151
|
Webb A, Knoblauch J, Sabankar N, Kallur AS, Hey J, Sethuraman A. The Pop-Gen Pipeline Platform: A Software Platform for Population Genomic Analyses. Mol Biol Evol 2021; 38:3478-3485. [PMID: 33950197 PMCID: PMC8321520 DOI: 10.1093/molbev/msab113] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
The Pop-Gen Pipeline Platform (PPP) is a software platform for population genomic analyses. The PPP was designed as a collection of scripts that facilitate common population genomic workflows in a consistent and standardized Python environment. Functions were developed to encompass entire workflows, including input preparation, file format conversion, various population genomic analyses, and output generation. The platform has also been developed with reproducibility and extensibility of analyses in mind. The PPP is an open-source package that is available for download and use at https://ppp.readthedocs.io/en/latest/PPP_pages/install.html.
Collapse
Affiliation(s)
- Andrew Webb
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, USA
| | - Jared Knoblauch
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, USA
| | - Nitesh Sabankar
- Department of Biological Sciences, California State University San Marcos, San Marcos, CA, USA
| | - Apeksha Sukesh Kallur
- Department of Biological Sciences, California State University San Marcos, San Marcos, CA, USA
| | - Jody Hey
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, USA
| | - Arun Sethuraman
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, USA.,Department of Biological Sciences, California State University San Marcos, San Marcos, CA, USA
| |
Collapse
|
152
|
Flores-Bello A, Bauduer F, Salaberria J, Oyharçabal B, Calafell F, Bertranpetit J, Quintana-Murci L, Comas D. Genetic origins, singularity, and heterogeneity of Basques. Curr Biol 2021; 31:2167-2177.e4. [DOI: 10.1016/j.cub.2021.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/22/2021] [Accepted: 03/02/2021] [Indexed: 02/09/2023]
|
153
|
Xie HB, Wang LG, Fan CY, Zhang LC, Adeniyi CA, Yin X, Zeng ZB, Wang LX, Zhang YP. Genetic architecture underlying nascent speciation - The evolution of Eurasian pigs under domestication. Mol Biol Evol 2021; 38:3556-3566. [PMID: 33892509 PMCID: PMC8382894 DOI: 10.1093/molbev/msab117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Speciation is a process whereby the evolution of reproductive barriers leads to isolated species. Although many studies have addressed large-effect genetic footprints in the advanced stages of speciation, the genetics of reproductive isolation in nascent stage of speciation remains unclear. Here, we show that pig domestication offers an interesting model for studying the early stages of speciation in great details. Pig breeds have not evolved the large X-effect of hybrid incompatibility commonly observed between “good species.” Instead, deleterious epistatic interactions among multiple autosomal loci are common. These weak Dobzhansky–Muller incompatibilities confer partial hybrid inviability with sex biases in crosses between European and East Asian domestic pigs. The genomic incompatibility is enriched in pathways for angiogenesis, androgen receptor signaling and immunity, with an observation of many highly differentiated cis-regulatory variants. Our study suggests that partial hybrid inviability caused by pervasive but weak interactions among autosomal loci may be a hallmark of nascent speciation in mammals.
Collapse
Affiliation(s)
- Hai-Bing Xie
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Li-Gang Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Chen-Yu Fan
- State Key Laboratory for Conservation and Utilization of Bio-resource, School of Life Science, Yunnan University, Kunming, China
| | - Long-Chao Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - C Adeola Adeniyi
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Xue Yin
- State Key Laboratory for Conservation and Utilization of Bio-resource, School of Life Science, Yunnan University, Kunming, China
| | - Zhao-Bang Zeng
- Bioinformatics Research Center, Department of Horticultural Science, North Carolina State University, Raleigh, NC, USA
| | - Li-Xian Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,State Key Laboratory for Conservation and Utilization of Bio-resource, School of Life Science, Yunnan University, Kunming, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
| |
Collapse
|
154
|
van Keulen D, van Koeverden ID, Boltjes A, Princen HMG, van Gool AJ, de Borst GJ, Asselbergs FW, Tempel D, Pasterkamp G, van der Laan SW. Common Variants Associated With OSMR Expression Contribute to Carotid Plaque Vulnerability, but Not to Cardiovascular Disease in Humans. Front Cardiovasc Med 2021; 8:658915. [PMID: 33959646 PMCID: PMC8093786 DOI: 10.3389/fcvm.2021.658915] [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: 01/26/2021] [Accepted: 03/09/2021] [Indexed: 01/15/2023] Open
Abstract
Background and Aims: Oncostatin M (OSM) signaling is implicated in atherosclerosis, however the mechanism remains unclear. We investigated the impact of common genetic variants in OSM and its receptors, OSMR and LIFR, on overall plaque vulnerability, plaque phenotype, intraplaque OSMR and LIFR expression, coronary artery calcification burden and cardiovascular disease susceptibility. Methods and Results: We queried Genotype-Tissue Expression data and found that rs13168867 (C allele) was associated with decreased OSMR expression and that rs10491509 (A allele) was associated with increased LIFR expression in arterial tissues. No variant was significantly associated with OSM expression. We associated these two variants with plaque characteristics from 1,443 genotyped carotid endarterectomy patients in the Athero-Express Biobank Study. After correction for multiple testing, rs13168867 was significantly associated with an increased overall plaque vulnerability (β = 0.118 ± s.e. = 0.040, p = 3.00 × 10-3, C allele). Looking at individual plaque characteristics, rs13168867 showed strongest associations with intraplaque fat (β = 0.248 ± s.e. = 0.088, p = 4.66 × 10-3, C allele) and collagen content (β = -0.259 ± s.e. = 0.095, p = 6.22 × 10-3, C allele), but these associations were not significant after correction for multiple testing. rs13168867 was not associated with intraplaque OSMR expression. Neither was intraplaque OSMR expression associated with plaque vulnerability and no known OSMR eQTLs were associated with coronary artery calcification burden, or cardiovascular disease susceptibility. No associations were found for rs10491509 in the LIFR locus. Conclusions: Our study suggests that rs1316887 in the OSMR locus is associated with increased plaque vulnerability, but not with coronary calcification or cardiovascular disease risk. It remains unclear through which precise biological mechanisms OSM signaling exerts its effects on plaque morphology. However, the OSM-OSMR/LIFR pathway is unlikely to be causally involved in lifetime cardiovascular disease susceptibility.
Collapse
Affiliation(s)
- Danielle van Keulen
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
- Central Diagnostics Laboratory, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
- Quorics B.V., Rotterdam, Netherlands
- TNO-Metabolic Health Research, Gaubius Laboratory, Leiden, Netherlands
| | - Ian D. van Koeverden
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Arjan Boltjes
- Central Diagnostics Laboratory, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | | | - Alain J. van Gool
- Translational Metabolic Laboratory, Radboudumc, Nijmegen, Netherlands
- TNO- Microbiology & Systems Biology, Zeist, Netherlands
| | - Gert J. de Borst
- Department of Vascular Surgery, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Folkert W. Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London, United Kingdom
- Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom
| | - Dennie Tempel
- Central Diagnostics Laboratory, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
- Quorics B.V., Rotterdam, Netherlands
- SkylineDx B.V., Rotterdam, Netherlands
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| |
Collapse
|
155
|
Belbin GM, Cullina S, Wenric S, Soper ER, Glicksberg BS, Torre D, Moscati A, Wojcik GL, Shemirani R, Beckmann ND, Cohain A, Sorokin EP, Park DS, Ambite JL, Ellis S, Auton A, Bottinger EP, Cho JH, Loos RJF, Abul-Husn NS, Zaitlen NA, Gignoux CR, Kenny EE. Toward a fine-scale population health monitoring system. Cell 2021; 184:2068-2083.e11. [PMID: 33861964 DOI: 10.1016/j.cell.2021.03.034] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/18/2020] [Accepted: 03/12/2021] [Indexed: 12/22/2022]
Abstract
Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.
Collapse
Affiliation(s)
- Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sinead Cullina
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Stephane Wenric
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Emily R Soper
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Denis Torre
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Arden Moscati
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Genevieve L Wojcik
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Ruhollah Shemirani
- Information Science Institute, University of Southern California, Marina del Rey, CA 90089, USA
| | - Noam D Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ariella Cohain
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Elena P Sorokin
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Danny S Park
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jose-Luis Ambite
- Information Science Institute, University of Southern California, Marina del Rey, CA 90089, USA
| | - Steve Ellis
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adam Auton
- Department of Genetics, Albert Einstein College of Medicine, New York, NY 10461, USA
| | - Erwin P Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Judy H Cho
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth J F Loos
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Noura S Abul-Husn
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Noah A Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA 90033, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| |
Collapse
|
156
|
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: 4.0] [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.
Collapse
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
| |
Collapse
|
157
|
Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D, Bonder MJ, Sulovari A, Ebler J, Zhou W, Serra Mari R, Yilmaz F, Zhao X, Hsieh P, Lee J, Kumar S, Lin J, Rausch T, Chen Y, Ren J, Santamarina M, Höps W, Ashraf H, Chuang NT, Yang X, Munson KM, Lewis AP, Fairley S, Tallon LJ, Clarke WE, Basile AO, Byrska-Bishop M, Corvelo A, Evani US, Lu TY, Chaisson MJP, Chen J, Li C, Brand H, Wenger AM, Ghareghani M, Harvey WT, Raeder B, Hasenfeld P, Regier AA, Abel HJ, Hall IM, Flicek P, Stegle O, Gerstein MB, Tubio JMC, Mu Z, Li YI, Shi X, Hastie AR, Ye K, Chong Z, Sanders AD, Zody MC, Talkowski ME, Mills RE, Devine SE, Lee C, Korbel JO, Marschall T, Eichler EE. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 2021; 372:eabf7117. [PMID: 33632895 PMCID: PMC8026704 DOI: 10.1126/science.abf7117] [Citation(s) in RCA: 403] [Impact Index Per Article: 100.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.
Collapse
Affiliation(s)
- Peter Ebert
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Bernardo Rodriguez-Martin
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Marc Jan Bonder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Jana Ebler
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Weichen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Rebecca Serra Mari
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Feyza Yilmaz
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Joyce Lee
- Bionano Genomics, San Diego, CA 92121, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jiadong Lin
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Tobias Rausch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Yu Chen
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Jingwen Ren
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin Santamarina
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Hufsah Ashraf
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Nelson T Chuang
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Xiaofei Yang
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Luke J Tallon
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | | | | | | | | | | | - Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Junjie Chen
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Chong Li
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aaron M Wenger
- Pacific Biosciences of California, Menlo Park, CA 94025, USA
| | - Maryam Ghareghani
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123 Saarbrücken, Germany
- Saarbrücken Graduate School of Computer Science, Saarland University, Saarland Informatics Campus E1.3, 66123 Saarbrücken, Germany
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Benjamin Raeder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Allison A Regier
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Haley J Abel
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Ira M Hall
- Department of Genetics, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jose M C Tubio
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Zepeng Mu
- Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | | | - Kai Ye
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Zechen Chong
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | | | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China
- Department of Graduate Studies-Life Sciences, Ewha Womans University, Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, South Korea
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tobias Marschall
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| |
Collapse
|
158
|
Obesity and ADHD: Exploring the role of body composition, BMI polygenic risk score, and reward system genes. J Psychiatr Res 2021; 136:529-536. [PMID: 33127071 DOI: 10.1016/j.jpsychires.2020.10.026] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 10/10/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
The association between obesity and attention-deficit hyperactivity disorder (ADHD) has been extensively reported in the literature. However, the potential mechanisms underlying this association are not completely understood. This study aimed to evaluate the association between body composition and ADHD and explore the possible genetic mechanisms involved. We used data from the 1982 Pelotas (Brazil) Birth Cohort at age 30-year follow-up (N = 3630). We first used logistic regression analysis to test whether body mass index (BMI), fat mass (FM), and fat-free mass (FFM) were associated with ADHD. We further tested the association between BMI polygenic risk score (BMI-PRS) and ADHD and the role of the genes upregulated in the reward system using a gene-set association approach. BMI (odds ratio [OR] = 1.05; 95% confidence interval [CI], 1.00-1.09; p = 0.038) and FM (OR = 1.04; 95% CI, 1.00-1.07; p = 0.043) were associated with ADHD. The BMI-PRS was associated with ADHD (using p-value threshold (PT) = 0.4; OR = 1.65; 95% CI, 1.02-2.65) at a nominal level. In gene-set analysis, the reward system genes were associated with BMI in subjects with a high BMI-PRS score, considering PT = 0.4 (p = 0.014). The results suggest that BMI genetic components, especially those genes related to the reward system, may be involved in this association.
Collapse
|
159
|
Secolin R, Gonsales MC, Rocha CS, Naslavsky M, De Marco L, Bicalho MAC, Vazquez VL, Zatz M, Silva WA, Lopes-Cendes I. Exploring a Region on Chromosome 8p23.1 Displaying Positive Selection Signals in Brazilian Admixed Populations: Additional Insights Into Predisposition to Obesity and Related Disorders. Front Genet 2021; 12:636542. [PMID: 33841501 PMCID: PMC8027303 DOI: 10.3389/fgene.2021.636542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
We recently reported a deviation of local ancestry on the chromosome (ch) 8p23.1, which led to positive selection signals in a Brazilian population sample. The deviation suggested that the genetic variability of candidate genes located on ch 8p23.1 may have been evolutionarily advantageous in the early stages of the admixture process. In the present work, we aim to extend the previous work by studying additional Brazilian admixed individuals and examining DNA sequencing data from the ch 8p23.1 candidate region. Thus, we inferred the local ancestry of 125 exomes from individuals born in five towns within the Southeast region of Brazil (São Paulo, Campinas, Barretos, and Ribeirão Preto located in the state of São Paulo and Belo Horizonte, the capital of the state of Minas Gerais), and compared to data from two public Brazilian reference genomic databases, BIPMed and ABraOM, and with information from the 1000 Genomes Project phase 3 and gnomAD databases. Our results revealed that ancestry is similar among individuals born in the five Brazilian towns assessed; however, an increased proportion of sub-Saharan African ancestry was observed in individuals from Belo Horizonte. In addition, individuals from the five towns considered, as well as those from the ABRAOM dataset, had the same overrepresentation of Native-American ancestry on the ch 8p23.1 locus that was previously reported for the BIPMed reference sample. Sequencing analysis of ch 8p23.1 revealed the presence of 442 non-synonymous variants, including frameshift, inframe deletion, start loss, stop gain, stop loss, and splicing site variants, which occurred in 24 genes. Among these genes, 13 were associated with obesity, type II diabetes, lipid levels, and waist circumference (PRAG1, MFHAS1, PPP1R3B, TNKS, MSRA, PRSS55, RP1L1, PINX1, MTMR9, FAM167A, BLK, GATA4, and CTSB). These results strengthen the hypothesis that a set of variants located on ch 8p23.1 that result from positive selection during early admixture events may influence obesity-related disease predisposition in admixed individuals of the Brazilian population. Furthermore, we present evidence that the exploration of local ancestry deviation in admixed individuals may provide information with the potential to be translated into health care improvement.
Collapse
Affiliation(s)
- Rodrigo Secolin
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Marina C Gonsales
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Cristiane S Rocha
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Michel Naslavsky
- Departament of Genetics and Evolutive Biology, Human Genome and Stem Cell Research Center, Institute of Bioscience, University of São Paulo (USP), São Paulo, Brazil
| | - Luiz De Marco
- Department of Surgery, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Maria A C Bicalho
- Department of Clinical Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Vinicius L Vazquez
- Molecular Oncology Research Center (CPOM) - Barretos Cancer Hospital, Barretos, Brazil
| | - Mayana Zatz
- Departament of Genetics and Evolutive Biology, Human Genome and Stem Cell Research Center, Institute of Bioscience, University of São Paulo (USP), São Paulo, Brazil
| | - Wilson A Silva
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo at Ribeirão Preto (USP), Ribeirão Preto, Brazil
| | - Iscia Lopes-Cendes
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| |
Collapse
|
160
|
Ávila-Arcos MC, McManus KF, Sandoval K, Rodríguez-Rodríguez JE, Villa-Islas V, Martin AR, Luisi P, Peñaloza-Espinosa RI, Eng C, Huntsman S, Burchard EG, Gignoux CR, Bustamante CD, Moreno-Estrada A. Population History and Gene Divergence in Native Mexicans Inferred from 76 Human Exomes. Mol Biol Evol 2021; 37:994-1006. [PMID: 31848607 PMCID: PMC7086176 DOI: 10.1093/molbev/msz282] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Native American genetic variation remains underrepresented in most catalogs of human genome sequencing data. Previous genotyping efforts have revealed that Mexico’s Indigenous population is highly differentiated and substructured, thus potentially harboring higher proportions of private genetic variants of functional and biomedical relevance. Here we have targeted the coding fraction of the genome and characterized its full site frequency spectrum by sequencing 76 exomes from five Indigenous populations across Mexico. Using diffusion approximations, we modeled the demographic history of Indigenous populations from Mexico with northern and southern ethnic groups splitting 7.2 KYA and subsequently diverging locally 6.5 and 5.7 KYA, respectively. Selection scans for positive selection revealed BCL2L13 and KBTBD8 genes as potential candidates for adaptive evolution in Rarámuris and Triquis, respectively. BCL2L13 is highly expressed in skeletal muscle and could be related to physical endurance, a well-known phenotype of the northern Mexico Rarámuri. The KBTBD8 gene has been associated with idiopathic short stature and we found it to be highly differentiated in Triqui, a southern Indigenous group from Oaxaca whose height is extremely low compared to other Native populations.
Collapse
Affiliation(s)
- María C Ávila-Arcos
- International Laboratory for Human Genome Research (LIIGH), UNAM Juriquilla, Queretaro, Mexico.,Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Kimberly F McManus
- Department of Biology, Stanford University, Stanford, CA.,Department of Biomedical Informatics, Stanford School of Medicine, Stanford, CA
| | - Karla Sandoval
- National Laboratory of Genomics for Biodiversity (LANGEBIO), UGA, CINVESTAV, Irapuato, Guanajuato 36821, Mexico
| | | | - Viridiana Villa-Islas
- International Laboratory for Human Genome Research (LIIGH), UNAM Juriquilla, Queretaro, Mexico
| | - Alicia R Martin
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Pierre Luisi
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas, Consejo Nacional de Investigaciones Científicas y Técnicas, Córdoba, Argentina.,Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Rosenda I Peñaloza-Espinosa
- Division of Biological and Health Sciences, Department of Biological Systems, Universidad Autónoma Metropolitana-Xochimilco, Mexico City, Mexico
| | - Celeste Eng
- Department Bioengineering & Therapeutic Sciences and Medicine, University of California San Francisco, San Francisco, CA
| | - Scott Huntsman
- Department Bioengineering & Therapeutic Sciences and Medicine, University of California San Francisco, San Francisco, CA
| | - Esteban G Burchard
- Department Bioengineering & Therapeutic Sciences and Medicine, University of California San Francisco, San Francisco, CA
| | - Christopher R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO
| | - Carlos D Bustamante
- Department of Genetics, Stanford University School of Medicine, Stanford, CA
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), UGA, CINVESTAV, Irapuato, Guanajuato 36821, Mexico
| |
Collapse
|
161
|
Font-Porterias N, Caro-Consuegra R, Lucas-Sánchez M, Lopez M, Giménez A, Carballo-Mesa A, Bosch E, Calafell F, Quintana-Murci L, Comas D. The Counteracting Effects of Demography on Functional Genomic Variation: The Roma Paradigm. Mol Biol Evol 2021; 38:2804-2817. [PMID: 33713133 PMCID: PMC8233508 DOI: 10.1093/molbev/msab070] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Demographic history plays a major role in shaping the distribution of genomic variation. Yet the interaction between different demographic forces and their effects in the genomes is not fully resolved in human populations. Here, we focus on the Roma population, the largest transnational ethnic minority in Europe. They have a South Asian origin and their demographic history is characterized by recent dispersals, multiple founder events, and extensive gene flow from non-Roma groups. Through the analyses of new high-coverage whole exome sequences and genome-wide array data for 89 Iberian Roma individuals together with forward simulations, we show that founder effects have reduced their genetic diversity and proportion of rare variants, gene flow has counteracted the increase in mutational load, runs of homozygosity show ancestry-specific patterns of accumulation of deleterious homozygotes, and selection signals primarily derive from preadmixture adaptation in the Roma population sources. The present study shows how two demographic forces, bottlenecks and admixture, act in opposite directions and have long-term balancing effects on the Roma genomes. Understanding how demography and gene flow shape the genome of an admixed population provides an opportunity to elucidate how genomic variation is modeled in human populations.
Collapse
Affiliation(s)
- Neus Font-Porterias
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Rocio Caro-Consuegra
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Marcel Lucas-Sánchez
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Marie Lopez
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Aaron Giménez
- Facultat de Sociologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Elena Bosch
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Reus, Spain
| | - Francesc Calafell
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Lluís Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.,Human Genomics and Evolution, Collège de France, Paris, France
| | - David Comas
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| |
Collapse
|
162
|
Bhattacharya A, Li Y, Love MI. MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies. PLoS Genet 2021; 17:e1009398. [PMID: 33684137 PMCID: PMC7971899 DOI: 10.1371/journal.pgen.1009398] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 03/18/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023] Open
Abstract
Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.
Collapse
Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, University of California-Los Angeles, Los Angeles, California, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| |
Collapse
|
163
|
Coignard J, Lush M, Beesley J, O'Mara TA, Dennis J, Tyrer JP, Barnes DR, McGuffog L, Leslie G, Bolla MK, Adank MA, Agata S, Ahearn T, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Arnold N, Aronson KJ, Arun BK, Augustinsson A, Azzollini J, Barrowdale D, Baynes C, Becher H, Bermisheva M, Bernstein L, Białkowska K, Blomqvist C, Bojesen SE, Bonanni B, Borg A, Brauch H, Brenner H, Burwinkel B, Buys SS, Caldés T, Caligo MA, Campa D, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Chung WK, Claes KBM, Clarke CL, Collée JM, Conroy DM, Czene K, Daly MB, Devilee P, Diez O, Ding YC, Domchek SM, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Flyger H, Fostira F, Friedman E, Fritschi L, Frost D, Gago-Dominguez M, Gapstur SM, Garber J, Garcia-Barberan V, García-Closas M, García-Sáenz JA, Gaudet MM, Gayther SA, Gehrig A, Georgoulias V, Giles GG, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Greene MH, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hart SN, He W, Hogervorst FBL, Hollestelle A, Hopper JL, Horcasitas DJ, Hulick PJ, et alCoignard J, Lush M, Beesley J, O'Mara TA, Dennis J, Tyrer JP, Barnes DR, McGuffog L, Leslie G, Bolla MK, Adank MA, Agata S, Ahearn T, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Arnold N, Aronson KJ, Arun BK, Augustinsson A, Azzollini J, Barrowdale D, Baynes C, Becher H, Bermisheva M, Bernstein L, Białkowska K, Blomqvist C, Bojesen SE, Bonanni B, Borg A, Brauch H, Brenner H, Burwinkel B, Buys SS, Caldés T, Caligo MA, Campa D, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Chung WK, Claes KBM, Clarke CL, Collée JM, Conroy DM, Czene K, Daly MB, Devilee P, Diez O, Ding YC, Domchek SM, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Flyger H, Fostira F, Friedman E, Fritschi L, Frost D, Gago-Dominguez M, Gapstur SM, Garber J, Garcia-Barberan V, García-Closas M, García-Sáenz JA, Gaudet MM, Gayther SA, Gehrig A, Georgoulias V, Giles GG, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Greene MH, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hart SN, He W, Hogervorst FBL, Hollestelle A, Hopper JL, Horcasitas DJ, Hulick PJ, Hunter DJ, Imyanitov EN, Jager A, Jakubowska A, James PA, Jensen UB, John EM, Jones ME, Kaaks R, Kapoor PM, Karlan BY, Keeman R, Khusnutdinova E, Kiiski JI, Ko YD, Kosma VM, Kraft P, Kurian AW, Laitman Y, Lambrechts D, Le Marchand L, Lester J, Lesueur F, Lindstrom T, Lopez-Fernández A, Loud JT, Luccarini C, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Mebirouk N, Meindl A, Miller A, Milne RL, Montagna M, Nathanson KL, Neuhausen SL, Nevanlinna H, Nielsen FC, O'Brien KM, Olopade OI, Olson JE, Olsson H, Osorio A, Ottini L, Park-Simon TW, Parsons MT, Pedersen IS, Peshkin B, Peterlongo P, Peto J, Pharoah PDP, Phillips KA, Polley EC, Poppe B, Presneau N, Pujana MA, Punie K, Radice P, Rantala J, Rashid MU, Rennert G, Rennert HS, Robson M, Romero A, Rossing M, Saloustros E, Sandler DP, Santella R, Scheuner MT, Schmidt MK, Schmidt G, Scott C, Sharma P, Soucy P, Southey MC, Spinelli JJ, Steinsnyder Z, Stone J, Stoppa-Lyonnet D, Swerdlow A, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Teulé A, Thull DL, Tischkowitz M, Toland AE, Torres D, Trainer AH, Truong T, Tung N, Vachon CM, Vega A, Vijai J, Wang Q, Wappenschmidt B, Weinberg CR, Weitzel JN, Wendt C, Wolk A, Yadav S, Yang XR, Yannoukakos D, Zheng W, Ziogas A, Zorn KK, Park SK, Thomassen M, Offit K, Schmutzler RK, Couch FJ, Simard J, Chenevix-Trench G, Easton DF, Andrieu N, Antoniou AC. A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers. Nat Commun 2021; 12:1078. [PMID: 33990587 PMCID: PMC7890067 DOI: 10.1038/s41467-020-20496-3] [Show More Authors] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 11/19/2020] [Indexed: 02/02/2023] Open
Abstract
Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10-8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers.
Collapse
Affiliation(s)
- Juliette Coignard
- Genetic Epidemiology of Cancer team, Inserm, U900, Paris, France
- Institut Curie Paris, Paris, France
- Mines ParisTech Fontainebleau, Paris, France
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- PSL University Paris, Paris, France
- Paris Sud University, Orsay, France
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Beesley
- Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tracy A O'Mara
- Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Daniel R Barnes
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Lesley McGuffog
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Muriel A Adank
- Family Cancer Clinic, The Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
| | - Simona Agata
- Immunology and Molecular Oncology, Unit Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Kristiina Aittomäki
- Department of Clinical Genetics, Helsinki University Hospital University of Helsinki, Helsinki, Finland
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics University of Toronto, Toronto, ON, Canada
| | - Hoda Anton-Culver
- Department of Epidemiology, Genetic Epidemiology Research Institute University of California Irvine, Irvine, CA, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Norbert Arnold
- Department of Gynaecology and Obstetrics University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel, Germany
- Institute of Clinical Molecular Biology University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute Queen's University, Kingston, ON, Canada
| | - Banu K Arun
- Department of Breast Medical Oncology University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Annelie Augustinsson
- Department of Cancer Epidemiology, Clinical Sciences Lund University, Lund, 22242, Sweden
| | - Jacopo Azzollini
- Unit of Medical Genetics, Department of Medical Oncology and Hematology Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Daniel Barrowdale
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Caroline Baynes
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Heiko Becher
- Institute for Medical Biometrics and Epidemiology University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
| | - Leslie Bernstein
- Department of Population Sciences Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Katarzyna Białkowska
- Department of Genetics and Pathology Pomeranian Medical University Szczecin, Szczecin, Poland
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital University of Helsinki, Helsinki, Finland
- Department of Oncology Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences University of Copenhagen, Copenhagen, Denmark
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Ake Borg
- Department of Oncology Lund University and Skåne University Hospital, Lund, Sweden
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- iFIT-Cluster of Excellence University of Tübingen, Tübingen, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, C080 German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg University of Heidelberg, Heidelberg, Germany
| | - Saundra S Buys
- Department of Medicine Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Trinidad Caldés
- Molecular Oncology Laboratory CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Maria A Caligo
- SOD Genetica Molecolare University Hospital, Pisa, Italy
| | - Daniele Campa
- Department of Biology University of Pisa, Pisa, Italy
- Division of Cancer Epidemiology German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Brian D Carter
- Behavioral and Epidemiology Research Group American Cancer Society Atlanta, Atlanta, GA, USA
| | - Jose E Castelao
- Oncology and Genetics Unit Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH) University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | | | - Christine L Clarke
- Westmead Institute for Medical Research University of Sydney, Sydney, NSW, Australia
| | - J Margriet Collée
- Department of Clinical Genetics Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Don M Conroy
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics Fox Chase Cancer Center Philadelphia, Philadelphia, PA, USA
| | - Peter Devilee
- Department of Pathology Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics Leiden University Medical Center, Leiden, The Netherlands
| | - Orland Diez
- Oncogenetics Group Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
- Clinical and Molecular Genetics Area University Hospital Vall d'Hebron, Barcelona, Spain
| | - Yuan Chun Ding
- Department of Population Sciences Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Susan M Domchek
- Basser Center for BRCA, Abramson Cancer Center University of Pennsylvania, Philadelphia, PA, USA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology London School of Hygiene and Tropical Medicine, London, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Miriam Dwek
- School of Life Sciences University of Westminster, London, UK
| | - Diana M Eccles
- Faculty of Medicine University of Southampton, Southampton, UK
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Leipzig, Germany
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - D Gareth Evans
- Genomic Medicine, Division of Evolution and Genomic Sciences The University of Manchester, Manchester Academic Health Science Centre, Manchester Universities Foundation Trust, St Mary's Hospital, Manchester, UK
- Genomic Medicine, North West Genomics hub Manchester Academic Health Science Centre, Manchester Universities Foundation Trust, St Mary's Hospital, Manchester, UK
| | - Peter A Fasching
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology University of California at Los Angeles, Los Angeles, CA, USA
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN University Hospital Erlangen, Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
| | - Florentia Fostira
- Molecular Diagnostics Laboratory, INRASTES National Centre for Scientific Research íDemokritosí, Athens, Greece
| | - Eitan Friedman
- The Susanne Levy Gertner Oncogenetics Unit Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine Tel Aviv University, Ramat Aviv, Israel
| | - Lin Fritschi
- School of Public Health Curtin University, Perth, Western Australia, Australia
| | - Debra Frost
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
- Moores Cancer Center University of California, San Diego La Jolla, CA, USA
| | - Susan M Gapstur
- Behavioral and Epidemiology Research Group American Cancer Society Atlanta, Atlanta, GA, USA
| | - Judy Garber
- Division of Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Vanesa Garcia-Barberan
- Medical Oncology Department, Hospital Clínico San Carlos Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Mia M Gaudet
- Behavioral and Epidemiology Research Group American Cancer Society Atlanta, Atlanta, GA, USA
| | - Simon A Gayther
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrea Gehrig
- Department of Human Genetics University Würzburg, Würzburg, Germany
| | | | - Graham G Giles
- Cancer Epidemiology Division Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health Monash University, Clayton, VIC, Australia
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Mark S Goldberg
- Department of Medicine, McGill University, Montréal, QC, Canada
- Division of Clinical Epidemiology, Royal Victoria Hospital McGill University Montréal, Montréal, QC, Canada
| | - David E Goldgar
- Huntsman Cancer Institute and Department of Dermatology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Anna González-Neira
- Human Cancer Genetics Programme Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Mark H Greene
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics National Cancer Institute, Bethesda, MD, USA
| | - Pascal Guénel
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP) INSERM, University Paris-Sud, University Paris-Saclay, Villejuif, France
| | - Lothar Haeberle
- Department of Gynaecology and Obstetrics, University Hospital Erlangen Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Eric Hahnen
- Center for Hereditary Breast and Ovarian Cancer Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO) Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine University of Southern California, Los Angeles, CA, USA
| | | | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patricia A Harrington
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Steven N Hart
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Frans B L Hogervorst
- Family Cancer Clinic, The Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
| | - Antoinette Hollestelle
- Department of Medical Oncology, Family Cancer Clinic Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Darling J Horcasitas
- New Mexico Oncology Hematology Consultants, University of New Mexico, Albuquerque, NM, USA
| | - Peter J Hulick
- Center for Medical Genetics NorthShore University HealthSystem, Evanston, IL, USA
- The University of Chicago Pritzker School of Medicine Chicago, Chicago, IL, USA
| | - David J Hunter
- Department of Epidemiology Harvard TH Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics Harvard TH Chan School of Public Health Boston, Boston, MA, USA
- Nuffield Department of Population Health University of Oxford, Oxford, UK
| | | | - Agnes Jager
- Department of Medical Oncology, Family Cancer Clinic Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Anna Jakubowska
- Department of Genetics and Pathology Pomeranian Medical University Szczecin, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics Pomeranian Medical University, Szczecin, Poland
| | - Paul A James
- Sir Peter MacCallum Department of Oncology The University of Melbourne, Melbourne, VIC, Australia
- Parkville Familial Cancer Centre Peter MacCallum Cancer Center, Melbourne, VIC, Australia
| | - Uffe Birk Jensen
- Department of Clinical Genetics Aarhus, University Hospital, Aarhus, Denmark
| | - Esther M John
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael E Jones
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Rudolf Kaaks
- Division of Cancer Epidemiology German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pooja Middha Kapoor
- Division of Cancer Epidemiology German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Genetics and Epidemiology The Institute of Cancer Research, London, UK
| | - Beth Y Karlan
- Faculty of Medicine University of Heidelberg, Heidelberg, Germany
- David Geffen School of Medicine, Department of Obstetrics and Gynecology University of California at Los Angeles, Los Angeles, CA, USA
| | - Renske Keeman
- Womenís Cancer Program at the Samuel Oschin Comprehensive Cancer Institute Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Division of Molecular Pathology The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Johanna I Kiiski
- Department of Genetics and Fundamental Medicine Bashkir State Medical University, Ufa, Russia
| | - Yon-Dschun Ko
- Department of Obstetrics and Gynecology, Helsinki University Hospital University of Helsinki, Helsinki, Finland
| | - Veli-Matti Kosma
- Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH Johanniter Krankenhaus, Bonn, Germany
- Translational Cancer Research Area University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine University of Eastern Finland, Kuopio, Finland
| | - Peter Kraft
- Department of Epidemiology Harvard TH Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics Harvard TH Chan School of Public Health Boston, Boston, MA, USA
| | - Allison W Kurian
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Yael Laitman
- The Susanne Levy Gertner Oncogenetics Unit Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Diether Lambrechts
- VIB Center for Cancer Biology, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics University of Leuven, Leuven, Belgium
| | - Loic Le Marchand
- Epidemiology Program University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Jenny Lester
- Faculty of Medicine University of Heidelberg, Heidelberg, Germany
- David Geffen School of Medicine, Department of Obstetrics and Gynecology University of California at Los Angeles, Los Angeles, CA, USA
| | - Fabienne Lesueur
- Genetic Epidemiology of Cancer team, Inserm, U900, Paris, France
- Institut Curie Paris, Paris, France
- Mines ParisTech Fontainebleau, Paris, France
- PSL University Paris, Paris, France
| | - Tricia Lindstrom
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Adria Lopez-Fernández
- High Risk and Cancer Prevention Group Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Jennifer T Loud
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics National Cancer Institute, Bethesda, MD, USA
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Arto Mannermaa
- Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH Johanniter Krankenhaus, Bonn, Germany
- Translational Cancer Research Area University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine University of Eastern Finland, Kuopio, Finland
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset Karolinska Institutet, Stockholm, Sweden
| | - John W M Martens
- Department of Medical Oncology, Family Cancer Clinic Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Noura Mebirouk
- Genetic Epidemiology of Cancer team, Inserm, U900, Paris, France
- Institut Curie Paris, Paris, France
- Mines ParisTech Fontainebleau, Paris, France
- PSL University Paris, Paris, France
| | - Alfons Meindl
- Department of Gynecology and Obstetrics University of Munich, Campus Grosshadern, Munich, Germany
| | - Austin Miller
- NRG Oncology, Statistics and Data Management Center Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Roger L Milne
- Cancer Epidemiology Division Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health Monash University, Clayton, VIC, Australia
| | - Marco Montagna
- Immunology and Molecular Oncology, Unit Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Katherine L Nathanson
- Basser Center for BRCA, Abramson Cancer Center University of Pennsylvania, Philadelphia, PA, USA
| | - Susan L Neuhausen
- Department of Population Sciences Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Heli Nevanlinna
- Department of Genetics and Fundamental Medicine Bashkir State Medical University, Ufa, Russia
| | - Finn C Nielsen
- Center for Genomic Medicine Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Katie M O'Brien
- Epidemiology Branch National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
| | | | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences Lund University, Lund, 22242, Sweden
| | - Ana Osorio
- Human Cancer Genetics Programme Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Laura Ottini
- Department of Molecular Medicine University La Sapienza, Rome, Italy
| | | | - Michael T Parsons
- Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Inge Sokilde Pedersen
- Molecular Diagnostics Aalborg University Hospital, Aalborg, Denmark
- Clinical Cancer Research Center Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine Aalborg University, Aalborg, Denmark
| | - Beth Peshkin
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Paolo Peterlongo
- Genome Diagnostics Program IFOM - the FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology, Milan, Italy
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology London School of Hygiene and Tropical Medicine, London, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Kelly-Anne Phillips
- Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology The University of Melbourne, Melbourne, VIC, Australia
| | - Eric C Polley
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Bruce Poppe
- Centre for Medical Genetics Ghent University, Gent, Belgium
| | - Nadege Presneau
- School of Life Sciences University of Westminster, London, UK
| | - Miquel Angel Pujana
- Translational Research Laboratory IDIBELL (Bellvitge Biomedical Research Institute), Catalan Institute of Oncology, CIBERONC, Barcelona, Spain
| | - Kevin Punie
- Leuven Multidisciplinary Breast Center, Department of Oncology Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | | | - Muhammad U Rashid
- Molecular Genetics of Breast Cancer German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Basic Sciences Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Lahore, Pakistan
| | - Gad Rennert
- Clalit National Cancer Control Center Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Hedy S Rennert
- Clalit National Cancer Control Center Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Mark Robson
- Clinical Genetics Service, Department of Medicine Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Atocha Romero
- Medical Oncology Department Hospital Universitario Puerta de Hierro, Madrid, Spain
| | - Maria Rossing
- Center for Genomic Medicine Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Dale P Sandler
- Epidemiology Branch National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
| | - Regina Santella
- Department of Epidemiology, Mailman School of Public Health Columbia University, New York, NY, USA
| | - Maren T Scheuner
- Cancer Genetics and Prevention Program University of California San Francisco, San Francisco, CA, USA
| | - Marjanka K Schmidt
- Womenís Cancer Program at the Samuel Oschin Comprehensive Cancer Institute Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Psychosocial Research and Epidemiology The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
| | - Gunnar Schmidt
- Institute of Human Genetics Hannover Medical School, Hannover, Germany
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Priyanka Sharma
- Department of Internal Medicine, Division of Medical Oncology University of Kansas Medical Center, Westwood, KS, USA
| | - Penny Soucy
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Québec City, QC, Canada
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology The University of Melbourne, Melbourne, VIC, Australia
| | - John J Spinelli
- Population Oncology BC Cancer, Vancouver, BC, Canada
- School of Population and Public Health University of British Columbia, Vancouver, BC, Canada
| | - Zoe Steinsnyder
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- The Curtin UWA Centre for Genetic Origins of Health and Disease Curtin University and University of Western Australia, Perth, Western Australia, Australia
| | - Dominique Stoppa-Lyonnet
- Service de Génétique Institut Curie, Paris, France
- Department of Tumour Biology INSERM U830, Paris, France
- Université Paris Descartes, Paris, France
| | - Anthony Swerdlow
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Division of Breast Cancer Research Institute of Cancer Research, London, UK
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology Harvard TH Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics Harvard TH Chan School of Public Health Boston, Boston, MA, USA
| | | | - Jack A Taylor
- Epidemiology Branch National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
- Epigenetic and Stem Cell Biology Laboratory National Institute of Environmental Health Sciences, NIH Research Triangle Park, Triangle Park, NC, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health Columbia University, New York, NY, USA
| | - Alex Teulé
- Hereditary Cancer Program ONCOBELL-IDIBELL-IDIBGI-IGTP, Catalan Institute of Oncology, CIBERONC, Barcelona, Spain
| | - Darcy L Thull
- Department of Medicine Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Marc Tischkowitz
- Program in Cancer Genetics, Departments of Human Genetics and Oncology McGill University, Montréal, QC, Canada
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Center, University of Cambridge, Cambridge, UK
| | - Amanda E Toland
- Department of Cancer Biology and Genetics The Ohio State University, Columbus, OH, USA
| | - Diana Torres
- Molecular Genetics of Breast Cancer German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Human Genetics Pontificia Universidad Javeriana, Bogota, Colombia
| | - Alison H Trainer
- Parkville Familial Cancer Centre Peter MacCallum Cancer Center, Melbourne, VIC, Australia
- Department of medicine University Of Melbourne, Melbourne, VIC, Australia
| | - Thérèse Truong
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP) INSERM, University Paris-Sud, University Paris-Saclay, Villejuif, France
| | - Nadine Tung
- Department of Medical Oncology Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Celine M Vachon
- Department of Health Science Research, Division of Epidemiology Mayo Clinic, Rochester, MN, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica-SERGAS, Instituto de Investigación Sanitaria Santiago de Compostela (IDIS); CIBERER, Santiago de Compostela, Spain
| | - Joseph Vijai
- Clinical Genetics Service, Department of Medicine Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Barbara Wappenschmidt
- Center for Hereditary Breast and Ovarian Cancer Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO) Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch National Institute of Environmental Health Sciences, NIH Research Triangle Park, Triangle Park, NC, USA
| | | | - Camilla Wendt
- Department of Clinical Science and Education, Södersjukhuset Karolinska Institutet, Stockholm, Sweden
| | - Alicja Wolk
- Institute of Environmental Medicine Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences Uppsala University, Uppsala, Sweden
| | | | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES National Centre for Scientific Research íDemokritosí, Athens, Greece
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Argyrios Ziogas
- Department of Epidemiology, Genetic Epidemiology Research Institute University of California Irvine, Irvine, CA, USA
| | - Kristin K Zorn
- Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sue K Park
- Department of Preventive Medicine Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Sciences Seoul National University Graduate School, Seoul, Korea
- Cancer Research Institute Seoul National University, Seoul, Korea
| | - Mads Thomassen
- Department of Clinical Genetics Odense University Hospital, Odence C, Denmark
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Rita K Schmutzler
- Center for Hereditary Breast and Ovarian Cancer Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO) Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology Mayo Clinic, Rochester, MN, USA
| | - Jacques Simard
- Department of Epidemiology, Genetic Epidemiology Research Institute University of California Irvine, Irvine, CA, USA
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge, Cambridge, UK
| | - Nadine Andrieu
- Genetic Epidemiology of Cancer team, Inserm, U900, Paris, France.
- Institut Curie Paris, Paris, France.
- Mines ParisTech Fontainebleau, Paris, France.
- PSL University Paris, Paris, France.
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| |
Collapse
|
164
|
Díez-Obrero V, Dampier CH, Moratalla-Navarro F, Devall M, Plummer SJ, Díez-Villanueva A, Peters U, Bien S, Huyghe JR, Kundaje A, Ibáñez-Sanz G, Guinó E, Obón-Santacana M, Carreras-Torres R, Casey G, Moreno V. Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci. Cell Mol Gastroenterol Hepatol 2021; 12:181-197. [PMID: 33601062 PMCID: PMC8102177 DOI: 10.1016/j.jcmgh.2021.02.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND & AIMS The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource. METHODS We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses. RESULTS We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application. CONCLUSIONS We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.
Collapse
Affiliation(s)
- Virginia Díez-Obrero
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Christopher H Dampier
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia; Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - Ferran Moratalla-Navarro
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Matthew Devall
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Sarah J Plummer
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Anna Díez-Villanueva
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Ulrike Peters
- Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephanie Bien
- Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jeroen R Huyghe
- Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, California
| | - Gemma Ibáñez-Sanz
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Gastroenterology Department, Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Elisabeth Guinó
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Mireia Obón-Santacana
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Robert Carreras-Torres
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.
| | - Víctor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
| |
Collapse
|
165
|
Bonfante B, Faux P, Navarro N, Mendoza-Revilla J, Dubied M, Montillot C, Wentworth E, Poloni L, Varón-González C, Jones P, Xiong Z, Fuentes-Guajardo M, Palmal S, Chacón-Duque JC, Hurtado M, Villegas V, Granja V, Jaramillo C, Arias W, Barquera R, Everardo-Martínez P, Sánchez-Quinto M, Gómez-Valdés J, Villamil-Ramírez H, Silva de Cerqueira CC, Hünemeier T, Ramallo V, Liu F, Weinberg SM, Shaffer JR, Stergiakouli E, Howe LJ, Hysi PG, Spector TD, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Thauvin-Robinet C, Faivre L, Costedoat C, Balding D, Cox T, Kayser M, Duplomb L, Yalcin B, Cotney J, Adhikari K, Ruiz-Linares A. A GWAS in Latin Americans identifies novel face shape loci, implicating VPS13B and a Denisovan introgressed region in facial variation. SCIENCE ADVANCES 2021; 7:eabc6160. [PMID: 33547071 PMCID: PMC7864580 DOI: 10.1126/sciadv.abc6160] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 12/17/2020] [Indexed: 05/25/2023]
Abstract
To characterize the genetic basis of facial features in Latin Americans, we performed a genome-wide association study (GWAS) of more than 6000 individuals using 59 landmark-based measurements from two-dimensional profile photographs and ~9,000,000 genotyped or imputed single-nucleotide polymorphisms. We detected significant association of 32 traits with at least 1 (and up to 6) of 32 different genomic regions, more than doubling the number of robustly associated face morphology loci reported until now (from 11 to 23). These GWAS hits are strongly enriched in regulatory sequences active specifically during craniofacial development. The associated region in 1p12 includes a tract of archaic adaptive introgression, with a Denisovan haplotype common in Native Americans affecting particularly lip thickness. Among the nine previously unidentified face morphology loci we identified is the VPS13B gene region, and we show that variants in this region also affect midfacial morphology in mice.
Collapse
Affiliation(s)
- Betty Bonfante
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France
| | - Pierre Faux
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France
| | - Nicolas Navarro
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, Dijon 21078, France
- EPHE, PSL University, Paris 75014, France
| | - Javier Mendoza-Revilla
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
- Unit of Human Evolutionary Genetics, Institut Pasteur, Paris 75015, France
| | - Morgane Dubied
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, Dijon 21078, France
| | - Charlotte Montillot
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
| | - Emma Wentworth
- Department of Genetics and Genome Sciences, University of Connecticut Health, Farmington, CT 06030, USA
| | - Lauriane Poloni
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, Dijon 21078, France
- EPHE, PSL University, Paris 75014, France
| | - Ceferino Varón-González
- Institut de Systématique, Évolution, Biodiversité, ISYEB-UMR 7205-CNRS, MNHN, UPMC, EPHE, UA, Muséum National d'Histoire Naturelle, Sorbonne Universités, Paris 75005, France
| | - Philip Jones
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Ziyi Xiong
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3015GD, Netherlands
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica 1000000, Chile
| | - Sagnik Palmal
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France
| | - Juan Camilo Chacón-Duque
- Division of Vertebrates and Anthropology, Department of Earth Sciences, Natural History Museum, London SW7 5BD, UK
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Rodrigo Barquera
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), Jena 07745, Germany
| | - Paola Everardo-Martínez
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
| | - Mirsha Sánchez-Quinto
- Forensic Science, Faculty of Medicine, UNAM (Universidad Nacional Autónoma de México), Mexico City 06320, Mexico
| | - Jorge Gómez-Valdés
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
| | - Hugo Villamil-Ramírez
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | | | - Tábita Hünemeier
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP 05508-090, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brasil
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
| | - Fan Liu
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3015GD, Netherlands
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100864, China
- University of Chinese Academy of Sciences, Beijing 100864, China
| | - Seth M Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - John R Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS1 2LY, UK
- School of Oral and Dental Sciences, University of Bristol, Bristol BS1 2LY, UK
| | - Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS1 2LY, UK
| | - Pirro G Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Rolando Gonzalez-José
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
| | - Lavinia Schüler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brasil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brasil
| | - Victor Acuña-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
| | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Arica 1000000, Chile
| | - Christel Thauvin-Robinet
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
- Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs" de l'Est, Centre de Génétique, FHU TRANSLAD, CHU Dijon, Dijon 21000, France
| | - Laurence Faivre
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
- Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs" de l'Est, Centre de Génétique, FHU TRANSLAD, CHU Dijon, Dijon 21000, France
| | | | - David Balding
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- Melbourne Integrative Genomics, Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Timothy Cox
- Department of Oral and Craniofacial Sciences, School of Dentistry and Department of Pediatrics, School of Medicine, University of Missouri, Kansas City, MO 64108, USA
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3015GD, Netherlands
| | - Laurence Duplomb
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
| | - Binnaz Yalcin
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
| | - Justin Cotney
- Department of Genetics and Genome Sciences, University of Connecticut Health, Farmington, CT 06030, USA
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes MK7 6AA, UK.
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Andrés Ruiz-Linares
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France.
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- 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, Yangpu District, Shanghai, China
| |
Collapse
|
166
|
van Eeden G, Uren C, Möller M, Henn BM. Inferring recombination patterns in African populations. Hum Mol Genet 2021; 30:R11-R16. [PMID: 33445180 DOI: 10.1093/hmg/ddab020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/04/2021] [Accepted: 01/06/2021] [Indexed: 11/14/2022] Open
Abstract
Although several high-resolution recombination maps exist for European-descent populations, the recombination landscape of African populations remains relatively understudied. Given that there is high genetic divergence among groups in Africa, it is possible that recombination hotspots also diverge significantly. Both limitations and opportunities exist for developing recombination maps for these populations. In this review, we discuss various recombination inference methods, and the strengths and weaknesses of these methods in analyzing recombination in African-descent populations. Furthermore, we provide a decision tree and recommendations for which inference method to use in various research contexts. Establishing an appropriate methodology for recombination rate inference in a particular study will improve the accuracy of various downstream analyses including but not limited to local ancestry inference, haplotype phasing, fine-mapping of GWAS loci and genome assemblies.
Collapse
Affiliation(s)
- Gerald van Eeden
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California Davis, Davis, CA 95616, USA
| |
Collapse
|
167
|
Chatterjee A, Basu A, Das K, Chowdhury A, Basu P. Exome-wide scan identifies significant association of rs4788084 in IL27 promoter with increase in hepatic fat content among Indians. Gene 2021; 775:145431. [PMID: 33444683 DOI: 10.1016/j.gene.2021.145431] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 11/23/2020] [Accepted: 01/05/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is a global epidemic that often progresses to liver cirrhosis and hepatocellular carcinoma. In contrast to most world populations where NAFLD is mostly prevalent among obese, NAFLD among Indians and generally among South and South-East Asians is unique and highly prevalent among individuals who are lean. Genetics of NAFLD in Indian populations is understudied. In this study, we have used an exome-wide approach to identify genetic determinants of hepatic fat content (HFC) in India. METHODS HFC was measured in 244 participants using Proton magnetic resonance spectroscopy (H1-MRS). Quantitative trait loci (QTL) mapping was done exome-wide, to identify SNPs associated with HFC. The effects of the interaction between adiposity and QTLs on HFC were studied using a regression model. Association of the significant loci with disease severity was studied in 146 NAFLD patients among 244 participants, who underwent liver biopsy. RESULTS Our study identified 4 significantly associated SNPs (rs738409 and rs2281135 (PNPLA3), rs3761472 (SAMM50), rs17513722 (FAM161A) and rs4788084), with HFC after adjusting for the effects of covariates (p-value < 0.0005). rs738409, rs2281135 (PNPLA3), and rs3761472 (SAMM50) were associated with hepatocyte ballooning, lobular and portal inflammation and non-alcoholic steatohepatitis (NASH) (p-value < 0.05). rs4788048 is an eQTL for IL27 and SULT1A2 genes, both of which are highly expressed in healthy livers and are likely to be involved in NAFLD pathogenesis. CONCLUSIONS Our study identified the novel association of rs4788084 with HFC, which regulates the expression of IL-27, an immune regulatory gene. We further showed that adiposity affected the HFC, irrespective of the genetic predisposition.
Collapse
Affiliation(s)
- Ankita Chatterjee
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Kausik Das
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | - Abhijit Chowdhury
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | - Priyadarshi Basu
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India.
| |
Collapse
|
168
|
Hassan S, Surakka I, Taskinen MR, Salomaa V, Palotie A, Wessman M, Tukiainen T, Pirinen M, Palta P, Ripatti S. High-resolution population-specific recombination rates and their effect on phasing and genotype imputation. Eur J Hum Genet 2020; 29:615-624. [PMID: 33249422 PMCID: PMC8114909 DOI: 10.1038/s41431-020-00768-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 10/01/2020] [Accepted: 10/20/2020] [Indexed: 11/24/2022] Open
Abstract
Previous research has shown that using population-specific reference panels has a significant effect on downstream population genomic analyses like haplotype phasing, genotype imputation, and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10 and 50 kb scale using high-coverage (20–30×) whole-genome sequenced data of 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. We found that Finnish recombination rates have a moderately high correlation (Spearman’s ρ = 0.67–0.79) with NFE, although on average (across all autosomal chromosomes), Finnish rates (2.268 ± 0.4209 cM/Mb) are 12–14% lower than NFE (2.641 ± 0.5032 cM/Mb). Finnish recombination map was found to have no significant effect in haplotype phasing accuracy (switch error rates ~2%) and average imputation concordance rates (97–98% for common, 92–96% for low frequency and 78–90% for rare variants). Our results suggest that haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps. Even though recombination rate estimates had some differences between the Finnish and NFE populations, haplotyping and imputation had not been noticeably affected by the recombination map used. Therefore, the currently available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.
Collapse
Affiliation(s)
- Shabbeer Hassan
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Ida Surakka
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Marja-Riitta Taskinen
- Clinical and molecular metabolism, Research program unit, University of Helsinki, Helsinki, Finland
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.,Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Maija Wessman
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Department of Public Health, Faculty of Medicine, Clinicum, University of Helsinki, Helsinki, Finland.,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Priit Palta
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland. .,Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. .,Department of Public Health, Faculty of Medicine, Clinicum, University of Helsinki, Helsinki, Finland.
| |
Collapse
|
169
|
Samuels DC, Below JE, Ness S, Yu H, Leng S, Guo Y. Alternative Applications of Genotyping Array Data Using Multivariant Methods. Trends Genet 2020; 36:857-867. [PMID: 32773169 PMCID: PMC7572808 DOI: 10.1016/j.tig.2020.07.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 10/23/2022]
Abstract
One of the forerunners that pioneered the revolution of high-throughput genomic technologies is the genotyping microarray technology, which can genotype millions of single-nucleotide variants simultaneously. Owing to apparent benefits, such as high speed, low cost, and high throughput, the genotyping array has gained lasting applications in genome-wide association studies (GWAS) and thus accumulated an enormous amount of data. Empowered by continuous manufactural upgrades and analytical innovation, unconventional applications of genotyping array data have emerged to address more diverse genetic problems, holding promise of boosting genetic research into human diseases through the re-mining of the rich accumulated data. Here, we review several unconventional genotyping array analysis techniques that have been built on the idea of large-scale multivariant analysis and provide empirical application examples. These unconventional outcomes of genotyping arrays include polygenic score, runs of homozygosity (ROH)/heterozygosity ratio, distant pedigree computation, and mitochondrial DNA (mtDNA) copy number inference.
Collapse
Affiliation(s)
- David C Samuels
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Jennifer E Below
- Devision of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Scott Ness
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA
| | - Hui Yu
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA
| | - Shuguang Leng
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA
| | - Yan Guo
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA.
| |
Collapse
|
170
|
Kwon YC, Lim J, Bang SY, Ha E, Hwang MY, Yoon K, Choe JY, Yoo DH, Lee SS, Lee J, Chung WT, Kim TH, Sung YK, Shim SC, Choi CB, Jun JB, Kang YM, Shin JM, Lee YK, Cho SK, Kim BJ, Lee HS, Kim K, Bae SC. Genome-wide association study in a Korean population identifies six novel susceptibility loci for rheumatoid arthritis. Ann Rheum Dis 2020; 79:1438-1445. [PMID: 32723749 PMCID: PMC7569386 DOI: 10.1136/annrheumdis-2020-217663] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 06/30/2020] [Accepted: 06/30/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Genome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case-control population. METHODS We newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations. RESULTS We identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta<5×10-8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases. CONCLUSION This study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.
Collapse
Affiliation(s)
- Young-Chang Kwon
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
| | - Jiwoo Lim
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea
| | - So-Young Bang
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Eunji Ha
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Research, Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Kyungheon Yoon
- Division of Genome Research, Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Jung-Yoon Choe
- Department of Rheumatology, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Dae-Hyun Yoo
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Shin-Seok Lee
- Division of Rheumatology, Department of Internal Medicine, Chonnam National University Medical School and Hospital, Gwangju, Republic of Korea
| | - Jisoo Lee
- Division of Rheumatology, Ewha Womans University School of Medicine, Seoul, Republic of Korea
| | - Won Tae Chung
- Department of Internal Medicine, Dong-A University Hospital, Busan, Republic of Korea
| | - Tae-Hwan Kim
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Yoon-Kyoung Sung
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Seung-Cheol Shim
- Division of Rheumatology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Chan-Bum Choi
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Jae-Bum Jun
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Young Mo Kang
- Division of Rheumatology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Jung-Min Shin
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Yeon-Kyung Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Soo-Kyung Cho
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Research, Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Hye-Soon Lee
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Kwangwoo Kim
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Cheol Bae
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| |
Collapse
|
171
|
Utsunomiya YT, Milanesi M, Barbato M, Utsunomiya ATH, Sölkner J, Ajmone‐Marsan P, Garcia JF. Unsupervised detection of ancestry tracks with the GHap
r
package. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13467] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yuri Tani Utsunomiya
- Department of Support, Production and Animal Health School of Veterinary Medicine of Araçatuba São Paulo State University (Unesp) Araçatuba/SP Brazil
- International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics Araçatuba/SP Brazil
| | - Marco Milanesi
- Department of Support, Production and Animal Health School of Veterinary Medicine of Araçatuba São Paulo State University (Unesp) Araçatuba/SP Brazil
- International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics Araçatuba/SP Brazil
| | - Mario Barbato
- Department of Animal Science Food and Nutrition—DIANA and Nutrigenomics and Proteomics Research Center Università Cattolica del Sacro Cuore Piacenza Italy
| | - Adam Taiti Harth Utsunomiya
- Department of Support, Production and Animal Health School of Veterinary Medicine of Araçatuba São Paulo State University (Unesp) Araçatuba/SP Brazil
- International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics Araçatuba/SP Brazil
| | - Johann Sölkner
- Division of Livestook Sciences Department of Sustainable Agriculture System BOKU—University of Natural Resources and Life Sciences Vienna Austria
| | - Paolo Ajmone‐Marsan
- Department of Animal Science Food and Nutrition—DIANA and Nutrigenomics and Proteomics Research Center Università Cattolica del Sacro Cuore Piacenza Italy
| | - José Fernando Garcia
- Department of Support, Production and Animal Health School of Veterinary Medicine of Araçatuba São Paulo State University (Unesp) Araçatuba/SP Brazil
- International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics Araçatuba/SP Brazil
- Department of Preventive Veterinary Medicine and Animal Reproduction School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp) Jaboticabal/SP Brazil
| |
Collapse
|
172
|
Choudhury A, Aron S, Botigué LR, Sengupta D, Botha G, Bensellak T, Wells G, Kumuthini J, Shriner D, Fakim YJ, Ghoorah AW, Dareng E, Odia T, Falola O, Adebiyi E, Hazelhurst S, Mazandu G, Nyangiri OA, Mbiyavanga M, Benkahla A, Kassim SK, Mulder N, Adebamowo SN, Chimusa ER, Muzny D, Metcalf G, Gibbs RA, Rotimi C, Ramsay M, Adeyemo AA, Lombard Z, Hanchard NA. High-depth African genomes inform human migration and health. Nature 2020; 586:741-748. [PMID: 33116287 PMCID: PMC7759466 DOI: 10.1038/s41586-020-2859-7] [Citation(s) in RCA: 228] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/07/2020] [Indexed: 01/05/2023]
Abstract
The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals-comprising 50 ethnolinguistic groups, including previously unsampled populations-to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon-but in other genes, variants denoted as 'likely pathogenic' in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health.
Collapse
Affiliation(s)
- Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laura R Botigué
- Center for Research in Agricultural Genomics (CRAG), Plant and Animal Genomics Program, CSIC-IRTA-UAB-UB, Barcelona, Spain
| | - Dhriti Sengupta
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gerrit Botha
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Taoufik Bensellak
- System and Data Engineering Team, Abdelmalek Essaadi University, ENSA, Tangier, Morocco
| | - Gordon Wells
- Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa.,South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.,Africa Health Research Institute, Durban, South Africa
| | - Judit Kumuthini
- Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa.,South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yasmina J Fakim
- Department of Agriculture and Food Science, Faculty of Agriculture, University of Mauritius, Reduit, Mauritius.,Department of Digital Technologies,Faculty of Information, Communication & Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Anisah W Ghoorah
- Department of Digital Technologies,Faculty of Information, Communication & Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Eileen Dareng
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Institute of Human Virology Nigeria, Abuja, Nigeria
| | - Trust Odia
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Oluwadamilare Falola
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria.,Department of Computer and Information Sciences, Covenant University, Ota, Nigeria
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Gaston Mazandu
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Oscar A Nyangiri
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Mamana Mbiyavanga
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institute Pasteur of Tunis, Tunis, Tunisia
| | - Samar K Kassim
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Abbaseya, Cairo, Egypt
| | - Nicola Mulder
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Sally N Adebamowo
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, University of Maryland Baltimore, Baltimore, MD, USA.,University of Maryland Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute for Infectious, Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | | | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Zané Lombard
- Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Neil A Hanchard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
| |
Collapse
|
173
|
Rowe M, Whittington E, Borziak K, Ravinet M, Eroukhmanoff F, Sætre GP, Dorus S. Molecular Diversification of the Seminal Fluid Proteome in a Recently Diverged Passerine Species Pair. Mol Biol Evol 2020; 37:488-506. [PMID: 31665510 PMCID: PMC6993853 DOI: 10.1093/molbev/msz235] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Seminal fluid proteins (SFPs) mediate an array of postmating reproductive processes that influence fertilization and fertility. As such, it is widely held that SFPs may contribute to postmating, prezygotic reproductive barriers between closely related taxa. We investigated seminal fluid (SF) diversification in a recently diverged passerine species pair (Passer domesticus and Passer hispaniolensis) using a combination of proteomic and comparative evolutionary genomic approaches. First, we characterized and compared the SF proteome of the two species, revealing consistencies with known aspects of SFP biology and function in other taxa, including the presence and diversification of proteins involved in immunity and sperm maturation. Second, using whole-genome resequencing data, we assessed patterns of genomic differentiation between house and Spanish sparrows. These analyses detected divergent selection on immunity-related SF genes and positive selective sweeps in regions containing a number of SF genes that also exhibited protein abundance diversification between species. Finally, we analyzed the molecular evolution of SFPs across 11 passerine species and found a significantly higher rate of positive selection in SFPs compared with the rest of the genome, as well as significant enrichments for functional pathways related to immunity in the set of positively selected SF genes. Our results suggest that selection on immunity pathways is an important determinant of passerine SF composition and evolution. Assessing the role of immunity genes in speciation in other recently diverged taxa should be prioritized given the potential role for immunity-related proteins in reproductive incompatibilities in Passer sparrows.
Collapse
Affiliation(s)
- Melissah Rowe
- Natural History Museum, University of Oslo, Oslo, Norway.,Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway.,Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Emma Whittington
- Center for Reproductive Evolution, Department of Biology, Syracuse University, Syracuse, NY
| | - Kirill Borziak
- Center for Reproductive Evolution, Department of Biology, Syracuse University, Syracuse, NY
| | - Mark Ravinet
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Fabrice Eroukhmanoff
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Glenn-Peter Sætre
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway
| | - Steve Dorus
- Center for Reproductive Evolution, Department of Biology, Syracuse University, Syracuse, NY
| |
Collapse
|
174
|
Bates S, Sesia M, Sabatti C, Candès E. Causal inference in genetic trio studies. Proc Natl Acad Sci U S A 2020; 117:24117-24126. [PMID: 32948695 PMCID: PMC7533659 DOI: 10.1073/pnas.2007743117] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/11/2020] [Indexed: 12/26/2022] Open
Abstract
We introduce a method to draw causal inferences-inferences immune to all possible confounding-from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.
Collapse
Affiliation(s)
- Stephen Bates
- Department of Statistics, Stanford University, Stanford, CA 94305;
| | - Matteo Sesia
- Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA 90089
| | - Chiara Sabatti
- Department of Statistics, Stanford University, Stanford, CA 94305
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Emmanuel Candès
- Department of Statistics, Stanford University, Stanford, CA 94305;
- Department of Mathematics, Stanford University, Stanford, CA 94305
| |
Collapse
|
175
|
Semmes EC, Shen E, Cohen JL, Zhang C, Wei Q, Hurst JH, Walsh KM. Genetic variation associated with childhood and adult stature and risk of MYCN-amplified neuroblastoma. Cancer Med 2020; 9:8216-8225. [PMID: 32945147 PMCID: PMC7643638 DOI: 10.1002/cam4.3458] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/07/2020] [Accepted: 08/25/2020] [Indexed: 12/16/2022] Open
Abstract
Background Neuroblastoma is the most common pediatric solid tumor. MYCN‐amplification is an important negative prognostic indicator and inherited genetic contributions to risk are incompletely understood. Genetic determinants of stature increase risk of several adult and childhood cancers, but have not been studied in neuroblastoma despite elevated neuroblastoma incidence in children with congenital overgrowth syndromes. Methods We investigated the association between genetic determinants of height and neuroblastoma risk in 1538 neuroblastoma cases, stratified by MYCN‐amplification status, and compared to 3390 European‐ancestry controls using polygenic scores for birth length (five variants), childhood height (six variants), and adult height (413 variants). We further examined the UK Biobank to evaluate the association of known neuroblastoma risk loci and stature. Results An increase in the polygenic score for childhood stature, corresponding to a ~0.5 cm increase in pre‐pubertal height, was associated with greater risk of MYCN‐amplified neuroblastoma (OR = 1.14, P = .047). An increase in the polygenic score for adult stature, corresponding to a ~1.7 cm increase in adult height attainment, was associated with decreased risk of MYCN‐amplified neuroblastoma (OR = 0.87, P = .047). These associations persisted in case‐case analyses comparing MYCN‐amplified to MYCN‐unamplified neuroblastoma. No polygenic height scores were associated with MYCN‐unamplified neuroblastoma risk. Previously identified genome‐wide association study hits for neuroblastoma (N = 10) were significantly enriched for association with both childhood (P = 4.0 × 10−3) and adult height (P = 8.9 × 10−3) in >250 000 UK Biobank study participants. Conclusions Genetic propensity to taller childhood height and shorter adult height were associated with MYCN‐amplified neuroblastoma risk, suggesting that biological pathways affecting growth trajectories and pubertal timing may contribute to MYCN‐amplified neuroblastoma etiology.
Collapse
Affiliation(s)
- Eleanor C Semmes
- Medical Scientist Training Program, Duke University, Durham, NC, USA.,Department of Pediatrics, Children's Health and Discovery Institute, Duke University, Durham, NC, USA
| | - Erica Shen
- Division of Neuro-epidemiology, Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Jennifer L Cohen
- Division of Medical Genetics, Department of Pediatrics, Duke University, Durham, NC, USA
| | - Chenan Zhang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Qingyi Wei
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.,Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Jillian H Hurst
- Department of Pediatrics, Children's Health and Discovery Institute, Duke University, Durham, NC, USA
| | - Kyle M Walsh
- Department of Pediatrics, Children's Health and Discovery Institute, Duke University, Durham, NC, USA.,Division of Neuro-epidemiology, Department of Neurosurgery, Duke University, Durham, NC, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.,Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| |
Collapse
|
176
|
Ferraro NM, Strober BJ, Einson J, Abell NS, Aguet F, Barbeira AN, Brandt M, Bucan M, Castel SE, Davis JR, Greenwald E, Hess GT, Hilliard AT, Kember RL, Kotis B, Park Y, Peloso G, Ramdas S, Scott AJ, Smail C, Tsang EK, Zekavat SM, Ziosi M, Aradhana, Ardlie KG, Assimes TL, Bassik MC, Brown CD, Correa A, Hall I, Im HK, Li X, Natarajan P, Lappalainen T, Mohammadi P, Montgomery SB, Battle A. Transcriptomic signatures across human tissues identify functional rare genetic variation. Science 2020; 369:eaaz5900. [PMID: 32913073 PMCID: PMC7646251 DOI: 10.1126/science.aaz5900] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 07/31/2020] [Indexed: 12/18/2022]
Abstract
Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.
Collapse
Affiliation(s)
- Nicole M Ferraro
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Benjamin J Strober
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jonah Einson
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Nathan S Abell
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Margot Brandt
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Maja Bucan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephane E Castel
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Joe R Davis
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Emily Greenwald
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Gaelen T Hess
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Austin T Hilliard
- Palo Alto Veterans Institute for Research, Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Rachel L Kember
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Bence Kotis
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - YoSon Park
- Department of Systems Pharmacology and Translational Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Gina Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Shweta Ramdas
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandra J Scott
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Craig Smail
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Emily K Tsang
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Seyedeh M Zekavat
- Medical & Population Genomics, Yale School of Medicine and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Aradhana
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Themistocles L Assimes
- Palo Alto Veterans Institute for Research, Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Ira Hall
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Xin Li
- Department of Pathology, Stanford University, Stanford, CA, USA
- Shanghai Institutes for Biological Sciences, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Pejman Mohammadi
- New York Genome Center, New York, NY, USA.
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- Scripps Translational Science Institute, La Jolla, CA, USA
| | - Stephen B Montgomery
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
177
|
Bodelon C, Oh H, Derkach A, Sampson JN, Sprague BL, Vacek P, Weaver DL, Fan S, Palakal M, Papathomas D, Xiang J, Patel DA, Linville L, Clare SE, Visscher DW, Mies C, Hewitt SM, Brinton LA, Storniolo AMV, He C, Chanock SJ, Garcia-Closas M, Gierach GL, Figueroa JD. Polygenic risk score for the prediction of breast cancer is related to lesser terminal duct lobular unit involution of the breast. NPJ Breast Cancer 2020; 6:41. [PMID: 32964115 PMCID: PMC7477555 DOI: 10.1038/s41523-020-00184-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 08/06/2020] [Indexed: 12/26/2022] Open
Abstract
Terminal duct lobular units (TDLUs) are the predominant anatomical structures where breast cancers originate. Having lesser degrees of age-related TDLU involution, measured as higher TDLUs counts or more epithelial TDLU substructures (acini), is related to increased breast cancer risk among women with benign breast disease (BBD). We evaluated whether a recently developed polygenic risk score (PRS) based on 313-common variants for breast cancer prediction is related to TDLU involution in the background, normal breast tissue, as this could provide mechanistic clues on the genetic predisposition to breast cancer. Among 1398 women without breast cancer, higher values of the PRS were significantly associated with higher TDLU counts (P = 0.004), but not with acini counts (P = 0.808), in histologically normal tissue samples from donors and diagnostic BBD biopsies. Mediation analysis indicated that TDLU counts may explain a modest proportion (≤10%) of the association of the 313-variant PRS with breast cancer risk. These findings suggest that TDLU involution might be an intermediate step in the association between common genetic variation and breast cancer risk.
Collapse
Affiliation(s)
- Clara Bodelon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Hannah Oh
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
- Division of Health Policy and Management, College of Health Sciences, Korea University, Seoul, Korea
| | - Andriy Derkach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Joshua N. Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Brian L. Sprague
- University of Vermont College of Medicine and Vermont Cancer Center, Burlington, VT USA
| | - Pamela Vacek
- University of Vermont College of Medicine and Vermont Cancer Center, Burlington, VT USA
| | - Donald L. Weaver
- University of Vermont College of Medicine and Vermont Cancer Center, Burlington, VT USA
| | - Shaoqi Fan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Maya Palakal
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Daphne Papathomas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Jackie Xiang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Deesha A. Patel
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Laura Linville
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Susan E. Clare
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Daniel W. Visscher
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN USA
| | - Carolyn Mies
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Stephen M. Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA
| | - Louise A. Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Anna Maria V. Storniolo
- Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center, Indianapolis, IN USA
| | - Chunyan He
- Department Internal Medicine, Division of Medical Oncology, College of Medicine, University of Kentucky, Lexington, KY USA
- Markey Cancer Center, University of Kentucky, Lexington, KY USA
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | | | - Gretchen L. Gierach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Jonine D. Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
- Usher Institute of Population Health Sciences and Informatics and Cancer Research UK Edinburgh Centre, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
178
|
Lin SH, Sampson JN, Grünewald TGP, Surdez D, Reynaud S, Mirabeau O, Karlins E, Rubio RA, Zaidi S, Grossetête-Lalami S, Ballet S, Lapouble E, Laurence V, Michon J, Pierron G, Kovar H, Kontny U, González-Neira A, Alonso J, Patino-Garcia A, Corradini N, Bérard PM, Miller J, Freedman ND, Rothman N, Carter BD, Dagnall CL, Burdett L, Jones K, Manning M, Wyatt K, Zhou W, Yeager M, Cox DG, Hoover RN, Khan J, Armstrong GT, Leisenring WM, Bhatia S, Robison LL, Kulozik AE, Kriebel J, Meitinger T, Metzler M, Krumbholz M, Hartmann W, Strauch K, Kirchner T, Dirksen U, Mirabello L, Tucker MA, Tirode F, Morton LM, Chanock SJ, Delattre O, Machiela MJ. Low-frequency variation near common germline susceptibility loci are associated with risk of Ewing sarcoma. PLoS One 2020; 15:e0237792. [PMID: 32881892 PMCID: PMC7470401 DOI: 10.1371/journal.pone.0237792] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/03/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Ewing sarcoma (EwS) is a rare, aggressive solid tumor of childhood, adolescence and young adulthood associated with pathognomonic EWSR1-ETS fusion oncoproteins altering transcriptional regulation. Genome-wide association studies (GWAS) have identified 6 common germline susceptibility loci but have not investigated low-frequency inherited variants with minor allele frequencies below 5% due to limited genotyped cases of this rare tumor. METHODS We investigated the contribution of rare and low-frequency variation to EwS susceptibility in the largest EwS genome-wide association study to date (733 EwS cases and 1,346 unaffected controls of European ancestry). RESULTS We identified two low-frequency variants, rs112837127 and rs2296730, on chromosome 20 that were associated with EwS risk (OR = 0.186 and 2.038, respectively; P-value < 5×10-8) and located near previously reported common susceptibility loci. After adjusting for the most associated common variant at the locus, only rs112837127 remained a statistically significant independent signal (OR = 0.200, P-value = 5.84×10-8). CONCLUSIONS These findings suggest rare variation residing on common haplotypes are important contributors to EwS risk. IMPACT Motivate future targeted sequencing studies for a comprehensive evaluation of low-frequency and rare variation around common EwS susceptibility loci.
Collapse
Affiliation(s)
- Shu-Hong Lin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Joshua N. Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Thomas G. P. Grünewald
- Max-Eder Research Group for Pediatric Sarcoma Biology, Ludwig Maximilians Universität (LMU), Munich, Germany
- Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Didier Surdez
- Inserm U830, Équipe Labellisés LNCC, PSL Université, Institut Curie, Paris, France
| | | | - Olivier Mirabeau
- Inserm U830, Équipe Labellisés LNCC, PSL Université, Institut Curie, Paris, France
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | - Eric Karlins
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Rebeca Alba Rubio
- Max-Eder Research Group for Pediatric Sarcoma Biology, Ludwig Maximilians Universität (LMU), Munich, Germany
| | - Sakina Zaidi
- Inserm U830, Équipe Labellisés LNCC, PSL Université, Institut Curie, Paris, France
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | - Sandrine Grossetête-Lalami
- Inserm U830, Équipe Labellisés LNCC, PSL Université, Institut Curie, Paris, France
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | - Stelly Ballet
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | - Eve Lapouble
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | | | - Jean Michon
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | | | - Heinrich Kovar
- Children’s Cancer Research Institute, St. Anna Kinderkrebsforschung, Vienna, Austria
| | - Udo Kontny
- Division of Pediatric Hematology, Oncology and Stem Cell Transplantation, Uniklinik RWTH Aachen, Aachen, Germany
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Javier Alonso
- Unidad de Tumores Solidos Infantiles (IIER-ISCIII) & Centro de Investigación Biomédica en Red de Enfermedades Raras (CB06/07/1009; CIBERER-ISCIII), Instituto de Salud Carlos III, Majadahonda, Spain
| | - Ana Patino-Garcia
- Laboratory of Pediatrics, University Clinic of Navarra, Program in Solid Tumors, Center for Applied Medical Research (CIMA) and Navarra's Health Research Institute (IdiSNA), Pamplona, Spain
| | - Nadège Corradini
- Institute for Paediatric Haematology and Oncology, Leon Bérard Cancer Centre, University of Lyon, Lyon, France
| | - Perrine Marec Bérard
- Institute for Paediatric Haematology and Oncology, Leon Bérard Cancer Centre, University of Lyon, Lyon, France
| | - Jeremy Miller
- Information Management Services, Inc., Calverton, MD, United States of America
| | - Neal D. Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Brian D. Carter
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, United States of America
| | - Casey L. Dagnall
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Laurie Burdett
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Kristine Jones
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Michelle Manning
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Kathleen Wyatt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, United States of America
| | - David G. Cox
- Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
| | - Robert N. Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Javed Khan
- Genetics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, United States of America
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, United States of America
| | - Wendy M. Leisenring
- Cancer Prevention and Clinical Statistics Programs, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Leslie L. Robison
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, United States of America
| | - Andreas E. Kulozik
- Department of Pediatric Oncology, Hematology and Immunology and Hopp Children Cancer Center, University of Heidelberg, Heidelberg, Germany
| | - Jennifer Kriebel
- 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
- German Center for Diabetes Research (DZD), München, Neuherberg, Germany
| | - Thomas Meitinger
- German Research Center for Environmental Health, Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Markus Metzler
- Department of Paediatrics and Adolescent Medicine, University Hospital of Erlangen, Erlangen, Germany
| | - Manuela Krumbholz
- Department of Paediatrics and Adolescent Medicine, University Hospital of Erlangen, Erlangen, Germany
| | - Wolfgang Hartmann
- Division of Translational Pathology, Gerhard-Domagk Institute of Pathology, University Hospital of Münster, Münster, Germany
| | | | - Thomas Kirchner
- Institute of Pathology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Uta Dirksen
- Pediatrics III, West German Cancer Centre, University Hospital Essen, Essen, Germany
- German Cancer Consortium (DKTK), Center Essen, Heidelberg, Germany
| | - Lisa Mirabello
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Margaret A. Tucker
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Franck Tirode
- Inserm U830, Équipe Labellisés LNCC, PSL Université, Institut Curie, Paris, France
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | - Lindsay M. Morton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| | - Olivier Delattre
- Inserm U830, Équipe Labellisés LNCC, PSL Université, Institut Curie, Paris, France
- SIREDO Oncology Centre, Institut Curie, Paris, France
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America
| |
Collapse
|
179
|
Wu FL, Strand AI, Cox LA, Ober C, Wall JD, Moorjani P, Przeworski M. A comparison of humans and baboons suggests germline mutation rates do not track cell divisions. PLoS Biol 2020; 18:e3000838. [PMID: 32804933 PMCID: PMC7467331 DOI: 10.1371/journal.pbio.3000838] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 09/02/2020] [Accepted: 07/28/2020] [Indexed: 12/19/2022] Open
Abstract
In humans, most germline mutations are inherited from the father. This observation has been widely interpreted as reflecting the replication errors that accrue during spermatogenesis. If so, the male bias in mutation should be substantially lower in a closely related species with similar rates of spermatogonial stem cell divisions but a shorter mean age of reproduction. To test this hypothesis, we resequenced two 3-4 generation nuclear families (totaling 29 individuals) of olive baboons (Papio anubis), who reproduce at approximately 10 years of age on average, and analyzed the data in parallel with three 3-generation human pedigrees (26 individuals). We estimated a mutation rate per generation in baboons of 0.57×10-8 per base pair, approximately half that of humans. Strikingly, however, the degree of male bias in germline mutations is approximately 4:1, similar to that of humans-indeed, a similar male bias is seen across mammals that reproduce months, years, or decades after birth. These results mirror the finding in humans that the male mutation bias is stable with parental ages and cast further doubt on the assumption that germline mutations track cell divisions. Our mutation rate estimates for baboons raise a further puzzle, suggesting a divergence time between apes and Old World monkeys of 65 million years, too old to be consistent with the fossil record; reconciling them now requires not only a slowdown of the mutation rate per generation in humans but also in baboons.
Collapse
Affiliation(s)
- Felix L. Wu
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, New York, United States of America
| | - Alva I. Strand
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Laura A. Cox
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Carole Ober
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Jeffrey D. Wall
- Institute for Human Genetics, Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, California, United States of America
| | - Priya Moorjani
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Molly Przeworski
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| |
Collapse
|
180
|
Whalen A, Gorjanc G, Hickey JM. AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data. Bioinformatics 2020; 36:4369-4371. [PMID: 32467963 PMCID: PMC7520044 DOI: 10.1093/bioinformatics/btaa499] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 04/22/2020] [Accepted: 05/25/2020] [Indexed: 12/12/2022] Open
Abstract
SUMMARY AlphaFamImpute is an imputation package for calling, phasing and imputing genome-wide genotypes in outbred full-sib families from single nucleotide polymorphism (SNP) array and genotype-by-sequencing (GBS) data. GBS data are increasingly being used to genotype individuals, especially when SNP arrays do not exist for a population of interest. Low-coverage GBS produces data with a large number of missing or incorrect naïve genotype calls, which can be improved by identifying shared haplotype segments between full-sib individuals. Here, we present AlphaFamImpute, an algorithm specifically designed to exploit the genetic structure of full-sib families. It performs imputation using a two-step approach. In the first step, it phases and imputes parental genotypes based on the segregation states of their offspring (i.e. which pair of parental haplotypes the offspring inherited). In the second step, it phases and imputes the offspring genotypes by detecting which haplotype segments the offspring inherited from their parents. With a series of simulations, we find that AlphaFamImpute obtains high-accuracy genotypes, even when the parents are not genotyped and individuals are sequenced at <1x coverage. AVAILABILITY AND IMPLEMENTATION AlphaFamImpute is available as a Python package from the AlphaGenes website http://www.AlphaGenes.roslin.ed.ac.uk/AlphaFamImpute. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Andrew Whalen
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
| | - John M Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
| |
Collapse
|
181
|
Biagini SA, Ramos-Luis E, Comas D, Calafell F. The place of metropolitan France in the European genomic landscape. Hum Genet 2020; 139:1091-1105. [DOI: 10.1007/s00439-020-02158-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/25/2020] [Indexed: 10/24/2022]
|
182
|
Polimanti R, Walters RK, Johnson EC, McClintick JN, Adkins AE, Adkins DE, Bacanu SA, Bierut LJ, Bigdeli TB, Brown S, Bucholz KK, Copeland WE, Costello EJ, Degenhardt L, Farrer LA, Foroud TM, Fox L, Goate AM, Grucza R, Hack LM, Hancock DB, Hartz SM, Heath AC, Hewitt JK, Hopfer CJ, Johnson EO, Kendler KS, Kranzler HR, Krauter K, Lai D, Madden PAF, Martin NG, Maes HH, Nelson EC, Peterson RE, Porjesz B, Riley BP, Saccone N, Stallings M, Wall TL, Webb BT, Wetherill L, Edenberg HJ, Agrawal A, Gelernter J. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol Psychiatry 2020; 25:1673-1687. [PMID: 32099098 PMCID: PMC7392789 DOI: 10.1038/s41380-020-0677-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/15/2020] [Accepted: 01/30/2020] [Indexed: 01/17/2023]
Abstract
To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
Collapse
Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jeanette N McClintick
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Amy E Adkins
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Daniel E Adkins
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Silviu-Alin Bacanu
- Virginia Commonwealth University Alcohol Research Center, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Sandra Brown
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - William E Copeland
- Department of Psychiatry, University of Vermont Medical Center, Burlington, VT, USA
| | - E Jane Costello
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Louis Fox
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Richard Grucza
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Dana B Hancock
- Center for Omics Discovery and Epidemiology, RTI International, Research Triangle Park, NC, USA
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Christian J Hopfer
- Department of Psychiatry, University of Colorado Denver, Aurora, CO, USA
| | - Eric O Johnson
- Center for Omics Discovery and Epidemiology, RTI International, Research Triangle Park, NC, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Kenneth Krauter
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Hermine H Maes
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Elliot C Nelson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Brien P Riley
- Virginia Commonwealth University Alcohol Research Center, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Nancy Saccone
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
| | - Michael Stallings
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Tamara L Wall
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Bradley T Webb
- Virginia Commonwealth University Alcohol Research Center, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA.
| |
Collapse
|
183
|
Streicher SA, Klein AP, Olson SH, Kurtz RC, Amundadottir LT, DeWan AT, Zhao H, Risch HA. A pooled genome-wide association study identifies pancreatic cancer susceptibility loci on chromosome 19p12 and 19p13.3 in the full-Jewish population. Hum Genet 2020; 140:309-319. [PMID: 32671597 DOI: 10.1007/s00439-020-02205-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/07/2020] [Indexed: 12/20/2022]
Abstract
Jews are estimated to be at increased risk of pancreatic cancer compared to non-Jews, but their observed 50-80% excess risk is not explained by known non-genetic or genetic risk factors. We conducted a GWAS in a case-control sample of American Jews, largely Ashkenazi, including 406 pancreatic cancer patients and 2332 controls, identified in the dbGaP, PanScan I/II, PanC4 and GERA data sets. We then examined resulting SNPs with P < 10-7 in an expanded sample set, of 539 full- or part-Jewish pancreatic cancer patients and 4117 full- or part-Jewish controls from the same data sets. Jewish ancestries were genetically determined using seeded FastPCA. Among the full Jews, a novel genome-wide significant association was detected on chromosome 19p12 (rs66562280, per-allele OR = 1.55, 95% CI = 1.33-1.81, P = 10-7.6). A suggestive relatively independent association was detected on chromosome 19p13.3 (rs2656937, OR = 1.53, 95% CI = 1.31-1.78, P = 10-7.0). Similar associations were seen for these SNPs among the full and part Jews combined. This is the first GWAS conducted for pancreatic cancer in the increased-risk Jewish population. The SNPs rs66562280 and rs2656937 are located in introns of ZNF100-like and ARRDC5, respectively, and are known to alter regulatory motifs of genes that play integral roles in pancreatic carcinogenesis.
Collapse
Affiliation(s)
- Samantha A Streicher
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
| | - Alison P Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, 401 North Broadway, Baltimore, MD, 21287, USA.,Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, 401 North Broadway, Baltimore, MD, 21287, USA
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY, 10017, USA
| | - Robert C Kurtz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Laufey T Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health,, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Andrew T DeWan
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, Connecticut, 06520, USA.,Program of Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT, 06520, USA
| | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA.
| |
Collapse
|
184
|
Ravinet M, Kume M, Ishikawa A, Kitano J. Patterns of genomic divergence and introgression between Japanese stickleback species with overlapping breeding habitats. J Evol Biol 2020; 34:114-127. [PMID: 32557887 DOI: 10.1111/jeb.13664] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/29/2020] [Accepted: 06/05/2020] [Indexed: 11/26/2022]
Abstract
With only a few absolute geographic barriers in marine environments, the factors maintaining reproductive isolation among marine organisms remain elusive. However, spatial structuring in breeding habitat can contribute to reproductive isolation. This is particularly important for marine organisms that migrate to use fresh- or brackish water environments to breed. The Japanese Gasterosteus stickleback species, the Pacific Ocean three-spined stickleback (G. aculeatus) and the Japan Sea stickleback (G. nipponicus) overwinter in the sea, but migrate to rivers for spawning. Although they co-occur at several locations across the Japanese islands, they are reproductively isolated. Our previous studies in Bekanbeushi River showed that the Japan Sea stickleback spawns in the estuary, while the Pacific Ocean stickleback mainly spawns further upstream in freshwater. Overall genomic divergence was very high with many interspersed regions of introgression. Here, we investigated genomic divergence and introgression between the sympatric species in the much shorter Tokotan River, where they share spawning sites. The levels of genome-wide divergence were reduced and introgression was increased, suggesting that habitat isolation substantially contributes to a reduction in gene flow. We also found that genomic regions of introgression were largely shared between the two systems. Furthermore, some regions of introgression were located near loci with a heterozygote advantage for juvenile survival. Taken together, introgression may be partially driven by adaptation in this system. Although, the two species remain clearly genetically differentiated. Regions with low recombination rates showed especially low introgression. Speciation reversal is therefore likely prevented by barriers other than habitat isolation.
Collapse
Affiliation(s)
- Mark Ravinet
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Manabu Kume
- Kyoto University Field Science Education and Research Center, Kyoto, Japan
| | - Asano Ishikawa
- Ecological Genetics Laboratory, National Institute of Genetics, Mishima, Japan
| | - Jun Kitano
- Ecological Genetics Laboratory, National Institute of Genetics, Mishima, Japan
| |
Collapse
|
185
|
Money D, Wilson D, Jenko J, Whalen A, Thorn S, Gorjanc G, Hickey JM. Extending long-range phasing and haplotype library imputation algorithms to large and heterogeneous datasets. Genet Sel Evol 2020; 52:38. [PMID: 32640985 PMCID: PMC7346379 DOI: 10.1186/s12711-020-00558-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 06/26/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND We describe the latest improvements to the long-range phasing (LRP) and haplotype library imputation (HLI) algorithms for successful phasing of both datasets with one million individuals and datasets genotyped using different sets of single nucleotide polymorphisms (SNPs). Previous publicly available implementations of the LRP algorithm implemented in AlphaPhase could not phase large datasets due to the computational cost of defining surrogate parents by exhaustive all-against-all searches. Furthermore, the AlphaPhase implementations of LRP and HLI were not designed to deal with large amounts of missing data that are inherent when using multiple SNP arrays. METHODS We developed methods that avoid the need for all-against-all searches by performing LRP on subsets of individuals and then concatenating the results. We also extended LRP and HLI algorithms to enable the use of different sets of markers, including missing values, when determining surrogate parents and identifying haplotypes. We implemented and tested these extensions in an updated version of AlphaPhase, and compared its performance to the software package Eagle2. RESULTS A simulated dataset with one million individuals genotyped with the same 6711 SNPs for a single chromosome took less than a day to phase, compared to more than seven days for Eagle2. The percentage of correctly phased alleles at heterozygous loci was 90.2 and 99.9% for AlphaPhase and Eagle2, respectively. A larger dataset with one million individuals genotyped with 49,579 SNPs for a single chromosome took AlphaPhase 23 days to phase, with 89.9% of alleles at heterozygous loci phased correctly. The phasing accuracy was generally lower for datasets with different sets of markers than with one set of markers. For a simulated dataset with three sets of markers, 1.5% of alleles at heterozygous positions were phased incorrectly, compared to 0.4% with one set of markers. CONCLUSIONS The improved LRP and HLI algorithms enable AlphaPhase to quickly and accurately phase very large and heterogeneous datasets. AlphaPhase is an order of magnitude faster than the other tested packages, although Eagle2 showed a higher level of phasing accuracy. The speed gain will make phasing achievable for very large genomic datasets in livestock, enabling more powerful breeding and genetics research and application.
Collapse
Affiliation(s)
- Daniel Money
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - David Wilson
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Janez Jenko
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Andrew Whalen
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Steve Thorn
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - John M. Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| |
Collapse
|
186
|
Ludwig-Słomczyńska AH, Seweryn MT, Kapusta P, Pitera E, Handelman SK, Mantaj U, Cyganek K, Gutaj P, Dobrucka Ł, Wender-Ożegowska E, Małecki MT, Wołkow PP. Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients. BMC Med Genomics 2020; 13:97. [PMID: 32635923 PMCID: PMC7341625 DOI: 10.1186/s12920-020-00752-7] [Citation(s) in RCA: 10] [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: 08/02/2019] [Accepted: 06/30/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND BMI is a strong indicator of complications from type I diabetes, especially under intensive treatment. METHODS We have genotyped 435 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays and performed mitoGWAS on BMI. We identified additive interactions between mitochondrial and nuclear variants in genes associated with mitochondrial functioning MitoCarta2.0 and confirmed and refined the results on external cohorts: the Framingham Heart Study (FHS) and GTEx data. Linear mixed model analysis was performed using the GENESIS package in R/Bioconductor. RESULTS We find a borderline significant association between the mitochondrial variant rs28357980, localized to MT-ND2, and BMI (β = - 0.69, p = 0.056). This BMI association was confirmed on 1889 patients from FHS cohort (β = - 0.312, p = 0.047). Next, we searched for additive interactions between mitochondrial and nuclear variants. MT-ND2 variants interacted with variants in the genes SIRT3, ATP5B, CYCS, TFB2M and POLRMT. TFB2M is a mitochondrial transcription factor and together with TFAM creates a transcription promoter complex for the mitochondrial polymerase POLRMT. We have found an interaction between rs3021088 in MT-ND2 and rs6701836 in TFB2M leading to BMI decrease (inter_pval = 0.0241), while interaction of rs3021088 in MT-ND2 and rs41542013 in POLRMT led to BMI increase (inter_pval = 0.0004). The influence of these interactions on BMI was confirmed in external cohorts. CONCLUSIONS Here, we have shown that variants in the mitochondrial genome as well as additive interactions between mitochondrial and nuclear SNPs influence BMI in T1DM and general cohorts.
Collapse
Affiliation(s)
| | - Michał T Seweryn
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland
- The Ohio State University Wexner Medical Center, Department of Cancer Biology and Genetics, Columbus, OH, USA
| | - Przemysław Kapusta
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland
| | - Ewelina Pitera
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland
| | - Samuel K Handelman
- Department of Internal Medicine, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Urszula Mantaj
- Division of Reproduction, Poznań University of Medical Sciences, Poznań, Poland
| | - Katarzyna Cyganek
- Department of Metabolic Diseases, University Hospital Kraków, Kraków, Poland
| | - Paweł Gutaj
- Division of Reproduction, Poznań University of Medical Sciences, Poznań, Poland
| | - Łucja Dobrucka
- Department of Metabolic Diseases, University Hospital Kraków, Kraków, Poland
| | | | - Maciej T Małecki
- Department of Metabolic Diseases, University Hospital Kraków, Kraków, Poland
- Department of Metabolic Diseases, Jagiellonian University Medical College, Kraków, Poland
| | - Paweł P Wołkow
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland.
| |
Collapse
|
187
|
Zhou Y, Browning BL, Browning SR. Population-Specific Recombination Maps from Segments of Identity by Descent. Am J Hum Genet 2020; 107:137-148. [PMID: 32533945 PMCID: PMC7332656 DOI: 10.1016/j.ajhg.2020.05.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/20/2020] [Indexed: 12/26/2022] Open
Abstract
Recombination rates vary significantly across the genome, and estimates of recombination rates are needed for downstream analyses such as haplotype phasing and genotype imputation. Existing methods for recombination rate estimation are limited by insufficient amounts of informative genetic data or by high computational cost. We present a method and software, called IBDrecomb, for using segments of identity by descent to infer recombination rates. IBDrecomb can be applied to sequenced population cohorts to obtain high-resolution, population-specific recombination maps. In simulated admixed data, IBDrecomb obtains higher accuracy than admixture-based estimation of recombination rates. When applied to 2,500 simulated individuals, IBDrecomb obtains similar accuracy to a linkage-disequilibrium (LD)-based method applied to 96 individuals (the largest number for which computation is tractable). Compared to LD-based maps, our IBD-based maps have the advantage of estimating recombination rates in the recent past rather than the distant past. We used IBDrecomb to generate new recombination maps for European Americans and for African Americans from TOPMed sequence data from the Framingham Heart Study (1,626 unrelated individuals) and the Jackson Heart Study (2,046 unrelated individuals), and we compare them to LD-based, admixture-based, and family-based maps.
Collapse
Affiliation(s)
- Ying Zhou
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Brian L Browning
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| |
Collapse
|
188
|
Ioannidis AG, Blanco-Portillo J, Sandoval K, Hagelberg E, Miquel-Poblete JF, Moreno-Mayar JV, Rodríguez-Rodríguez JE, Quinto-Cortés CD, Auckland K, Parks T, Robson K, Hill AVS, Avila-Arcos MC, Sockell A, Homburger JR, Wojcik GL, Barnes KC, Herrera L, Berríos S, Acuña M, Llop E, Eng C, Huntsman S, Burchard EG, Gignoux CR, Cifuentes L, Verdugo RA, Moraga M, Mentzer AJ, Bustamante CD, Moreno-Estrada A. Native American gene flow into Polynesia predating Easter Island settlement. Nature 2020; 583:572-577. [PMID: 32641827 PMCID: PMC8939867 DOI: 10.1038/s41586-020-2487-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 05/22/2020] [Indexed: 11/08/2022]
Abstract
The possibility of voyaging contact between prehistoric Polynesian and Native American populations has long intrigued researchers. Proponents have pointed to the existence of New World crops, such as the sweet potato and bottle gourd, in the Polynesian archaeological record, but nowhere else outside the pre-Columbian Americas1-6, while critics have argued that these botanical dispersals need not have been human mediated7. The Norwegian explorer Thor Heyerdahl controversially suggested that prehistoric South American populations had an important role in the settlement of east Polynesia and particularly of Easter Island (Rapa Nui)2. Several limited molecular genetic studies have reached opposing conclusions, and the possibility continues to be as hotly contested today as it was when first suggested8-12. Here we analyse genome-wide variation in individuals from islands across Polynesia for signs of Native American admixture, analysing 807 individuals from 17 island populations and 15 Pacific coast Native American groups. We find conclusive evidence for prehistoric contact of Polynesian individuals with Native American individuals (around AD 1200) contemporaneous with the settlement of remote Oceania13-15. Our analyses suggest strongly that a single contact event occurred in eastern Polynesia, before the settlement of Rapa Nui, between Polynesian individuals and a Native American group most closely related to the indigenous inhabitants of present-day Colombia.
Collapse
Affiliation(s)
- Alexander G Ioannidis
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico.
| | - Javier Blanco-Portillo
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico
| | - Karla Sandoval
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico
| | - Erika Hagelberg
- Department of Biosciences, University of Oslo, Blindern, Oslo, Norway
| | | | | | | | - Consuelo D Quinto-Cortés
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico
| | - Kathryn Auckland
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Tom Parks
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Kathryn Robson
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Adrian V S Hill
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - María C Avila-Arcos
- International Laboratory for Human Genome Research (LIIGH), UNAM Juriquilla, Queretaro, Mexico
| | - Alexandra Sockell
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
| | - Julian R Homburger
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
| | - Genevieve L Wojcik
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
| | - Kathleen C Barnes
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Luisa Herrera
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Soledad Berríos
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Mónica Acuña
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Elena Llop
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Celeste Eng
- Program in Pharmaceutical Sciences and Pharmacogenomics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Program in Pharmaceutical Sciences and Pharmacogenomics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Esteban G Burchard
- Program in Pharmaceutical Sciences and Pharmacogenomics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Christopher R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Lucía Cifuentes
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Ricardo A Verdugo
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
- Basic-Applied Oncology Department, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Mauricio Moraga
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
- Department of Anthropology, Faculty of Social Sciences, University of Chile, Santiago, Chile
| | - Alexander J Mentzer
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Carlos D Bustamante
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico.
| |
Collapse
|
189
|
Lee Yu HL, Fan TW, Hsing IM. Oligonucleotide hybridization with magnetic separation assay for multiple SNP phasing. Anal Chim Acta X 2020; 5:100050. [PMID: 33117988 PMCID: PMC7587028 DOI: 10.1016/j.acax.2020.100050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 10/29/2022] Open
Abstract
Since humans have two copies of each gene, multiple mutations in different loci may or may not be found on the same strand of DNA (i.e., inherited from one parent). When a person is heterozygous at more than one position, the placement of these mutations, also called the haplotype phase, (i.e., cis for the same strand and trans for different strands) can result in the expression of different amount and type of proteins. In this work, we described an enzyme-free method to phase two single nucleotide polymorphisms (SNPs) using two fluorophore/quencher-labelled probes, where one of which was biotinylated. The fluorescence signal was obtained twice: first, after the addition of the labelled probes and second, after the addition of the magnetic beads. The first signal was shown to be proportional to the total number of SNP A and SNP B present in the target analyte, while the second signal showed a marked decrease of the fluorescence signal from the non-biotinylated probe when the SNPs were in trans, showing that the probe immobilized on the magnetic bead selectively captures targets with SNPs in a cis configuration. We then mimic the nature of the human genome which consists of two haplotype copies of each gene, and showed that 250 nM of the 10 possible pairs of haplotypes could be differentiated using a combination of fluorescence microscopy and fluorescence detection.
Collapse
Affiliation(s)
- Henson L. Lee Yu
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Tsz Wing Fan
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - I-Ming Hsing
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| |
Collapse
|
190
|
Bretherick AD, Canela-Xandri O, Joshi PK, Clark DW, Rawlik K, Boutin TS, Zeng Y, Amador C, Navarro P, Rudan I, Wright AF, Campbell H, Vitart V, Hayward C, Wilson JF, Tenesa A, Ponting CP, Baillie JK, Haley C. Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet 2020; 16:e1008785. [PMID: 32628676 PMCID: PMC7337286 DOI: 10.1371/journal.pgen.1008785] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 04/21/2020] [Indexed: 01/25/2023] Open
Abstract
To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.
Collapse
Affiliation(s)
- Andrew D. Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Peter K. Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - David W. Clark
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Konrad Rawlik
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Thibaud S. Boutin
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Carmen Amador
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Alan F. Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - James F. Wilson
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Albert Tenesa
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Chris P. Ponting
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - J. Kenneth Baillie
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| |
Collapse
|
191
|
Suvanto M, Beesley J, Blomqvist C, Chenevix-Trench G, Khan S, Nevanlinna H. SNPs in lncRNA Regions and Breast Cancer Risk. Front Genet 2020; 11:550. [PMID: 32714364 PMCID: PMC7340126 DOI: 10.3389/fgene.2020.00550] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 05/07/2020] [Indexed: 01/18/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in human physiology, and have been found to be associated with various cancers. Transcribed ultraconserved regions (T-UCRs) are a subgroup of lncRNAs conserved in several species, and are often located in cancer-related regions. Breast cancer is the most common cancer in women worldwide and the leading cause of female cancer deaths. We investigated the association of genetic variants in lncRNA and T-UCR regions with breast cancer risk to uncover candidate loci for further analysis. Our focus was on low-penetrance variants that can be discovered in a large dataset. We selected 565 regions of lncRNAs and T-UCRs that are expressed in breast or breast cancer tissue, or show expression correlation to major breast cancer associated genes. We studied the association of single nucleotide polymorphisms (SNPs) in these regions with breast cancer risk in the 122970 case samples and 105974 controls of the Breast Cancer Association Consortium's genome-wide data, and also by in silico functional analyses using Integrated Expression Quantitative trait and in silico prediction of GWAS targets (INQUISIT) and expression quantitative trait loci (eQTL) analysis. The eQTL analysis was carried out using the METABRIC dataset and analyses from GTEx and ncRNA eQTL databases. We found putative breast cancer risk variants (p < 1 × 10-5) targeting the lncRNA GABPB1-AS1 in INQUISIT and eQTL analysis. In addition, putative breast cancer risk associated SNPs (p < 1 × 10-5) in the region of two T-UCRs, uc.184 and uc.313, located in protein coding genes CPEB4 and TIAL1, respectively, targeted these genes in INQUISIT and in eQTL analysis. Other non-coding regions containing SNPs with the defined p-value and highly significant false discovery rate (FDR) for breast cancer risk association were discovered that may warrant further studies. These results suggest candidate lncRNA loci for further research on breast cancer risk and the molecular mechanisms.
Collapse
Affiliation(s)
- Maija Suvanto
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jonathan Beesley
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QL, Australia
| | - Carl Blomqvist
- Department of Oncology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QL, Australia
| | - Sofia Khan
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|
192
|
Yang DW, Wang TM, Zhang JB, Li XZ, He YQ, Xiao R, Xue WQ, Zheng XH, Zhang PF, Zhang SD, Hu YZ, Shen GP, Chen M, Sun Y, Jia WH. Genome-wide association study identifies genetic susceptibility loci and pathways of radiation-induced acute oral mucositis. J Transl Med 2020; 18:224. [PMID: 32503578 PMCID: PMC7275566 DOI: 10.1186/s12967-020-02390-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/28/2020] [Indexed: 12/17/2022] Open
Abstract
Background Radiation-induced oral mucositis (OM) is one of the most common acute complications for head and neck cancer. Severe OM is associated with radiation treatment breaks, which harms successful tumor management. Radiogenomics studies have indicated that genetic variants are associated with adverse effects of radiotherapy. Methods A large-scale genome-wide scan was performed in 1467 nasopharyngeal carcinoma patients, including 753 treated with 2D-CRT from Genetic Architecture of the Radiotherapy Toxicity and Prognosis (GARTP) cohort and 714 treated with IMRT (192 from the GARTP and 522 newly recruited). Subgroup analysis by radiotherapy technique was further performed in the top associations. We also performed physical and regulatory mapping of the risk loci and gene set enrichment analysis of the candidate target genes. Results We identified 50 associated genomic loci and 64 genes via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping and gene-based analysis, and 36 of these loci were replicated in subgroup analysis. Interestingly, one of the top loci located in TNKS, a gene relevant to radiation toxicity, was associated with increased OM risk with OR = 3.72 of the lead SNP rs117157809 (95% CI 2.10–6.57; P = 6.33 × 10−6). Gene set analyses showed that the 64 candidate target genes were enriched in the biological processes of regulating telomere capping and maintenance and telomerase activity (Top P = 7.73 × 10−7). Conclusions These results enhance the biological understanding of radiotherapy toxicity. The association signals enriched in telomere function regulation implicate the potential underlying mechanism and warrant further functional investigation and potential individual radiotherapy applications.
Collapse
Affiliation(s)
- Da-Wei Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Tong-Min Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Jiang-Bo Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Xi-Zhao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Yong-Qiao He
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Ruowen Xiao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Wen-Qiong Xue
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Xiao-Hui Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Pei-Fen Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Shao-Dan Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Ye-Zhu Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Guo-Ping Shen
- Department of Radiation Oncology, Sun Yat-sen University First Affiliated Hospital, Guangzhou, People's Republic of China
| | - Mingyuan Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China. .,School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China. .,Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, People's Republic of China.
| |
Collapse
|
193
|
Manichaikul A, Peres LC, Wang XQ, Barnard ME, Chyn D, Sheng X, Du Z, Tyrer J, Dennis J, Schwartz AG, Cote ML, Peters E, Moorman PG, Bondy M, Barnholtz-Sloan JS, Terry P, Alberg AJ, Bandera EV, Funkhouser E, Wu AH, Pearce CL, Pike M, Setiawan VW, Haiman CA, African American Breast Cancer Consortium (AABC), African Ancestry Prostate Cancer Consortium (AAPC), Palmer JR, LeMarchand L, Wilkens LR, Berchuck A, Doherty JA, Modugno F, Ness R, Moysich K, Karlan BY, Whittemore AS, McGuire V, Sieh W, Lawrenson K, Gayther S, Sellers TA, Pharoah P, Schildkraut JM. Identification of novel epithelial ovarian cancer loci in women of African ancestry. Int J Cancer 2020; 146:2987-2998. [PMID: 31469419 PMCID: PMC7523187 DOI: 10.1002/ijc.32653] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 07/29/2019] [Accepted: 08/12/2019] [Indexed: 12/11/2022]
Abstract
Women of African ancestry have lower incidence of epithelial ovarian cancer (EOC) yet worse survival compared to women of European ancestry. We conducted a genome-wide association study in African ancestry women with 755 EOC cases, including 537 high-grade serous ovarian carcinomas (HGSOC) and 1,235 controls. We identified four novel loci with suggestive evidence of association with EOC (p < 1 × 10-6 ), including rs4525119 (intronic to AKR1C3), rs7643459 (intronic to LOC101927394), rs4286604 (12 kb 3' of UGT2A2) and rs142091544 (5 kb 5' of WWC1). For HGSOC, we identified six loci with suggestive evidence of association including rs37792 (132 kb 5' of follistatin [FST]), rs57403204 (81 kb 3' of MAGEC1), rs79079890 (LOC105376360 intronic), rs66459581 (5 kb 5' of PRPSAP1), rs116046250 (GABRG3 intronic) and rs192876988 (32 kb 3' of GK2). Among the identified variants, two are near genes known to regulate hormones and diseases of the ovary (AKR1C3 and FST), and two are linked to cancer (AKR1C3 and MAGEC1). In follow-up studies of the 10 identified variants, the GK2 region SNP, rs192876988, showed an inverse association with EOC in European ancestry women (p = 0.002), increased risk of ER positive breast cancer in African ancestry women (p = 0.027) and decreased expression of GK2 in HGSOC tissue from African ancestry women (p = 0.004). A European ancestry-derived polygenic risk score showed positive associations with EOC and HGSOC in women of African ancestry suggesting shared genetic architecture. Our investigation presents evidence of variants for EOC shared among European and African ancestry women and identifies novel EOC risk loci in women of African ancestry.
Collapse
Affiliation(s)
- Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Lauren C. Peres
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Xin-Qun Wang
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Mollie E. Barnard
- Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt Lake City, UT
| | - Deanna Chyn
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Xin Sheng
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA
| | - Zhaohui Du
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA
| | - Jonathan Tyrer
- Strangeways Research Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Joseph Dennis
- Strangeways Research Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Ann G. Schwartz
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI
| | - Michele L. Cote
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI
| | - Edward Peters
- Epidemiology Program, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA
| | - Patricia G. Moorman
- Department of Community and Family Medicine, Duke University Medical Center, Durham, NC
| | - Melissa Bondy
- Cancer Prevention and Population Sciences Program, Baylor College of Medicine, Houston, TX
| | - Jill S. Barnholtz-Sloan
- Department of Population and Quantitative Health Sciences and Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH
| | - Paul Terry
- Department of Medicine, University of Tennessee Medical Center – Knoxville, Knoxville, TN
| | - Anthony J. Alberg
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - Elisa V. Bandera
- Department of Population Science, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
| | - Ellen Funkhouser
- Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Anna H. Wu
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA
| | - Malcom Pike
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | | | - Andrew Berchuck
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC
| | - Jennifer A. Doherty
- Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt Lake City, UT
| | - Francesmary Modugno
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA
- Ovarian Cancer Center of Excellence, Womens Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, PA
| | - Roberta Ness
- The University of Texas School of Public Health, Houston, TX
| | - Kirsten Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY
| | - Beth Y. Karlan
- Department of Obstetrics and Gynecology, Ronald Regan UCLA Medical Center, Los Angeles, CA
| | - Alice S. Whittemore
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA
| | - Valerie McGuire
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA
| | - Weiva Sieh
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, NY, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York
| | - Kate Lawrenson
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Simon Gayther
- Center for Bioinformatics and Functional Genomics and the Cedars-Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Thomas A. Sellers
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Paul Pharoah
- Strangeways Research Laboratory, University of Cambridge, Cambridge, United Kingdom
| | | |
Collapse
|
194
|
Pinto EM, Figueiredo BC, Chen W, Galvao HC, Formiga MN, Fragoso MCB, Ashton-Prolla P, Ribeiro EM, Felix G, Costa TE, Savage SA, Yeager M, Palmero EI, Volc S, Salvador H, Fuster-Soler JL, Lavarino C, Chantada G, Vaur D, Odone-Filho V, Brugières L, Else T, Stoffel EM, Maxwell KN, Achatz MI, Kowalski L, de Andrade KC, Pappo A, Letouze E, Latronico AC, Mendonca BB, Almeida MQ, Brondani VB, Bittar CM, Soares EW, Mathias C, Ramos CR, Machado M, Zhou W, Jones K, Vogt A, Klincha PP, Santiago KM, Komechen H, Paraizo MM, Parise IZ, Hamilton KV, Wang J, Rampersaud E, Clay MR, Murphy AJ, Lalli E, Nichols KE, Ribeiro RC, Rodriguez-Galindo C, Korbonits M, Zhang J, Thomas MG, Connelly JP, Pruett-Miller S, Diekmann Y, Neale G, Wu G, Zambetti GP. XAF1 as a modifier of p53 function and cancer susceptibility. SCIENCE ADVANCES 2020; 6:eaba3231. [PMID: 32637605 PMCID: PMC7314530 DOI: 10.1126/sciadv.aba3231] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 05/14/2020] [Indexed: 05/15/2023]
Abstract
Cancer risk is highly variable in carriers of the common TP53-R337H founder allele, possibly due to the influence of modifier genes. Whole-genome sequencing identified a variant in the tumor suppressor XAF1 (E134*/Glu134Ter/rs146752602) in a subset of R337H carriers. Haplotype-defining variants were verified in 203 patients with cancer, 582 relatives, and 42,438 newborns. The compound mutant haplotype was enriched in patients with cancer, conferring risk for sarcoma (P = 0.003) and subsequent malignancies (P = 0.006). Functional analyses demonstrated that wild-type XAF1 enhances transactivation of wild-type and hypomorphic TP53 variants, whereas XAF1-E134* is markedly attenuated in this activity. We propose that cosegregation of XAF1-E134* and TP53-R337H mutations leads to a more aggressive cancer phenotype than TP53-R337H alone, with implications for genetic counseling and clinical management of hypomorphic TP53 mutant carriers.
Collapse
Affiliation(s)
- Emilia M. Pinto
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | | | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | | | | | | | | | | | | | | | | | | | | | - Sahlua Volc
- Hospital de Cancer de Barretos, Barretos, SP, Brazil
| | - Hector Salvador
- Pediatric Oncology Department, Sant Joan de Deu Hospital, Barcelona, Spain
| | | | - Cinzia Lavarino
- Pediatric Oncology Department, Sant Joan de Deu Hospital, Barcelona, Spain
| | - Guillermo Chantada
- Department of Global Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Dominique Vaur
- Comprehensive Cancer Center François Baclesse, Caen, France
| | - Vicente Odone-Filho
- ITACI–Instituto de Tratamento do Câncer Infantil do Departamento de Pediatria da Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, SP, Brazil
| | | | | | | | - Kara N. Maxwell
- Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Alberto Pappo
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Eric Letouze
- Centre de Recherche des Cordeliers, Paris, France
| | | | | | | | | | - Camila M. Bittar
- Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | | | | | | | - Weiyin Zhou
- National Cancer Institute, Rockville, MD, USA
| | | | | | | | | | - Heloisa Komechen
- Instituto de Pesquisa Pelé Pequeno Principe, Curitiba, PR, Brazil
| | | | - Ivy Z.S. Parise
- Instituto de Pesquisa Pelé Pequeno Principe, Curitiba, PR, Brazil
| | - Kayla V. Hamilton
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Jinling Wang
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Evadnie Rampersaud
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Michael R. Clay
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Andrew J. Murphy
- Department of Surgery, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Enzo Lalli
- Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France
| | - Kim E. Nichols
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Raul C. Ribeiro
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Carlos Rodriguez-Galindo
- Department of Global Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Marta Korbonits
- Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jinghui Zhang
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Mark G. Thomas
- Research Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Jon P. Connelly
- Center for Advanced Genome Engineering, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Shondra Pruett-Miller
- Center for Advanced Genome Engineering, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Yoan Diekmann
- Research Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Geoffrey Neale
- Hartwell Center for Bioinformatics and Biotechnology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Gerard P. Zambetti
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| |
Collapse
|
195
|
Dapas M, Lin FTJ, Nadkarni GN, Sisk R, Legro RS, Urbanek M, Hayes MG, Dunaif A. Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis. PLoS Med 2020; 17:e1003132. [PMID: 32574161 PMCID: PMC7310679 DOI: 10.1371/journal.pmed.1003132] [Citation(s) in RCA: 146] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Polycystic ovary syndrome (PCOS) is a common, complex genetic disorder affecting up to 15% of reproductive-age women worldwide, depending on the diagnostic criteria applied. These diagnostic criteria are based on expert opinion and have been the subject of considerable controversy. The phenotypic variation observed in PCOS is suggestive of an underlying genetic heterogeneity, but a recent meta-analysis of European ancestry PCOS cases found that the genetic architecture of PCOS defined by different diagnostic criteria was generally similar, suggesting that the criteria do not identify biologically distinct disease subtypes. We performed this study to test the hypothesis that there are biologically relevant subtypes of PCOS. METHODS AND FINDINGS Using biochemical and genotype data from a previously published PCOS genome-wide association study (GWAS), we investigated whether there were reproducible phenotypic subtypes of PCOS with subtype-specific genetic associations. Unsupervised hierarchical cluster analysis was performed on quantitative anthropometric, reproductive, and metabolic traits in a genotyped cohort of 893 PCOS cases (median and interquartile range [IQR]: age = 28 [25-32], body mass index [BMI] = 35.4 [28.2-41.5]). The clusters were replicated in an independent, ungenotyped cohort of 263 PCOS cases (median and IQR: age = 28 [24-33], BMI = 35.7 [28.4-42.3]). The clustering revealed 2 distinct PCOS subtypes: a "reproductive" group (21%-23%), characterized by higher luteinizing hormone (LH) and sex hormone binding globulin (SHBG) levels with relatively low BMI and insulin levels, and a "metabolic" group (37%-39%), characterized by higher BMI, glucose, and insulin levels with lower SHBG and LH levels. We performed a GWAS on the genotyped cohort, limiting the cases to either the reproductive or metabolic subtypes. We identified alleles in 4 loci that were associated with the reproductive subtype at genome-wide significance (PRDM2/KAZN, P = 2.2 × 10-10; IQCA1, P = 2.8 × 10-9; BMPR1B/UNC5C, P = 9.7 × 10-9; CDH10, P = 1.2 × 10-8) and one locus that was significantly associated with the metabolic subtype (KCNH7/FIGN, P = 1.0 × 10-8). We developed a predictive model to classify a separate, family-based cohort of 73 women with PCOS (median and IQR: age = 28 [25-33], BMI = 34.3 [27.8-42.3]) and found that the subtypes tended to cluster in families and that carriers of previously reported rare variants in DENND1A, a gene that regulates androgen biosynthesis, were significantly more likely to have the reproductive subtype of PCOS. Limitations of our study were that only PCOS cases of European ancestry diagnosed by National Institutes of Health (NIH) criteria were included, the sample sizes for the subtype GWAS were small, and the GWAS findings were not replicated. CONCLUSIONS In conclusion, we have found reproducible reproductive and metabolic subtypes of PCOS. Furthermore, these subtypes were associated with novel, to our knowledge, susceptibility loci. Our results suggest that these subtypes are biologically relevant because they appear to have distinct genetic architecture. This study demonstrates how phenotypic subtyping can be used to gain additional insights from GWAS data.
Collapse
Affiliation(s)
- Matthew Dapas
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Frederick T. J. Lin
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Girish N. Nadkarni
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ryan Sisk
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Richard S. Legro
- Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Margrit Urbanek
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Center for Reproductive Science, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - M. Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Anthropology, Northwestern University, Evanston, Illinois, United States of America
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- * E-mail:
| |
Collapse
|
196
|
Semmes EC, Vijayakrishnan J, Zhang C, Hurst JH, Houlston RS, Walsh KM. Leveraging Genome and Phenome-Wide Association Studies to Investigate Genetic Risk of Acute Lymphoblastic Leukemia. Cancer Epidemiol Biomarkers Prev 2020; 29:1606-1614. [PMID: 32467347 DOI: 10.1158/1055-9965.epi-20-0113] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/23/2020] [Accepted: 05/06/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) of childhood cancers remain limited, highlighting the need for novel analytic strategies. We describe a hybrid GWAS and phenome-wide association study (PheWAS) approach to uncover genotype-phenotype relationships and candidate risk loci, applying it to acute lymphoblastic leukemia (ALL). METHODS PheWAS was performed for 12 ALL SNPs identified by prior GWAS and two control SNP-sets using UK Biobank data. PheWAS-traits significantly associated with ALL SNPs compared with control SNPs were assessed for association with ALL risk (959 cases, 2,624 controls) using polygenic score and Mendelian randomization analyses. Trait-associated SNPs were tested for association with ALL risk in single-SNP analyses, with replication in an independent case-control dataset (1,618 cases, 9,409 controls). RESULTS Platelet count was the trait most enriched for association with known ALL risk loci. A polygenic score for platelet count (223 SNPs) was not associated with ALL risk (P = 0.82) and Mendelian randomization did not suggest a causal relationship. However, twelve platelet count-associated SNPs were nominally associated with ALL risk in COG data and three were replicated in UK data (rs10058074, rs210142, rs2836441). CONCLUSIONS In our hybrid GWAS-PheWAS approach, we identify pleiotropic genetic variation contributing to ALL risk and platelet count. Three SNPs known to influence platelet count were reproducibly associated with ALL risk, implicating genomic regions containing IRF1, proapoptotic protein BAK1, and ERG in platelet production and leukemogenesis. IMPACT Incorporating PheWAS data into association studies can leverage genetic pleiotropy to identify cancer risk loci, highlighting the utility of our novel approach.
Collapse
Affiliation(s)
- Eleanor C Semmes
- Medical Scientist Training Program, Duke University, Durham, North Carolina.,Children's Health and Discovery Initiative, Department of Pediatrics, Duke University, Durham, North Carolina
| | - Jayaram Vijayakrishnan
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Chenan Zhang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Jillian H Hurst
- Children's Health and Discovery Initiative, Department of Pediatrics, Duke University, Durham, North Carolina
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Kyle M Walsh
- Children's Health and Discovery Initiative, Department of Pediatrics, Duke University, Durham, North Carolina. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California.,Department of Neurosurgery, Duke University, Durham, North Carolina.,Duke Cancer Institute, Duke University, Durham, North Carolina
| |
Collapse
|
197
|
Gzara C, Dallmann-Sauer M, Orlova M, Van Thuc N, Thai VH, Fava VM, Bihoreau MT, Boland A, Abel L, Alcaïs A, Schurr E, Cobat A. Family-based genome-wide association study of leprosy in Vietnam. PLoS Pathog 2020; 16:e1008565. [PMID: 32421744 PMCID: PMC7259797 DOI: 10.1371/journal.ppat.1008565] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/29/2020] [Accepted: 04/20/2020] [Indexed: 12/14/2022] Open
Abstract
Leprosy is a chronic infectious disease of the skin and peripheral nerves with a strong genetic predisposition. Recent genome-wide approaches have identified numerous common variants associated with leprosy, almost all in the Chinese population. We conducted the first family-based genome-wide association study of leprosy in 622 affected offspring from Vietnam, followed by replication in an independent sample of 1181 leprosy cases and 668 controls of the same ethnic origin. The most significant results were observed within the HLA region, in which six SNPs displayed genome-wide significant associations, all of which were replicated in the independent case/control sample. We investigated the signal in the HLA region in more detail, by conducting a multivariate analysis on the case/control sample of 319 GWAS-suggestive HLA hits for which evidence for replication was obtained. We identified three independently associated SNPs, two located in the HLA class I region (rs1265048: OR = 0.69 [0.58-0.80], combined p-value = 5.53x10-11; and rs114598080: OR = 1.47 [1.46-1.48], combined p-value = 8.77x10-13), and one located in the HLA class II region (rs3187964 (OR = 1.67 [1.55-1.80], combined p-value = 8.35x10-16). We also validated two previously identified risk factors for leprosy: the missense variant rs3764147 in the LACC1 gene (OR = 1.52 [1.41-1.63], combined p-value = 5.06x10-14), and the intergenic variant rs6871626 located close to the IL12B gene (OR = 0.73 [0.61-0.84], combined p-value = 6.44x10-8). These results shed new light on the genetic control of leprosy, by dissecting the influence of HLA SNPs, and validating the independent role of two additional variants in a large Vietnamese sample.
Collapse
Affiliation(s)
- Chaima Gzara
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris, France
- Université de Paris, Imagine Institute, Paris, France
| | - Monica Dallmann-Sauer
- McGill International TB Centre, Montreal, QC, Canada
- Program in Infectious Diseases and Immunity in Global Health, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine and Human Genetics, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Marianna Orlova
- McGill International TB Centre, Montreal, QC, Canada
- Program in Infectious Diseases and Immunity in Global Health, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine and Human Genetics, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Nguyen Van Thuc
- Hospital for Dermato-Venereology, District, Ho Chi Minh City, Vietnam
| | - Vu Hong Thai
- Hospital for Dermato-Venereology, District, Ho Chi Minh City, Vietnam
| | - Vinicius M. Fava
- McGill International TB Centre, Montreal, QC, Canada
- Program in Infectious Diseases and Immunity in Global Health, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Marie-Thérèse Bihoreau
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry, France
| | - Laurent Abel
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris, France
- Université de Paris, Imagine Institute, Paris, France
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, United States of America
| | - Alexandre Alcaïs
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris, France
- Université de Paris, Imagine Institute, Paris, France
| | - Erwin Schurr
- McGill International TB Centre, Montreal, QC, Canada
- Program in Infectious Diseases and Immunity in Global Health, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine and Human Genetics, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Aurélie Cobat
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris, France
- Université de Paris, Imagine Institute, Paris, France
- * E-mail:
| |
Collapse
|
198
|
Ciani E, Mastrangelo S, Da Silva A, Marroni F, Ferenčaković M, Ajmone-Marsan P, Baird H, Barbato M, Colli L, Delvento C, Dovenski T, Gorjanc G, Hall SJG, Hoda A, Li MH, Marković B, McEwan J, Moradi MH, Ruiz-Larrañaga O, Ružić-Muslić D, Šalamon D, Simčič M, Stepanek O, Curik I, Cubric-Curik V, Lenstra JA. On the origin of European sheep as revealed by the diversity of the Balkan breeds and by optimizing population-genetic analysis tools. Genet Sel Evol 2020; 52:25. [PMID: 32408891 PMCID: PMC7227234 DOI: 10.1186/s12711-020-00545-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 04/30/2020] [Indexed: 11/26/2022] Open
Abstract
Background In the Neolithic, domestic sheep migrated into Europe and subsequently spread in westerly and northwesterly directions. Reconstruction of these migrations and subsequent genetic events requires a more detailed characterization of the current phylogeographic differentiation. Results We collected 50 K single nucleotide polymorphism (SNP) profiles of Balkan sheep that are currently found near the major Neolithic point of entry into Europe, and combined these data with published genotypes from southwest-Asian, Mediterranean, central-European and north-European sheep and from Asian and European mouflons. We detected clines, ancestral components and admixture by using variants of common analysis tools: geography-informative supervised principal component analysis (PCA), breed-specific admixture analysis, across-breed \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$f_{4}$$\end{document}f4 profiles and phylogenetic analysis of regional pools of breeds. The regional Balkan sheep populations exhibit considerable genetic overlap, but are clearly distinct from the breeds in surrounding regions. The Asian mouflon did not influence the differentiation of the European domestic sheep and is only distantly related to present-day sheep, including those from Iran where the mouflons were sampled. We demonstrate the occurrence, from southeast to northwest Europe, of a continuously increasing ancestral component of up to 20% contributed by the European mouflon, which is assumed to descend from the original Neolithic domesticates. The overall patterns indicate that the Balkan region and Italy served as post-domestication migration hubs, from which wool sheep reached Spain and north Italy with subsequent migrations northwards. The documented dispersal of Tarentine wool sheep during the Roman period may have been part of this process. Our results also reproduce the documented 18th century admixture of Spanish Merino sheep into several central-European breeds. Conclusions Our results contribute to a better understanding of the events that have created the present diversity pattern, which is relevant for the management of the genetic resources represented by the European sheep population.
Collapse
Affiliation(s)
- Elena Ciani
- Dipartamento Bioscienze, Biotecnologie, Biofarmaceutica, Universita. degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Salvatore Mastrangelo
- Dipartimento Scienze Agrarie, Alimentari e Forestali, Universita Studi di Palermo, Palermo, Italy
| | - Anne Da Silva
- Université de Limoges, INRAE, Pereine EA7500, USC1061 Gamaa, 87000, Limoges, France
| | - Fabio Marroni
- Dipartamento Scienze Agroalimentari, Ambientali e Animali, Universita Udine, Udine, Italy
| | | | - Paolo Ajmone-Marsan
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Universita Cattolica del S. Cuore di Piacenza, Piacenza, Italy
| | - Hayley Baird
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
| | - Mario Barbato
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Universita Cattolica del S. Cuore di Piacenza, Piacenza, Italy
| | - Licia Colli
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Universita Cattolica del S. Cuore di Piacenza, Piacenza, Italy
| | - Chiara Delvento
- Dipartamento Bioscienze, Biotecnologie, Biofarmaceutica, Universita. degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Toni Dovenski
- Department of Reproduction and Biomedicine, Faculty of Veterinary Medicine, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | - Gregor Gorjanc
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, Scotland, UK
| | | | - Anila Hoda
- Department of Animal Production, Faculty of Agriculture and Environment, Agricultural University ofTirana, Tirana, Albania
| | - Meng-Hua Li
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | | | - John McEwan
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Universita Cattolica del S. Cuore di Piacenza, Piacenza, Italy
| | - Mohammad H Moradi
- Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran
| | - Otsanda Ruiz-Larrañaga
- Department of Genetics, Physical Anthropology and Animal Physiology, University of Basque Country, Leioa, Spain
| | | | - Dragica Šalamon
- Department of Animal Science, University of Zagreb, Zagreb, Croatia
| | - Mojca Simčič
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | | | | | - Ino Curik
- Department of Animal Science, University of Zagreb, Zagreb, Croatia
| | | | - Johannes A Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
| |
Collapse
|
199
|
Mashbat B, Bellos E, Hodeib S, Bidmos F, Thwaites RS, Lu Y, Wright VJ, Herberg JA, Klobassa DS, Walton WG, Zenz W, Hansel TT, Nadel S, Langford PR, Schlapbach LJ, Li MS, Redinbo MR, Di YP, Levin M, Sancho-Shimizu V. A Rare Mutation in SPLUNC1 Affects Bacterial Adherence and Invasion in Meningococcal Disease. Clin Infect Dis 2020; 70:2045-2053. [PMID: 31504285 PMCID: PMC7201419 DOI: 10.1093/cid/ciz600] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 06/28/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Neisseria meningitidis (Nm) is a nasopharyngeal commensal carried by healthy individuals. However, invasive infections occurs in a minority of individuals, with devastating consequences. There is evidence that common polymorphisms are associated with invasive meningococcal disease (IMD), but the contributions of rare variants other than those in the complement system have not been determined. METHODS We identified familial cases of IMD in the UK meningococcal disease study and the European Union Life-Threatening Infectious Disease Study. Candidate genetic variants were identified by whole-exome sequencing of 2 patients with familial IMD. Candidate variants were further validated by in vitro assays. RESULTS Exomes of 2 siblings with IMD identified a novel heterozygous missense mutation in BPIFA1/SPLUNC1. Sequencing of 186 other nonfamilial cases identified another unrelated IMD patient with the same mutation. SPLUNC1 is an innate immune defense protein expressed in the nasopharyngeal epithelia; however, its role in invasive infections is unknown. In vitro assays demonstrated that recombinant SPLUNC1 protein inhibits biofilm formation by Nm, and impedes Nm adhesion and invasion of human airway cells. The dominant negative mutant recombinant SPLUNC1 (p.G22E) showed reduced antibiofilm activity, increased meningococcal adhesion, and increased invasion of cells, compared with wild-type SPLUNC1. CONCLUSIONS A mutation in SPLUNC1 affecting mucosal attachment, biofilm formation, and invasion of mucosal epithelial cells is a new genetic cause of meningococcal disease.
Collapse
Affiliation(s)
- Bayarchimeg Mashbat
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Evangelos Bellos
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Stephanie Hodeib
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Fadil Bidmos
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Ryan S Thwaites
- National Heart and Lung Institute, Imperial College London, United Kingdom
| | - Yaxuan Lu
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Victoria J Wright
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Jethro A Herberg
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Daniela S Klobassa
- Department of Pediatric and Adolescence Surgery, Division of General Pediatric Surgery, Medical University Graz, Austria
| | - William G Walton
- Paediatric Intensive Care Unit, St. Mary’s Hospital, Imperial College Healthcare Trust, London, United Kingdom
| | - Werner Zenz
- Department of Pediatric and Adolescence Surgery, Division of General Pediatric Surgery, Medical University Graz, Austria
| | - Trevor T Hansel
- National Heart and Lung Institute, Imperial College London, United Kingdom
| | - Simon Nadel
- Paediatric Intensive Care Unit, St. Mary’s Hospital, Imperial College Healthcare Trust, London, United Kingdom
| | - Paul R Langford
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Luregn J Schlapbach
- Faculty of Medicine Brisbane, The University of Queensland Brisbane, Australia
- Paediatric Critical Care Research Group, The University of Queensland Brisbane, Australia
- Paediatric Intensive Care Unit, Lady Cilento Children’s Hospital, Children’s Health Queensland, Brisbane, Australia
- Department of Pediatrics, Bern University Hospital, Inselspital, University of Bern, Switzerland
| | - Ming-Shi Li
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Matthew R Redinbo
- Department of Chemistry, University of North Carolina, Chapel Hill
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
| | - Y Peter Di
- Department of Environmental and Occupational Health, University of Pittsburgh, Pennsylvania
| | - Michael Levin
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| | - Vanessa Sancho-Shimizu
- Department of Paediatric Infectious Diseases, Division of Medicine, Imperial College London, Norfolk Place, United Kingdom
| |
Collapse
|
200
|
Arauna LR, Hellenthal G, Comas D. Dissecting human North African gene-flow into its western coastal surroundings. Proc Biol Sci 2020; 286:20190471. [PMID: 31039721 DOI: 10.1098/rspb.2019.0471] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
North African history and populations have exerted a pivotal influence on surrounding geographical regions, although scant genetic studies have addressed this issue. Our aim is to understand human historical migrations in the coastal surroundings of North Africa. We built a refined genome-wide dataset of North African populations to unearth the fine-scale genetic structure of the region, using haplotype information. The results suggest that the gene-flow from North Africa into the European Mediterranean coast (Tuscany and the Iberian Peninsula) arrived mainly from the Mediterranean coast of North Africa. In Tuscany, this North African admixture date estimate suggests the movement of peoples during the fall of the Roman Empire around the fourth century. In the Iberian Peninsula, the North African component probably reflects the impact of the Arab expansion since the seventh century and the subsequent expansion of the Christian Kingdoms. By contrast, the North African component in the Canary Islands has a source genetically related to present-day people from the Atlantic North African coast. We also find sub-Saharan gene-flow from the Senegambia region in the Canary Islands. Specifically, we detect a complex signal of admixture involving Atlantic, Senegambian and European sources intermixing around the fifteenth century, soon after the Castilian conquest. Our results highlight the differential genetic influence of North Africa into the surrounding coast and show that specific historical events have not only had a socio-cultural impact but additionally modified the gene pool of the populations.
Collapse
Affiliation(s)
- Lara R Arauna
- 1 Departament de Ciències Experimentals i de la Salut, Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra , Barcelona , Spain
| | - Garrett Hellenthal
- 2 UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London , London , UK
| | - David Comas
- 1 Departament de Ciències Experimentals i de la Salut, Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra , Barcelona , Spain
| |
Collapse
|