1
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Rong S, Root E, Reilly SK. Massively parallel approaches for characterizing noncoding functional variation in human evolution. Curr Opin Genet Dev 2024; 88:102256. [PMID: 39217658 DOI: 10.1016/j.gde.2024.102256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 08/02/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
The genetic differences underlying unique phenotypes in humans compared to our closest primate relatives have long remained a mystery. Similarly, the genetic basis of adaptations between human groups during our expansion across the globe is poorly characterized. Uncovering the downstream phenotypic consequences of these genetic variants has been difficult, as a substantial portion lies in noncoding regions, such as cis-regulatory elements (CREs). Here, we review recent high-throughput approaches to measure the functions of CREs and the impact of variation within them. CRISPR screens can directly perturb CREs in the genome to understand downstream impacts on gene expression and phenotypes, while massively parallel reporter assays can decipher the regulatory impact of sequence variants. Machine learning has begun to be able to predict regulatory function from sequence alone, further scaling our ability to characterize genome function. Applying these tools across diverse phenotypes, model systems, and ancestries is beginning to revolutionize our understanding of noncoding variation underlying human evolution.
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Affiliation(s)
- Stephen Rong
- Department of Genetics, Yale University, New Haven, CT, USA.
| | - Elise Root
- Department of Genetics, Yale University, New Haven, CT, USA
| | - Steven K Reilly
- Department of Genetics, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
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2
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Arora UP, Dumont BL. Molecular evolution of the mammalian kinetochore complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.600994. [PMID: 38979348 PMCID: PMC11230421 DOI: 10.1101/2024.06.27.600994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Mammalian centromeres are satellite-rich chromatin domains that serve as sites for kinetochore complex assembly. Centromeres are highly variable in sequence and satellite organization across species, but the processes that govern the co-evolutionary dynamics between rapidly evolving centromeres and their associated kinetochore proteins remain poorly understood. Here, we pursue a course of phylogenetic analyses to investigate the molecular evolution of the complete kinetochore complex across primate and rodent species with divergent centromere repeat sequences and features. We show that many protein components of the core centromere associated network (CCAN) harbor signals of adaptive evolution, consistent with their intimate association with centromere satellite DNA and roles in the stability and recruitment of additional kinetochore proteins. Surprisingly, CCAN and outer kinetochore proteins exhibit comparable rates of adaptive divergence, suggesting that changes in centromere DNA can ripple across the kinetochore to drive adaptive protein evolution within distant domains of the complex. Our work further identifies kinetochore proteins subject to lineage-specific adaptive evolution, including rapidly evolving proteins in species with centromere satellites characterized by higher-order repeat structure and lacking CENP-B boxes. Thus, features of centromeric chromatin beyond the linear DNA sequence may drive selection on kinetochore proteins. Overall, our work spotlights adaptively evolving proteins with diverse centromere-associated functions, including centromere chromatin structure, kinetochore protein assembly, kinetochore-microtubule association, cohesion maintenance, and DNA damage response pathways. These adaptively evolving kinetochore protein candidates present compelling opportunities for future functional investigations exploring how their concerted changes with centromere DNA ensure the maintenance of genome stability.
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Affiliation(s)
- Uma P Arora
- The Jackson Laboratory, 600 Main Street, Bar Harbor ME 04609
- Tufts University, Graduate School of Biomedical Sciences, 136 Harrison Ave, Boston MA 02111
| | - Beth L Dumont
- The Jackson Laboratory, 600 Main Street, Bar Harbor ME 04609
- Tufts University, Graduate School of Biomedical Sciences, 136 Harrison Ave, Boston MA 02111
- Graduate School of Biomedical Science and Engineering, The University of Maine, Orono, Maine, 04469
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3
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Riley R, Mathieson I, Mathieson S. Interpreting generative adversarial networks to infer natural selection from genetic data. Genetics 2024; 226:iyae024. [PMID: 38386895 PMCID: PMC10990424 DOI: 10.1093/genetics/iyae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 02/24/2024] Open
Abstract
Understanding natural selection and other forms of non-neutrality is a major focus for the use of machine learning in population genetics. Existing methods rely on computationally intensive simulated training data. Unlike efficient neutral coalescent simulations for demographic inference, realistic simulations of selection typically require slow forward simulations. Because there are many possible modes of selection, a high dimensional parameter space must be explored, with no guarantee that the simulated models are close to the real processes. Finally, it is difficult to interpret trained neural networks, leading to a lack of understanding about what features contribute to classification. Here we develop a new approach to detect selection and other local evolutionary processes that requires relatively few selection simulations during training. We build upon a generative adversarial network trained to simulate realistic neutral data. This consists of a generator (fitted demographic model), and a discriminator (convolutional neural network) that predicts whether a genomic region is real or fake. As the generator can only generate data under neutral demographic processes, regions of real data that the discriminator recognizes as having a high probability of being "real" do not fit the neutral demographic model and are therefore candidates for targets of selection. To incentivize identification of a specific mode of selection, we fine-tune the discriminator with a small number of custom non-neutral simulations. We show that this approach has high power to detect various forms of selection in simulations, and that it finds regions under positive selection identified by state-of-the-art population genetic methods in three human populations. Finally, we show how to interpret the trained networks by clustering hidden units of the discriminator based on their correlation patterns with known summary statistics.
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Affiliation(s)
- Rebecca Riley
- Department of Computer Science, Haverford College, Haverford, PA 19041, USA
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sara Mathieson
- Department of Computer Science, Haverford College, Haverford, PA 19041, USA
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4
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Venkatesh SS, Wittemans LBL, Palmer DS, Baya NA, Ferreira T, Hill B, Lassen FH, Parker MJ, Reibe S, Elhakeem A, Banasik K, Bruun MT, Erikstrup C, Jensen BA, Juul A, Mikkelsen C, Nielsen HS, Ostrowski SR, Pedersen OB, Rohde PD, Sorensen E, Ullum H, Westergaard D, Haraldsson A, Holm H, Jonsdottir I, Olafsson I, Steingrimsdottir T, Steinthorsdottir V, Thorleifsson G, Figueredo J, Karjalainen MK, Pasanen A, Jacobs BM, Hubers N, Lippincott M, Fraser A, Lawlor DA, Timpson NJ, Nyegaard M, Stefansson K, Magi R, Laivuori H, van Heel DA, Boomsma DI, Balasubramanian R, Seminara SB, Chan YM, Laisk T, Lindgren CM. Genome-wide analyses identify 21 infertility loci and over 400 reproductive hormone loci across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304530. [PMID: 38562841 PMCID: PMC10984039 DOI: 10.1101/2024.03.19.24304530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Genome-wide association studies (GWASs) may help inform treatments for infertility, whose causes remain unknown in many cases. Here we present GWAS meta-analyses across six cohorts for male and female infertility in up to 41,200 cases and 687,005 controls. We identified 21 genetic risk loci for infertility (P≤5E-08), of which 12 have not been reported for any reproductive condition. We found positive genetic correlations between endometriosis and all-cause female infertility (rg=0.585, P=8.98E-14), and between polycystic ovary syndrome and anovulatory infertility (rg=0.403, P=2.16E-03). The evolutionary persistence of female infertility-risk alleles in EBAG9 may be explained by recent directional selection. We additionally identified up to 269 genetic loci associated with follicle-stimulating hormone (FSH), luteinising hormone, oestradiol, and testosterone through sex-specific GWAS meta-analyses (N=6,095-246,862). While hormone-associated variants near FSHB and ARL14EP colocalised with signals for anovulatory infertility, we found no rg between female infertility and reproductive hormones (P>0.05). Exome sequencing analyses in the UK Biobank (N=197,340) revealed that women carrying testosterone-lowering rare variants in GPC2 were at higher risk of infertility (OR=2.63, P=1.25E-03). Taken together, our results suggest that while individual genes associated with hormone regulation may be relevant for fertility, there is limited genetic evidence for correlation between reproductive hormones and infertility at the population level. We provide the first comprehensive view of the genetic architecture of infertility across multiple diagnostic criteria in men and women, and characterise its relationship to other health conditions.
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Affiliation(s)
- Samvida S Venkatesh
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Nikolas A Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Frederik Heymann Lassen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Melody J Parker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Saskia Reibe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Bitten A Jensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Anders Juul
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Copenhagen, Denmark
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Henriette S Nielsen
- Department of Obstetrics and Gynecology, The Fertility Clinic, Hvidovre University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Kge, Denmark
| | - Palle D Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Erik Sorensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Asgeir Haraldsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Children's Hospital Iceland, Landspitali University Hospital, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Ingileif Jonsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali University Hospital, Reykjavik, Iceland
| | | | | | - Jessica Figueredo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minna K Karjalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Finland
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anu Pasanen
- Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Benjamin M Jacobs
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University London, London, EC1M 6BQ, United Kingdom
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Margaret Lippincott
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mette Nyegaard
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Finland
| | - David A van Heel
- Blizard Institute, Queen Mary University London, London, E1 2AT, United Kingdom
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ravikumar Balasubramanian
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephanie B Seminara
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yee-Ming Chan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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5
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Liu S, Luo H, Zhang P, Li Y, Hao D, Zhang S, Song T, Xu T, He S. Adaptive Selection of Cis-regulatory Elements in the Han Chinese. Mol Biol Evol 2024; 41:msae034. [PMID: 38377343 PMCID: PMC10917166 DOI: 10.1093/molbev/msae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/18/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Cis-regulatory elements have an important role in human adaptation to the living environment. However, the lag in population genomic cohort studies and epigenomic studies, hinders the research in the adaptive analysis of cis-regulatory elements in human populations. In this study, we collected 4,013 unrelated individuals and performed a comprehensive analysis of adaptive selection of genome-wide cis-regulatory elements in the Han Chinese. In total, 12.34% of genomic regions are under the influence of adaptive selection, where 1.00% of enhancers and 2.06% of promoters are under positive selection, and 0.06% of enhancers and 0.02% of promoters are under balancing selection. Gene ontology enrichment analysis of these cis-regulatory elements under adaptive selection reveals that many positive selections in the Han Chinese occur in pathways involved in cell-cell adhesion processes, and many balancing selections are related to immune processes. Two classes of adaptive cis-regulatory elements related to cell adhesion were in-depth analyzed, one is the adaptive enhancers derived from neanderthal introgression, leads to lower hyaluronidase level in skin, and brings better performance on UV-radiation resistance to the Han Chinese. Another one is the cis-regulatory elements regulating wound healing, and the results suggest the positive selection inhibits coagulation and promotes angiogenesis and wound healing in the Han Chinese. Finally, we found that many pathogenic alleles, such as risky alleles of type 2 diabetes or schizophrenia, remain in the population due to the hitchhiking effect of positive selections. Our findings will help deepen our understanding of the adaptive evolution of genome regulation in the Han Chinese.
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Affiliation(s)
- Shuai Liu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sijia Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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6
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Brown EA, Kales S, Boyle MJ, Vitti J, Kotliar D, Schaffner S, Tewhey R, Sabeti PC. Three linked variants have opposing regulatory effects on isovaleryl-CoA dehydrogenase gene expression. Hum Mol Genet 2024; 33:270-283. [PMID: 37930192 PMCID: PMC10800014 DOI: 10.1093/hmg/ddad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
While genome-wide association studies (GWAS) and positive selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected the IVD gene locus-which encodes the isovaleryl-CoA dehydrogenase enzyme-implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and a deletion tiling MPRA strategy across regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects on IVD expression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated with IVD expression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity.
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Affiliation(s)
- Elizabeth A Brown
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Susan Kales
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Michael James Boyle
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
| | - Joseph Vitti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Dylan Kotliar
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Steve Schaffner
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Ryan Tewhey
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Pardis C Sabeti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
- Howard Hughes Medical Institute, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
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7
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Urnikyte A, Masiulyte A, Pranckeniene L, Kučinskas V. Disentangling archaic introgression and genomic signatures of selection at human immunity genes. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2023; 116:105528. [PMID: 37977419 DOI: 10.1016/j.meegid.2023.105528] [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: 05/16/2023] [Revised: 11/04/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023]
Abstract
Pathogens and infectious diseases have imposed exceptionally strong selective pressure on ancient and modern human genomes and contributed to the current variation in many genes. There is evidence that modern humans acquired immune variants through interbreeding with ancient hominins, but the impact of such variants on human traits is not fully understood. The main objectives of this research were to infer the genetic signatures of positive selection that may be involved in adaptation to infectious diseases and to investigate the function of Neanderthal alleles identified within a set of 50 Lithuanian genomes. Introgressed regions were identified using the machine learning tool ArchIE. Recent positive selection signatures were analysed using iHS. We detected high-scoring signals of positive selection at innate immunity genes (EMB, PARP8, HLAC, and CDSN) and evaluated their interactions with the structural proteins of pathogens. Interactions with human immunodeficiency virus (HIV) 1 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified. Overall, genomic regions introgressed from Neanderthals were shown to be enriched in genes related to immunity, keratinocyte differentiation, and sensory perception.
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Affiliation(s)
- Alina Urnikyte
- Faculty of Medicine, Department of Human and Medical Genetics, Institute of Biomedical Sciences, Vilnius University, Santariskiu Street 2, Vilnius LT-08661, Lithuania.
| | - Abigaile Masiulyte
- Faculty of Medicine, Department of Human and Medical Genetics, Institute of Biomedical Sciences, Vilnius University, Santariskiu Street 2, Vilnius LT-08661, Lithuania
| | - Laura Pranckeniene
- Faculty of Medicine, Department of Human and Medical Genetics, Institute of Biomedical Sciences, Vilnius University, Santariskiu Street 2, Vilnius LT-08661, Lithuania.
| | - Vaidutis Kučinskas
- Faculty of Medicine, Department of Human and Medical Genetics, Institute of Biomedical Sciences, Vilnius University, Santariskiu Street 2, Vilnius LT-08661, Lithuania.
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8
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Mikhailova SV. Problems with studying directional natural selection in humans. Vavilovskii Zhurnal Genet Selektsii 2023; 27:684-693. [PMID: 38023807 PMCID: PMC10643113 DOI: 10.18699/vjgb-23-79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 12/01/2023] Open
Abstract
The review describes the main methods for assessing directional selection in human populations. These include bioinformatic analysis of DNA sequences via detection of linkage disequilibrium and of deviations from the random distribution of frequencies of genetic variants, demographic and anthropometric studies based on a search for a correlation between fertility and phenotypic traits, genome-wide association studies on fertility along with genetic loci and polygenic risk scores, and a comparison of allele frequencies between generations (in modern samples and in those obtained from burials). Each approach has its limitations and is applicable to different periods in the evolution of Homo sapiens. The main source of error in such studies is thought to be sample stratification, the small number of studies on nonwhite populations, the impossibility of a complete comparison of the associations found and functionally significant causative variants, and the difficulty with taking into account all nongenetic determinants of fertility in contemporary populations. The results obtained by various methods indicate that the direction of human adaptation to new food products has not changed during evolution since the Neolithic; many variants of immunity genes associated with inflammatory and autoimmune diseases in modern populations have undergone positive selection over the past 2-3 thousand years owing to the spread of bacterial and viral infections. For some genetic variants and polygenic traits, an alteration of the direction of natural selection in Europe has been documented, e. g., for those associated with an immune response and cognitive abilities. Examination of the correlation between fertility and educational attainment yields conflicting results. In modern populations, to a greater extent than previously, there is selection for variants of genes responsible for social adaptation and behavioral phenotypes. In particular, several articles have shown a positive correlation of fertility with polygenic risk scores of attention deficit/hyperactivity disorder.
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Affiliation(s)
- S V Mikhailova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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9
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Tanaka T, Hayakawa T, Teshima KM. Power of neutrality tests for detecting natural selection. G3 (BETHESDA, MD.) 2023; 13:jkad161. [PMID: 37481468 PMCID: PMC10542275 DOI: 10.1093/g3journal/jkad161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/24/2023]
Abstract
Detection of natural selection is one of the main interests in population genetics. Thus, many tests have been developed for detecting natural selection using genomic data. Although it is recognized that the utility of tests depends on several evolutionary factors, such as the timing of selection, strength of selection, frequency of selected alleles, demographic events, and initial frequency of selected allele when selection started acting (softness of selection), the relationships between such evolutionary factors and the power of tests are not yet entirely clear. In this study, we investigated the power of 4 tests: Tajiama's D, Fay and Wu's H, relative extended haplotype homozygosity (rEHH), and integrated haplotype score (iHS), under ranges of evolutionary parameters and demographic models to quantitatively expand the understanding of approaches for detecting selection. The results show that each test detects selection within a limited parameter range, and there are still wide ranges of parameters for which none of these tests work effectively. In addition, the parameter space in which each test shows the highest power overlaps the empirical results of previous research. These results indicate that our present perspective of adaptation is limited to only a part of actual adaptation.
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Affiliation(s)
- Tomotaka Tanaka
- Graduate School of System Life Science, Kyushu University, Fukuoka 819-0395, Japan
| | - Toshiyuki Hayakawa
- Graduate School of System Life Science, Kyushu University, Fukuoka 819-0395, Japan
- Faculty of Arts and Science, Kyushu University, Fukuoka 819-0395, Japan
| | - Kosuke M Teshima
- Department of Biology, Faculty of Science, Kyushu University, Fukuoka 819-0395, Japan
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10
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Roca-Umbert A, Garcia-Calleja J, Vogel-González M, Fierro-Villegas A, Ill-Raga G, Herrera-Fernández V, Bosnjak A, Muntané G, Gutiérrez E, Campelo F, Vicente R, Bosch E. Human genetic adaptation related to cellular zinc homeostasis. PLoS Genet 2023; 19:e1010950. [PMID: 37747921 PMCID: PMC10553801 DOI: 10.1371/journal.pgen.1010950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/05/2023] [Accepted: 08/31/2023] [Indexed: 09/27/2023] Open
Abstract
SLC30A9 encodes a ubiquitously zinc transporter (ZnT9) and has been consistently suggested as a candidate for positive selection in humans. However, no direct adaptive molecular phenotype has been demonstrated. Our results provide evidence for directional selection operating in two major complementary haplotypes in Africa and East Asia. These haplotypes are associated with differential gene expression but also differ in the Met50Val substitution (rs1047626) in ZnT9, which we show is found in homozygosis in the Denisovan genome and displays accompanying signatures suggestive of archaic introgression. Although we found no significant differences in systemic zinc content between individuals with different rs1047626 genotypes, we demonstrate that the expression of the derived isoform (ZnT9 50Val) in HEK293 cells shows a gain of function when compared with the ancestral (ZnT9 50Met) variant. Notably, the ZnT9 50Val variant was found associated with differences in zinc handling by the mitochondria and endoplasmic reticulum, with an impact on mitochondrial metabolism. Given the essential role of the mitochondria in skeletal muscle and since the derived allele at rs1047626 is known to be associated with greater susceptibility to several neuropsychiatric traits, we propose that adaptation to cold may have driven this selection event, while also impacting predisposition to neuropsychiatric disorders in modern humans.
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Affiliation(s)
- Ana Roca-Umbert
- Institut de Biologia Evolutiva (UPF-CSIC), Departament de Medicina i Ciències de la Vida, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Jorge Garcia-Calleja
- Institut de Biologia Evolutiva (UPF-CSIC), Departament de Medicina i Ciències de la Vida, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Marina Vogel-González
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Alejandro Fierro-Villegas
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Gerard Ill-Raga
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Víctor Herrera-Fernández
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Anja Bosnjak
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Gerard Muntané
- Institut de Biologia Evolutiva (UPF-CSIC), Departament de Medicina i Ciències de la Vida, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
| | - Esteban Gutiérrez
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Felix Campelo
- ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Rubén Vicente
- Laboratory of Molecular Physiology, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Elena Bosch
- Institut de Biologia Evolutiva (UPF-CSIC), Departament de Medicina i Ciències de la Vida, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
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11
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Riley R, Mathieson I, Mathieson S. INTERPRETING GENERATIVE ADVERSARIAL NETWORKS TO INFER NATURAL SELECTION FROM GENETIC DATA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531546. [PMID: 36945387 PMCID: PMC10028936 DOI: 10.1101/2023.03.07.531546] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Understanding natural selection in humans and other species is a major focus for the use of machine learning in population genetics. Existing methods rely on computationally intensive simulated training data. Unlike efficient neutral coalescent simulations for demographic inference, realistic simulations of selection typically requires slow forward simulations. Because there are many possible modes of selection, a high dimensional parameter space must be explored, with no guarantee that the simulated models are close to the real processes. Mismatches between simulated training data and real test data can lead to incorrect inference. Finally, it is difficult to interpret trained neural networks, leading to a lack of understanding about what features contribute to classification. Here we develop a new approach to detect selection that requires relatively few selection simulations during training. We use a Generative Adversarial Network (GAN) trained to simulate realistic neutral data. The resulting GAN consists of a generator (fitted demographic model) and a discriminator (convolutional neural network). For a genomic region, the discriminator predicts whether it is "real" or "fake" in the sense that it could have been simulated by the generator. As the "real" training data includes regions that experienced selection and the generator cannot produce such regions, regions with a high probability of being real are likely to have experienced selection. To further incentivize this behavior, we "fine-tune" the discriminator with a small number of selection simulations. We show that this approach has high power to detect selection in simulations, and that it finds regions under selection identified by state-of-the art population genetic methods in three human populations. Finally, we show how to interpret the trained networks by clustering hidden units of the discriminator based on their correlation patterns with known summary statistics. In summary, our approach is a novel, efficient, and powerful way to use machine learning to detect natural selection.
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Affiliation(s)
- Rebecca Riley
- Department of Computer Science, Haverford College, Haverford PA, 19041 USA
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, 19104 USA
| | - Sara Mathieson
- Department of Computer Science, Haverford College, Haverford PA, 19041 USA
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12
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Tang J, Zhang H, Zhang H, Zhu H. PopTradeOff: A database for exploring population-specificity of adaptive evolution, disease susceptibility, and drug responsiveness. Comput Struct Biotechnol J 2023; 21:3443-3451. [PMID: 37448726 PMCID: PMC10338148 DOI: 10.1016/j.csbj.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
The influence of adaptive evolution on disease susceptibility has drawn attention; however, the extent of the influence, whether favored mutations also influence drug responses, and whether the associations between the three are population-specific remain unknown. Using a reported deep learning network to integrate seven statistical tests for detecting selection signals, we predicted favored mutations in the genomes of 17 human populations and integrated these favored mutations with reported GWAS sites and drug response-related variants into the database PopTradeOff (http://www.gaemons.net/PopFMIntro). The database also contains genome annotation information on the SNP, sequence, gene, and pathway levels. The preliminary data analyses suggest that substantial associations exist between adaptive evolution, disease susceptibility, and drug responses and that the associations are highly population-specific. The database may be valuable for disease studies, drug development, and personalized medicine.
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Affiliation(s)
- Ji Tang
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Huanlin Zhang
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Hai Zhang
- Network Center, Southern Medical University, Guangzhou 510515, China
| | - Hao Zhu
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
- Guangdong Provincial Key Lab of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, China
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13
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Mathieson I, Day FR, Barban N, Tropf FC, Brazel DM, Vaez A, van Zuydam N, Bitarello BD, Gardner EJ, Akimova ET, Azad A, Bergmann S, Bielak LF, Boomsma DI, Bosak K, Brumat M, Buring JE, Cesarini D, Chasman DI, Chavarro JE, Cocca M, Concas MP, Davey Smith G, Davies G, Deary IJ, Esko T, Faul JD, Franco O, Ganna A, Gaskins AJ, Gelemanovic A, de Geus EJC, Gieger C, Girotto G, Gopinath B, Grabe HJ, Gunderson EP, Hayward C, He C, van Heemst D, Hill WD, Hoffmann ER, Homuth G, Hottenga JJ, Huang H, Hyppӧnen E, Ikram MA, Jansen R, Johannesson M, Kamali Z, Kardia SLR, Kavousi M, Kifley A, Kiiskinen T, Kraft P, Kühnel B, Langenberg C, Liew G, Lind PA, Luan J, Mägi R, Magnusson PKE, Mahajan A, Martin NG, Mbarek H, McCarthy MI, McMahon G, Medland SE, Meitinger T, Metspalu A, Mihailov E, Milani L, Missmer SA, Mitchell P, Møllegaard S, Mook-Kanamori DO, Morgan A, van der Most PJ, de Mutsert R, Nauck M, Nolte IM, Noordam R, Penninx BWJH, Peters A, Peyser PA, Polašek O, Power C, Pribisalic A, Redmond P, Rich-Edwards JW, Ridker PM, Rietveld CA, Ring SM, Rose LM, Rueedi R, Shukla V, Smith JA, Stankovic S, Stefánsson K, Stöckl D, Strauch K, Swertz MA, Teumer A, Thorleifsson G, Thorsteinsdottir U, Thurik AR, Timpson NJ, Turman C, Uitterlinden AG, Waldenberger M, Wareham NJ, Weir DR, Willemsen G, Zhao JH, Zhao W, Zhao Y, Snieder H, den Hoed M, Ong KK, Mills MC, Perry JRB. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus. Nat Hum Behav 2023; 7:790-801. [PMID: 36864135 DOI: 10.1038/s41562-023-01528-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/12/2023] [Indexed: 03/04/2023]
Abstract
Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.
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Affiliation(s)
- Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicola Barban
- Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Felix C Tropf
- Nuffield College, University of Oxford, Oxford, UK
- École Nationale de la Statistique et de L'administration Économique (ENSAE), Paris, France
- Center for Research in Economics and Statistics (CREST), Paris, France
| | - David M Brazel
- Nuffield College, University of Oxford, Oxford, UK
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Ahmad Vaez
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Natalie van Zuydam
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Bárbara D Bitarello
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Evelina T Akimova
- Nuffield College, University of Oxford, Oxford, UK
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Ajuna Azad
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, the Netherlands
| | | | - Marco Brumat
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Julie E Buring
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - David Cesarini
- Department of Economics, New York University, New York, NY, USA
- Research Institute for Industrial Economics, Stockholm, Sweden
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Daniel I Chasman
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jorge E Chavarro
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | | | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Oscar Franco
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Audrey J Gaskins
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Giorgia Girotto
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Bamini Gopinath
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Hans Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Erica P Gunderson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Chunyan He
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA
- Department of Internal Medicine, Division of Medical Oncology, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Eva R Hoffmann
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hongyang Huang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Elina Hyppӧnen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Annette Kifley
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Gerald Liew
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Hamdi Mbarek
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Qatar Genome Programme, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - George McMahon
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | | | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Stacey A Missmer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Adolescent and Young Adult Medicine, Department of Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Obstetrics, Gynecology, and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA
| | - Paul Mitchell
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Stine Møllegaard
- Department of Sociology, University of Copenhagen, Copenhagen, Denmark
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Anna Morgan
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam, the Netherlands
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Ozren Polašek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Chris Power
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | | | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Janet W Rich-Edwards
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paul M Ridker
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Cornelius A Rietveld
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vallari Shukla
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Stasa Stankovic
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | | | - Doris Stöckl
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Morris A Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | | | | | - A Roy Thurik
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
- Montpellier Business School, Montpellier, France
| | | | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - André G Uitterlinden
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jing Hau Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marcel den Hoed
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melinda C Mills
- Nuffield College, University of Oxford, Oxford, UK.
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK.
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Department of Economics, Econometrics and Finance, University of Groningen, Groningen, the Netherlands.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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14
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Pandey D, Harris M, Garud NR, Narasimhan VM. Understanding natural selection in Holocene Europe using multi-locus genotype identity scans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538113. [PMID: 37163039 PMCID: PMC10168228 DOI: 10.1101/2023.04.24.538113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Ancient DNA (aDNA) has been a revolutionary technology in understanding human history but has not been used extensively to study natural selection as large sample sizes to study allele frequency changes over time have thus far not been available. Here, we examined a time transect of 708 published samples over the past 7,000 years of European history using multi-locus genotype-based selection scans. As aDNA data is affected by high missingness, ascertainment bias, DNA damage, random allele calling, and is unphased, we first validated our selection scan, G 12 a n c i e n t , on simulated data resembling aDNA under a demographic model that captures broad features of the allele frequency spectrum of European genomes as well as positive controls that have been previously identified and functionally validated in modern European datasets on data from ancient individuals from time periods very close to the present time. We then applied our statistic to the aDNA time transect to detect and resolve the timing of natural selection occurring genome wide and found several candidates of selection across the different time periods that had not been picked up by selection scans using single SNP allele frequency approaches. In addition, enrichment analysis discovered multiple categories of complex traits that might be under adaptation across these periods. Our results demonstrate the utility of applying different types of selection scans to aDNA to uncover putative selection signals at loci in the ancient past that might have been masked in modern samples.
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Affiliation(s)
- Devansh Pandey
- Department of Integrative Biology, The University of Texas at Austin
| | - Mariana Harris
- Department of Computational Medicine, University of California, Los Angeles
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles
- Department of Human Genetics, University of California, Los Angeles
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin
- Department of Statistics and Data Science, The University of Texas at Austin
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15
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Garcia OA, Arslanian K, Whorf D, Thariath S, Shriver M, Li JZ, Bigham AW. The Legacy of Infectious Disease Exposure on the Genomic Diversity of Indigenous Southern Mexicans. Genome Biol Evol 2023; 15:7023365. [PMID: 36726304 PMCID: PMC10016042 DOI: 10.1093/gbe/evad015] [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: 04/15/2022] [Revised: 12/19/2022] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
To characterize host risk factors for infectious disease in Mesoamerican populations, we interrogated 857,481 SNPs assayed using the Affymetrix 6.0 genotyping array for signatures of natural selection in immune response genes. We applied three statistical tests to identify signatures of natural selection: locus-specific branch length (LSBL), the cross-population extended haplotype homozygosity (XP-EHH), and the integrated haplotype score (iHS). Each of the haplotype tests (XP-EHH and iHS) were paired with LSBL and significance was determined at the 1% level. For the paired analyses, we identified 95 statistically significant windows for XP-EHH/LSBL and 63 statistically significant windows for iHS/LSBL. Among our top immune response loci, we found evidence of recent directional selection associated with the major histocompatibility complex (MHC) and the peroxisome proliferator-activated receptor gamma (PPAR-γ) signaling pathway. These findings illustrate that Mesoamerican populations' immunity has been shaped by exposure to infectious disease. As targets of selection, these variants are likely to encode phenotypes that manifest themselves physiologically and therefore may contribute to population-level variation in immune response. Our results shed light on past selective events influencing the host response to modern diseases, both pathogenic infection as well as autoimmune disorders.
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Affiliation(s)
- Obed A Garcia
- Department of Anthropology, University of Michigan, Ann Arbor, Michigan.,Department of Biomedical Data Science, Stanford University, Stanford, California
| | | | - Daniel Whorf
- College of Medicine, University of Illinois, Peoria, Illinois
| | - Serena Thariath
- Department of Anthropology, University of Tennessee, Knoxville, Tennessee
| | - Mark Shriver
- Department of Anthropology, Penn State University, State College, Pennsylvania
| | - Jun Z Li
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan
| | - Abigail W Bigham
- Department of Anthropology, University of California, Los Angeles, California
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16
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Ancestry, diversity, and genetics of health-related traits in African-derived communities (quilombos) from Brazil. Funct Integr Genomics 2023; 23:74. [PMID: 36867305 PMCID: PMC9982798 DOI: 10.1007/s10142-023-00999-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
Brazilian quilombos are communities formed by enslaved Africans and their descendants all over the country during slavery and shortly after its abolition. Quilombos harbor a great fraction of the largely unknown genetic diversity of the African diaspora in Brazil. Thus, genetic studies in quilombos have the potential to provide important insights not only into the African roots of the Brazilian population but also into the genetic bases of complex traits and human adaptation to diverse environments. This review summarizes the main results of genetic studies performed on quilombos so far. Here, we analyzed the patterns of African, Amerindian, European, and subcontinental ancestry (within Africa) of quilombos from the five different geographic regions of Brazil. In addition, uniparental markers (from the mtDNA and the Y chromosome) studies are analyzed together to reveal demographic processes and sex-biased admixture that occurred during the formation of these unique populations. Lastly, the prevalence of known malaria-adaptive African mutations and other African-specific variants discovered in quilombos, as well as the genetic bases of health-related traits, are discussed here, together with their implication for the health of populations of African descent.
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17
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Boitard S, Liaubet L, Paris C, Fève K, Dehais P, Bouquet A, Riquet J, Mercat MJ. Whole-genome sequencing of cryopreserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines. Genet Sel Evol 2023; 55:13. [PMID: 36864379 PMCID: PMC9979506 DOI: 10.1186/s12711-023-00789-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 02/15/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Numerous genomic scans for positive selection have been performed in livestock species within the last decade, but often a detailed characterization of the detected regions (gene or trait under selection, timing of selection events) is lacking. Cryopreserved resources stored in reproductive or DNA gene banks offer a great opportunity to improve this characterization by providing direct access to recent allele frequency dynamics, thereby differentiating between signatures from recent breeding objectives and those related to more ancient selection constraints. Improved characterization can also be achieved by using next-generation sequencing data, which helps narrowing the size of the detected regions while reducing the number of associated candidate genes. METHODS We estimated genetic diversity and detected signatures of recent selection in French Large White pigs by sequencing the genomes of 36 animals from three distinct cryopreserved samples: two recent samples from dam (LWD) and sire (LWS) lines, which had diverged from 1995 and were selected under partly different objectives, and an older sample from 1977 prior to the divergence. RESULTS French LWD and LWS lines have lost approximately 5% of the SNPs that segregated in the 1977 ancestral population. Thirty-eight genomic regions under recent selection were detected in these lines and the corresponding selection events were further classified as convergent between lines (18 regions), divergent between lines (10 regions), specific to the dam line (6 regions) or specific to the sire line (4 regions). Several biological functions were found to be significantly enriched among the genes included in these regions: body size, body weight and growth regardless of the category, early life survival and calcium metabolism more specifically in the signatures in the dam line and lipid and glycogen metabolism more specifically in the signatures in the sire line. Recent selection on IGF2 was confirmed and several other regions were linked to a single candidate gene (ARHGAP10, BMPR1B, GNA14, KATNA1, LPIN1, PKP1, PTH, SEMA3E or ZC3HAV1, among others). CONCLUSIONS These results illustrate that sequencing the genome of animals at several recent time points generates considerable insight into the traits, genes and variants under recent selection in a population. This approach could be applied to other livestock populations, e.g. by exploiting the rich biological resources stored in cryobanks.
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Affiliation(s)
- Simon Boitard
- CBGP, CIRAD, INRAE, Institut Agro, IRD, Université de Montpellier, Montferrier-sur-Lez, France. .,GenPhySE, INRAE, INP, Université de Toulouse, Castanet-Tolosan, France.
| | - Laurence Liaubet
- grid.507621.7GenPhySE, INRAE, INP, Université de Toulouse, Castanet-Tolosan, France
| | - Cyriel Paris
- grid.507621.7GenPhySE, INRAE, INP, Université de Toulouse, Castanet-Tolosan, France
| | - Katia Fève
- grid.507621.7GenPhySE, INRAE, INP, Université de Toulouse, Castanet-Tolosan, France
| | - Patrice Dehais
- grid.507621.7GenPhySE, INRAE, INP, Université de Toulouse, Castanet-Tolosan, France
| | - Alban Bouquet
- IFIP Institut du porc/Alliance R & D, Le Rheu, France
| | - Juliette Riquet
- grid.507621.7GenPhySE, INRAE, INP, Université de Toulouse, Castanet-Tolosan, France
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18
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Pappas F, Kurta K, Vanhala T, Jeuthe H, Hagen Ø, Beirão J, Palaiokostas C. Whole‐genome re‐sequencing provides key genomic insights in farmed Arctic charr (
Salvelinus alpinus
) populations of anadromous and landlocked origin from Scandinavia. Evol Appl 2023; 16:797-813. [PMID: 37124091 PMCID: PMC10130564 DOI: 10.1111/eva.13537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/05/2022] [Accepted: 02/12/2023] [Indexed: 03/03/2023] Open
Abstract
Arctic charr (Salvelinus alpinus) is a niche-market high-value species for Nordic aquaculture. Similar to other salmonids, both anadromous and landlocked populations are encountered. Whole-genome re-sequencing (22X coverage) was performed on two farmed populations of anadromous (Sigerfjord; n = 24) and landlocked (Arctic Superior; n = 24) origin from Norway and Sweden respectively. More than 5 million SNPs were used to study their genetic diversity and to scan for selection signatures. The two populations were clearly distinguished through principal component analysis, with the mean fixation index being ~0.12. Furthermore, the levels of genomic inbreeding estimated from runs of homozygosity were 6.23% and 8.66% for the Norwegian and the Swedish population respectively. Biological processes that could be linked to selection pressure associated primarily with the anadromous background and/or secondarily with domestication were suggested. Overall, our study provided insights regarding the genetic composition of two main strains of farmed Arctic charr from Scandinavia. At the same time, ample genomic resources were produced in the magnitude of millions of SNPs that could assist the transition of Nordic Arctic charr farming in the genomics era.
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Affiliation(s)
- Fotis Pappas
- Department of Animal Breeding and Genetics Swedish University of Agricultural Sciences Uppsala Sweden
| | - Khrystyna Kurta
- Department of Animal Breeding and Genetics Swedish University of Agricultural Sciences Uppsala Sweden
| | - Tytti Vanhala
- Department of Animal Breeding and Genetics Swedish University of Agricultural Sciences Uppsala Sweden
| | - Henrik Jeuthe
- Department of Animal Breeding and Genetics Swedish University of Agricultural Sciences Uppsala Sweden
- Aquaculture Center North Kälarne Sweden
| | - Ørjan Hagen
- Faculty of Bioscience and Aquaculture Nord University Bodø Norway
| | - José Beirão
- Faculty of Bioscience and Aquaculture Nord University Bodø Norway
| | - Christos Palaiokostas
- Department of Animal Breeding and Genetics Swedish University of Agricultural Sciences Uppsala Sweden
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19
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Non-synonymous variation and protein structure of candidate genes associated with selection in farm and wild populations of turbot (Scophthalmus maximus). Sci Rep 2023; 13:3019. [PMID: 36810752 PMCID: PMC9944912 DOI: 10.1038/s41598-023-29826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Abstract
Non-synonymous variation (NSV) of protein coding genes represents raw material for selection to improve adaptation to the diverse environmental scenarios in wild and livestock populations. Many aquatic species face variations in temperature, salinity and biological factors throughout their distribution range that is reflected by the presence of allelic clines or local adaptation. The turbot (Scophthalmus maximus) is a flatfish of great commercial value with a flourishing aquaculture which has promoted the development of genomic resources. In this study, we developed the first atlas of NSVs in the turbot genome by resequencing 10 individuals from Northeast Atlantic Ocean. More than 50,000 NSVs where detected in the ~ 21,500 coding genes of the turbot genome, and we selected 18 NSVs to be genotyped using a single Mass ARRAY multiplex on 13 wild populations and three turbot farms. We detected signals of divergent selection on several genes related to growth, circadian rhythms, osmoregulation and oxygen binding in the different scenarios evaluated. Furthermore, we explored the impact of NSVs identified on the 3D structure and functional relationship of the correspondent proteins. In summary, our study provides a strategy to identify NSVs in species with consistently annotated and assembled genomes to ascertain their role in adaptation.
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20
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Prioritizing autoimmunity risk variants for functional analyses by fine-mapping mutations under natural selection. Nat Commun 2022; 13:7069. [PMID: 36400766 PMCID: PMC9674589 DOI: 10.1038/s41467-022-34461-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 10/25/2022] [Indexed: 11/19/2022] Open
Abstract
Pathogen-driven selection shaped adaptive mutations in immunity genes, including those contributing to inflammatory disorders. Functional characterization of such adaptive variants can shed light on disease biology and past adaptations. This popular idea, however, was difficult to test due to challenges in pinpointing adaptive mutations in selection footprints. In this study, using a local-tree-based approach, we show that 28% of risk loci (153/535) in 21 inflammatory disorders bear footprints of moderate and weak selection, and part of them are population specific. Weak selection footprints allow partial fine-mapping, and we show that in 19% (29/153) of the risk loci under selection, candidate disease variants are hitchhikers, and only in 39% of cases they are likely selection targets. We predict function for a subset of these selected SNPs and highlight examples of antagonistic pleiotropy. We conclude by offering disease variants under selection that can be tested functionally using infectious agents and other stressors to decipher the poorly understood link between environmental stressors and genetic risk in inflammatory conditions.
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21
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Fiteni E, Durand K, Gimenez S, Meagher RL, Legeai F, Kergoat GJ, Nègre N, d’Alençon E, Nam K. Host-plant adaptation as a driver of incipient speciation in the fall armyworm (Spodoptera frugiperda). BMC Ecol Evol 2022; 22:133. [DOI: 10.1186/s12862-022-02090-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/02/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Divergent selection on host-plants is one of the main evolutionary forces driving ecological speciation in phytophagous insects. The ecological speciation might be challenging in the presence of gene flow and assortative mating because the direction of divergence is not necessarily the same between ecological selection (through host-plant adaptation) and assortative mating. The fall armyworm (FAW), a major lepidopteran pest species, is composed of two sympatric strains, corn and rice strains, named after two of their preferred host-plants. These two strains have been hypothesized to undergo incipient speciation, based on (i) several lines of evidence encompassing both pre- and post-zygotic reproductive isolation, and (ii) the presence of a substantial level of genetic differentiation. Even though the status of these two strains has been established a long time ago, it is still yet to be found whether these two strains indeed exhibit a marked level of genetic differentiation from a large number of genomic loci. Here, we analyzed whole genome sequences from 56 FAW individuals either collected from pasture grasses (a part of the favored host range of the rice strain) or corn to assess the role of host-plant adaptation in incipient speciation.
Results
Principal component analysis of whole genome data shows that the pattern of divergence in the fall armyworm is predominantly explained by the genetic differentiation associated with host-plants. The level of genetic differentiation between corn and rice strains is particularly marked in the Z chromosome. We identified one autosomal locus and two Z chromosome loci targeted by selective sweeps specific to rice strain and corn strain, respectively. The autosomal locus has both increased DXY and FST while the Z chromosome loci had decreased DXY and increased FST.
Conclusion
These results show that the FAW population structure is dominated by the genetic differentiation between corn and rice strains. This differentiation involves divergent selection targeting at least three loci, which include a locus potentially causing reproductive isolation. Taken together, these results suggest the evolutionary scenario that host-plant speciation is a driver of incipient speciation in the fall armyworm.
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22
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Koller D, Wendt FR, Pathak GA, De Lillo A, De Angelis F, Cabrera-Mendoza B, Tucci S, Polimanti R. Denisovan and Neanderthal archaic introgression differentially impacted the genetics of complex traits in modern populations. BMC Biol 2022; 20:249. [PMID: 36344982 PMCID: PMC9641937 DOI: 10.1186/s12915-022-01449-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Introgression from extinct Neanderthal and Denisovan human species has been shown to contribute to the genetic pool of modern human populations and their phenotypic spectrum. Evidence of how Neanderthal introgression shaped the genetics of human traits and diseases has been extensively studied in populations of European descent, with signatures of admixture reported for instance in genes associated with pigmentation, immunity, and metabolic traits. However, limited information is currently available about the impact of archaic introgression on other ancestry groups. Additionally, to date, no study has been conducted with respect to the impact of Denisovan introgression on the health and disease of modern populations. Here, we compare the way evolutionary pressures shaped the genetics of complex traits in East Asian and European populations, and provide evidence of the impact of Denisovan introgression on the health of East Asian and Central/South Asian populations. RESULTS Leveraging genome-wide association statistics from the Biobank Japan and UK Biobank, we assessed whether Denisovan and Neanderthal introgression together with other evolutionary genomic signatures were enriched for the heritability of physiological and pathological conditions in populations of East Asian and European descent. In EAS, Denisovan-introgressed loci were enriched for coronary artery disease heritability (1.69-fold enrichment, p=0.003). No enrichment for archaic introgression was observed in EUR. We also performed a phenome-wide association study of Denisovan and Neanderthal alleles in six ancestry groups available in the UK Biobank. In EAS, the Denisovan-introgressed SNP rs62391664 in the major histocompatibility complex region was associated with albumin/globulin ratio (beta=-0.17, p=3.57×10-7). Neanderthal-introgressed alleles were associated with psychiatric and cognitive traits in EAS (e.g., "No Bipolar or Depression"-rs79043717 beta=-1.5, p=1.1×10-7), and with blood biomarkers (e.g., alkaline phosphatase-rs11244089 beta=0.1, p=3.69×10-116) and red hair color (rs60733936 beta=-0.86, p=4.49×10-165) in EUR. In the other ancestry groups, Neanderthal alleles were associated with several traits, also including the use of certain medications (e.g., Central/South East Asia: indapamide - rs732632 beta=-2.38, p=5.22×10-7). CONCLUSIONS Our study provides novel evidence regarding the impact of archaic introgression on the genetics of complex traits in worldwide populations, highlighting the specific contribution of Denisovan introgression in EAS populations.
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Affiliation(s)
- Dora Koller
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Catalonia, 08028, Spain
| | - Frank R Wendt
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Antonella De Lillo
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA
- Department of Biology, University of Rome "Tor Vergata", Rome, 00133, Italy
| | - Flavio De Angelis
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
- Department of Biology, University of Rome "Tor Vergata", Rome, 00133, Italy
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Serena Tucci
- Department of Anthropology, Yale University, New Haven, CT, 06511, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, 06516, USA.
- VA CT Healthcare Center, West Haven, CT, 06516, USA.
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23
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Li J, Abedi V, Zand R. Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine. J Clin Med 2022; 11:jcm11205980. [PMID: 36294301 PMCID: PMC9604604 DOI: 10.3390/jcm11205980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 12/03/2022] Open
Abstract
Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.
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Affiliation(s)
- Jiang Li
- Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA 17822, USA
| | - Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
- Correspondence: (V.A.); (R.Z.)
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
- Neuroscience Institute, Geisinger Health System, 100 North Academy Avenue, Danville, PA 17822, USA
- Correspondence: (V.A.); (R.Z.)
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24
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Fedorova L, Khrunin A, Khvorykh G, Lim J, Thornton N, Mulyar OA, Limborska S, Fedorov A. Analysis of Common SNPs across Continents Reveals Major Genomic Differences between Human Populations. Genes (Basel) 2022; 13:genes13081472. [PMID: 36011383 PMCID: PMC9408407 DOI: 10.3390/genes13081472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 12/03/2022] Open
Abstract
Common alleles tend to be more ancient than rare alleles. These common SNPs appeared thousands of years ago and reflect intricate human evolution including various adaptations, admixtures, and migration events. Eighty-four thousand abundant region-specific alleles (ARSAs) that are common in one continent but absent in the rest of the world have been characterized by processing 3100 genomes from 230 populations. Also computed were 17,446 polymorphic sites with regional absence of common alleles (RACAs), which are widespread globally but absent in one region. A majority of these region-specific SNPs were found in Africa. America has the second greatest number of ARSAs (3348) and is even ahead of Europe (1911). Surprisingly, East Asia has the highest number of RACAs (10,524) and the lowest number of ARSAs (362). ARSAs and RACAs have distinct compositions of ancestral versus derived alleles in different geographical regions, reflecting their unique evolution. Genes associated with ARSA and RACA SNPs were identified and their functions were analyzed. The core 100 genes shared by multiple populations and associated with region-specific natural selection were examined. The largest part of them (42%) are related to the nervous system. ARSA and RACA SNPs are important for both association and human evolution studies.
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Affiliation(s)
| | - Andrey Khrunin
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Gennady Khvorykh
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Jan Lim
- CRI Genetics LLC, Santa Monica, CA 90404, USA
| | | | | | - Svetlana Limborska
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Alexei Fedorov
- CRI Genetics LLC, Santa Monica, CA 90404, USA
- Department of Medicine, University of Toledo, Toledo, OH 43606, USA
- Correspondence: ; Tel.: +1-419-383-5270
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25
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Quiver MH, Lachance J. Adaptive eQTLs reveal the evolutionary impacts of pleiotropy and tissue-specificity while contributing to health and disease. HGG ADVANCES 2022; 3:100083. [PMID: 35047867 PMCID: PMC8756519 DOI: 10.1016/j.xhgg.2021.100083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/21/2021] [Indexed: 11/24/2022] Open
Abstract
Large numbers of expression quantitative trait loci (eQTLs) have recently been identified in humans, and many of these regulatory variants have large allele frequency differences between populations. Here, we conducted genome-wide scans of selection to identify adaptive eQTLs (i.e., eQTLs with large population branch statistics). We then tested if tissue pleiotropy affects whether eQTLs are more or less likely to be adaptive and identified tissues that have been key targets of positive selection during the last 100,000 years. Top adaptive eQTL outliers include rs1043809, rs66899053, and rs2814778 (a SNP that is associated with malaria resistance). We found that effect sizes of eQTLs were negatively correlated with population branch statistics and that adaptive eQTLs affect two-thirds as many tissues as do non-adaptive eQTLs. Because the tissue breadth of an eQTL can be viewed as a measure of pleiotropy, these results imply that pleiotropy inhibits adaptation. The proportion of eQTLs that are adaptive varies by tissue, and we found that eQTLs that regulate expression in testis, thyroid, blood, or sun-exposed skin are enriched for signatures of positive selection. By contrast, eQTLs that regulate expression in the cerebrum or female-specific tissues have a relative lack of adaptive outliers. Scans of selections also reveal that many adaptive eQTLs are closely linked to disease-associated loci. Taken together, our results indicate that eQTLs have played an important role in recent human evolution.
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Affiliation(s)
- Melanie H Quiver
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Joseph Lachance
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
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26
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Laval G, Patin E, Boutillier P, Quintana-Murci L. Sporadic occurrence of recent selective sweeps from standing variation in humans as revealed by an approximate Bayesian computation approach. Genetics 2021; 219:6377789. [PMID: 34849862 DOI: 10.1093/genetics/iyab161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 09/01/2021] [Indexed: 12/14/2022] Open
Abstract
During their dispersals over the last 100,000 years, modern humans have been exposed to a large variety of environments, resulting in genetic adaptation. While genome-wide scans for the footprints of positive Darwinian selection have increased knowledge of genes and functions potentially involved in human local adaptation, they have globally produced evidence of a limited contribution of selective sweeps in humans. Conversely, studies based on machine learning algorithms suggest that recent sweeps from standing variation are widespread in humans, an observation that has been recently questioned. Here, we sought to formally quantify the number of recent selective sweeps in humans, by leveraging approximate Bayesian computation and whole-genome sequence data. Our computer simulations revealed suitable ABC estimations, regardless of the frequency of the selected alleles at the onset of selection and the completion of sweeps. Under a model of recent selection from standing variation, we inferred that an average of 68 (from 56 to 79) and 140 (from 94 to 198) sweeps occurred over the last 100,000 years of human history, in African and Eurasian populations, respectively. The former estimation is compatible with human adaptation rates estimated since divergence with chimps, and reveals numbers of sweeps per generation per site in the range of values estimated in Drosophila. Our results confirm the rarity of selective sweeps in humans and show a low contribution of sweeps from standing variation to recent human adaptation.
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Affiliation(s)
- Guillaume Laval
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, CNRS, Paris 75015, France
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, CNRS, Paris 75015, France
| | - Pierre Boutillier
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR 2000, CNRS, Paris 75015, France.,Human Genomics and Evolution, Collège de France, 75005 Paris, France
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27
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Colbran LL, Johnson MR, Mathieson I, Capra JA. Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment. Genome Biol Evol 2021; 13:evab237. [PMID: 34718543 PMCID: PMC8576593 DOI: 10.1093/gbe/evab237] [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] [Accepted: 10/16/2021] [Indexed: 12/16/2022] Open
Abstract
As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting PrediXcan to increase robustness to incomplete data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes proposed to be involved in diet-related local adaptation, and the predicted effects on regulation often suggest explanations for known signals of selection, for example, at FADS1, GPX1, and LEPR. In contrast, skin pigmentation genes show little regulatory change over a 38,000-year time series of 2,999 ancient Europeans, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates that combining aDNA with present-day genomes is informative about the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between genetic diversity and complex traits.
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Affiliation(s)
- Laura L Colbran
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Maya R Johnson
- School for Science and Math at Vanderbilt, Vanderbilt University, USA
- Department of Computer Science, Bryn Mawr College, Pennsylvania, USA
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, USA
| | - John A Capra
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, USA
- Department of Biological Sciences, Vanderbilt University, USA
- Department of Biomedical Informatics, Vanderbilt University, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
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28
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Villegas-Mirón P, Acosta S, Nye J, Bertranpetit J, Laayouni H. Chromosome X-wide Analysis of Positive Selection in Human Populations: Common and Private Signals of Selection and its Impact on Inactivated Genes and Enhancers. Front Genet 2021; 12:714491. [PMID: 34646300 PMCID: PMC8502928 DOI: 10.3389/fgene.2021.714491] [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: 05/25/2021] [Accepted: 09/08/2021] [Indexed: 01/22/2023] Open
Abstract
The ability of detecting adaptive (positive) selection in the genome has opened the possibility of understanding the genetic basis of population-specific adaptations genome-wide. Here, we present the analysis of recent selective sweeps, specifically in the X chromosome, in human populations from the third phase of the 1,000 Genomes Project using three different haplotype-based statistics. We describe instances of recent positive selection that fit the criteria of hard or soft sweeps, and detect a higher number of events among sub-Saharan Africans than non-Africans (Europe and East Asia). A global enrichment of neural-related processes is observed and numerous genes related to fertility appear among the top candidates, reflecting the importance of reproduction in human evolution. Commonalities with previously reported genes under positive selection are found, while particularly strong new signals are reported in specific populations or shared across different continental groups. We report an enrichment of signals in genes that escape X chromosome inactivation, which may contribute to the differentiation between sexes. We also provide evidence of a widespread presence of soft-sweep-like signatures across the chromosome and a global enrichment of highly scoring regions that overlap potential regulatory elements. Among these, enhancers-like signatures seem to present putative signals of positive selection which might be in concordance with selection in their target genes. Also, particularly strong signals appear in regulatory regions that show differential activities, which might point to population-specific regulatory adaptations.
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Affiliation(s)
- Pablo Villegas-Mirón
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Sandra Acosta
- Department Pathology and Experimental Therapeutics, Medical School, University of Barcelona, Barcelona, Spain
| | - Jessica Nye
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Jaume Bertranpetit
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Hafid Laayouni
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain.,Bioinformatics Studies, ESCI-UPF, Barcelona, Spain
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29
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Griesemer D, Xue JR, Reilly SK, Ulirsch JC, Kukreja K, Davis JR, Kanai M, Yang DK, Butts JC, Guney MH, Luban J, Montgomery SB, Finucane HK, Novina CD, Tewhey R, Sabeti PC. Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution. Cell 2021; 184:5247-5260.e19. [PMID: 34534445 PMCID: PMC8487971 DOI: 10.1016/j.cell.2021.08.025] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 05/25/2021] [Accepted: 08/19/2021] [Indexed: 12/11/2022]
Abstract
3' untranslated region (3'UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3'UTRs (MPRAu) to sensitively assay 12,173 3'UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3'UTR function, suggesting that simple sequences predominately explain 3'UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3'UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.
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Affiliation(s)
- Dustin Griesemer
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - James R Xue
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02143, USA.
| | - Steven K Reilly
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02143, USA
| | - Jacob C Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kalki Kukreja
- Department of Molecular and Cell Biology, Harvard University, Cambridge, MA 02138, USA
| | - Joe R Davis
- BigHat Biosciences, San Carlos, CA 94070, USA
| | - Masahiro Kanai
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - David K Yang
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA
| | - John C Butts
- The Jackson Laboratory, Bar Harbor, ME 04609, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA
| | - Mehmet H Guney
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Jeremy Luban
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA; Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Carl D Novina
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME 04609, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA; Tufts University School of Medicine, Boston, MA 02111, USA
| | - Pardis C Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02143, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
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30
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Shauli T, Brandes N, Linial M. Evolutionary and functional lessons from human-specific amino acid substitution matrices. NAR Genom Bioinform 2021; 3:lqab079. [PMID: 34541526 PMCID: PMC8445205 DOI: 10.1093/nargab/lqab079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/02/2021] [Accepted: 09/14/2021] [Indexed: 12/26/2022] Open
Abstract
Human genetic variation in coding regions is fundamental to the study of protein structure and function. Most methods for interpreting missense variants consider substitution measures derived from homologous proteins across different species. In this study, we introduce human-specific amino acid (AA) substitution matrices that are based on genetic variations in the modern human population. We analyzed the frequencies of >4.8M single nucleotide variants (SNVs) at codon and AA resolution and compiled human-centric substitution matrices that are fundamentally different from classic cross-species matrices (e.g. BLOSUM, PAM). Our matrices are asymmetric, with some AA replacements showing significant directional preference. Moreover, these AA matrices are only partly predicted by nucleotide substitution rates. We further test the utility of our matrices in exposing functional signals of experimentally-validated protein annotations. A significant reduction in AA transition frequencies was observed across nine post-translational modification (PTM) types and four ion-binding sites. Our results propose a purifying selection signal in the human proteome across a diverse set of functional protein annotations and provide an empirical baseline for interpreting human genetic variation in coding regions.
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Affiliation(s)
- Tair Shauli
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Nadav Brandes
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
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31
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Nischalke HD, Fischer J, Klüners A, Matz-Soja M, Krämer B, Langhans B, Goeser F, Soyka M, Stickel F, Spengler U, Nattermann J, Strassburg CP, Berg T, Lutz P. A genetic variant in toll-like receptor 5 is linked to chemokine levels and hepatocellular carcinoma in steatohepatitis. Liver Int 2021; 41:2139-2148. [PMID: 34051061 DOI: 10.1111/liv.14980] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 04/26/2021] [Accepted: 05/19/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND & AIMS Bacterial translocation drives liver disease progression. We investigated whether functional genetic variants in toll-like receptor 5 (TLR5), the receptor for bacterial flagellin, affect the risk for hepatocellular carcinoma (HCC). METHODS Healthy controls (n = 212), patients with alcohol abuse without liver disease (n = 382), and patients from a discovery cohort of alcohol-associated cirrhosis (n = 372 including 79 HCC cases), a validation cohort of alcohol-associated cirrhosis (n = 355 including 132 HCC cases), and a cohort of cirrhosis due to nonalcoholic steatohepatitis (NASH) (n = 145 including 62 HCC cases) were genotyped for the TLR5 rs5744174 and rs5744168 polymorphisms. Chemokine levels were measured by ELISA in patients' sera and supernatants of flagellin-stimulated healthy monocytes. RESULTS Frequency of the TLR5 rs5744174 TT genotype was similar in healthy controls (33%), controls with alcohol abuse (34%), and patients with alcohol-associated cirrhosis in the discovery (28%), validation (33%), and NASH cohort (31%). The TT genotype was enriched in patients with versus without HCC in the discovery, validation, and NASH cohort (41% vs 25%; 39% vs 29%; 40% vs 24%; p < .05 each). This genotype remained a risk factor for HCC (OR = 1.9; p = .01) after multivariate correction for age, gender, diabetes, and carriage of the PNPLA3 148M variant. Interleukin-8 induction in monocytes from healthy controls and serum levels of interleukin-8 and CXCL1 from cirrhotic patients with the TT genotype were significantly increased versus C allele carriers. CONCLUSION The TLR5 rs5744174 polymorphism, affecting immune response to flagellin, is linked to occurrence of HCC in cirrhosis caused by steatohepatitis.
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Affiliation(s)
- Hans Dieter Nischalke
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Janett Fischer
- Division of Hepatology, Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, Germany
| | - Alexandra Klüners
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Madlen Matz-Soja
- Division of Hepatology, Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, Germany
| | - Benjamin Krämer
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Bettina Langhans
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Felix Goeser
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Michael Soyka
- Psychiatric Hospital, Ludwig Maximilians University, Munich, Germany
| | - Felix Stickel
- Department of Gastroenterology and Hepatology, University Hospital of Zürich, Zürich, Switzerland
| | - Ulrich Spengler
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Jacob Nattermann
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Christian P Strassburg
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
| | - Thomas Berg
- Division of Hepatology, Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, Germany
| | - Philipp Lutz
- Department of Internal Medicine I, University Hospital, University of Bonn, Bonn, Germany
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32
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Canny SP, Jackson SW. B Cells in Systemic Lupus Erythematosus: From Disease Mechanisms to Targeted Therapies. Rheum Dis Clin North Am 2021; 47:395-413. [PMID: 34215370 PMCID: PMC8357318 DOI: 10.1016/j.rdc.2021.04.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
B cells exert a prominent contribution to the pathogenesis of systemic lupus erythematosus (SLE). Here, we review the immune mechanisms underlying autoreactive B cell activation in SLE, focusing on how B cell receptor and Toll-like receptor signals integrate to drive breaks in tolerance to nuclear antigens. In addition, we discuss autoantibody-dependent and autoantibody-independent B cell effector functions during lupus pathogenesis. Finally, we address efforts to target B cells therapeutically in human SLE. Despite initial disappointing clinical trials testing B cell depletion in lupus, more recent studies show promise, emphasizing how greater understanding of underlying immune mechanisms can yield clinical benefits.
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Affiliation(s)
- Susan P Canny
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA; Benaroya Research Institute, 1201 Ninth Avenue, Seattle, WA 98101, USA
| | - Shaun W Jackson
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA; Seattle Children's Research Institute, Seattle, WA, USA.
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33
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Policarpo R, Sierksma A, De Strooper B, d'Ydewalle C. From Junk to Function: LncRNAs in CNS Health and Disease. Front Mol Neurosci 2021; 14:714768. [PMID: 34349622 PMCID: PMC8327212 DOI: 10.3389/fnmol.2021.714768] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 06/25/2021] [Indexed: 12/26/2022] Open
Abstract
Recent advances in RNA sequencing technologies helped to uncover the existence of tens of thousands of long non-coding RNAs (lncRNAs) that arise from the dark matter of the genome. These lncRNAs were originally thought to be transcriptional noise but an increasing number of studies demonstrate that these transcripts can modulate protein-coding gene expression by a wide variety of transcriptional and post-transcriptional mechanisms. The spatiotemporal regulation of lncRNA expression is particularly evident in the central nervous system, suggesting that they may directly contribute to specific brain processes, including neurogenesis and cellular homeostasis. Not surprisingly, lncRNAs are therefore gaining attention as putative novel therapeutic targets for disorders of the brain. In this review, we summarize the recent insights into the functions of lncRNAs in the brain, their role in neuronal maintenance, and their potential contribution to disease. We conclude this review by postulating how these RNA molecules can be targeted for the treatment of yet incurable neurological disorders.
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Affiliation(s)
- Rafaela Policarpo
- VIB-KU Leuven Center For Brain & Disease Research, Leuven, Belgium.,Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium.,Neuroscience Discovery, Janssen Research & Development, Janssen Pharmaceutica N.V., Beerse, Belgium
| | - Annerieke Sierksma
- VIB-KU Leuven Center For Brain & Disease Research, Leuven, Belgium.,Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Bart De Strooper
- VIB-KU Leuven Center For Brain & Disease Research, Leuven, Belgium.,Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium.,UK Dementia Research Institute, University College London, London, United Kingdom
| | - Constantin d'Ydewalle
- Neuroscience Discovery, Janssen Research & Development, Janssen Pharmaceutica N.V., Beerse, Belgium
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34
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Benítez-Burraco A, Chekalin E, Bruskin S, Tatarinova T, Morozova I. Recent selection of candidate genes for mammal domestication in Europeans and language change in Europe: a hypothesis. Ann Hum Biol 2021; 48:313-320. [PMID: 34241552 DOI: 10.1080/03014460.2021.1936634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND AND AIM Human evolution resulted from changes in our biology, behaviour, and culture. One source of these changes has been hypothesised to be our self-domestication (that is, the development in humans of features commonly found in domesticated strains of mammals, seemingly as a result of selection for reduced aggression). Signals of domestication, notably brain size reduction, have increased in recent times. METHODS In this paper, we compare whole-genome data between the Late Neolithic/Bronze Age individuals and modern Europeans. RESULTS We show that genes associated with mammal domestication and with neural crest development and function are significantly differently enriched in nonsynonymous single nucleotide polymorphisms between these two groups. CONCLUSION We hypothesise that these changes might account for the increased features of self-domestication in modern humans and, ultimately, for subtle recent changes in human cognition and behaviour, including language.
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Affiliation(s)
- Antonio Benítez-Burraco
- Department of Spanish, Linguistics, and Theory of Literature, Faculty of Philology, University of Seville, Seville, Spain
| | - Evgeny Chekalin
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Sergey Bruskin
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Tatiana Tatarinova
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.,Department of Biology, University of La Verne, La Verne, CA, USA.,A. A. Kharkevich Institute for Information Transmission Problems, Moscow, Russia.,Department of Fundamental Biology and Biotechnology, Siberian Federal University, Krasnoyarsk, Russia
| | - Irina Morozova
- Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
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35
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Nunes K, Maia MHT, Dos Santos EJM, Dos Santos SEB, Guerreiro JF, Petzl-Erler ML, Bedoya G, Gallo C, Poletti G, Llop E, Tsuneto L, Bortolini MC, Rothhammer F, Single R, Ruiz-Linares A, Rocha J, Meyer D. How natural selection shapes genetic differentiation in the MHC region: A case study with Native Americans. Hum Immunol 2021; 82:523-531. [PMID: 33812704 PMCID: PMC8217218 DOI: 10.1016/j.humimm.2021.03.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 02/15/2021] [Accepted: 03/09/2021] [Indexed: 12/19/2022]
Abstract
The Human Leukocyte Antigen (HLA) loci are extremely well documented targets of balancing selection, yet few studies have explored how selection affects population differentiation at these loci. In the present study we investigate genetic differentiation at HLA genes by comparing differentiation at microsatellites distributed genomewide to those in the MHC region. Our study uses a sample of 494 individuals from 30 human populations, 28 of which are Native Americans, all of whom were typed for genomewide and MHC region microsatellites. We find greater differentiation in the MHC than in the remainder of the genome (FST-MHC = 0.130 and FST-Genomic = 0.087), and use a permutation approach to show that this difference is statistically significant, and not accounted for by confounding factors. This finding lies in the opposite direction to the expectation that balancing selection reduces population differentiation. We interpret our findings as evidence that selection favors different sets of alleles in distinct localities, leading to increased differentiation. Thus, balancing selection at HLA genes simultaneously increases intra-population polymorphism and inter-population differentiation in Native Americans.
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Affiliation(s)
- Kelly Nunes
- Departamento de Genética e Biologia Evolutiva, Universidade de São Paulo, São Paulo, Brazil.
| | | | | | | | | | | | - Gabriel Bedoya
- Instituto de Biología, Universidad de Antioquia, Medellín, Colombia
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Giovanni Poletti
- Facultad de Medicina, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Elena Llop
- Instituto de Ciencias Biomédicas, Faculdad de Medicina, Universidade de Chile, Santiago, Chile
| | - Luiza Tsuneto
- Departamento de Ciências Básicas da Saúde, Universidade Estadual de Maringá, Maringá, Brazil
| | - Maria Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Richard Single
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200433, China; D Aix-Marseille University, CNRS, EFS, ADES, Marseille 13007, France
| | - Jorge Rocha
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal; CIBIO - Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Porto, Portugal.
| | - Diogo Meyer
- Departamento de Genética e Biologia Evolutiva, Universidade de São Paulo, São Paulo, Brazil.
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Mathyer ME, Brettmann EA, Schmidt AD, Goodwin ZA, Oh IY, Quiggle AM, Tycksen E, Ramakrishnan N, Matkovich SJ, Guttman-Yassky E, Edwards JR, de Guzman Strong C. Selective sweep for an enhancer involucrin allele identifies skin barrier adaptation out of Africa. Nat Commun 2021; 12:2557. [PMID: 33963188 PMCID: PMC8105351 DOI: 10.1038/s41467-021-22821-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 03/30/2021] [Indexed: 02/03/2023] Open
Abstract
The genetic modules that contribute to human evolution are poorly understood. Here we investigate positive selection in the Epidermal Differentiation Complex locus for skin barrier adaptation in diverse HapMap human populations (CEU, JPT/CHB, and YRI). Using Composite of Multiple Signals and iSAFE, we identify selective sweeps for LCE1A-SMCP and involucrin (IVL) haplotypes associated with human migration out-of-Africa, reaching near fixation in European populations. CEU-IVL is associated with increased IVL expression and a known epidermis-specific enhancer. CRISPR/Cas9 deletion of the orthologous mouse enhancer in vivo reveals a functional requirement for the enhancer to regulate Ivl expression in cis. Reporter assays confirm increased regulatory and additive enhancer effects of CEU-specific polymorphisms identified at predicted IRF1 and NFIC binding sites in the IVL enhancer (rs4845327) and its promoter (rs1854779). Together, our results identify a selective sweep for a cis regulatory module for CEU-IVL, highlighting human skin barrier evolution for increased IVL expression out-of-Africa.
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Affiliation(s)
- Mary Elizabeth Mathyer
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Erin A. Brettmann
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Alina D. Schmidt
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Zane A. Goodwin
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Inez Y. Oh
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Ashley M. Quiggle
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Eric Tycksen
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Natasha Ramakrishnan
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Scot J. Matkovich
- grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Emma Guttman-Yassky
- grid.59734.3c0000 0001 0670 2351Department of Dermatology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029 USA
| | - John R. Edwards
- grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
| | - Cristina de Guzman Strong
- grid.4367.60000 0001 2355 7002Division of Dermatology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for Pharmacogenomics, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Center for the Study of Itch & Sensory Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO 63110 USA
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African genetic diversity and adaptation inform a precision medicine agenda. Nat Rev Genet 2021; 22:284-306. [PMID: 33432191 DOI: 10.1038/s41576-020-00306-8] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 01/29/2023]
Abstract
The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.
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Feng Y, McQuillan MA, Tishkoff SA. Evolutionary genetics of skin pigmentation in African populations. Hum Mol Genet 2021; 30:R88-R97. [PMID: 33438000 PMCID: PMC8117430 DOI: 10.1093/hmg/ddab007] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 12/14/2022] Open
Abstract
Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.
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Affiliation(s)
- Yuanqing Feng
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A McQuillan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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Harris AM, DeGiorgio M. A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data. Mol Biol Evol 2021; 37:3023-3046. [PMID: 32392293 PMCID: PMC7530616 DOI: 10.1093/molbev/msaa115] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverages the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole-genome polymorphism data sets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.
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Affiliation(s)
- Alexandre M Harris
- Department of Biology, Pennsylvania State University, University Park, PA.,Molecular, Cellular, and Integrative Biosciences, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA
| | - Michael DeGiorgio
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL
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Scheinfeldt LB, Brangan A, Kusic DM, Kumar S, Gharani N. Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants. J Pers Med 2021; 11:jpm11020131. [PMID: 33669176 PMCID: PMC7919641 DOI: 10.3390/jpm11020131] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 12/15/2022] Open
Abstract
Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.
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Affiliation(s)
- Laura B. Scheinfeldt
- Coriell Institute for Medical Research, Camden, NJ 08003, USA; (A.B.); (D.M.K.); (N.G.)
- Correspondence:
| | - Andrew Brangan
- Coriell Institute for Medical Research, Camden, NJ 08003, USA; (A.B.); (D.M.K.); (N.G.)
| | - Dara M. Kusic
- Coriell Institute for Medical Research, Camden, NJ 08003, USA; (A.B.); (D.M.K.); (N.G.)
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA;
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
- Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah 21577, Saudi Arabia
| | - Neda Gharani
- Coriell Institute for Medical Research, Camden, NJ 08003, USA; (A.B.); (D.M.K.); (N.G.)
- Gharani Consulting, Surrey KT139PA, UK
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Umans BD, Battle A, Gilad Y. Where Are the Disease-Associated eQTLs? Trends Genet 2021; 37:109-124. [PMID: 32912663 PMCID: PMC8162831 DOI: 10.1016/j.tig.2020.08.009] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/07/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023]
Abstract
Most disease-associated variants, although located in putatively regulatory regions, do not have detectable effects on gene expression. One explanation could be that we have not examined gene expression in the cell types or conditions that are most relevant for disease. Even large-scale efforts to study gene expression across tissues are limited to human samples obtained opportunistically or postmortem, mostly from adults. In this review we evaluate recent findings and suggest an alternative strategy, drawing on the dynamic and highly context-specific nature of gene regulation. We discuss new technologies that can extend the standard regulatory mapping framework to more diverse, disease-relevant cell types and states.
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Affiliation(s)
- Benjamin D Umans
- Department of Medicine, University of Chicago, Chicago, IL, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Yoav Gilad
- Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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42
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Profile of David M. Kingsley. Proc Natl Acad Sci U S A 2021; 118:2025633118. [PMID: 33431699 DOI: 10.1073/pnas.2025633118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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43
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Hamid I, Korunes KL, Beleza S, Goldberg A. Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde. eLife 2021; 10:e63177. [PMID: 33393457 PMCID: PMC7815310 DOI: 10.7554/elife.63177] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/04/2021] [Indexed: 12/20/2022] Open
Abstract
Humans have undergone large migrations over the past hundreds to thousands of years, exposing ourselves to new environments and selective pressures. Yet, evidence of ongoing or recent selection in humans is difficult to detect. Many of these migrations also resulted in gene flow between previously separated populations. These recently admixed populations provide unique opportunities to study rapid evolution in humans. Developing methods based on distributions of local ancestry, we demonstrate that this sort of genetic exchange has facilitated detectable adaptation to a malaria parasite in the admixed population of Cabo Verde within the last ~20 generations. We estimate that the selection coefficient is approximately 0.08, one of the highest inferred in humans. Notably, we show that this strong selection at a single locus has likely affected patterns of ancestry genome-wide, potentially biasing demographic inference. Our study provides evidence of adaptation in a human population on historical timescales.
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Affiliation(s)
- Iman Hamid
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
| | | | - Sandra Beleza
- Department of Genetics and Genome Biology, University of LeicesterLeicesterUnited Kingdom
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
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Wendt FR, Pathak GA, Overstreet C, Tylee DS, Gelernter J, Atkinson EG, Polimanti R. Characterizing the effect of background selection on the polygenicity of brain-related traits. Genomics 2021; 113:111-119. [PMID: 33278486 PMCID: PMC7855394 DOI: 10.1016/j.ygeno.2020.11.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/20/2020] [Accepted: 11/30/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have demonstrated that psychopathology phenotypes are affected by many risk alleles with small effect (polygenicity). It is unclear how ubiquitously evolutionary pressures influence the genetic architecture of these traits. METHODS We partitioned SNP heritability to assess the contribution of background (BGS) and positive selection, Neanderthal local ancestry, functional significance, and genotype networks in 75 brain-related traits (8411 ≤ N ≤ 1,131,181, mean N = 205,289). We applied binary annotations by dichotomizing each measure based on top 2%, 1%, and 0.5% of all scores genome-wide. Effect size distribution features were calculated using GENESIS. We tested the relationship between effect size distribution descriptive statistics and natural selection. In a subset of traits, we explore the inclusion of diagnostic heterogeneity (e.g., number of diagnostic combinations and total symptoms) in the tested relationship. RESULTS SNP-heritability was enriched (false discovery rate q < 0.05) for loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and loss-of-function (LoF)-intolerant regions (67 phenotypes). These effects were strongest in GWAS of schizophrenia (1.90-fold BGS, 1.16-fold genic, and 1.92-fold LoF), educational attainment (1.86-fold BGS, 1.12-fold genic, and 1.79-fold LoF), and cognitive performance (2.29-fold BGS, 1.12-fold genic, and 1.79-fold LoF). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ2 parameter) in 75 brain-related traits (β = 4.39 × 10-5, p = 1.43 × 10-5, model r2 = 0.548). Considering the number of DSM-5 diagnostic combinations per psychiatric disorder improved model fit (σ2 ~ BTop2% × Genic × diagnostic combinations; model r2 = 0.661). CONCLUSIONS Brain-related phenotypes with larger variance in risk locus effect sizes are associated with loci under BGS. We show exploratory results suggesting that diagnostic complexity may also contribute to the increased polygenicity of psychiatric disorders.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA
| | - Cassie Overstreet
- National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA CT Healthcare System and Department of Psychiatry, Yale University School of Medicine, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA; Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Elizabeth G Atkinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA.
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Galimberti M, Leuenberger C, Wolf B, Szilágyi SM, Foll M, Wegmann D. Detecting Selection from Linked Sites Using an F-Model. Genetics 2020; 216:1205-1215. [PMID: 33067324 PMCID: PMC7768260 DOI: 10.1534/genetics.120.303780] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 10/03/2020] [Indexed: 11/18/2022] Open
Abstract
Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus- and population-specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations, we show that the statistical power of our method is up to twofold higher than that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).
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Affiliation(s)
- Marco Galimberti
- Department of Biology, University of Fribourg, 1700, Switzerland
- Swiss Institute of Bioinformatics, Fribourg, 1700, Switzerland
| | | | - Beat Wolf
- iCoSys, University of Applied Sciences Western Switzerland, Fribourg, 1700 Switzerland
| | - Sándor Miklós Szilágyi
- Department of Informatics, University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, 540139, Romania
| | - Matthieu Foll
- International Agency for Research on Cancer (IARC/WHO), Section of Genetics, 69372 Lyon, France
| | - Daniel Wegmann
- Department of Biology, University of Fribourg, 1700, Switzerland
- Swiss Institute of Bioinformatics, Fribourg, 1700, Switzerland
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Lee YG, Lee JY, Kim J, Kim YJ. Insertion variants missing in the human reference genome are widespread among human populations. BMC Biol 2020; 18:167. [PMID: 33187521 PMCID: PMC7666470 DOI: 10.1186/s12915-020-00894-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/09/2020] [Indexed: 01/07/2023] Open
Abstract
Background Structural variants comprise diverse genomic arrangements including deletions, insertions, inversions, and translocations, which can generally be detected in humans through sequence comparison to the reference genome. Among structural variants, insertions are the least frequently identified variants, mainly due to ascertainment bias in the reference genome, lack of previous sequence knowledge, and low complexity of typical insertion sequences. Though recent developments in long-read sequencing deliver promise in annotating individual non-reference insertions, population-level catalogues on non-reference insertion variants have not been identified and the possible functional roles of these hidden variants remain elusive. Results To detect non-reference insertion variants, we developed a pipeline, InserTag, which generates non-reference contigs by local de novo assembly and then infers the full-sequence of insertion variants by tracing contigs from non-human primates and other human genome assemblies. Application of the pipeline to data from 2535 individuals of the 1000 Genomes Project helped identify 1696 non-reference insertion variants and re-classify the variants as retention of ancestral sequences or novel sequence insertions based on the ancestral state. Genotyping of the variants showed that individuals had, on average, 0.92-Mbp sequences missing from the reference genome, 92% of the variants were common (allele frequency > 5%) among human populations, and more than half of the variants were major alleles. Among human populations, African populations were the most divergent and had the most non-reference sequences, which was attributed to the greater prevalence of high-frequency insertion variants. The subsets of insertion variants were in high linkage disequilibrium with phenotype-associated SNPs and showed signals of recent continent-specific selection. Conclusions Non-reference insertion variants represent an important type of genetic variation in the human population, and our developed pipeline, InserTag, provides the frameworks for the detection and genotyping of non-reference sequences missing from human populations. Supplementary information Supplementary information accompanies this paper at 10.1186/s12915-020-00894-1.
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Affiliation(s)
- Young-Gun Lee
- Department of Integrated Omics for Biomedical Science, WCU Graduate School, Yonsei University, Seoul, Republic of Korea
| | - Jin-Young Lee
- Department of Biochemistry, College of Life Science and Technology, Yonsei University, Seoul, Republic of Korea
| | - Junhyong Kim
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Young-Joon Kim
- Department of Integrated Omics for Biomedical Science, WCU Graduate School, Yonsei University, Seoul, Republic of Korea. .,Department of Biochemistry, College of Life Science and Technology, Yonsei University, Seoul, Republic of Korea.
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A Selective Sweep Conceals a MicroRNA with Broad Metabolic Effects. Cell Metab 2020; 32:697-698. [PMID: 33147481 DOI: 10.1016/j.cmet.2020.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The need for discovering new genes driving metabolic disease susceptibility is clear; even clearer is the need for their subsequent functional characterization. A new paper reports a role for miR-128-1 in metabolic control through a series of elegant mouse studies, and an intriguing hypothesis about its "thrifty" role in metabolism.
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Wang L, Sinnott-Armstrong N, Wagschal A, Wark AR, Camporez JP, Perry RJ, Ji F, Sohn Y, Oh J, Wu S, Chery J, Moud BN, Saadat A, Dankel SN, Mellgren G, Tallapragada DSP, Strobel SM, Lee MJ, Tewhey R, Sabeti PC, Schaefer A, Petri A, Kauppinen S, Chung RT, Soukas A, Avruch J, Fried SK, Hauner H, Sadreyev RI, Shulman GI, Claussnitzer M, Näär AM. A MicroRNA Linking Human Positive Selection and Metabolic Disorders. Cell 2020; 183:684-701.e14. [PMID: 33058756 PMCID: PMC8092355 DOI: 10.1016/j.cell.2020.09.017] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 05/08/2020] [Accepted: 09/03/2020] [Indexed: 01/09/2023]
Abstract
Positive selection in Europeans at the 2q21.3 locus harboring the lactase gene has been attributed to selection for the ability of adults to digest milk to survive famine in ancient times. However, the 2q21.3 locus is also associated with obesity and type 2 diabetes in humans, raising the possibility that additional genetic elements in the locus may have contributed to evolutionary adaptation to famine by promoting energy storage, but which now confer susceptibility to metabolic diseases. We show here that the miR-128-1 microRNA, located at the center of the positively selected locus, represents a crucial metabolic regulator in mammals. Antisense targeting and genetic ablation of miR-128-1 in mouse metabolic disease models result in increased energy expenditure and amelioration of high-fat-diet-induced obesity and markedly improved glucose tolerance. A thrifty phenotype connected to miR-128-1-dependent energy storage may link ancient adaptation to famine and modern metabolic maladaptation associated with nutritional overabundance.
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Affiliation(s)
- Lifeng Wang
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,These authors contributed equally,Present address: Cardiovascular & Metabolism, Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, PA 19477, USA
| | - Nasa Sinnott-Armstrong
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA,These authors contributed equally
| | - Alexandre Wagschal
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,Present address: Vertex Pharmaceuticals, Watertown, MA 02472, USA
| | - Abigail R. Wark
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Joao-Paulo Camporez
- Departments of Internal Medicine and Cellular & Molecular Physiology, Yale School of Medicine, New Haven, CT 06510, USA,Present address: Ribeirao Preto School of Medicine, University of Sao Paulo, Sao Paulo 14049-90, Brazil
| | - Rachel J. Perry
- Departments of Internal Medicine and Cellular & Molecular Physiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Fei Ji
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Yoojin Sohn
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,Present address: Vanderbilt University, Nashville, TN 37235, USA
| | - Justin Oh
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,Present address: Vertex Pharmaceuticals, Watertown, MA 02472, USA
| | - Su Wu
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,Present address: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Jessica Chery
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,Present address: Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Bahareh Nemati Moud
- Else Kroener-Fresenius-Center of Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Alham Saadat
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Simon N. Dankel
- Mohn Nutrition Research Laboratory, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway,Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, 5020 Bergen, Norway
| | - Gunnar Mellgren
- Mohn Nutrition Research Laboratory, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway,Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, 5020 Bergen, Norway
| | - Divya Sri Priyanka Tallapragada
- Mohn Nutrition Research Laboratory, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway,Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, 5020 Bergen, Norway
| | - Sophie Madlen Strobel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, 80992 Munich, Germany
| | - Mi-Jeong Lee
- Obesity Center, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA,Present address: Department of Human Nutrition, Food and Animal Sciences, University of Hawaii, Honolulu, HI 96822, USA
| | - Ryan Tewhey
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA,Present address: The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Pardis C. Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Anne Schaefer
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School ofMedicine atMount Sinai, New York, New York 10029, USA
| | - Andreas Petri
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, 2450 Copenhagen, Denmark
| | - Sakari Kauppinen
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, 2450 Copenhagen, Denmark
| | - Raymond T. Chung
- Liver Center, Gastrointestinal Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Alexander Soukas
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Department of Medicine, Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA,Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Joseph Avruch
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA,Department of Medicine, Harvard Medical School, Boston, MA 02114, USA,Diabetes unit, Medical Services, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Susan K. Fried
- Obesity Center, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA,Present address: Diabetes, Obesity and Metabolism Institute, Mt. Sinai School of Medicine, New York, NY 10029, USA
| | - Hans Hauner
- Else Kroener-Fresenius-Center of Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany,Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, 80992 Munich, Germany
| | - Ruslan I. Sadreyev
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gerald I. Shulman
- Departments of Internal Medicine and Cellular & Molecular Physiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Anders M. Näär
- Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,Present address: Department of Nutritional Sciences & Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA,Lead Contact,Correspondence: https://doi.org/10.1016/j.cell.2020.09.017
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49
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Horscroft C, Ennis S, Pengelly RJ, Sluckin TJ, Collins A. Sequencing era methods for identifying signatures of selection in the genome. Brief Bioinform 2020; 20:1997-2008. [PMID: 30053138 DOI: 10.1093/bib/bby064] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 05/16/2018] [Indexed: 12/12/2022] Open
Abstract
Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole-genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.
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Affiliation(s)
- Clare Horscroft
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
| | - Sarah Ennis
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
| | - Reuben J Pengelly
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
| | - Timothy J Sluckin
- Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK.,Mathematical Sciences, University of Southampton, Highfield, Southampton, UK
| | - Andrew Collins
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
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50
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Werren EA, Garcia O, Bigham AW. Identifying adaptive alleles in the human genome: from selection mapping to functional validation. Hum Genet 2020; 140:241-276. [PMID: 32728809 DOI: 10.1007/s00439-020-02206-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/07/2020] [Indexed: 12/19/2022]
Abstract
The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles also may be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.
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Affiliation(s)
- Elizabeth A Werren
- Department of Human Genetics, The University of Michigan, Ann Arbor, MI, USA
- Department of Anthropology, The University of Michigan, Ann Arbor, MI, USA
| | - Obed Garcia
- Department of Anthropology, The University of Michigan, Ann Arbor, MI, USA
| | - Abigail W Bigham
- Department of Anthropology, University of California Los Angeles, 341 Haines Hall, Los Angeles, CA, 90095, USA.
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