph J Anderson
- Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Yoshino Ando
- Field Science Center for Northern Biosphere, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Nigel R Andrew
- Natural History Museum, Zoology, University of New England, Armidale, NSW, Australia
| | - Fabio Angeoletto
- Programa de Pós-Graduação em Geografia da UFMT, campus de Rondonópolis, Cuiabá, Brazil
| | - Daniel N Anstett
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Julia Anstett
- Graduate Program in Genome Sciences and Technology, Genome Sciences Centre, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
| | | | | | - Mary T K Arroyo
- Departamento de Ciencias Ecológicas, Facultad de Ciencias, Universidad de Chile, Santiago, Chile.,Instituto de Ecología y Biodiversidad, Universidad de Chile, Santiago, Chile
| | - Emily J Austen
- Department of Biology, Mount Allison University, Sackville, NB, Canada
| | | | - Cory A Barker
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Howard A Baylis
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Julia M Beliz
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Biology, University of Miami, Miami, FL, USA
| | - Alfonso Benitez-Mora
- Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS), Universidad Bernardo O'Higgins, Santiago, Chile
| | - David Bickford
- Department of Biology, University of La Verne, La Verne, CA, USA
| | | | - Gwylim S Blackburn
- Département des sciences du bois et de la forêt, Université Laval, Quebec, QC, Canada
| | - Mannfred M A Boehm
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Stephen P Bonser
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Dries Bonte
- Department of Biology, Ghent University, Ghent, Belgium
| | - Jesse R Bragger
- Department of Biology, Monmouth University, West Long Branch, NJ, USA
| | - Cristina Branquinho
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
| | | | - Jorge C Bresciano
- School of Agriculture and Environment, Wildlife and Ecology group, Massey University, Palmerston North, Manawatu, New Zealand
| | - Peta D Brom
- Department of Biological Sciences, University of Cape Town, Cape Town, South Africa
| | - Anna Bucharova
- Institute of Landscape Ecology, University of Münster, Münster, Germany
| | - Briana Burt
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - James F Cahill
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | | | - Elizabeth J Carlen
- Louis Calder Center and Department of Biological Sciences, Fordham University, Armonk, NY, USA
| | - Diego Carmona
- Departamento de Ecología Tropical, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | | | - Giada Centenaro
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Izan Chalen
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador.,iBIOTROP Instituto de Biodiversidad Tropical, Universidad San Francisco de Quito, Quito, Ecuador
| | - Jaime A Chaves
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador.,Department of Biology, San Francisco State University, San Francisco, CA, USA
| | - Mariana Chávez-Pesqueira
- Unidad de Recursos Naturales, Centro de Investigación Científica de Yucatán AC, Mérida, Yucatán, México
| | - Xiao-Yong Chen
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China.,Shanghai Engineering Research Center of Sustainable Plant Innovation, Shanghai 200231, China
| | - Angela M Chilton
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Kristina M Chomiak
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Diego F Cisneros-Heredia
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador.,iBIOTROP Instituto de Biodiversidad Tropical, Universidad San Francisco de Quito, Quito, Ecuador
| | - Ibrahim K Cisse
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Aimée T Classen
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Hannah Corney
- Biology Department, Saint Mary's University, Halifax, NS, Canada
| | - Andrew J Crawford
- Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Kerri M Crawford
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Maxime Dahirel
- ECOBIO (Ecosystèmes, biodiversité, évolution), Université de Rennes, Rennes, France
| | - Santiago David
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Robert De Haan
- Department of Environmental Studies, Dordt University, Sioux Center, IA, USA
| | - Nicholas J Deacon
- Department of Biology, Minneapolis Community and Technical College, Minneapolis, MN, USA
| | - Clare Dean
- Department of Natural Sciences, Ecology and Environment Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Ek Del-Val
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, UNAM, Morelia, Mexico
| | | | - Derek Denney
- Department of Genetics, University of Georgia, Athens, GA, USA
| | | | - Michelle F DiLeo
- Faculty of Biological and Environmental Science, Organismal & Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Yuan-Yuan Ding
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
| | - Moisés E Domínguez-López
- Corporación Científica Ingeobosque, Medellín, Antioquia, Colombia.,GTA Colombia S.A.S. Envigado, Antioquia, Colombia
| | - Davide M Dominoni
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, Scotland, UK
| | | | - Karen Dyson
- Department of Urban Design and Planning, University of Washington, Seattle, WA, USA
| | - Jacintha Ellers
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Carlos I Espinosa
- Departamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, Loja, Ecuador
| | - Liliana Essi
- Departamento de Biologia, Universidade Federal de Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil
| | - Mohsen Falahati-Anbaran
- Department of Plant Sciences, School of Biology, College of Science, University of Tehran, Tehran, Iran.,NTNU University Museum, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Jéssica C F Falcão
- Red de Estudios Moleculares Avanzados, Instituto de Ecología A. C., Xalapa, Mexico
| | - Hayden T Fargo
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Mark D E Fellowes
- School of Biological Sciences, University of Reading, Whiteknights Park, Reading, Berkshire, UK
| | | | - Leah E Flaherty
- Department of Biological Sciences, MacEwan University, Edmonton, AB, Canada
| | - Pádraic J Flood
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - María F Flores
- Instituto de Ecología y Biodiversidad, Universidad de Chile, Santiago, Chile
| | - Juan Fornoni
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Amy G Foster
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | | | - Tracy L Fuentes
- Department of Urban Design and Planning, University of Washington, Seattle, WA, USA
| | - Justin R Fulkerson
- Alaska Center for Conservation Science, University of Alaska Anchorage, Anchorage, AK, USA
| | - Edeline Gagnon
- Tropical Diversity, Royal Botanical Garden of Edinburgh, Edinburgh, UK.,Département de biologie, Université de Moncton, Moncton, New Brunswick, Canada
| | - Frauke Garbsch
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Colin J Garroway
- Department of Biological Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Aleeza C Gerstein
- Departments of Microbiology & Statistics, University of Manitoba, Winnipeg, MB, Canada
| | - Mischa M Giasson
- Department of Biology, University of New Brunswick, Fredericton, NB, Canada
| | | | - Spyros Gkelis
- Department of Botany, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - William Godsoe
- BioProtection Research Centre, Lincoln University, Lincoln, Canterbury, New Zealand
| | | | - Mireille Golemiec
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - César González-Lagos
- Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS), Universidad Bernardo O'Higgins, Santiago, Chile.,Departamento de Ciencias, Facultad de Artes Liberales, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Amanda J Gorton
- Department of Ecology, Evolution, and Behaviour University of Minnesota, Minneapolis, MN, USA
| | - Kiyoko M Gotanda
- Department of Zoology, University of Cambridge, Cambridge, UK.,Department of Biological Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Gustaf Granath
- Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Stephan Greiner
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Joanna S Griffiths
- Department of Environmental Toxicology, University of California, Davis, CA, USA
| | - Filipa Grilo
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
| | - Pedro E Gundel
- IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Buenos Aires, Argentina.,ICB - University of Talca, Chile
| | - Benjamin Hamilton
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | | | - Tianhua He
- School of Molecular and Life Science, Curtin University, Perth, Australia.,College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Stephen B Heard
- Department of Biology, University of New Brunswick, Fredericton, NB, Canada
| | - André F Henriques
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
| | | | | | - Sarah J Hill
- Natural History Museum, Zoology, University of New England, Armidale, NSW, Australia
| | - Dieter F Hochuli
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Kathryn A Hodgins
- School of Biological Sciences, Monash University, Melbourne, VIC, Australia
| | - Glen R Hood
- Department of Biological Sciences, Wayne State University, Detroit, MI, USA
| | - Gareth R Hopkins
- Department of Biology, Western Oregon University, Monmouth, OR, USA
| | - Katherine A Hovanes
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA
| | - Ava R Howard
- Department of Biology, Western Oregon University, Monmouth, OR, USA
| | | | | | - Carlos Iñiguez-Armijos
- Departamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, Loja, Ecuador
| | - Paola Jara-Arancio
- Departamento de Ciencias Biológicas y Departamento de Ecología y Biodiversidad, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile.,Institute of Ecology and Biodiversity (IEB), Chile
| | - Benjamin J M Jarrett
- Department of Zoology, University of Cambridge, Cambridge, UK.,Department of Biology, Lund University, Lund, Sweden
| | - Manon Jeannot
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vania Jiménez-Lobato
- Escuela Superiro de Desarrollo Sustentable, Universidad Autónoma de Guerrero -CONACYT, Las Tunas, Mexico
| | - Mae Johnson
- Clarkson Secondary School, Peel District School Board, Mississauga, ON, Canada
| | - Oscar Johnson
- Homelands Sr. Public School, Peel District School Board, Mississauga, ON, Canada
| | - Philip P Johnson
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Reagan Johnson
- St. James Catholic Global Learning Centre, Dufferin-Peel Catholic District School Board, Mississauga ON, Canada
| | | | - Meen Chel Jung
- Department of Urban Design and Planning, University of Washington, Seattle, WA, USA
| | - Michael G Just
- Ecological Processes Branch, U.S. Army ERDC-CERL, Champaign, IL, USA
| | - Aapo Kahilainen
- Faculty of Biological and Environmental Science, Organismal & Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Otto S Kailing
- Department of Biology, Oberlin College, Oberlin, OH, USA
| | | | - Regina Karousou
- Department of Botany, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lauren A Kirn
- School of Biological Sciences, Monash University, Melbourne, VIC, Australia
| | - Anna Kirschbaum
- Institute of Evolution and Ecology, University of Tübingen, Tübingen, Germany
| | - Anna-Liisa Laine
- Faculty of Biological and Environmental Science, Organismal & Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse, Zurich, Switzerland
| | - Jalene M LaMontagne
- Department of Biological Sciences, DePaul University, Chicago, IL, USA.,Urban Wildlife Institute, Department of Conservation and Science, Lincoln Park Zoo, Chicago, IL, USA
| | - Christian Lampei
- Institute of Landscape Ecology, University of Münster, Münster, Germany
| | - Carlos Lara
- Departamento de Ecología, Universidad Católica de la Santísima Concepción, Concepción, Chile
| | - Erica L Larson
- Department of Biological Sciences, University of Denver, Denver, CO, USA
| | - Adrián Lázaro-Lobo
- Department of Biological Sciences, Mississippi State University, Starkville, MS, USA
| | - Jennifer H Le
- Department of Biology, Center for Computational & Integrative Biology, Rutgers University-Camden, Camden, NJ, USA
| | - Deleon S Leandro
- Programa de Pós-Graduação em Geografia da UFMT, campus de Rondonópolis, Brasil
| | - Christopher Lee
- School of Biological Sciences, Monash University, Melbourne, VIC, Australia
| | - Yunting Lei
- Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Carolina A León
- Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS), Universidad Bernardo O'Higgins, Santiago, Chile
| | | | - Danica C Levesque
- Department of Chemistry & Biochemistry, Laurentian University, Sudbury, ON, Canada
| | - Wan-Jin Liao
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China
| | - Megan Ljubotina
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Hannah Locke
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Martin T Lockett
- School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
| | - Tiffany C Longo
- Department of Biology, Monmouth University, West Long Branch, NJ, USA
| | | | - Thomas MacGillavry
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, Scotland, UK
| | | | - Alex R Mahmoud
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Isaac A Manju
- Department of Biology, Western Oregon University, Monmouth, OR, USA
| | - Janine Mariën
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - D Nayeli Martínez
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, UNAM, Morelia, Mexico.,Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Coyoacán, Mexico City, 04510, Mexico
| | - Marina Martínez-Bartolomé
- Department of Biological Sciences, Mississippi State University, Starkville, MS, USA.,Department of Biological Sciences, Auburn University, Auburn, AL, USA
| | - Emily K Meineke
- Department of Entomology and Nematology, University of California, Davis, CA, USA
| | | | - Thomas J S Merritt
- Department of Chemistry & Biochemistry, Laurentian University, Sudbury, ON, Canada
| | | | - Giuditta Migiani
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, Scotland, UK
| | - Emily S Minor
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Nora Mitchell
- Department of Biology, University of New Mexico, Albuquerque, NM, USA.,Department of Biology, University of Wisconsin - Eau Claire, Eau Claire, WI 54701
| | - Mitra Mohammadi Bazargani
- Agriculture Institute, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran
| | - Angela T Moles
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Julia D Monk
- School of the Environment, Yale University, New Haven, CT, USA
| | | | | | - Brook T Moyers
- Department of Biology, University of Massachusetts Boston, Boston, MA, USA.,Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - Miriam Muñoz-Rojas
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia.,Departamento de Biología Vegetal y Ecología, Facultad de Biología, Universidad de Sevilla, Av. Reina Mercedes s/n, 41012 Sevilla, Spain
| | - Jason Munshi-South
- Louis Calder Center and Department of Biological Sciences, Fordham University, Armonk, NY, USA
| | - Shannon M Murphy
- Department of Biological Sciences, University of Denver, Denver, CO, USA
| | - Maureen M Murúa
- Facultad de Estudios Interdisciplinarios, Centro GEMA- Genómica, Ecología y Medio Ambiente, Universidad Mayor, Santiago, Chile
| | - Melisa Neila
- Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS), Universidad Bernardo O'Higgins, Santiago, Chile
| | - Ourania Nikolaidis
- Department of Biology, Center for Computational & Integrative Biology, Rutgers University-Camden, Camden, NJ, USA
| | - Iva Njunjić
- Evolutionary Ecology Group, Naturalis Biodiversity Center, Leiden, Netherlands
| | - Peter Nosko
- Department of Biology and Chemistry, Nipissing University, North Bay, ON, Canada
| | - Juan Núñez-Farfán
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Takayuki Ohgushi
- Center for Ecological Research, Kyoto University, Otsu, Shiga, Japan
| | - Kenneth M Olsen
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Cristina Ornelas
- Bonanza Creek Long Term Ecological Research Program, University of Alaska Fairbanks, Fairbanks, AK, USA
| | - Amy L Parachnowitsch
- Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden.,Department of Biology, University of New Brunswick, Fredericton, NB, Canada
| | - Aaron S Paratore
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Angela M Parody-Merino
- School of Agriculture and Environment, Wildlife and Ecology group, Massey University, Palmerston North, Manawatu, New Zealand
| | - Juraj Paule
- Department of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt, Frankfurt am Main, Germany
| | - Octávio S Paulo
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
| | - João Carlos Pena
- Departamento de Biodiversidade, Instituto de Biociências, Univ Estadual Paulista - UNESP, Rio Claro, São Paulo, Brazil
| | - Vera W Pfeiffer
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI, USA
| | - Pedro Pinho
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
| | - Anthony Piot
- Département des sciences du bois et de la forêt, Université Laval, Quebec, QC, Canada
| | - Ilga M Porth
- Département des sciences du bois et de la forêt, Université Laval, Quebec, QC, Canada
| | - Nicholas Poulos
- Department of Biology, California State University, Northridge, Los Angeles, CA, USA
| | - Adriana Puentes
- Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Jiao Qu
- Department of Biology, Ghent University, Ghent, Belgium
| | | | - Steve M Raciti
- Department of Biology, Hofstra University, Long Island, NY, USA
| | | | - Krista M Raveala
- Faculty of Biological and Environmental Science, Organismal & Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Diana J Rennison
- Division of Biological Sciences, University of California San Diego, San Diego, CA, USA
| | - Milton C Ribeiro
- Departamento de Biodiversidade, Instituto de Biociências, Univ Estadual Paulista - UNESP, Rio Claro, São Paulo, Brazil
| | | | - Gonzalo Rivas-Torres
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador.,Estación de Biodiversidad Tiputini, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | | | - Adam B Roddy
- Department of Biological Sciences, Institute of Environment, Florida International University, Miami, FL, USA
| | | | | | - Laura S Rossi
- Department of Biology and Chemistry, Nipissing University, North Bay, ON, Canada
| | - Jennifer K Rowntree
- Department of Natural Sciences, Ecology and Environment Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Travis J Ryan
- Department of Biological Sciences and Center for Urban Ecology and Sustainability, Butler University, Indianapolis, IN, USA
| | | | - Nathan J Sanders
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - Amy M Savage
- Department of Biology, Center for Computational & Integrative Biology, Rutgers University-Camden, Camden, NJ, USA
| | - J F Scheepens
- Institute of Evolution and Ecology, University of Tübingen, Tübingen, Germany.,Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Adam C Schneider
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Department of Biology, Hendrix College, Conway, AR, USA
| | - Tiffany Scholier
- Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden.,Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Jared L Scott
- Department of Biology, University of Louisville, Louisville, KY, USA
| | - Summer A Shaheed
- Department of Biology, Monmouth University, West Long Branch, NJ, USA
| | - Richard P Shefferson
- Organization for Programs on Environmental Science, University of Tokyo, Tokyo, Japan
| | | | - Jacqui A Shykoff
- Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, 91405, Orsay, France
| | | | - Alexis D Smith
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Lizet Solis-Gabriel
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, UNAM, Morelia, Mexico
| | - Antonella Soro
- General Zoology, Institute for Biology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Katie V Spellman
- Bonanza Creek Long Term Ecological Research Program, University of Alaska Fairbanks, Fairbanks, AK, USA.,International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA
| | - Kaitlin Stack Whitney
- Science, Technology and Society Department, Rochester Institute of Technology, Rochester, NY, USA
| | - Indra Starke-Ottich
- Department of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt, Frankfurt am Main, Germany
| | - Jörg G Stephan
- Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden.,SLU Swedish Species Information Centre, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | | | - Justyna Szulc
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Marta Szulkin
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Ayco J M Tack
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Ítalo Tamburrino
- Instituto de Ecología y Biodiversidad, Universidad de Chile, Santiago, Chile
| | - Tayler D Tate
- Department of Biology, Western Oregon University, Monmouth, OR, USA
| | | | - Panagiotis Theodorou
- General Zoology, Institute for Biology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Ken A Thompson
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.,Department of Biology, Stanford University, Stanford, CA, USA
| | - Caragh G Threlfall
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia
| | | | | | - Xin Tong
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
| | - Léa Uroy
- ECOBIO (Ecosystèmes, biodiversité, évolution), Université de Rennes, Rennes, France.,UMR 0980 BAGAP, Agrocampus Ouest-ESA-INRA, Rennes, France
| | - Shunsuke Utsumi
- Field Science Center for Northern Biosphere, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Martijn L Vandegehuchte
- Department of Biology, Ghent University, Ghent, Belgium.,Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Acer VanWallendael
- Plant Biology Department, Michigan State University, East Lansing, MI, USA
| | - Paula M Vidal
- Instituto de Ecología y Biodiversidad, Universidad de Chile, Santiago, Chile
| | | | - Ai-Ying Wang
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China
| | - Nian Wang
- College of Horticulture and Forestry Sciences/ Hubei Engineering Technology Research Center for Forestry Information, Huazhong Agricultural University, Wuhan, China, Hubei, China
| | - Montana L Warbrick
- Department of Biology and Chemistry, Nipissing University, North Bay, ON, Canada
| | - Kenneth D Whitney
- Department of Biology, University of New Mexico, Albuquerque, NM, USA
| | - Miriam Wiesmeier
- School of Life Sciences, Technical University of Munich, Munich, Germany
| | | | - Jianqiang Wu
- Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Zoe A Xirocostas
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Zhaogui Yan
- College of Horticulture and Forestry Sciences/ Hubei Engineering Technology Research Center for Forestry Information, Huazhong Agricultural University, Wuhan, China, Hubei, China
| | - Jiahe Yao
- School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Jeremy B Yoder
- Department of Biology, California State University, Northridge, Los Angeles, CA, USA
| | - Owen Yoshida
- Biology Department, Saint Mary's University, Halifax, NS, Canada
| | - Jingxiong Zhang
- Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Zhigang Zhao
- School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Carly D Ziter
- Department of Biology, Concordia University, Montreal, QC, Canada
| | - Matthew P Zuellig
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - Rebecca A Zufall
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Juan E Zurita
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Sharon E Zytynska
- School of Life Sciences, Technical University of Munich, Munich, Germany.,Department of Evolution, Ecology and Behaviour, University of Liverpool, Liverpool, UK
| | - Marc T J Johnson
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada.,Centre for Urban Environments, University of Toronto Mississauga, Mississauga, ON, Canada
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21
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Harley IT, Sawalha AH. Systemic lupus erythematosus as a genetic disease. Clin Immunol 2022; 236:108953. [PMID: 35149194 PMCID: PMC9167620 DOI: 10.1016/j.clim.2022.108953] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 12/12/2022]
Abstract
Systemic lupus erythematosus is the prototypical systemic autoimmune disease, as it is characterized both by protean multi-organ system manifestations and by the uniform presence of pathogenic autoantibodies directed against components of the nucleus. Prior to the modern genetic era, the diverse clinical manifestations of SLE suggested to many that SLE patients were unlikely to share a common genetic risk basis. However, modern genetic studies have revealed that SLE usually arises when an environmental exposure occurs in an individual with a collection of genetic risk variants passing a liability threshold. Here, we summarize the current state of the field aimed at: (1) understanding the genetic architecture of this complex disease, (2) synthesizing how this genetic risk architecture impacts cellular and molecular disease pathophysiology, (3) providing illustrative examples that highlight the rich complexity of the pathobiology of this prototypical autoimmune disease and (4) communicating this complex etiopathogenesis to patients.
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Affiliation(s)
- Isaac T.W. Harley
- Division of Rheumatology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA,Human Immunology and Immunotherapy Initiative (HI3), Department of Immunology, University of Colorado School of Medicine, Aurora, CO, USA,Rocky Mountain Regional Veteran’s Administration Medical Center (VAMC), Medicine Service, Rheumatology Section, Aurora, CO, USA,Corresponding author at: Isaac TW Harley, MD, PhD, MS, Division of Rheumatology, University of Colorado Anschutz Medical Campus, Barbara Davis Center, Mail Stop B115, 1775 Aurora Court, Aurora, CO 80045, USA, (I.T.W. Harley)
| | - Amr H. Sawalha
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh School of Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA,Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA,Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA,Lupus Center of Excellence, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA,Corresponding author at: Amr H. Sawalha, MD, University of Pittsburgh, 7123 Rangos Research Center, 4401 Penn Avenue, Pittsburgh, PA 15224, USA, (A.H. Sawalha)
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22
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Sun S, Roth C, Floyd Averette A, Magwene PM, Heitman J. Epistatic genetic interactions govern morphogenesis during sexual reproduction and infection in a global human fungal pathogen. Proc Natl Acad Sci U S A 2022; 119:e2122293119. [PMID: 35169080 PMCID: PMC8872808 DOI: 10.1073/pnas.2122293119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/11/2022] [Indexed: 12/12/2022] Open
Abstract
Cellular development is orchestrated by evolutionarily conserved signaling pathways, which are often pleiotropic and involve intra- and interpathway epistatic interactions that form intricate, complex regulatory networks. Cryptococcus species are a group of closely related human fungal pathogens that grow as yeasts yet transition to hyphae during sexual reproduction. Additionally, during infection they can form large, polyploid titan cells that evade immunity and develop drug resistance. Multiple known signaling pathways regulate cellular development, yet how these are coordinated and interact with genetic variation is less well understood. Here, we conducted quantitative trait locus (QTL) analyses of a mapping population generated by sexual reproduction of two parents, only one of which is unisexually fertile. We observed transgressive segregation of the unisexual phenotype among progeny, as well as a large-cell phenotype under mating-inducing conditions. These large-cell progeny were found to produce titan cells both in vitro and in infected animals. Two major QTLs and corresponding quantitative trait genes (QTGs) were identified: RIC8 (encoding a guanine-exchange factor) and CNC06490 (encoding a putative Rho-GTPase activator), both involved in G protein signaling. The two QTGs interact epistatically with each other and with the mating-type locus in phenotypic determination. These findings provide insights into the complex genetics of morphogenesis during unisexual reproduction and pathogenic titan cell formation and illustrate how QTL analysis can be applied to identify epistasis between genes. This study shows that phenotypic outcomes are influenced by the genetic background upon which mutations arise, implicating dynamic, complex genotype-to-phenotype landscapes in fungal pathogens and beyond.
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Affiliation(s)
- Sheng Sun
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710
| | - Cullen Roth
- Department of Biology, Duke University, Durham, NC 27708
| | - Anna Floyd Averette
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710
| | - Paul M Magwene
- Department of Biology, Duke University, Durham, NC 27708
| | - Joseph Heitman
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710;
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23
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Rand DM, Mossman JA, Spierer AN, Santiago JA. Mitochondria as environments for the nuclear genome in Drosophila: mitonuclear G×G×E. J Hered 2022; 113:37-47. [PMID: 34964900 PMCID: PMC8851671 DOI: 10.1093/jhered/esab066] [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: 04/04/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022] Open
Abstract
Mitochondria evolved from a union of microbial cells belonging to distinct lineages that were likely anaerobic. The evolution of eukaryotes required a massive reorganization of the 2 genomes and eventual adaptation to aerobic environments. The nutrients and oxygen that sustain eukaryotic metabolism today are processed in mitochondria through coordinated expression of 37 mitochondrial genes and over 1000 nuclear genes. This puts mitochondria at the nexus of gene-by-gene (G×G) and gene-by-environment (G×E) interactions that sustain life. Here we use a Drosophila model of mitonuclear genetic interactions to explore the notion that mitochondria are environments for the nuclear genome, and vice versa. We construct factorial combinations of mtDNA and nuclear chromosomes to test for epistatic interactions (G×G), and expose these mitonuclear genotypes to altered dietary environments to examine G×E interactions. We use development time and genome-wide RNAseq analyses to assess the relative contributions of mtDNA, nuclear chromosomes, and environmental effects on these traits (mitonuclear G×G×E). We show that the nuclear transcriptional response to alternative mitochondrial "environments" (G×G) has significant overlap with the transcriptional response of mitonuclear genotypes to altered dietary environments. These analyses point to specific transcription factors (e.g., giant) that mediated these interactions, and identified coexpressed modules of genes that may account for the overlap in differentially expressed genes. Roughly 20% of the transcriptome includes G×G genes that are concordant with G×E genes, suggesting that mitonuclear interactions are part of an organism's environment.
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Affiliation(s)
- David M Rand
- Department of Ecology, Evolution and Organismal Biology, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
| | - James A Mossman
- Department of Ecology, Evolution and Organismal Biology, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
| | - Adam N Spierer
- Department of Ecology, Evolution and Organismal Biology, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
| | - John A Santiago
- Department of Molecular Biology, Cellular Biology, and Biochemistry, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
- Department of Pathology and Laboratory Medicine, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
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24
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Ogbunugafor CB. The mutation effect reaction norm (mu-rn) highlights environmentally dependent mutation effects and epistatic interactions. Evolution 2022; 76:37-48. [PMID: 34989399 DOI: 10.1111/evo.14428] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/23/2021] [Indexed: 11/27/2022]
Abstract
Since the modern synthesis, the fitness effects of mutations and epistasis have been central yet provocative concepts in evolutionary and population genetics. Studies of how the interactions between parcels of genetic information can change as a function of environmental context have added a layer of complexity to these discussions. Here I introduce the "mutation effect reaction norm" (Mu-RN), a new instrument through which one can analyze the phenotypic consequences of mutations and interactions across environmental contexts. It embodies the fusion of measurements of genetic interactions with the reaction norm, a classic depiction of the performance of genotypes across environments. I demonstrate the utility of the Mu-RN through the signature of a "compensatory ratchet" mutation that undermines reverse evolution of antimicrobial resistance. More broadly, I argue that the mutation effect reaction norm may help us resolve the dynamism and unpredictability of evolution, with implications for theoretical biology, genetic modification technology, and public health. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- C Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA
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25
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Abstract
PURPOSE OF REVIEW We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field. RECENT FINDINGS Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.
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26
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Singh RS. Decoding 'Unnecessary Complexity': A Law of Complexity and a Concept of Hidden Variation Behind "Missing Heritability" in Precision Medicine. J Mol Evol 2021; 89:513-526. [PMID: 34341835 PMCID: PMC8327892 DOI: 10.1007/s00239-021-10023-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/20/2021] [Indexed: 01/06/2023]
Abstract
The high hopes for the Human Genome Project and personalized medicine were not met because the relationship between genotypes and phenotypes turned out to be more complex than expected. In a previous study we laid the foundation of a theory of complexity and showed that because of the blind nature of evolution, and molecular and historical contingency, cells have accumulated unnecessary complexity, complexity beyond what is necessary and sufficient to describe an organism. Here we provide empirical evidence and show that unnecessary complexity has become integrated into the genome in the form of redundancy and is relevant to molecular evolution of phenotypic complexity. Unnecessary complexity creates uncertainty between molecular and phenotypic complexity, such that phenotypic complexity (CP) is higher than molecular complexity (CM), which is higher than DNA complexity (CD). The qualitative inequality in complexity is based on the following hierarchy: CP > CM > CD. This law-like relationship holds true for all complex traits, including complex diseases. We present a hypothesis of two types of variation, namely open and closed (hidden) systems, show that hidden variation provides a hitherto undiscovered "third source" of phenotypic variation, beside genotype and environment, and argue that "missing heritability" for some complex diseases is likely to be a case of "diluted heritability". There is a need for radically new ways of thinking about the principles of genotype-phenotype relationship. Understanding how cells use hidden, pathway variation to respond to stress can shed light on why two individuals who share the same risk factors may not develop the same disease, or how cancer cells escape death.
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Affiliation(s)
- Rama S Singh
- Department of Biology, and Origins Institute, McMaster University, 1280 Main Street West, Hamilton, ON, L8S4K1, Canada.
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27
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Inferring multilayer interactome networks shaping phenotypic plasticity and evolution. Nat Commun 2021; 12:5304. [PMID: 34489412 PMCID: PMC8421358 DOI: 10.1038/s41467-021-25086-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023] Open
Abstract
Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.
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28
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Gupta R, Karczewski KJ, Howrigan D, Neale BM, Mootha VK. Human genetic analyses of organelles highlight the nucleus in age-related trait heritability. eLife 2021; 10:68610. [PMID: 34467851 PMCID: PMC8476128 DOI: 10.7554/elife.68610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 08/30/2021] [Indexed: 12/15/2022] Open
Abstract
Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a 'hallmark' of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be 'haplosufficient,' while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration.
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Affiliation(s)
- Rahul Gupta
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, United States.,Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Konrad J Karczewski
- Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Daniel Howrigan
- Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Benjamin M Neale
- Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Vamsi K Mootha
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, United States.,Broad Institute of MIT and Harvard, Cambridge, United States
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29
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The Genetic Architecture of a Congenital Heart Defect Is Related to Its Fitness Cost. Genes (Basel) 2021; 12:genes12091368. [PMID: 34573350 PMCID: PMC8467714 DOI: 10.3390/genes12091368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/29/2021] [Indexed: 11/24/2022] Open
Abstract
In newborns, severe congenital heart defects are rarer than mild ones. This epidemiological relationship between heart defect severity and incidence lacks explanation. Here, an analysis of ~10,000 Nkx2-5+/− mice from two inbred strain crosses illustrates the fundamental role of epistasis. Modifier genes raise or lower the risk of specific defects via pairwise (G×GNkx) and higher-order (G×G×GNkx) interactions with Nkx2-5. Their effect sizes correlate with the severity of a defect. The risk loci for mild, atrial septal defects exert predominantly small G×GNkx effects, while the loci for severe, atrioventricular septal defects exert large G×GNkx and G×G×GNkx effects. The loci for moderately severe ventricular septal defects have intermediate effects. Interestingly, G×G×GNkx effects are three times more likely to suppress risk when the genotypes at the first two loci are from the same rather than different parental inbred strains. This suggests the genetic coadaptation of interacting G×G×GNkx loci, a phenomenon that Dobzhansky first described in Drosophila. Thus, epistasis plays dual roles in the pathogenesis of congenital heart disease and the robustness of cardiac development. The empirical results suggest a relationship between the fitness cost and genetic architecture of a disease phenotype and a means for phenotypic robustness to have evolved.
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30
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Lozovsky ER, Daniels RF, Heffernan GD, Jacobus DP, Hartl DL. Relevance of Higher-Order Epistasis in Drug Resistance. Mol Biol Evol 2021; 38:142-151. [PMID: 32745183 PMCID: PMC7782864 DOI: 10.1093/molbev/msaa196] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
We studied five chemically distinct but related 1,3,5-triazine antifolates with regard to their effects on growth of a set of mutants in dihydrofolate reductase. The mutants comprise a combinatorially complete data set of all 16 possible combinations of four amino acid replacements associated with resistance to pyrimethamine in the malaria parasite Plasmodium falciparum. Pyrimethamine was a mainstay medication for malaria for many years, and it is still in use in intermittent treatment during pregnancy or as a partner drug in artemisinin combination therapy. Our goal was to investigate the extent to which the alleles yield similar adaptive topographies and patterns of epistasis across chemically related drugs. We find that the adaptive topographies are indeed similar with the same or closely related alleles being fixed in computer simulations of stepwise evolution. For all but one of the drugs the topography features at least one suboptimal fitness peak. Our data are consistent with earlier results indicating that third order and higher epistatic interactions appear to contribute only modestly to the overall adaptive topography, and they are largely conserved. In regard to drug development, our data suggest that higher-order interactions are likely to be of little value as an advisory tool in the choice of lead compounds.
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Affiliation(s)
- Elena R Lozovsky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
| | - Rachel F Daniels
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA
| | | | | | - Daniel L Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
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31
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Wang H, Ye M, Fu Y, Dong A, Zhang M, Feng L, Zhu X, Bo W, Jiang L, Griffin CH, Liang D, Wu R. Modeling genome-wide by environment interactions through omnigenic interactome networks. Cell Rep 2021; 35:109114. [PMID: 33979624 DOI: 10.1016/j.celrep.2021.109114] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/11/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022] Open
Abstract
How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.
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Affiliation(s)
- Haojie Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yaru Fu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Miaomiao Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wenhao Bo
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dan Liang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
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32
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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33
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Liu L, Wang Y, Zhang D, Chen Z, Chen X, Su Z, He X. The Origin of Additive Genetic Variance Driven by Positive Selection. Mol Biol Evol 2021; 37:2300-2308. [PMID: 32243529 PMCID: PMC7403624 DOI: 10.1093/molbev/msaa085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Fisher's fundamental theorem of natural selection predicts no additive variance of fitness in a natural population. Consistently, studies in a variety of wild populations show virtually no narrow-sense heritability (h2) for traits important to fitness. However, counterexamples are occasionally reported, calling for a deeper understanding on the evolution of additive variance. In this study, we propose adaptive divergence followed by population admixture as a source of the additive genetic variance of evolutionarily important traits. We experimentally tested the hypothesis by examining a panel of ∼1,000 yeast segregants produced by a hybrid of two yeast strains that experienced adaptive divergence. We measured >400 yeast cell morphological traits and found a strong positive correlation between h2 and evolutionary importance. Because adaptive divergence followed by population admixture could happen constantly, particularly in species with wide geographic distribution and strong migratory capacity (e.g., humans), the finding reconciles the observation of abundant additive variances in evolutionarily important traits with Fisher's fundamental theorem of natural selection. Importantly, the revealed role of positive selection in promoting rather than depleting additive variance suggests a simple explanation for why additive genetic variance can be dominant in a population despite the ubiquitous between-gene epistasis observed in functional assays.
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Affiliation(s)
- Li Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Yayu Wang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Di Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhuoxin Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoshu Chen
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Zhijian Su
- Department of Cell Biology, Jinan University, Guangzhou, China
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
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34
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Hem IG, Selle ML, Gorjanc G, Fuglstad GA, Riebler A. Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge. Genetics 2021. [PMID: 33789346 DOI: 10.1101/2020.04.01.019497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.
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Affiliation(s)
- Ingeborg Gullikstad Hem
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Maria Lie Selle
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Edinburgh
| | - Geir-Arne Fuglstad
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Andrea Riebler
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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35
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Hem IG, Selle ML, Gorjanc G, Fuglstad GA, Riebler A. Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge. Genetics 2021; 217:iyab002. [PMID: 33789346 PMCID: PMC8045730 DOI: 10.1093/genetics/iyab002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/20/2020] [Indexed: 12/19/2022] Open
Abstract
We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.
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Affiliation(s)
- Ingeborg Gullikstad Hem
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Maria Lie Selle
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Edinburgh
| | - Geir-Arne Fuglstad
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Andrea Riebler
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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36
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A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits. G3-GENES GENOMES GENETICS 2020; 10:4553-4563. [PMID: 33023974 PMCID: PMC7718749 DOI: 10.1534/g3.120.401824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.
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37
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Kalyakulina A, Iannuzzi V, Sazzini M, Garagnani P, Jalan S, Franceschi C, Ivanchenko M, Giuliani C. Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions. Front Physiol 2020; 11:575968. [PMID: 33262703 PMCID: PMC7686538 DOI: 10.3389/fphys.2020.575968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/14/2020] [Indexed: 01/18/2023] Open
Abstract
Cold climates represent one of the major environmental challenges that anatomically modern humans faced during their dispersal out of Africa. The related adaptive traits have been achieved by modulation of thermogenesis and thermoregulation processes where nuclear (nuc) and mitochondrial (mt) genes play a major role. In human populations, mitonuclear genetic interactions are the result of both the peculiar genetic history of each human group and the different environments they have long occupied. This study aims to investigate mitonuclear genetic interactions by considering all the mitochondrial genes and 28 nuclear genes involved in brown adipose tissue metabolism, which have been previously hypothesized to be crucial for cold adaptation. For this purpose, we focused on three human populations (i.e., Finnish, British, and Central Italian people) of European ancestry from different biogeographical and climatic areas, and we used a machine learning approach to identify relevant nucDNA–mtDNA interactions that characterized each population. The obtained results are twofold: (i) at the methodological level, we demonstrated that a machine learning approach is able to detect patterns of genetic structure among human groups from different latitudes both at single genes and by considering combinations of mtDNA and nucDNA loci; (ii) at the biological level, the analysis identified population-specific nuclear genes and variants that likely play a relevant biological role in association with a mitochondrial gene (such as the “obesity gene” FTO in Finnish people). Further studies are needed to fully elucidate the evolutionary dynamics (e.g., migration, admixture, and/or local adaptation) that shaped these nucDNA–mtDNA interactions and their functional role.
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Affiliation(s)
- Alena Kalyakulina
- Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Vincenzo Iannuzzi
- Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate), University of Bologna, Bologna, Italy.,Laboratory of Molecular Anthropology and Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
| | - Marco Sazzini
- Laboratory of Molecular Anthropology and Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Sarika Jalan
- Complex Systems Laboratory, Discipline of Physics, Indian Institute of Technology Indore, Indore, India.,Center for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS), Daejeon, South Korea
| | - Claudio Franceschi
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Mikhail Ivanchenko
- Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Cristina Giuliani
- Laboratory of Molecular Anthropology and Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy.,School of Anthropology and Museum Ethnography, University of Oxford, Oxford, United Kingdom
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38
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Johnson LM, Smith OJ, Hahn DA, Baer CF. Short-term heritable variation overwhelms 200 generations of mutational variance for metabolic traits in Caenorhabditis elegans. Evolution 2020; 74:2451-2464. [PMID: 32989734 DOI: 10.1111/evo.14104] [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: 05/11/2020] [Revised: 08/05/2020] [Accepted: 09/21/2020] [Indexed: 10/23/2022]
Abstract
Metabolic disorders have a large heritable component, and have increased markedly in human populations over the past few generations. Genome-wide association studies of metabolic traits typically find a substantial unexplained fraction of total heritability, suggesting an important role of spontaneous mutation. An alternative explanation is that epigenetic effects contribute significantly to the heritable variation. Here, we report a study designed to quantify the cumulative effects of spontaneous mutation on adenosine metabolism in the nematode Caenorhabditis elegans, including both the activity and concentration of two metabolic enzymes and the standing pools of their associated metabolites. The only prior studies on the effects of mutation on metabolic enzyme activity, in Drosophila melanogaster, found that total enzyme activity presents a mutational target similar to that of morphological and life-history traits. However, those studies were not designed to account for short-term heritable effects. We find that the short-term heritable variance for most traits is of similar magnitude as the variance among MA lines. This result suggests that the potential heritable effects of epigenetic variation in metabolic disease warrant additional scrutiny.
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Affiliation(s)
- Lindsay M Johnson
- Department of Biology, University of Florida, Gainesville, Florida, 32611.,Ology Bioservices, Inc., Alachua, Florida, 32615
| | - Olivia J Smith
- Department of Biology, University of Florida, Gainesville, Florida, 32611
| | - Daniel A Hahn
- Department of Entomology and Nematology, University of Florida, Gainesville, Florida, 32611.,University of Florida Genetics Institute, Gainesville, Florida, 32611
| | - Charles F Baer
- Department of Biology, University of Florida, Gainesville, Florida, 32611.,University of Florida Genetics Institute, Gainesville, Florida, 32611
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39
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Despotović M, Jevtović Stoimenov T, Stanković I, Bašić J, Đorđević B. Genetic variants of vitamin D receptor and antioxidant enzyme genes in bronchial asthma: Epistatic interactions. Ann Allergy Asthma Immunol 2020; 125:701-703.e1. [PMID: 32730806 DOI: 10.1016/j.anai.2020.07.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/14/2020] [Accepted: 07/21/2020] [Indexed: 11/17/2022]
Affiliation(s)
- Milena Despotović
- Department of Biochemistry, Faculty of Medicine, University of Niš, Niš, Republic of Serbia.
| | | | - Ivana Stanković
- Clinic for Pulmonary Diseases and Tuberculosis, Clinical Centre Niš, University of Niš, Niš, Republic of Serbia
| | - Jelena Bašić
- Department of Biochemistry, Faculty of Medicine, University of Niš, Niš, Republic of Serbia
| | - Branka Đorđević
- Department of Biochemistry, Faculty of Medicine, University of Niš, Niš, Republic of Serbia
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40
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Alonge M, Wang X, Benoit M, Soyk S, Pereira L, Zhang L, Suresh H, Ramakrishnan S, Maumus F, Ciren D, Levy Y, Harel TH, Shalev-Schlosser G, Amsellem Z, Razifard H, Caicedo AL, Tieman DM, Klee H, Kirsche M, Aganezov S, Ranallo-Benavidez TR, Lemmon ZH, Kim J, Robitaille G, Kramer M, Goodwin S, McCombie WR, Hutton S, Van Eck J, Gillis J, Eshed Y, Sedlazeck FJ, van der Knaap E, Schatz MC, Lippman ZB. Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato. Cell 2020; 182:145-161.e23. [PMID: 32553272 PMCID: PMC7354227 DOI: 10.1016/j.cell.2020.05.021] [Citation(s) in RCA: 372] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/10/2020] [Accepted: 05/12/2020] [Indexed: 12/22/2022]
Abstract
Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.
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Affiliation(s)
- Michael Alonge
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xingang Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Matthias Benoit
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Sebastian Soyk
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Lara Pereira
- Center for Applied Genetic Technologies, Genetics & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Lei Zhang
- Center for Applied Genetic Technologies, Genetics & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Hamsini Suresh
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | | | - Florian Maumus
- URGI, INRA, Université Paris-Saclay, 78026 Versailles, France
| | - Danielle Ciren
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yuval Levy
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Tom Hai Harel
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Gili Shalev-Schlosser
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ziva Amsellem
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Hamid Razifard
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA; Department of Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Ana L Caicedo
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA; Department of Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Denise M Tieman
- Horticultural Sciences, Plant Innovation Center, University of Florida, Gainesville, FL 32611, USA
| | - Harry Klee
- Horticultural Sciences, Plant Innovation Center, University of Florida, Gainesville, FL 32611, USA
| | - Melanie Kirsche
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sergey Aganezov
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Zachary H Lemmon
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Jennifer Kim
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Gina Robitaille
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Melissa Kramer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - W Richard McCombie
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Samuel Hutton
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
| | - Joyce Van Eck
- Boyce Thompson Institute, Ithaca, NY 14853, USA; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yuval Eshed
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Esther van der Knaap
- Center for Applied Genetic Technologies, Genetics & Genomics, University of Georgia, Athens, GA 30602, USA; Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA 30602, USA; Department of Horticulture, University of Georgia, Athens, GA 30602, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Zachary B Lippman
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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41
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Genç Ö, An JY, Fetter RD, Kulik Y, Zunino G, Sanders SJ, Davis GW. Homeostatic plasticity fails at the intersection of autism-gene mutations and a novel class of common genetic modifiers. eLife 2020; 9:55775. [PMID: 32609087 PMCID: PMC7394548 DOI: 10.7554/elife.55775] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 06/07/2020] [Indexed: 01/08/2023] Open
Abstract
We identify a set of common phenotypic modifiers that interact with five independent autism gene orthologs (RIMS1, CHD8, CHD2, WDFY3, ASH1L) causing a common failure of presynaptic homeostatic plasticity (PHP) in Drosophila. Heterozygous null mutations in each autism gene are demonstrated to have normal baseline neurotransmission and PHP. However, PHP is sensitized and rendered prone to failure. A subsequent electrophysiology-based genetic screen identifies the first known heterozygous mutations that commonly genetically interact with multiple ASD gene orthologs, causing PHP to fail. Two phenotypic modifiers identified in the screen, PDPK1 and PPP2R5D, are characterized. Finally, transcriptomic, ultrastructural and electrophysiological analyses define one mechanism by which PHP fails; an unexpected, maladaptive up-regulation of CREG, a conserved, neuronally expressed, stress response gene and a novel repressor of PHP. Thus, we define a novel genetic landscape by which diverse, unrelated autism risk genes may converge to commonly affect the robustness of synaptic transmission.
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Affiliation(s)
- Özgür Genç
- Department of Biochemistry and Biophysics Kavli Institute for Fundamental Neuroscience University of California, San Francisco, San Francisco, United States
| | - Joon-Yong An
- Department of Psychiatry UCSF Weill Institute for Neurosciences University of California, San Francisco, San Francisco, United States.,School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul, Republic of Korea
| | - Richard D Fetter
- Department of Biochemistry and Biophysics Kavli Institute for Fundamental Neuroscience University of California, San Francisco, San Francisco, United States
| | - Yelena Kulik
- Department of Biochemistry and Biophysics Kavli Institute for Fundamental Neuroscience University of California, San Francisco, San Francisco, United States
| | - Giulia Zunino
- Department of Biochemistry and Biophysics Kavli Institute for Fundamental Neuroscience University of California, San Francisco, San Francisco, United States
| | - Stephan J Sanders
- Department of Psychiatry UCSF Weill Institute for Neurosciences University of California, San Francisco, San Francisco, United States
| | - Graeme W Davis
- Department of Biochemistry and Biophysics Kavli Institute for Fundamental Neuroscience University of California, San Francisco, San Francisco, United States
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42
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Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives. Mamm Genome 2020; 31:146-156. [PMID: 32529318 PMCID: PMC7369129 DOI: 10.1007/s00335-020-09841-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 06/03/2020] [Indexed: 12/12/2022]
Abstract
Thought to be directly and uniquely dependent from genotypes, the ontogeny of individual phenotypes is much more complicated. Individual genetics, environmental exposures, and their interaction are the three main determinants of individual's phenotype. This picture has been further complicated a decade ago when the Lamarckian theory of acquired inheritance has been rekindled with the discovery of epigenetic inheritance, according to which acquired phenotypes can be transmitted through fertilization and affect phenotypes across generations. The results of Genome-Wide Association Studies have also highlighted a big degree of missing heritability in genetics and have provided hints that not only acquired phenotypes, but also individual's genotypes affect phenotypes intergenerationally through indirect genetic effects. Here, we review available examples of indirect genetic effects in mammals, what is known of the underlying molecular mechanisms and their potential impact for our understanding of missing heritability, phenotypic variation. and individual disease risk.
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Abstract
The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.
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Affiliation(s)
- Marcus Seldin
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aldons J Lusis
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
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44
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Weisweiler M, Montaigu AD, Ries D, Pfeifer M, Stich B. Transcriptomic and presence/absence variation in the barley genome assessed from multi-tissue mRNA sequencing and their power to predict phenotypic traits. BMC Genomics 2019; 20:787. [PMID: 31664921 PMCID: PMC6819542 DOI: 10.1186/s12864-019-6174-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/06/2019] [Indexed: 02/04/2023] Open
Abstract
Background Barley is the world’s fourth most cultivated cereal and is an important crop model for genetic studies. One layer of genomic information that remains poorly explored in barley is presence/absence variation (PAV), which has been suggested to contribute to phenotypic variation of agronomic importance in various crops. Results An mRNA sequencing approach was used to study genomic PAV and transcriptomic variation in 23 spring barley inbreds. 1502 new genes identified here were physically absent from the Morex reference sequence, and 11,523 previously unannotated genes were not expressed in Morex. The procedure applied to detect expression PAV revealed that more than 50% of all genes of our data set are not expressed in all inbreds. Interestingly, expression PAV were not in strong linkage disequilibrium with neighboring sequence variants (SV), and therefore provided an additional layer of genetic information. Optimal combinations of expression PAV, SV, and gene abundance data could enhance the prediction accuracy of predicting three different agronomic traits. Conclusions Our results highlight the advantage of mRNA sequencing for genomic prediction over other technologies, as it allows extracting multiple layers of genomic data from a single sequencing experiment. Finally, we propose low coverage mRNA sequencing based characterization of breeding material harvested as seedlings in petri dishes as a powerful and cost efficient approach to replace current single nucleotide polymorphism (SNP) based characterizations.
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Affiliation(s)
- Marius Weisweiler
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, Düsseldorf, 40225, Germany
| | - Amaury de Montaigu
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, Düsseldorf, 40225, Germany
| | - David Ries
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, Düsseldorf, 40225, Germany
| | - Mara Pfeifer
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, Düsseldorf, 40225, Germany
| | - Benjamin Stich
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, Düsseldorf, 40225, Germany. .,Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Universitätsstraße 1, Düsseldorf, 40225, Germany.
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45
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Meijsen JJ, Rammos A, Campbell A, Hayward C, Porteous DJ, Deary IJ, Marioni RE, Nicodemus KK. Using tree-based methods for detection of gene-gene interactions in the presence of a polygenic signal: simulation study with application to educational attainment in the Generation Scotland Cohort Study. Bioinformatics 2019; 35:181-188. [PMID: 29931044 PMCID: PMC6330004 DOI: 10.1093/bioinformatics/bty462] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 06/14/2018] [Indexed: 11/13/2022] Open
Abstract
Motivation The genomic architecture of human complex diseases is thought to be attributable to single markers, polygenic components and epistatic components. No study has examined the ability of tree-based methods to detect epistasis in the presence of a polygenic signal. We sought to apply decision tree-based methods, C5.0 and logic regression, to detect epistasis under several simulated conditions, varying strength of interaction and linkage disequilibrium (LD) structure. We then applied the same methods to the phenotype of educational attainment in a large population cohort. Results LD pruning improved the power and reduced the type I error. C5.0 had a conservative type I error rate whereas logic regression had a type I error rate that exceeded 5%. Despite the more conservative type I error, C5.0 was observed to have higher power than logic regression across several conditions. In the presence of a polygenic signal, power was generally reduced. Applying both methods on educational attainment in a large population cohort yielded numerous interacting SNPs; notably a SNP in RCAN3 which is associated with reading and spelling and a SNP in NPAS3, a neurodevelopmental gene. Availability and implementation All methods used are implemented and freely available in R. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joeri J Meijsen
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Alexandros Rammos
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Department of Genetics, Smurfit Institute of Genetics and Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Kristin K Nicodemus
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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46
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Giuliani C, Garagnani P, Franceschi C. Genetics of Human Longevity Within an Eco-Evolutionary Nature-Nurture Framework. Circ Res 2019; 123:745-772. [PMID: 30355083 DOI: 10.1161/circresaha.118.312562] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Human longevity is a complex trait, and to disentangle its basis has a great theoretical and practical consequences for biomedicine. The genetics of human longevity is still poorly understood despite several investigations that used different strategies and protocols. Here, we argue that such rather disappointing harvest is largely because of the extraordinary complexity of the longevity phenotype in humans. The capability to reach the extreme decades of human lifespan seems to be the result of an intriguing mixture of gene-environment interactions. Accordingly, the genetics of human longevity is here described as a highly context-dependent phenomenon, within a new integrated, ecological, and evolutionary perspective, and is presented as a dynamic process, both historically and individually. The available literature has been scrutinized within this perspective, paying particular attention to factors (sex, individual biography, family, population ancestry, social structure, economic status, and education, among others) that have been relatively neglected. The strength and limitations of the most powerful and used tools, such as genome-wide association study and whole-genome sequencing, have been discussed, focusing on prominently emerged genes and regions, such as apolipoprotein E, Forkhead box O3, interleukin 6, insulin-like growth factor-1, chromosome 9p21, 5q33.3, and somatic mutations among others. The major results of this approach suggest that (1) the genetics of longevity is highly population specific; (2) small-effect alleles, pleiotropy, and the complex allele timing likely play a major role; (3) genetic risk factors are age specific and need to be integrated in the light of the geroscience perspective; (4) a close relationship between genetics of longevity and genetics of age-related diseases (especially cardiovascular diseases) do exist. Finally, the urgent need of a global approach to the largely unexplored interactions between the 3 genetics of human body, that is, nuclear, mitochondrial, and microbiomes, is stressed. We surmise that the comprehensive approach here presented will help in increasing the above-mentioned harvest.
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Affiliation(s)
- Cristina Giuliani
- From the Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology (C.G.), University of Bologna, Italy.,School of Anthropology and Museum Ethnography, University of Oxford, United Kingdom (C.G.).,Interdepartmental Centre 'L. Galvani' (CIG), University of Bologna, Italy (C.G.)
| | - Paolo Garagnani
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES) (P.G.), University of Bologna, Italy.,Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet at Huddinge University Hospital, Stockholm, Sweden (P.G.)
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47
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Discovering genetic interactions bridging pathways in genome-wide association studies. Nat Commun 2019; 10:4274. [PMID: 31537791 PMCID: PMC6753138 DOI: 10.1038/s41467-019-12131-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson's disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.
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48
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He L, Su M, Ou G, Wang L, Deng J, Zhuang H, Xiang K, Li T. The modulation of HBsAg level by sI126T is affected by additional amino acid substitutions in the S region of HBV. INFECTION GENETICS AND EVOLUTION 2019; 75:104006. [PMID: 31442597 DOI: 10.1016/j.meegid.2019.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/12/2019] [Accepted: 08/15/2019] [Indexed: 11/17/2022]
Abstract
The hepatitis B surface antigen (HBsAg) is a vital serum marker for hepatitis B virus (HBV) infection. Amino acid (AA) substitutions in small hepatitis B surface protein (SHBs) are known to affect HBsAg level. However, how the genetic backbones of SHBs sequences would affect the roles of a specific AA substitution on HBsAg level remains unclear. In this study, we found that sI126 had a very high substitution detection rate of 17.54% (40/228) in untreated chronic hepatitis B cohort with subgenotype C2 HBV infection. Among different substitution types at sI126, the sI126T (N = 28) was found to be associated with significantly lower serum HBsAg level. Clone sequencing revealed that sI126T-harboring SHBs sequences had varied genetic backbones with zero to nine additional AA substitutions. Thus, we constructed 24 HBsAg expression plasmids harboring sI126T without (plasmid 1, P1) or with (P2-P24) additional AA substitution(s) and studied them in the HepG2 cells. The HBsAg levels were determined by both ELISA and Western blot. In vitro experiments showed that P1 significantly reduced HBsAg level and its secretion (p < .05), however, P2-P24 showed various extracellular and intracellular HBsAg levels. No significant differences were detected among the HBsAg mRNA levels of nine representative mutant plasmids. Our findings suggest that the modulation of HBsAg level by sI126T is affected by additional AA substitution(s) in the S region of HBV. The effects of AA combination substitutions in SHBs sequences on HBsAg levels are worthwhile for more attentions in terms of HBV biology and its clinical application.
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Affiliation(s)
- Lingyuan He
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China
| | - Mingze Su
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China
| | - Guomin Ou
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China
| | - Luwei Wang
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China
| | - Juan Deng
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China
| | - Hui Zhuang
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China
| | - Kuanhui Xiang
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China.
| | - Tong Li
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Xueyuan Road 38, Haidian District, Beijing 100191, China.
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Meszaros VA, Miller-Dickson MD, Ogbunugafor CB. Lexical Landscapes as large in silico data for examining advanced properties of fitness landscapes. PLoS One 2019; 14:e0220891. [PMID: 31404101 PMCID: PMC6690511 DOI: 10.1371/journal.pone.0220891] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 07/25/2019] [Indexed: 11/19/2022] Open
Abstract
In silico approaches have served a central role in the development of evolutionary theory for generations. This especially applies to the concept of the fitness landscape, one of the most important abstractions in evolutionary genetics, and one which has benefited from the presence of large empirical data sets only in the last decade or so. In this study, we propose a method that allows us to generate enormous data sets that walk the line between in silico and empirical: word usage frequencies as catalogued by the Google ngram corpora. These data can be codified or analogized in terms of a multidimensional empirical fitness landscape towards the examination of advanced concepts-adaptive landscape by environment interactions, clonal competition, higher-order epistasis and countless others. We argue that the greater Lexical Landscapes approach can serve as a platform that offers an astronomical number of fitness landscapes for exploration (at least) or theoretical formalism (potentially) in evolutionary biology.
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Affiliation(s)
- Victor A. Meszaros
- Department of Ecology and Evolutionary Biology – Brown University, Providence, Rhode Island, United States of America
| | - Miles D. Miller-Dickson
- Department of Ecology and Evolutionary Biology – Brown University, Providence, Rhode Island, United States of America
| | - C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology – Brown University, Providence, Rhode Island, United States of America
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50
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Yan J, Risacher SL, Shen L, Saykin AJ. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 2019; 19:1370-1381. [PMID: 28679163 DOI: 10.1093/bib/bbx066] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Indexed: 11/14/2022] Open
Abstract
In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.
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Affiliation(s)
- Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
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