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Du S, Chen J, Li J, Qian W, Wu S, Peng Q, Liu Y, Pan T, Li Y, Hadi SS, Tan J, Yuan Z, Wang J, Tang K, Wang Z, Wen Y, Dong X, Zhou W, Ruiz-Linares A, Shi Y, Jin L, Liu F, Zhang M, Wang S. A multi-ancestry GWAS meta-analysis of facial features and its application in predicting archaic human features. J Genet Genomics 2024:S1673-8527(24)00181-4. [PMID: 39002897 DOI: 10.1016/j.jgg.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 07/06/2024] [Accepted: 07/06/2024] [Indexed: 07/15/2024]
Abstract
Facial morphology, a complex trait influenced by genetics, holds great significance in evolutionary research. However, due to limited fossil evidence, the facial characteristics of Neanderthals and Denisovans have remained largely unknown. In this study, we conducted a large-scale multi-ethnic meta-analysis of Genome-Wide Association Study (GWAS), including 9674 East Asians and 10,115 Europeans, quantitatively assessing 78 facial traits using 3D facial images. We identified 71 genomic loci associated with facial features, including 21 novel loci. We developed a facial polygenic score (FPS) that enables the prediction of facial features based on genetic information. Interestingly, the distribution of FPSs among populations from diverse continental groups exhibited significant correlations with observed facial features. Furthermore, we applied the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA, and aligned predictions with the fossil records. Our results suggested that Neanderthals and Denisovans likely shared similar facial features, such as a wider but shorter nose and a wider endocanthion distance. The decreased mouth width was characterized specifically in Denisovan. The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.
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Affiliation(s)
- Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Jieyi Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China; Center for Molecular Medicine, Pediatrics Research Institute, Children's Hospital of Fudan University, Shanghai, China
| | - Jiarui Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Wei Qian
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Sijie Wu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Qianqian Peng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Yu Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Ting Pan
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Sibte Syed Hadi
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University for Security Sciences, Kingdom of Saudi Arabia
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyu Yuan
- Fudan-Taizhou Institute of Health Sciences, Taizhou, Jiangsu 225326, China
| | - Jiucun Wang
- Fudan-Taizhou Institute of Health Sciences, Taizhou, Jiangsu 225326, China; Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China; Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences
| | - Kun Tang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yanqin Wen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatrics Research Institute, Children's Hospital of Fudan University, Shanghai, China
| | - Wenhao Zhou
- Center for Molecular Medicine, Pediatrics Research Institute, Children's Hospital of Fudan University, Shanghai, China; Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China; 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
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Li Jin
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China; Fudan-Taizhou Institute of Health Sciences, Taizhou, Jiangsu 225326, China; Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China; Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences
| | - Fan Liu
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University for Security Sciences, Kingdom of Saudi Arabia; Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
| | - Manfei Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China; Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223 China.
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Yuan M, Goovaerts S, Vanneste M, Matthews H, Hoskens H, Richmond S, Klein OD, Spritz RA, Hallgrimsson B, Walsh S, Shriver MD, Shaffer JR, Weinberg SM, Peeters H, Claes P. Mapping genes for human face shape: exploration of univariate phenotyping strategies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597731. [PMID: 38895298 PMCID: PMC11185724 DOI: 10.1101/2024.06.06.597731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits. Author Summary Advancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.
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Qiao H, Tan J, Yan J, Sun C, Yin X, Li Z, Wu J, Guan H, Wen S, Zhang M, Xu S, Jin L. A comprehensive evaluation of the phenotype-first and data-driven approaches in analyzing facial morphological traits. iScience 2024; 27:109325. [PMID: 38487017 PMCID: PMC10937830 DOI: 10.1016/j.isci.2024.109325] [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: 11/08/2023] [Revised: 01/17/2024] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
The phenotype-first approach (PFA) and data-driven approach (DDA) have both greatly facilitated anthropological studies and the mapping of trait-associated genes. However, the pros and cons of the two approaches are poorly understood. Here, we systematically evaluated the two approaches and analyzed 14,838 facial traits in 2,379 Han Chinese individuals. Interestingly, the PFA explained more facial variation than the DDA in the top 100 and 1,000 except in the top 10 phenotypes. Accordingly, the ratio of heterogeneous traits extracted from the PFA was much greater, while more homogenous traits were found using the DDA for different sex, age, and BMI groups. Notably, our results demonstrated that the sex factor accounted for 30% of phenotypic variation in all traits extracted. Furthermore, we linked DDA phenotypes to PFA phenotypes with explicit biological explanations. These findings provide new insights into the analysis of multidimensional phenotypes and expand the understanding of phenotyping approaches.
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Affiliation(s)
- Hui Qiao
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai 200438, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Jingze Tan
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai 200438, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Jun Yan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Chang Sun
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Xing Yin
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Zijun Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Jiazi Wu
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Haijuan Guan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
| | - Shaoqing Wen
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Menghan Zhang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 201203, China
- Institute of Modern Languages and Linguistics, Fudan University, Shanghai 200433, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai 200438, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai 200438, China
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Nguyen TT, Mitchell JM, Kiel MD, Kenny CP, Li H, Jones KL, Cornell RA, Williams TJ, Nichols JT, Van Otterloo E. TFAP2 paralogs regulate midfacial development in part through a conserved ALX genetic pathway. Development 2024; 151:dev202095. [PMID: 38063857 PMCID: PMC10820886 DOI: 10.1242/dev.202095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
Cranial neural crest development is governed by positional gene regulatory networks (GRNs). Fine-tuning of the GRN components underlies facial shape variation, yet how those networks in the midface are connected and activated remain poorly understood. Here, we show that concerted inactivation of Tfap2a and Tfap2b in the murine neural crest, even during the late migratory phase, results in a midfacial cleft and skeletal abnormalities. Bulk and single-cell RNA-seq profiling reveal that loss of both TFAP2 family members dysregulates numerous midface GRN components involved in midface morphogenesis, patterning and differentiation. Notably, Alx1, Alx3 and Alx4 (ALX) transcript levels are reduced, whereas ChIP-seq analyses suggest TFAP2 family members directly and positively regulate ALX gene expression. Tfap2a, Tfap2b and ALX co-expression in midfacial neural crest cells of both mouse and zebrafish implies conservation of this regulatory axis across vertebrates. Consistent with this notion, tfap2a zebrafish mutants present with abnormal alx3 expression patterns, Tfap2a binds ALX loci and tfap2a-alx3 genetic interactions are observed. Together, these data demonstrate TFAP2 paralogs regulate vertebrate midfacial development in part by activating expression of ALX transcription factor genes.
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Affiliation(s)
- Timothy T. Nguyen
- Iowa Institute for Oral Health Research, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA
- Department of Periodontics, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA
- Department of Anatomy and Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA 52242, USA
| | - Jennyfer M. Mitchell
- Department of Craniofacial Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michaela D. Kiel
- Iowa Institute for Oral Health Research, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA
- Department of Periodontics, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA
- Department of Anatomy and Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Colin P. Kenny
- Department of Surgery, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Hong Li
- Department of Craniofacial Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kenneth L. Jones
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children's Hospital Colorado, Aurora, CO 80045, USA
| | - Robert A. Cornell
- Department of Oral Health Sciences, University of Washington, School of Dentistry, Seattle, WA 98195, USA
| | - Trevor J. Williams
- Department of Craniofacial Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children's Hospital Colorado, Aurora, CO 80045, USA
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - James T. Nichols
- Department of Craniofacial Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Eric Van Otterloo
- Iowa Institute for Oral Health Research, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA
- Department of Periodontics, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA
- Department of Anatomy and Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA 52242, USA
- Craniofacial Anomalies Research Center, University of Iowa, Iowa City, IA 52242, USA
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5
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Kapila S, Vora SR, Rengasamy Venugopalan S, Elnagar MH, Akyalcin S. Connecting the dots towards precision orthodontics. Orthod Craniofac Res 2023; 26 Suppl 1:8-19. [PMID: 37968678 DOI: 10.1111/ocr.12725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/20/2023] [Indexed: 11/17/2023]
Abstract
Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualized clinical phenotyping and diagnosis combined with the delivery of care using advanced customized devices, technologies and biologics. From its historical origins as a mechanotherapy and materials driven profession, the most recent advances in orthodontics in the past three decades have been propelled by technological innovations including volumetric and surface 3D imaging and printing, advances in software that facilitate the derivation of diagnostic details, enhanced personalization of treatment plans and fabrication of custom appliances. Still, the use of these diagnostic and therapeutic technologies is largely phenotype driven, focusing mainly on facial/skeletal morphology and tooth positions. Future advances in orthodontics will involve comprehensive understanding of an individual's biology through omics, a field of biology that involves large-scale rapid analyses of DNA, mRNA, proteins and other biological regulators from a cell, tissue or organism. Such understanding will define individual biological attributes that will impact diagnosis, treatment decisions, risk assessment and prognostics of therapy. Equally important are the advances in artificial intelligence (AI) and machine learning, and its applications in orthodontics. AI is already being used to perform validation of approaches for diagnostic purposes such as landmark identification, cephalometric tracings, diagnosis of pathologies and facial phenotyping from radiographs and/or photographs. Other areas for future discoveries and utilization of AI will include clinical decision support, precision orthodontics, payer decisions and risk prediction. The synergies between deep 3D phenotyping and advances in materials, omics and AI will propel the technological and omics era towards achieving the goal of delivering optimized and predictable precision orthodontics.
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Affiliation(s)
- Sunil Kapila
- Strategic Initiatives and Operations, UCLA School of Dentistry, Los Angeles, California, USA
| | - Siddharth R Vora
- Oral Health Sciences, University of British Columbia, Vancouver, British Columbia, USA
| | | | - Mohammed H Elnagar
- Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Illinois, USA
| | - Sercan Akyalcin
- Department of Developmental Biology, Harvard School of Dental Medicine, Boston, Massachusetts, USA
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M'charek A. Curious about race: Generous methods and modes of knowing in practice. SOCIAL STUDIES OF SCIENCE 2023; 53:826-849. [PMID: 37916761 DOI: 10.1177/03063127231201178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
What is race? And how does it figure in different scientific practices? To answer these questions, I suggest that we need to know race differently. Rather than defining race or looking for one conclusive answer to what it is, I propose methods that are open-ended, that allow us to follow race around, while remaining curious as to what it is. I suggest that we pursue generous methods. Drawing on empirical examples of forensic identification technologies, I argue that the slipperiness of race-the way race and its politics inexorably shift and change-cannot be fully grasped as an 'object multiple'. Race, I show, is not race: The same word refers to different phenomena. To grasp this, I introduce the notion of the affinity concept. Drawing on the history of race, along with contemporary work in forensic genetics, the affinity concept helps us articulate how race indexes three different scientific realities: race as object, race as method, and race as theory. These three different, yet interconnected realities, contribute to race's slipperiness as well as its virulence.
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7
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Cho HW, Ban HJ, Jin HS, Cha S, Eom YB. A genome-wide association scan reveals novel loci for facial traits of Koreans. Genomics 2023; 115:110710. [PMID: 37734486 DOI: 10.1016/j.ygeno.2023.110710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
DNA-based prediction of externally visible characteristics (EVC) with SNPs is one of the research areas of interest in the forensic field. Based on a previous study performing GWAS on facial traits in a Korean population, herein, we present results stemming from GWA analysis with KoreanChip and novel genetic loci satisfying genome-wide significant level. We discovered a total of 20 signals and 12 loci were found to have novel associations with facial traits, including six loci located in intergenic regions and six loci located at UBE2O, HECTD2, CCDC108, TPK1, FCN2, and FRMPD1. Additionally, we performed a polygenic score analysis for 33 distance-related traits in facial phenotyping and determined genetic relationships between facial traits and SNPs using the GCTA program. The results of the current study offer an understanding of how facial morphology is influenced by complex genetic structures and provide insights into forensic investigation and population genetics.
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Affiliation(s)
- Hye-Won Cho
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Chungnam 31538, Republic of Korea
| | - Hyo-Jeong Ban
- Korea Medicine (KM) Data Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, Chungnam 31499, Republic of Korea
| | - Seongwon Cha
- Korea Medicine (KM) Data Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea.
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Chungnam 31538, Republic of Korea; Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, Chungnam 31538, Republic of Korea.
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8
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Selleri L, Rijli FM. Shaping faces: genetic and epigenetic control of craniofacial morphogenesis. Nat Rev Genet 2023; 24:610-626. [PMID: 37095271 DOI: 10.1038/s41576-023-00594-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2023] [Indexed: 04/26/2023]
Abstract
Major differences in facial morphology distinguish vertebrate species. Variation of facial traits underlies the uniqueness of human individuals, and abnormal craniofacial morphogenesis during development leads to birth defects that significantly affect quality of life. Studies during the past 40 years have advanced our understanding of the molecular mechanisms that establish facial form during development, highlighting the crucial roles in this process of a multipotent cell type known as the cranial neural crest cell. In this Review, we discuss recent advances in multi-omics and single-cell technologies that enable genes, transcriptional regulatory networks and epigenetic landscapes to be closely linked to the establishment of facial patterning and its variation, with an emphasis on normal and abnormal craniofacial morphogenesis. Advancing our knowledge of these processes will support important developments in tissue engineering, as well as the repair and reconstruction of the abnormal craniofacial complex.
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Affiliation(s)
- Licia Selleri
- Program in Craniofacial Biology, Department of Orofacial Sciences, School of Dentistry, University of California, San Francisco, CA, USA.
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA.
| | - Filippo M Rijli
- Laboratory of Developmental Neuroepigenetics, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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9
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Yuan M, Goovaerts S, Hoskens H, Richmond S, Walsh S, Shriver MD, Shaffer JR, Marazita ML, Weinberg SM, Peeters H, Claes P. Data-driven trait heritability-based extraction of human facial phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.13.553129. [PMID: 37645810 PMCID: PMC10462092 DOI: 10.1101/2023.08.13.553129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
A genome-wide association study (GWAS) of a complex, multi-dimensional morphological trait, such as the human face, typically relies on predefined and simplified phenotypic measurements, such as inter-landmark distances and angles. These measures are predominantly designed by human experts based on perceived biological or clinical knowledge. To avoid use handcrafted phenotypes (i.e., a priori expert-identified phenotypes), alternative automatically extracted phenotypic descriptors, such as features derived from dimension reduction techniques (e.g., principal component analysis), are employed. While the features generated by such computational algorithms capture the geometric variations of the biological shape, they are not necessarily genetically relevant. Therefore, genetically informed data-driven phenotyping is desirable. Here, we propose an approach where phenotyping is done through a data-driven optimization of trait heritability, defined as the degree of variation in a phenotypic trait in a population that is due to genetic variation. The resulting phenotyping process consists of two steps: 1) constructing a feature space that models shape variations using dimension reduction techniques, and 2) searching for directions in the feature space exhibiting high trait heritability using a genetic search algorithm (i.e., heuristic inspired by natural selection). We show that the phenotypes resulting from the proposed trait heritability-optimized training differ from those of principal components in the following aspects: 1) higher trait heritability, 2) higher SNP heritability, and 3) identification of the same number of independent genetic loci with a smaller number of effective traits. Our results demonstrate that data-driven trait heritability-based optimization enables the automatic extraction of genetically relevant phenotypes, as shown by their increased power in genome-wide association scans.
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10
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Li Y, Xiong Z, Zhang M, Hysi PG, Qian Y, Adhikari K, Weng J, Wu S, Du S, Gonzalez-Jose R, Schuler-Faccini L, Bortolini MC, Acuna-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Wang J, Tan J, Yuan Z, Jin L, Uitterlinden AG, Ghanbari M, Ikram MA, Nijsten T, Zhu X, Lei Z, Jia P, Ruiz-Linares A, Spector TD, Wang S, Kayser M, Liu F. Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci. PLoS Genet 2023; 19:e1010786. [PMID: 37459304 PMCID: PMC10351707 DOI: 10.1371/journal.pgen.1010786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/16/2023] [Indexed: 07/20/2023] Open
Abstract
Human ear morphology, a complex anatomical structure represented by a multidimensional set of correlated and heritable phenotypes, has a poorly understood genetic architecture. In this study, we quantitatively assessed 136 ear morphology traits using deep learning analysis of digital face images in 14,921 individuals from five different cohorts in Europe, Asia, and Latin America. Through GWAS meta-analysis and C-GWASs, a recently introduced method to effectively combine GWASs of many traits, we identified 16 genetic loci involved in various ear phenotypes, eight of which have not been previously associated with human ear features. Our findings suggest that ear morphology shares genetic determinants with other surface ectoderm-derived traits such as facial variation, mono eyebrow, and male pattern baldness. Our results enhance the genetic understanding of human ear morphology and shed light on the shared genetic contributors of different surface ectoderm-derived phenotypes. Additionally, gene editing experiments in mice have demonstrated that knocking out the newly ear-associated gene (Intu) and a previously ear-associated gene (Tbx15) causes deviating mouse ear morphology.
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Affiliation(s)
- Yi Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
| | - Ziyi Xiong
- Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
| | - Manfei Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
| | - Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology, King’s College London, United Kingdom
| | - Yu Qian
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
- Beijing No.8 High School, Beijing, China
| | - Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, United Kingdom
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, United Kingdom
| | - Jun Weng
- Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China
| | - Sijie Wu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- University of Chinese Academy of Sciences, China
| | - Rolando Gonzalez-Jose
- Instituto Patagonico de Ciencias Sociales y Humanas, Centro Nacional Patagonico, CONICET, Argentina
| | | | | | - Victor Acuna-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico
| | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Quimica, UNAM-Instituto Nacional de Medicina Genomica, Mexico
| | - Carla Gallo
- Laboratorios de Investigacion y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Peru
| | - Giovanni Poletti
- Laboratorios de Investigacion y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Peru
| | - Gabriel Bedoya
- GENMOL (Genetica Molecular), Universidad de Antioquia, Medellin, Colombia
| | | | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
| | - Ziyu Yuan
- Fudan-Taizhou Institute of Health Sciences, China
| | - Li Jin
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
- Fudan-Taizhou Institute of Health Sciences, China
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
| | - Tamar Nijsten
- Department of Dermatology, Erasmus MC, University Medical Center, the Netherlands
| | - Xiangyu Zhu
- Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Zhen Lei
- Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
| | - Andres Ruiz-Linares
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, United Kingdom
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
- Aix-Marseille Universite, CNRS, EFS, ADES, France
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, United Kingdom
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, China
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
- Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
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11
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Heinrich J, Berger C, Berger B, Hecht W, Phillips C, Parson W. The LASSIE MPS panel: Predicting externally visible traits in dogs for forensic purposes. Forensic Sci Int Genet 2023; 66:102893. [PMID: 37290253 DOI: 10.1016/j.fsigen.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/28/2023] [Accepted: 06/01/2023] [Indexed: 06/10/2023]
Abstract
Predicting the outward appearance of dogs via their DNA, also known as Canine DNA Phenotyping, is a young, emerging field of research in forensic genetics. The few previous studies published in this respect were restricted to the consecutive analysis of single DNA markers, a process that is time- and sample-consuming and therefore not a viable option for limited forensic specimens. Here, we report on the development and evaluation of a Massively Parallel Sequencing (MPS) based molecular genetic assay, the LASSIE MPS Panel. This panel aims to predict externally visible as well as skeletal traits, which include coat color, coat pattern, coat structure, tail morphology, skull shape, ear shape, eye color and body size from DNA using 44 genetic markers in a single molecular genetic assay. A biostatistical naïve Bayes classification approach was applied to identify the most informative marker combinations for predicting phenotypes. Overall, the predictive performance was characterized by a very high classification success for some of the trait categories, and high to moderate success for others. The performance of the developed predictive framework was further evaluated using blind samples from three randomly selected dog individuals, whose appearance was well predicted.
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Affiliation(s)
- Josephin Heinrich
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Cordula Berger
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Burkhard Berger
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Werner Hecht
- Institute of Veterinary Pathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria; Forensic Science Program, The Pennsylvania State University, University Park, PA, USA.
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12
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Li Q, Chen J, Faux P, Delgado ME, Bonfante B, Fuentes-Guajardo M, Mendoza-Revilla J, 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, Wu S, Du S, Giardina A, Paria SS, Khokan MR, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Rojas W, Rothhammer F, Navarro N, Wang S, Adhikari K, Ruiz-Linares A. Automatic landmarking identifies new loci associated with face morphology and implicates Neanderthal introgression in human nasal shape. Commun Biol 2023; 6:481. [PMID: 37156940 PMCID: PMC10167347 DOI: 10.1038/s42003-023-04838-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/12/2023] [Indexed: 05/10/2023] Open
Abstract
We report a genome-wide association study of facial features in >6000 Latin Americans based on automatic landmarking of 2D portraits and testing for association with inter-landmark distances. We detected significant associations (P-value <5 × 10-8) at 42 genome regions, nine of which have been previously reported. In follow-up analyses, 26 of the 33 novel regions replicate in East Asians, Europeans, or Africans, and one mouse homologous region influences craniofacial morphology in mice. The novel region in 1q32.3 shows introgression from Neanderthals and we find that the introgressed tract increases nasal height (consistent with the differentiation between Neanderthals and modern humans). Novel regions include candidate genes and genome regulatory elements previously implicated in craniofacial development, and show preferential transcription in cranial neural crest cells. The automated approach used here should simplify the collection of large study samples from across the world, facilitating a cosmopolitan characterization of the genetics of facial features.
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Affiliation(s)
- Qing Li
- 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, 200438, China
| | - Jieyi Chen
- 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, 200438, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Pierre Faux
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille, 13005, France
| | - Miguel Eduardo Delgado
- 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, 200438, China
- División Antropología, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, República Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET, Buenos Aires, República Argentina
| | - Betty Bonfante
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille, 13005, France
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, 1000000, Chile
| | - 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
| | - J 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, 6600, 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, 6600, 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, 6600, 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, Brazil
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - Sijie Wu
- 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, 200438, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Andrea Giardina
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - Soumya Subhra Paria
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - Mahfuzur Rahman Khokan
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - 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, Brazil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 90040-060, Brazil
| | - Victor Acuña-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City, 14050, Mexico, 6600, 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
| | - Winston Rojas
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Arica, 1000000, Chile
| | - Nicolas Navarro
- Biogéosciences, UMR 6282 CNRS, Université de Bourgogne, Dijon, 21000, France
- EPHE, PSL University, Paris, 75014, France
| | - Sijia Wang
- 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, 200438, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom.
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Yangpu District, Shanghai, 200438, China.
- 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.
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13
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Echeverry-Quiceno LM, Candelo E, Gómez E, Solís P, Ramírez D, Ortiz D, González A, Sevillano X, Cuéllar JC, Pachajoa H, Martínez-Abadías N. Population-specific facial traits and diagnosis accuracy of genetic and rare diseases in an admixed Colombian population. Sci Rep 2023; 13:6869. [PMID: 37106005 PMCID: PMC10140286 DOI: 10.1038/s41598-023-33374-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Up to 40% of rare disorders (RD) present facial dysmorphologies, and visual assessment is commonly used for clinical diagnosis. Quantitative approaches are more objective, but mostly rely on European descent populations, disregarding diverse population ancestry. Here, we assessed the facial phenotypes of Down (DS), Morquio (MS), Noonan (NS) and Neurofibromatosis type 1 (NF1) syndromes in a Latino-American population, recording the coordinates of 18 landmarks in 2D images from 79 controls and 51 patients. We quantified facial differences using Euclidean Distance Matrix Analysis, and assessed the diagnostic accuracy of Face2Gene, an automatic deep-learning algorithm. Individuals diagnosed with DS and MS presented severe phenotypes, with 58.2% and 65.4% of significantly different facial traits. The phenotype was milder in NS (47.7%) and non-significant in NF1 (11.4%). Each syndrome presented a characteristic dysmorphology pattern, supporting the diagnostic potential of facial biomarkers. However, population-specific traits were detected in the Colombian population. Diagnostic accuracy was 100% in DS, moderate in NS (66.7%) but lower in comparison to a European population (100%), and below 10% in MS and NF1. Moreover, admixed individuals showed lower facial gestalt similarities. Our results underscore that incorporating populations with Amerindian, African and European ancestry is crucial to improve diagnostic methods of rare disorders.
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Affiliation(s)
- Luis M Echeverry-Quiceno
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Av. Diagonal, 643. Planta 2, 08028, Barcelona, Spain
| | - Estephania Candelo
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad ICESI, Cali, Colombia
- Servicio de Genética Clínica, Fundación Valle del Lili, Cali, Colombia
| | - Eidith Gómez
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad ICESI, Cali, Colombia
| | - Paula Solís
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad ICESI, Cali, Colombia
| | - Diana Ramírez
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad ICESI, Cali, Colombia
| | - Diana Ortiz
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad ICESI, Cali, Colombia
| | - Alejandro González
- HER - Human-Environment Research Group, La Salle - Universitat Ramon Llull, Barcelona, Spain
| | - Xavier Sevillano
- HER - Human-Environment Research Group, La Salle - Universitat Ramon Llull, Barcelona, Spain
| | | | - Harry Pachajoa
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad ICESI, Cali, Colombia
- Servicio de Genética Clínica, Fundación Valle del Lili, Cali, Colombia
| | - Neus Martínez-Abadías
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Av. Diagonal, 643. Planta 2, 08028, Barcelona, Spain.
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14
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Advancement in Human Face Prediction Using DNA. Genes (Basel) 2023; 14:genes14010136. [PMID: 36672878 PMCID: PMC9858985 DOI: 10.3390/genes14010136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/15/2022] [Accepted: 12/21/2022] [Indexed: 01/05/2023] Open
Abstract
The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.
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15
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Abstract
This review paper covers the forensic-relevant literature in biological sciences from 2019 to 2022 as a part of the 20th INTERPOL International Forensic Science Managers Symposium. Topics reviewed include rapid DNA testing, using law enforcement DNA databases plus investigative genetic genealogy DNA databases along with privacy/ethical issues, forensic biology and body fluid identification, DNA extraction and typing methods, mixture interpretation involving probabilistic genotyping software (PGS), DNA transfer and activity-level evaluations, next-generation sequencing (NGS), DNA phenotyping, lineage markers (Y-chromosome, mitochondrial DNA, X-chromosome), new markers and approaches (microhaplotypes, proteomics, and microbial DNA), kinship analysis and human identification with disaster victim identification (DVI), and non-human DNA testing including wildlife forensics. Available books and review articles are summarized as well as 70 guidance documents to assist in quality control that were published in the past three years by various groups within the United States and around the world.
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16
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Xiong Z, Gao X, Chen Y, Feng Z, Pan S, Lu H, Uitterlinden AG, Nijsten T, Ikram A, Rivadeneira F, Ghanbari M, Wang Y, Kayser M, Liu F. Combining genome-wide association studies highlight novel loci involved in human facial variation. Nat Commun 2022; 13:7832. [PMID: 36539420 PMCID: PMC9767941 DOI: 10.1038/s41467-022-35328-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
Standard genome-wide association studies (GWASs) rely on analyzing a single trait at a time. However, many human phenotypes are complex and composed by multiple correlated traits. Here we introduce C-GWAS, a method for combining GWAS summary statistics of multiple potentially correlated traits. Extensive computer simulations demonstrated increased statistical power of C-GWAS compared to the minimal p-values of multiple single-trait GWASs (MinGWAS) and the current state-of-the-art method for combining single-trait GWASs (MTAG). Applying C-GWAS to a meta-analysis dataset of 78 single trait facial GWASs from 10,115 Europeans identified 56 study-wide suggestively significant loci with multi-trait effects on facial morphology of which 17 are novel loci. Using data from additional 13,622 European and Asian samples, 46 (82%) loci, including 9 (53%) novel loci, were replicated at nominal significance with consistent allele effects. Functional analyses further strengthen the reliability of our C-GWAS findings. Our study introduces the C-GWAS method and makes it available as computationally efficient open-source R package for widespread future use. Our work also provides insights into the genetic architecture of human facial appearance.
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Affiliation(s)
- Ziyi Xiong
- grid.5645.2000000040459992XDepartment of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.5645.2000000040459992XDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Xingjian Gao
- grid.9227.e0000000119573309CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China ,grid.440259.e0000 0001 0115 7868National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing, Jiangsu China
| | - Yan Chen
- grid.5645.2000000040459992XDepartment of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.9227.e0000000119573309CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Zhanying Feng
- grid.9227.e0000000119573309CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Siyu Pan
- grid.9227.e0000000119573309CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Haojie Lu
- grid.5645.2000000040459992XDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.5645.2000000040459992XDepartment of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Andre G. Uitterlinden
- grid.5645.2000000040459992XDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.5645.2000000040459992XDepartment of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Tamar Nijsten
- grid.5645.2000000040459992XDepartment of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Arfan Ikram
- grid.5645.2000000040459992XDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Fernando Rivadeneira
- grid.5645.2000000040459992XDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.5645.2000000040459992XDepartment of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.5645.2000000040459992XDepartment of Oral and Maxillofacial Surgery, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mohsen Ghanbari
- grid.5645.2000000040459992XDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Yong Wang
- grid.9227.e0000000119573309CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Manfred Kayser
- grid.5645.2000000040459992XDepartment of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Fan Liu
- grid.5645.2000000040459992XDepartment of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ,grid.9227.e0000000119573309CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
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17
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Feng Z, Duren Z, Xin J, Yuan Q, He Y, Su B, Wong WH, Wang Y. Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification. eLife 2022; 11:82535. [PMID: 36525361 PMCID: PMC9810332 DOI: 10.7554/elife.82535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP-associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes' relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar, copy archived at swh:1:rev:cf27438d3f8245c34c357ec5f077528e6befe829.
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Affiliation(s)
- Zhanying Feng
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson UniversityGreenwoodUnited States
| | - Jingxue Xin
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford UniversityStanfordUnited States
| | - Qiuyue Yuan
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson UniversityGreenwoodUnited States
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
| | - Wing Hung Wong
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford UniversityStanfordUnited States
| | - Yong Wang
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of SciencesHangzhouChina
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18
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Joshi RS, Rigau M, García-Prieto CA, Castro de Moura M, Piñeyro D, Moran S, Davalos V, Carrión P, Ferrando-Bernal M, Olalde I, Lalueza-Fox C, Navarro A, Fernández-Tena C, Aspandi D, Sukno FM, Binefa X, Valencia A, Esteller M. Look-alike humans identified by facial recognition algorithms show genetic similarities. Cell Rep 2022; 40:111257. [PMID: 36001980 DOI: 10.1016/j.celrep.2022.111257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 06/05/2022] [Accepted: 08/01/2022] [Indexed: 11/03/2022] Open
Abstract
The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of "look-alike" humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics.
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Affiliation(s)
- Ricky S Joshi
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain
| | - Maria Rigau
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Carlos A García-Prieto
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain; Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | | | - David Piñeyro
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain; Centro de Investigacion Biomedica en Red Cancer (CIBERONC), 28029 Madrid, Spain
| | - Sebastian Moran
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain
| | - Veronica Davalos
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain
| | - Pablo Carrión
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Manuel Ferrando-Bernal
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Iñigo Olalde
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Carles Lalueza-Fox
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Arcadi Navarro
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain; Centre for Genomic Regulation (CNAG-CRG), 08003 Barcelona, Catalonia, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
| | | | - Decky Aspandi
- Departament de Tecnologies de la Informació i les Comunicaciones (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain
| | - Federico M Sukno
- Departament de Tecnologies de la Informació i les Comunicaciones (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain
| | - Xavier Binefa
- Departament de Tecnologies de la Informació i les Comunicaciones (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain; Centro de Investigacion Biomedica en Red Cancer (CIBERONC), 28029 Madrid, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain; Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), L'Hospitalet, 08907 Barcelona, Spain.
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19
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Zhou H, Yang Y, Wang L, Ye S, Liu J, Gong P, Qian Y, Zeng H, Chen X. Integrated multi-omic data reveal the potential molecular mechanisms of the nutrition and flavor in Liancheng white duck meat. Front Genet 2022; 13:939585. [PMID: 36046229 PMCID: PMC9421069 DOI: 10.3389/fgene.2022.939585] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/14/2022] [Indexed: 12/01/2022] Open
Abstract
The Liancheng white (LW) duck is one of the most valued Chinese indigenous poultry breeds. Its meat is rich in nutrients and has distinct flavors, but the molecular mechanisms behind them are unknown. To address this issue, we measured and compared multi-omic data (genome, transcriptome, and metabolome) of breast meat from LW ducks and the Mianyang Shelduck (MS) ducks. We found that the LW duck has distinct breed-specific genetic features, including numerous mutant genes with differential expressions associated with amino acid metabolism and transport activities. The metabolome driven by genetic materials was also seen to differ between the two breeds. For example, several amino acids that are beneficial for human health, such as L-Arginine, L-Ornithine, and L-lysine, were found in considerably higher concentrations in LW muscle than in MS duck muscle (p < 0.05). SLC7A6, a mutant gene, was substantially upregulated in the LW group (p < 0.05), which may lead to excessive L-arginine and L-ornithine accumulation in LW duck meat through transport regulation. Further, guanosine monophosphate (GMP), an umami-tasting molecule, was considerably higher in LW muscle (p < 0.05), while L-Aspartic acid was significantly abundant in MS duck meat (p < 0.05), showing that the LW duck has a different umami formation. Overall, this study contributed to our understanding of the molecular mechanisms driving the enriched nutrients and distinct umami of LW duck meat, which will provide a useful reference for duck breeding.
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Affiliation(s)
- Hao Zhou
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Yang
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Lixia Wang
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Shengqiang Ye
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Jiajia Liu
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Ping Gong
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Yunguo Qian
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Huijun Zeng
- Wuhan Institute for Food and Cosmetic Control, Wuhan, China
- Key Laboratory of Edible Oil Quality and Safety for State Market Regulation, Wuhan, China
- *Correspondence: Huijun Zeng, ; Xing Chen,
| | - Xing Chen
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
- *Correspondence: Huijun Zeng, ; Xing Chen,
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20
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Davies KJM, Richmond S, Medeiros-Mirra RJ, Abbas HH, Wilson-Nagrani CE, Davis MG, Zhurov A. Applying an automated method of classifying lip morphological traits. J Orthod 2022; 49:412-419. [PMID: 35796491 DOI: 10.1177/14653125221106489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To apply an automated computerised method to categorise and determine the prevalence of different types of lip traits, and to explore associations between lip traits and sex differences. DESIGN Observational descriptive study utilising an automated method of facial assessment. SETTING AND PARTICIPANTS A total of 4747 children from the Avon Longitudinal Study of Parents and Children (ALSPAC) who each had 3D facial scans carried out at 15 years of age. METHODS Each of the participants was automatically categorised regarding predetermined lip morphological traits. Descriptive statistics were applied to report the prevalence of the different types of each trait, and chi-square tests were used to investigate sex differences and associations between traits. RESULTS A total of 4730 individuals were assessed (47% male, 53% female). Eight predetermined lip traits have been reported previously. There were differences in prevalence for all lip traits in male and female patients (all P ⩽ 0.0002), with differences between the sexes described for each trait. For example, a deeply grooved philtrum of average width was more prevalent in boys, and an indentation near the upper vermilion border was more prevalent in girls. Each of the traits was significantly associated with the other traits (all P < 0.0001), with particularly strong associations seen between traits in the same region (e.g. upper lip). Individual associations between traits are reported; for example, a straight lip contour was found to be associated with no true vermilion border in both the upper and lower lip regions. CONCLUSION The automated computerised method described is an invaluable tool for the categorisation of lip morphological traits. The prevalence of various types of traits has been described. Sexual dimorphism exists for all the lip traits assessed. Generally, each of the traits are associated with all other traits, with individual associations reported.
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Affiliation(s)
| | - Stephen Richmond
- Department of Orthodontics, School of Dentistry, Cardiff University, Cardiff, UK
| | | | - Hawraa Hassan Abbas
- Department of Orthodontics, School of Dentistry, Cardiff University, Cardiff, UK
| | | | - Megan Gael Davis
- Department of Orthodontics, School of Dentistry, Cardiff University, Cardiff, UK
| | - Alexei Zhurov
- Department of Orthodontics, School of Dentistry, Cardiff University, Cardiff, UK
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21
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Naqvi S, Hoskens H, Wilke F, Weinberg SM, Shaffer JR, Walsh S, Shriver MD, Wysocka J, Claes P. Decoding the Human Face: Challenges and Progress in Understanding the Genetics of Craniofacial Morphology. Annu Rev Genomics Hum Genet 2022; 23:383-412. [PMID: 35483406 PMCID: PMC9482780 DOI: 10.1146/annurev-genom-120121-102607] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Variations in the form of the human face, which plays a role in our individual identities and societal interactions, have fascinated scientists and artists alike. Here, we review our current understanding of the genetics underlying variation in craniofacial morphology and disease-associated dysmorphology, synthesizing decades of progress on Mendelian syndromes in addition to more recent results from genome-wide association studies of human facial shape and disease risk. We also discuss the various approaches used to phenotype and quantify facial shape, which are of particular importance due to the complex, multipartite nature of the craniofacial form. We close by discussing how experimental studies have contributed and will further contribute to our understanding of human genetic variation and then proposing future directions and applications for the field.
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Affiliation(s)
- Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, USA; , .,Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Hanne Hoskens
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium; , .,Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Franziska Wilke
- Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA; ,
| | - Seth M Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; , .,Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Anthropology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - John R Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; , .,Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Susan Walsh
- Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA; ,
| | - Mark D Shriver
- Department of Anthropology, The Pennsylvania State University, University Park, Pennsylvania, USA;
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, USA; , .,Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, USA.,Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Peter Claes
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium; , .,Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium.,Murdoch Children's Research Institute, Melbourne, Victoria, Australia
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22
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Fernández-Rhodes L, Graff M, Buchanan VL, Justice AE, Highland HM, Guo X, Zhu W, Chen HH, Young KL, Adhikari K, Palmer ND, Below JE, Bradfield J, Pereira AC, Glover L, Kim D, Lilly AG, Shrestha P, Thomas AG, Zhang X, Chen M, Chiang CW, Pulit S, Horimoto A, Krieger JE, Guindo-Martínez M, Preuss M, Schumann C, Smit RA, Torres-Mejía G, Acuña-Alonzo V, Bedoya G, Bortolini MC, Canizales-Quinteros S, Gallo C, González-José R, Poletti G, Rothhammer F, Hakonarson H, Igo R, Adler SG, Iyengar SK, Nicholas SB, Gogarten SM, Isasi CR, Papnicolaou G, Stilp AM, Qi Q, Kho M, Smith JA, Langefeld CD, Wagenknecht L, Mckean-Cowdin R, Gao XR, Nousome D, Conti DV, Feng Y, Allison MA, Arzumanyan Z, Buchanan TA, Ida Chen YD, Genter PM, Goodarzi MO, Hai Y, Hsueh W, Ipp E, Kandeel FR, Lam K, Li X, Nadler JL, Raffel LJ, Roll K, Sandow K, Tan J, Taylor KD, Xiang AH, Yao J, Audirac-Chalifour A, de Jesus Peralta Romero J, Hartwig F, Horta B, Blangero J, Curran JE, Duggirala R, Lehman DE, Puppala S, Fejerman L, John EM, Aguilar-Salinas C, Burtt NP, Florez JC, García-Ortíz H, González-Villalpando C, Mercader J, Orozco L, Tusié-Luna T, Blanco E, Gahagan S, Cox NJ, Hanis C, Butte NF, Cole SA, Comuzzie AG, Voruganti VS, Rohde R, Wang Y, Sofer T, Ziv E, Grant SF, Ruiz-Linares A, Rotter JI, Haiman CA, Parra EJ, Cruz M, Loos RJ, North KE. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium. HGG ADVANCES 2022; 3:100099. [PMID: 35399580 PMCID: PMC8990175 DOI: 10.1016/j.xhgg.2022.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/06/2022] [Indexed: 02/05/2023] Open
Abstract
Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.
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Affiliation(s)
- Lindsay Fernández-Rhodes
- Department of Biobehavioral Health, Pennsylvania State University, 219 Biobehavioral Health Building, University Park, PA 16802, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Victoria L. Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Anne E. Justice
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, PA 17822, USA
| | - Heather M. Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Wanying Zhu
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kristin L. Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, MK7 6AA Milton Keynes, UK
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Jennifer E. Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jonathan Bradfield
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Alexandre C. Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo 05508-220, Brazil
| | - LáShauntá Glover
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Daeeun Kim
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Adam G. Lilly
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Poojan Shrestha
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alvin G. Thomas
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xinruo Zhang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Minhui Chen
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charleston W.K. Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Sara Pulit
- Vertex Pharmaceuticals, W2 6BD Oxford, UK
| | - Andrea Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo 05508-220, Brazil
| | - Jose E. Krieger
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo 05508-220, Brazil
| | - Marta Guindo-Martínez
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Novo Nordisk Center for Basic Metabolic Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Michael Preuss
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Claudia Schumann
- Hasso Plattner Institute, University of Potsdam, Digital Health Center, 14482 Potsdam, Germany
| | - Roelof A.J. Smit
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gabriela Torres-Mejía
- Department of Research in Cardiovascular Diseases, Diabetes Mellitus, and Cancer, Population Health Research Center, National Institute of Public Health, Cuernavaca, Morelos 62100, Mexico
| | | | - Gabriel Bedoya
- Molecular Genetics Investigation Group, University of Antioquia, Medellín 1226, Colombia
| | - Maria-Cátira Bortolini
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, Brazil
| | - Samuel Canizales-Quinteros
- Population Genomics Applied to Health Unit, The National Institute of Genomic Medicine and the Faculty of Chemistry at the National Autonomous University of Mexico, Mexico City 04510, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Rolando González-José
- Patagonian Institute of the Social and Human Sciences, Patagonian National Center, Puerto Madryn U9120, Argentina
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | | | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Robert Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Sharon G. Adler
- Division of Nephrology and Hypertension, Harbor-University of California Los Angeles Medical Center, Torrance, CA 90502, USA
| | - Sudha K. Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Susanne B. Nicholas
- Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | | | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | | | - Adrienne M. Stilp
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Carl D. Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Roberta Mckean-Cowdin
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Xiaoyi Raymond Gao
- Department of Ophthalmology and Visual Sciences, Department of Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH 43210, USA
| | - Darryl Nousome
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - David V. Conti
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Ye Feng
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Matthew A. Allison
- Department of Family Medicine, University of California, San Diego, CA 92161, USA
| | - Zorayr Arzumanyan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Thomas A. Buchanan
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Pauline M. Genter
- Department of Medicine, Division of Endocrinology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Willa Hsueh
- Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Eli Ipp
- Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
- Department of Medicine, Division of Endocrinology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Fouad R. Kandeel
- Department of Translational Research & Cellular Therapeutics, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Kelvin Lam
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Xiaohui Li
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Jerry L. Nadler
- Department of Pharmacology at New York Medical College School of Medicine, Valhalla, NY 10595, USA
| | - Leslie J. Raffel
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, CA 92697, USA
| | - Kathryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Anny H. Xiang
- Research and Evaluation Branch, Kaiser Permanente of Southern California, Pasadena, CA 91101, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Astride Audirac-Chalifour
- Medical Research Unit in Biochemistry, Specialty Hospital, National Medical Center of the Twenty-First Century, Mexican Institute of Social Security, Mexico City 06725, Mexico
| | - Jose de Jesus Peralta Romero
- Medical Research Unit in Biochemistry, Specialty Hospital, National Medical Center of the Twenty-First Century, Mexican Institute of Social Security, Mexico City 06725, Mexico
| | - Fernando Hartwig
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas 96010-610, Brazil
| | - Bernando Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas 96010-610, Brazil
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville and Edinburg, TX 78520 and 78539, USA
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville and Edinburg, TX 78520 and 78539, USA
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville and Edinburg, TX 78520 and 78539, USA
| | - Donna E. Lehman
- Department of Medicine, School of Medicine, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Sobha Puppala
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27109, USA
| | - Laura Fejerman
- Department of Public Health Sciences, School of Medicine, and the Comprehensive Cancer Center, University of California Davis, Davis, CA 95616, USA
| | - Esther M. John
- Departments of Epidemiology & Population Health and Medicine-Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Carlos Aguilar-Salinas
- Division of Nutrition, Salvador Zubirán National Institute of Health Sciences and Nutrition, Mexico City 14080, Mexico
| | - Noël P. Burtt
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Humberto García-Ortíz
- Laboratory of Immunogenomics and Metabolic Diseases, National Institute of Genomic Medicine, Mexico City 14610, Mexico
| | - Clicerio González-Villalpando
- Center for Diabetes Studies, Research Unit for Diabetes and Cardiovascular Risk, Center for Population Health Studies, National Institute of Public Health, Mexico City 14080, Mexico
| | - Josep Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lorena Orozco
- Laboratory of Immunogenomics and Metabolic Diseases, National Institute of Genomic Medicine, Mexico City 14610, Mexico
| | - Teresa Tusié-Luna
- Molecular Biology and Medical Genomics Unity, Institute of Biomedical Research, The National Autonomous University of Mexico and the Salvador Zubirán National Institute of Health Sciences and Nutrition, Mexico City 14080, Mexico
| | - Estela Blanco
- Center for Community Health, Division of Academic General Pediatrics, University of California at San Diego, San Diego, CA 92093, USA
| | - Sheila Gahagan
- Center for Community Health, Division of Academic General Pediatrics, University of California at San Diego, San Diego, CA 92093, USA
| | - Nancy J. Cox
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Craig Hanis
- University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Nancy F. Butte
- United States Department of Agriculture, Agricultural Research Service, The Children’s Nutrition Research Center, and the Department Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shelley A. Cole
- Population Health Program, Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | | | - V. Saroja Voruganti
- Department of Nutrition and Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC 28081, USA
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yujie Wang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tamar Sofer
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Elad Ziv
- Division of General Internal Medicine, Department of Medicine, Helen Diller Family Comprehensive Cancer Center, Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94115, USA
| | - Struan F.A. Grant
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Andres Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200438, China
- Department of Genetics, Evolution and Environment, and Genetics Institute of the University College London, London WC1E 6BT, UK
- Laboratory of Biocultural Anthropology, Law, Ethics, and Health, Aix-Marseille University, Marseille 13385, France
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto- Mississauga, Mississauga, ON L5L 1C6, Canada
| | - Miguel Cruz
- Medical Research Unit in Biochemistry, Specialty Hospital, National Medical Center of the Twenty-First Century, Mexican Institute of Social Security, Mexico City 06725, Mexico
| | - Ruth J.F. Loos
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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23
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Zhang M, Wu S, Du S, Qian W, Chen J, Qiao L, Yang Y, Tan J, Yuan Z, Peng Q, Liu Y, Navarro N, Tang K, Ruiz-Linares A, Wang J, Claes P, Jin L, Li J, Wang S. Genetic variants underlying differences in facial morphology in East Asian and European populations. Nat Genet 2022; 54:403-411. [PMID: 35393595 DOI: 10.1038/s41588-022-01038-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 01/19/2022] [Accepted: 02/25/2022] [Indexed: 11/09/2022]
Abstract
Facial morphology-a conspicuous feature of human appearance-is highly heritable. Previous studies on the genetic basis of facial morphology were performed mainly in European-ancestry cohorts (EUR). Applying a data-driven phenotyping and multivariate genome-wide scanning protocol to a large collection of three-dimensional facial images of individuals with East Asian ancestry (EAS), we identified 244 variants in 166 loci (62 new) associated with typical-range facial variation. A newly proposed polygenic shape analysis indicates that the effects of the variants on facial shape in EAS can be generalized to EUR. Based on this, we further identified 13 variants related to differences between facial shape in EUR and EAS populations. Evolutionary analyses suggest that the difference in nose shape between EUR and EAS populations is caused by a directional selection, due mainly to a local adaptation in Europeans. Our results illustrate the underlying genetic basis for facial differences across populations.
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Affiliation(s)
- Manfei Zhang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Computer Science, Fudan University, Shanghai, China
| | - Sijie Wu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wei Qian
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Computer Science, Fudan University, Shanghai, China
| | - Jieyi Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Lu Qiao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yajun Yang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyu Yuan
- Fudan-Taizhou Institute of Health Sciences, Taizhou, China
| | - Qianqian Peng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Nicolas Navarro
- Biogéosciences, UMR 6282 CNRS-EPHE, Université Bourgogne Franche-Comté, Dijon, France.,Ecole Pratique des Hautes Etudes, PSL University, Paris, France
| | - Kun Tang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Andrés Ruiz-Linares
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.,Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.,Aix-Marseille Université, CNRS, EFS, ADES, Marseille, France.,Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, UK
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.,Fudan-Taizhou Institute of Health Sciences, Taizhou, China
| | - Peter Claes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.,Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium.,Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China. .,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,Fudan-Taizhou Institute of Health Sciences, Taizhou, China.
| | - Jiarui Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium. .,Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
| | - Sijia Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China. .,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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24
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Dabas P, Jain S, Khajuria H, Nayak BP. Forensic DNA phenotyping: Inferring phenotypic traits from crime scene DNA. J Forensic Leg Med 2022; 88:102351. [DOI: 10.1016/j.jflm.2022.102351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/01/2022] [Accepted: 04/04/2022] [Indexed: 10/18/2022]
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25
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Qian W, Zhang M, Wan K, Xie Y, Du S, Li J, Mu X, Qiu J, Xue X, Zhuang X, Wu Y, Liu F, Wang S. Genetic evidence for facial variation being a composite phenotype of cranial variation and facial soft tissue thickness. J Genet Genomics 2022; 49:934-942. [DOI: 10.1016/j.jgg.2022.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 02/17/2022] [Accepted: 02/20/2022] [Indexed: 10/18/2022]
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26
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Boer EF, Maclary ET, Shapiro MD. Complex genetic architecture of three-dimensional craniofacial shape variation in domestic pigeons. Evol Dev 2021; 23:477-495. [PMID: 34914861 PMCID: PMC9119316 DOI: 10.1111/ede.12395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 10/28/2021] [Accepted: 11/24/2021] [Indexed: 11/29/2022]
Abstract
Deciphering the genetic basis of vertebrate craniofacial variation is a longstanding biological problem with broad implications in evolution, development, and human pathology. One of the most stunning examples of craniofacial diversification is the adaptive radiation of birds, in which the beak serves essential roles in virtually every aspect of their life histories. The domestic pigeon (Columba livia) provides an exceptional opportunity to study the genetic underpinnings of craniofacial variation because of its unique balance of experimental accessibility and extraordinary phenotypic diversity within a single species. We used traditional and geometric morphometrics to quantify craniofacial variation in an F2 laboratory cross derived from the straight-beaked Pomeranian Pouter and curved-beak Scandaroon pigeon breeds. Using a combination of genome-wide quantitative trait locus scans and multi-locus modeling, we identified a set of genetic loci associated with complex shape variation in the craniofacial skeleton, including beak shape, braincase shape, and mandible shape. Some of these loci control coordinated changes between different structures, while others explain variation in the size and shape of specific skull and jaw regions. We find that in domestic pigeons, a complex blend of both independent and coupled genetic effects underlie three-dimensional craniofacial morphology.
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Affiliation(s)
- Elena F Boer
- School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Emily T Maclary
- School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Michael D Shapiro
- School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA
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27
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Correlational research on facial and clinical characteristics of adolescents with obsessive-compulsive disorder. BMC Psychiatry 2021; 21:623. [PMID: 34895185 PMCID: PMC8666025 DOI: 10.1186/s12888-021-03612-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 11/22/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The neurodevelopmental model of obsessive-compulsive disorder (OCD) suggests that the neurodevelopmental changes in the ventral striatal circuit of the prefrontal lobe are associated with the initial symptoms of OCD. Facial morphology is one of the most consistent anatomical phenotypes of neurodevelopmental disorders, which can reflect brain structure and function. Facial deformity, an easily measured index of brain malformation, can reflect abnormal brain structure and function. Therefore, this study aims to explore the relationship between clinical features and neurodevelopment of adolescents with OCD through facial morphology. METHODS The enrolled study sample comprised 40 adolescents diagnosed with OCD using the Obsessive Compulsive Inventory-Child Version (OCI-CV) and 38 healthy controls (HCs). Facial photos, 21 facial diameters, and 9 facial angles were collected using image software. RESULTS In males, lower lip red height was significantly lower in OCD patients than in HCs (P < 0.025); no significant differences were observed in other facial indicators (all P > 0.025). In females, the nasolabial angle was smaller in OCD patients than in HCs (P < 0.025); no significant differences were observed in other facial indicators (all P > 0.025). The difference in lower lip red height between the OCD group and HC group was positively correlated with neutralizing symptoms (r = 0.401, P < 0.05). CONCLUSIONS Male OCD patients had a thinner lower lip and female OCD patients had smaller nasolabial angles. The facial features of adolescents with OCD were positively correlated with lower lip redness and neutralizing symptoms.
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28
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Orchard P, Manickam N, Ventresca C, Vadlamudi S, Varshney A, Rai V, Kaplan J, Lalancette C, Mohlke KL, Gallagher K, Burant CF, Parker SCJ. Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. Genome Res 2021; 31:2258-2275. [PMID: 34815310 PMCID: PMC8647829 DOI: 10.1101/gr.268482.120] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/16/2021] [Indexed: 12/12/2022]
Abstract
Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.
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Affiliation(s)
- Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Nandini Manickam
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Christa Ventresca
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Swarooparani Vadlamudi
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Vivek Rai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Jeremy Kaplan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Claudia Lalancette
- Epigenomics Core, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Katherine Gallagher
- Department of Surgery, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
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29
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Qian Y, Xiong Z, Li Y, Kayser M, Liu L, Liu F. The effects of Tbx15 and Pax1 on facial and other physical morphology in mice. FASEB Bioadv 2021; 3:1011-1019. [PMID: 34938962 PMCID: PMC8664010 DOI: 10.1096/fba.2021-00094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/19/2021] [Accepted: 08/23/2021] [Indexed: 12/13/2022] Open
Abstract
DNA variants in or close to the human TBX15 and PAX1 genes have been repeatedly associated with facial morphology in independent genome-wide association studies, while their functional roles in determining facial morphology remain to be understood. We generated Tbx15 knockout (Tbx15 -/-) and Pax1 knockout (Pax1 -/-) mice by applying the one-step CRISPR/Cas9 method. A total of 75 adult mice were used for subsequent phenotype analysis, including 38 Tbx15 mice (10 homozygous Tbx15 -/-, 18 heterozygous Tbx15 +/-, 10 wild-type Tbx15 +/+ WT littermates) and 37 Pax1 mice (12 homozygous Pax1 -/-, 15 heterozygous Pax1 +/-, 10 Pax1 +/+ WT littermates). Facial and other physical morphological phenotypes were obtained from three-dimensional (3D) images acquired with the HandySCAN BLACK scanner. Compared to WT littermates, the Tbx15 -/- mutant mice had significantly shorter faces (p = 1.08E-8, R2 = 0.61) and their ears were in a significantly lower position (p = 3.54E-8, R2 = 0.62) manifesting a "droopy ear" characteristic. Besides these face alternations, Tbx15 -/- mutant mice displayed significantly lower weight as well as shorter body and limb length. Pax1 -/- mutant mice showed significantly longer noses (p = 1.14E-5, R2 = 0.46) relative to WT littermates, but otherwise displayed less obvious morphological alterations than Tbx15 -/- mutant mice did. We provide the first direct functional evidence that two well-known and replicated human face genes, Tbx15 and Pax1, impact facial and other body morphology in mice. The general agreement between our findings in knock-out mice with those from previous GWASs suggests that the functional evidence we established here in mice may also be relevant in humans.
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Affiliation(s)
- Yu Qian
- CAS Key Laboratory of Genomic and Precision MedicineBeijing Institute of GenomicsChinese Academy of SciencesBeijingChina
- China National Center for BioinformationBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Ziyi Xiong
- Department of Genetic IdentificationErasmus MC University Medical Center RotterdamRotterdamthe Netherlands
- Department of EpidemiologyErasmus MC University Medical Center RotterdamRotterdamthe Netherlands
| | - Yi Li
- CAS Key Laboratory of Genomic and Precision MedicineBeijing Institute of GenomicsChinese Academy of SciencesBeijingChina
- China National Center for BioinformationBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Manfred Kayser
- Department of Genetic IdentificationErasmus MC University Medical Center RotterdamRotterdamthe Netherlands
| | - Lei Liu
- Department of Plastic and Burn SurgeryThe Second HospitalCheeloo College of MedicineShandong UniversityJinanChina
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision MedicineBeijing Institute of GenomicsChinese Academy of SciencesBeijingChina
- China National Center for BioinformationBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- Department of Genetic IdentificationErasmus MC University Medical Center RotterdamRotterdamthe Netherlands
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30
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Aponte JD, Katz DC, Roth DM, Vidal-García M, Liu W, Andrade F, Roseman CC, Murray SA, Cheverud J, Graf D, Marcucio RS, Hallgrímsson B. Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape. eLife 2021; 10:68623. [PMID: 34779766 PMCID: PMC8631940 DOI: 10.7554/elife.68623] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 11/12/2021] [Indexed: 12/20/2022] Open
Abstract
Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait – craniofacial shape – in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.
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Affiliation(s)
- Jose D Aponte
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - David C Katz
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Daniela M Roth
- School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Marta Vidal-García
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Wei Liu
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Fernando Andrade
- Department of Biology, Loyola University Chicago, Chicago, United States
| | - Charles C Roseman
- Department of Biology, Loyola University Chicago, Chicago, United States
| | | | - James Cheverud
- Department of Biology, Loyola University Chicago, Chicago, United States
| | - Daniel Graf
- School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Ralph S Marcucio
- Department of Orthopaedic Surgery, School of Medicine, University of California, San Francisco, San Francisco, United States
| | - Benedikt Hallgrímsson
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Animal Biology, University of Illinois Urbana Champaign, Urbana, United States
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31
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Knol MJ, Pawlak MA, Lamballais S, Terzikhan N, Hofer E, Xiong Z, Klaver CCW, Pirpamer L, Vernooij MW, Ikram MA, Schmidt R, Kayser M, Evans TE, Adams HHH. Genetic architecture of orbital telorism. Hum Mol Genet 2021; 31:1531-1543. [PMID: 34791242 PMCID: PMC9071440 DOI: 10.1093/hmg/ddab334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/19/2022] Open
Abstract
The interocular distance, or orbital telorism, is a distinctive craniofacial trait that also serves as a clinically informative measure. While its extremes, hypo- and hypertelorism, have been linked to monogenic disorders and are often syndromic, little is known about the genetic determinants of interocular distance within the general population. We derived orbital telorism measures from cranial magnetic resonance imaging by calculating the distance between the eyeballs’ centre of gravity, which showed a good reproducibility with an intraclass correlation coefficient of 0.991 (95% confidence interval 0.985–0.994). Heritability estimates were 76% (standard error = 12%) with a family-based method (N = 364) and 39% (standard error = 2.4%) with a single nucleotide polymorphism-based method (N = 34 130) and were unaffected by adjustment for height (model II) and intracranial volume (model III) or head width (model IV). Genome-wide association studies in 34 130 European individuals identified 56 significantly associated genomic loci (P < 5 × 10−8) across four different models of which 46 were novel for facial morphology, and overall these findings replicated in an independent sample (N = 10 115) with telorism-related horizontal facial distance measures. Genes located nearby these 56 identified genetic loci were 4.9-fold enriched for Mendelian hypotelorism and hypertelorism genes, underlining their biological relevance. This study provides novel insights into the genetic architecture underlying interocular distance in particular, and the face in general, and explores its potential for applications in a clinical setting.
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Affiliation(s)
- Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Mikolaj A Pawlak
- Department of Neurology and Cerebrovascular Disorders, Poznan University of Medical Sciences, Poznan, Poland.,Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Sander Lamballais
- Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Edith Hofer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Auenbruggerplatz 22, 8036 Graz, Austria.,Institute of Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Ziyi Xiong
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands.,Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Caroline C W Klaver
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands.,Department of Ophthalmology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Lukas Pirpamer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
| | - Hieab H H Adams
- Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, 3015 CE, the Netherlands
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32
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Liu C, Lee MK, Naqvi S, Hoskens H, Liu D, White JD, Indencleef K, Matthews H, Eller RJ, Li J, Mohammed J, Swigut T, Richmond S, Manyama M, Hallgrímsson B, Spritz RA, Feingold E, Marazita ML, Wysocka J, Walsh S, Shriver MD, Claes P, Weinberg SM, Shaffer JR. Genome scans of facial features in East Africans and cross-population comparisons reveal novel associations. PLoS Genet 2021; 17:e1009695. [PMID: 34411106 PMCID: PMC8375984 DOI: 10.1371/journal.pgen.1009695] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 07/02/2021] [Indexed: 12/16/2022] Open
Abstract
Facial morphology is highly variable, both within and among human populations, and a sizable portion of this variation is attributable to genetics. Previous genome scans have revealed more than 100 genetic loci associated with different aspects of normal-range facial variation. Most of these loci have been detected in Europeans, with few studies focusing on other ancestral groups. Consequently, the degree to which facial traits share a common genetic basis across diverse sets of humans remains largely unknown. We therefore investigated the genetic basis of facial morphology in an East African cohort. We applied an open-ended data-driven phenotyping approach to a sample of 2,595 3D facial images collected on Tanzanian children. This approach segments the face into hierarchically arranged, multivariate features that capture the shape variation after adjusting for age, sex, height, weight, facial size and population stratification. Genome scans of these multivariate shape phenotypes revealed significant (p < 2.5 × 10−8) signals at 20 loci, which were enriched for active chromatin elements in human cranial neural crest cells and embryonic craniofacial tissue, consistent with an early developmental origin of the facial variation. Two of these associations were in highly conserved regions showing craniofacial-specific enhancer activity during embryological development (5q31.1 and 12q21.31). Six of the 20 loci surpassed a stricter threshold accounting for multiple phenotypes with study-wide significance (p < 6.25 × 10−10). Cross-population comparisons indicated 10 association signals were shared with Europeans (seven sharing the same associated SNP), and facilitated fine-mapping of causal variants at previously reported loci. Taken together, these results may point to both shared and population-specific components to the genetic architecture of facial variation. Genetic factors play an important role in shaping human facial features. Over the last decade, studies have identified numerous genes associated with various facial traits. The vast majority of these studies have focused on European or Asian populations, while African populations have been underrepresented. Increasing the diversity of these analyses can reveal novel associations and cross-population analyses can help deepen our understanding of known genetic associations. We therefore performed a genome scan of 3D facial features in African children from Tanzania and then compared our results to Europeans. We found 20 regions of the genome associated with facial shape in Tanzanian children, 10 of which were also present in Europeans, indicating evidence for a partly shared genetic basis for human facial shape across populations. In addition, about half of the genetic associations observed in Tanzanians were not present in Europeans, and some of the shared signals differed between populations in the specific genetic variants associated or specific facial traits affected. These results shed light on the shared and population-specific genetic contributors to normal-range facial variation.
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Affiliation(s)
- Chenxing Liu
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Myoung Keun Lee
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Hanne Hoskens
- Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium
- Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Dongjing Liu
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Julie D. White
- Department of Anthropology, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Karlijne Indencleef
- Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium
- Processing Speech & Images, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Harold Matthews
- Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium
- Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
| | - Ryan J. Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Jiarui Li
- Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium
- Processing Speech & Images, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Jaaved Mohammed
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Tomek Swigut
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, United Kingdom
| | - Mange Manyama
- Anatomy in Radiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Benedikt Hallgrímsson
- Department of Anatomy and Cell Biology, Alberta Children´s Hospital Research Institute, University of Calgary, Calgary, Canada
| | - Richard A. Spritz
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Eleanor Feingold
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Mary L. Marazita
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, United States of America
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Mark D. Shriver
- Department of Anthropology, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Peter Claes
- Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium
- Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- Processing Speech & Images, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
- Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
| | - Seth M. Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Anthropology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (SMW); (JRS)
| | - John R. Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (SMW); (JRS)
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33
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Hoskens H, Liu D, Naqvi S, Lee MK, Eller RJ, Indencleef K, White JD, Li J, Larmuseau MHD, Hens G, Wysocka J, Walsh S, Richmond S, Shriver MD, Shaffer JR, Peeters H, Weinberg SM, Claes P. 3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies. PLoS Genet 2021; 17:e1009528. [PMID: 33983923 PMCID: PMC8118281 DOI: 10.1371/journal.pgen.1009528] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 04/01/2021] [Indexed: 12/12/2022] Open
Abstract
The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17-0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation.
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Affiliation(s)
- Hanne Hoskens
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
| | - Dongjing Liu
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Myoung Keun Lee
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ryan J. Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Karlijne Indencleef
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Department of Otorhinolaryngology, KU Leuven, Leuven, Belgium
| | - Julie D. White
- Department of Anthropology, The Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Jiarui Li
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Maarten H. D. Larmuseau
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Department of Biology, Laboratory of Socioecology and Social Evolution, KU Leuven, Leuven, Belgium
- Histories vzw, Mechelen, Belgium
| | - Greet Hens
- Department of Otorhinolaryngology, KU Leuven, Leuven, Belgium
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, United States of America
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, United States of America
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, United Kingdom
| | - Mark D. Shriver
- Department of Anthropology, The Pennsylvania State University, State College, Pennsylvania, United States of America
| | - John R. Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Seth M. Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Anthropology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
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34
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Feng Z, Duren Z, Xiong Z, Wang S, Liu F, Wong WH, Wang Y. hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context. Commun Biol 2021; 4:442. [PMID: 33824393 PMCID: PMC8024315 DOI: 10.1038/s42003-021-01970-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 03/09/2021] [Indexed: 12/19/2022] Open
Abstract
Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC .
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Affiliation(s)
- Zhanying Feng
- CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, China.,School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Zhana Duren
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, USA.,Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford University, Stanford, CA, USA
| | - Ziyi Xiong
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Sijia Wang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China. .,China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
| | - Wing Hung Wong
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford University, Stanford, CA, USA.
| | - Yong Wang
- CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, China. .,School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China. .,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China.
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35
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Abstract
Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by the recent availability of high-throughput genomics and multi-omics analyses and the companion tools of advanced informatics and computational science. Understanding of the human genome and its influence on phenotype continues to advance through genotyping large populations and using “light phenotyping” approaches in combination with smaller subsets of the population being evaluated using “deep phenotyping” approaches. Using our capability to integrate and jointly analyze genomic data with other multi-omic data, the knowledge of genotype-phenotype relationships and associated genetic pathways and functions is being advanced. Understanding genotype-phenotype relationships that discriminate human health from disease is speculated to facilitate predictive, precision health care and change modes of health care delivery. The American Association for Dental Research Fall Focused Symposium assembled experts to discuss how studies of genotype-phenotype relationships are illuminating the pathophysiology of craniofacial diseases and developmental biology. Although the breadth of the topic did not allow all areas of dental, oral, and craniofacial research to be addressed (e.g., cancer), the importance and power of integrating genomic, phenomic, and other -omic data are illustrated using a variety of examples. The 8 Fall Focused talks presented different methodological approaches for ascertaining study populations and evaluating population variance and phenotyping approaches. These advances are reviewed in this summary.
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Affiliation(s)
- J T Wright
- Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - M C Herzberg
- Department of Diagnostic and Biological Sciences, School of Dentistry, University of Minnesota, Minneapolis, MN, USA
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36
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Facial shape affects self-perceived facial attractiveness. PLoS One 2021; 16:e0245557. [PMID: 33534847 PMCID: PMC7857636 DOI: 10.1371/journal.pone.0245557] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 01/03/2021] [Indexed: 02/07/2023] Open
Abstract
Facial appearance expresses numerous cues about physical qualities as well as psychosocial and personality traits. Attractive faces are recognized clearly when seen and are often viewed advantageously in professional, social and romantic relationships. On the other hand, self-perceived attractiveness is not well understood and has been mainly attributed to psychological and cognitive factors. Here we use 3-dimensional facial surface data of a large young adult population (n = 601) to thoroughly assess the effect of facial shape on self-perceived facial attractiveness. Our results show that facial shape had a measurable effect on self-perception of facial attractiveness in both sexes. In females, self-perceived facial attractiveness was linked to decreased facial width, fuller anterior part of the lower facial third and more pronounced middle forehead and root of the nose. Males favored a well-defined chin, flatter cheeks and zygomas, and more pronounced eyebrow ridges, nose and middle forehead. The findings of this study support the notion that self-perceived facial attractiveness is not only motivated by psychological traits, but objectively measured phenotypic traits also contribute significantly. The role of social stereotypes for facial attractiveness in modern society is also inferred and discussed.
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Shen LL, Mangalesh S, McGeehan B, Tai V, Sarin N, El-Dairi MA, Freedman SF, Maguire MG, Toth CA. Birth Weight Is a Significant Predictor of Retinal Nerve Fiber Layer Thickness at 36 Weeks Postmenstrual Age in Preterm Infants. Am J Ophthalmol 2021; 222:41-53. [PMID: 32891695 PMCID: PMC7930155 DOI: 10.1016/j.ajo.2020.08.043] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To assess retinal nerve fiber layer (RNFL) thickness in preterm infants. DESIGN Prospective observational study. METHODS We imaged 83 awake infants (159 eyes) at 36 ± 1 weeks postmenstrual age (defined as the time elapsed between the first day of the last maternal menstrual period and the time at imaging) using a handheld optical coherence tomography (OCT) system at the bedside. Blinded graders semi-automatically segmented RNFL in the papillomacular bundle (-15 to +15° relative to the fovea-optic nerve axis). We correlated RNFL thickness and 7 characteristics of interest (sex, race, ethnicity, gestational age, birth weight, stage of retinopathy at prematurity, and presence of pre-plus or plus disease) via univariable and multivariable regressions. RESULTS RNFL was 3.4 μm thicker in the right eyes than in the left eyes (P < .001). Among 7 characteristics, birth weight was the only independent predictor of RNFL thickness (P < .001). A 250-g increase in birth weight was associated with 5.2 μm (95% confidence interval: 3.3-7.0) increase in RNFL thickness. Compared with very preterm infants, extremely preterm infants had thinner RNFL (58.0 ± 10.7 μm vs 63.4 ± 10.7 μm, P = .03), but the statistical significance disappeared after adjustment for birth weight (P = .25). RNFL thickness was 11.2 μm thinner in extremely low birth weight infants than in very low birth weight infants (55.5 ± 8.3 μm vs. 66.7 ± 10.2 μm; P < .001). The difference remained statistically significant after adjustment for gestational age. CONCLUSION Birth weight is a significant independent predictor of RNFL thickness near birth, implying that the retinal ganglion cells reserve is affected by intrauterine processes that affect birth weight.
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Affiliation(s)
- Liangbo L Shen
- Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Shwetha Mangalesh
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Brendan McGeehan
- Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vincent Tai
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Neeru Sarin
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Mays A El-Dairi
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sharon F Freedman
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Maureen G Maguire
- Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Cynthia A Toth
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA.
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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: 24] [Impact Index Per Article: 8.0] [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.
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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
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39
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White JD, Indencleef K, Naqvi S, Eller RJ, Hoskens H, Roosenboom J, Lee MK, Li J, Mohammed J, Richmond S, Quillen EE, Norton HL, Feingold E, Swigut T, Marazita ML, Peeters H, Hens G, Shaffer JR, Wysocka J, Walsh S, Weinberg SM, Shriver MD, Claes P. Insights into the genetic architecture of the human face. Nat Genet 2021; 53:45-53. [PMID: 33288918 PMCID: PMC7796995 DOI: 10.1038/s41588-020-00741-7] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 10/23/2020] [Indexed: 01/28/2023]
Abstract
The human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study meta-analysis of 8,246 European individuals, we identified 203 genome-wide-significant signals (120 also study-wide significant) associated with normal-range facial variation. Follow-up analyses indicate that the regions surrounding these signals are enriched for enhancer activity in cranial neural crest cells and craniofacial tissues, several regions harbor multiple signals with associations to different facial phenotypes, and there is evidence for potential coordinated actions of variants. In summary, our analyses provide insights into the understanding of how complex morphological traits are shaped by both individual and coordinated genetic actions.
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Affiliation(s)
- Julie D White
- Department of Anthropology, Pennsylvania State University, State College, PA, USA.
| | - Karlijne Indencleef
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan J Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Hanne Hoskens
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Jasmien Roosenboom
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Myoung Keun Lee
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiarui Li
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
| | - Jaaved Mohammed
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Ellen E Quillen
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather L Norton
- Department of Anthropology, University of Cincinnati, Cincinnati, OH, USA
| | - Eleanor Feingold
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tomek Swigut
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mary L Marazita
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Greet Hens
- Department of Neurosciences, Experimental Oto-Rhino-Laryngology, KU Leuven, Leuven, Belgium
| | - John R Shaffer
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Seth M Weinberg
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Peter Claes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
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40
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Murillo-Rincón AP, Kaucka M. Insights Into the Complexity of Craniofacial Development From a Cellular Perspective. Front Cell Dev Biol 2020; 8:620735. [PMID: 33392208 PMCID: PMC7775397 DOI: 10.3389/fcell.2020.620735] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
The head represents the most complex part of the body and a distinctive feature of the vertebrate body plan. This intricate structure is assembled during embryonic development in the four-dimensional process of morphogenesis. The head integrates components of the central and peripheral nervous system, sensory organs, muscles, joints, glands, and other specialized tissues in the framework of a complexly shaped skull. The anterior part of the head is referred to as the face, and a broad spectrum of facial shapes across vertebrate species enables different feeding strategies, communication styles, and diverse specialized functions. The face formation starts early during embryonic development and is an enormously complex, multi-step process regulated on a genomic, molecular, and cellular level. In this review, we will discuss recent discoveries that revealed new aspects of facial morphogenesis from the time of the neural crest cell emergence till the formation of the chondrocranium, the primary design of the individual facial shape. We will focus on molecular mechanisms of cell fate specification, the role of individual and collective cell migration, the importance of dynamic and continuous cellular interactions, responses of cells and tissues to generated physical forces, and their morphogenetic outcomes. In the end, we will examine the spatiotemporal activity of signaling centers tightly regulating the release of signals inducing the formation of craniofacial skeletal elements. The existence of these centers and their regulation by enhancers represent one of the core morphogenetic mechanisms and might lay the foundations for intra- and inter-species facial variability.
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Affiliation(s)
| | - Marketa Kaucka
- Max Planck Research Group Craniofacial Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
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41
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Huang Y, Li D, Qiao L, Liu Y, Peng Q, Wu S, Zhang M, Yang Y, Tan J, Xu S, Jin L, Wang S, Tang K, Grünewald S. A genome-wide association study of facial morphology identifies novel genetic loci in Han Chinese. J Genet Genomics 2020; 48:198-207. [PMID: 33593615 DOI: 10.1016/j.jgg.2020.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 10/23/2022]
Abstract
The human face is a heritable surface with many complex sensory organs. In recent years, many genetic loci associated with facial features have been reported in different populations, yet there is a lack of studies on the Han Chinese population. Here, we report a genome-wide association study of 3D normal human faces of 2,659 Han Chinese with autosegment phenotypes of facial morphology. We identify single-nucleotide polymorphisms (SNPs) encompassing four genomic regions showing significant associations with different facial regions, including SNPs in DENND1B associated with the chin, SNPs among PISRT1 associated with eyes, SNPs between DCHS2 and SFRP2 associated with the nose, and SNPs in VPS13B associated with the nose. We replicate 24 SNPs from previously reported genetic loci in different populations, whose candidate genes are DCHS2, SUPT3H, HOXD1, SOX9, PAX3, and EDAR. These results provide a more comprehensive understanding of the genetic basis of variation in human facial morphology.
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Affiliation(s)
- Yin Huang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China
| | - Dan Li
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; DeepBlue Technology (Shanghai) Co., Ltd, Shanghai 200336, China
| | - Lu Qiao
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China
| | - Yu Liu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China
| | - Qianqian Peng
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China
| | - Sijie Wu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Manfei Zhang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China; Fudan-Taizhou Institute of Health Sciences, Taizhou 225300, China
| | - Jingze Tan
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China; Fudan-Taizhou Institute of Health Sciences, Taizhou 225300, China
| | - Shuhua Xu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Li Jin
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China; Fudan-Taizhou Institute of Health Sciences, Taizhou 225300, China; Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
| | - Sijia Wang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.
| | - Kun Tang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China; DeepBlue Technology (Shanghai) Co., Ltd, Shanghai 200336, China.
| | - Stefan Grünewald
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai 200031, China.
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Nakatomi M, Ludwig KU, Knapp M, Kist R, Lisgo S, Ohshima H, Mangold E, Peters H. Msx1 deficiency interacts with hypoxia and induces a morphogenetic regulation during mouse lip development. Development 2020; 147:dev189175. [PMID: 32467233 DOI: 10.1242/dev.189175] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 05/16/2020] [Indexed: 12/19/2022]
Abstract
Nonsyndromic clefts of the lip and palate are common birth defects resulting from gene-gene and gene-environment interactions. Mutations in human MSX1 have been linked to orofacial clefting and we show here that Msx1 deficiency causes a growth defect of the medial nasal process (Mnp) in mouse embryos. Although this defect alone does not disrupt lip formation, Msx1-deficient embryos develop a cleft lip when the mother is transiently exposed to reduced oxygen levels or to phenytoin, a drug known to cause embryonic hypoxia. In the absence of interacting environmental factors, the Mnp growth defect caused by Msx1 deficiency is modified by a Pax9-dependent 'morphogenetic regulation', which modulates Mnp shape, rescues lip formation and involves a localized abrogation of Bmp4-mediated repression of Pax9 Analyses of GWAS data revealed a genome-wide significant association of a Gene Ontology morphogenesis term (including assigned roles for MSX1, MSX2, PAX9, BMP4 and GREM1) specifically for nonsyndromic cleft lip with cleft palate. Our data indicate that MSX1 mutations could increase the risk for cleft lip formation by interacting with an impaired morphogenetic regulation that adjusts Mnp shape, or through interactions that inhibit Mnp growth.
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Affiliation(s)
- Mitsushiro Nakatomi
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
- Division of Anatomy, Department of Health Promotion, Kyushu Dental University, Kitakyushu 803-8580, Japan
| | - Kerstin U Ludwig
- Institute of Human Genetics, University Hospital Bonn, 53127 Bonn, Germany
| | - Michael Knapp
- Institute of Medical Biometry, Informatics and Epidemiology, University of Bonn, 53127 Bonn, Germany
| | - Ralf Kist
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
- School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4BW, UK
| | - Steven Lisgo
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
| | - Hayato Ohshima
- Division of Anatomy and Cell Biology of the Hard Tissue, Department of Tissue Regeneration and Reconstruction, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8514, Japan
| | - Elisabeth Mangold
- Institute of Human Genetics, University Hospital Bonn, 53127 Bonn, Germany
| | - Heiko Peters
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
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