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Ferrão MAG, da Fonseca AFA, Volpi PS, de Souza LC, Comério M, Filho ACV, Riva-Souza EM, Munoz PR, Ferrão RG, Ferrão LFV. Genomic-assisted breeding for climate-smart coffee. THE PLANT GENOME 2024; 17:e20321. [PMID: 36946358 DOI: 10.1002/tpg2.20321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/25/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.
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Affiliation(s)
- Maria Amélia G Ferrão
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
- Empresa Brasileira de Pesquisa Agropecuária-Embrapa Café, Brasília, Brazil
| | - Aymbire F A da Fonseca
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
- Empresa Brasileira de Pesquisa Agropecuária-Embrapa Café, Brasília, Brazil
| | - Paulo S Volpi
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
| | - Lucimara C de Souza
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
| | - Marcone Comério
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
| | - Abraão C Verdin Filho
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
| | - Elaine M Riva-Souza
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
| | - Patricio R Munoz
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
| | - Romário G Ferrão
- Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural-Incaper, ES, Brazil
- Multivix Group, ES, Brazil
| | - Luís Felipe V Ferrão
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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2
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Xu C, Ganesh SK, Zhou X. mtPGS: Leverage multiple correlated traits for accurate polygenic score construction. Am J Hum Genet 2023; 110:1673-1689. [PMID: 37716346 PMCID: PMC10577082 DOI: 10.1016/j.ajhg.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/18/2023] [Accepted: 08/27/2023] [Indexed: 09/18/2023] Open
Abstract
Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.
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Affiliation(s)
- Chang Xu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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3
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Khurshid S, Lazarte J, Pirruccello JP, Weng LC, Choi SH, Hall AW, Wang X, Friedman SF, Nauffal V, Biddinger KJ, Aragam KG, Batra P, Ho JE, Philippakis AA, Ellinor PT, Lubitz SA. Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass. Nat Commun 2023; 14:1558. [PMID: 36944631 PMCID: PMC10030590 DOI: 10.1038/s41467-023-37173-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/04/2023] [Indexed: 03/23/2023] Open
Abstract
Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.
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Affiliation(s)
- Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Julieta Lazarte
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - James P Pirruccello
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Lu-Chen Weng
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amelia W Hall
- Gene Regulation Observatory, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xin Wang
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Victor Nauffal
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Kiran J Biddinger
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Krishna G Aragam
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jennifer E Ho
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Anthony A Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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4
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Conditional GWAS of non-CG transposon methylation in Arabidopsis thaliana reveals major polymorphisms in five genes. PLoS Genet 2022; 18:e1010345. [PMID: 36084135 PMCID: PMC9491579 DOI: 10.1371/journal.pgen.1010345] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/21/2022] [Accepted: 07/16/2022] [Indexed: 11/19/2022] Open
Abstract
Genome-wide association studies (GWAS) have revealed that the striking natural variation for DNA CHH-methylation (mCHH; H is A, T, or C) of transposons has oligogenic architecture involving major alleles at a handful of known methylation regulators. Here we use a conditional GWAS approach to show that CHG-methylation (mCHG) has a similar genetic architecture—once mCHH is statistically controlled for. We identify five key trans-regulators that appear to modulate mCHG levels, and show that they interact with a previously identified modifier of mCHH in regulating natural transposon mobilization. DNA methylation is an epigenetic mark common across eukaryotes. It is associated with transcriptional silencing, in particular of transposable elements. Multiple elements, including epigenetic inheritance, shape DNA methylation patterns, and the complexity makes it challenging to dissect the regulation. Our work on the 1001 Arabidopsis Epigenomes project led to the unexpected discovery that much of the natural variation for CHH methylation (mCHH; H is A, T, or C) on transposable elements could be attributed to allelic variation in three genes known to be involved in epigenetic regulation. However, our analysis of methylation in other sequence contexts (mCHG and mCG) revealed no genetic regulator. Here we show that if mCHG variation is analyzed while taking mCHH into account, mCHG is also strongly influenced by allelic variation in a small number of genes with known or highly plausible direct roles in epigenetic regulation. The presence of common allelic variation of large effect is suggestive of some form of local adaptation. The nature of this adaptation remains obscure, but we present further evidence that allelic variation regulating DNA methylation influences transposon mobilization.
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5
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Couvy-Duchesne B, Zhang F, Kemper KE, Sidorenko J, Wray NR, Visscher PM, Colliot O, Yang J. Parsimonious model for mass-univariate vertexwise analysis. J Med Imaging (Bellingham) 2022; 9:052404. [PMID: 35610986 PMCID: PMC9122091 DOI: 10.1117/1.jmi.9.5.052404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/26/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses. Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. Results: We showed that when performed on a large sample ( N = 8662 , UK Biobank), GLMs yielded greatly inflated false positive rate (cluster false discovery rate > 0.6 ). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate but at a cost of increased computation. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence, and smoking status) and LMM yielded fewer and more localized associations. We identified 19 significant clusters displaying small associations with age, sex, and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes. Conclusions: The published literature could contain a large proportion of redundant (possibly confounded) associations that are largely prevented using LMMs. The parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance.
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Affiliation(s)
- Baptiste Couvy-Duchesne
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia.,Sorbonne University, Paris Brain Institute (ICM), CNRS, INRIA, INSERM, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Futao Zhang
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia
| | - Kathryn E Kemper
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia
| | - Julia Sidorenko
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia
| | - Naomi R Wray
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia
| | - Peter M Visscher
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia
| | - Olivier Colliot
- Sorbonne University, Paris Brain Institute (ICM), CNRS, INRIA, INSERM, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Jian Yang
- University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia.,Westlake University, School of Life Sciences, Hangzhou, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
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6
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Hohl DM, González R, Di Santo Meztler GP, Patiño-Rico J, Dejean C, Avena S, Gutiérrez MDLÁ, Catanesi CI. Applicability of the IrisPlex system for eye color prediction in an admixed population from Argentina. Ann Hum Genet 2022; 86:297-327. [PMID: 35946314 DOI: 10.1111/ahg.12480] [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: 10/12/2021] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 11/29/2022]
Abstract
Eye color prediction based on an individual's genetic information is of interest in the field of forensic genetics. In recent years, researchers have studied different genes and markers associated with this externally visible characteristic and have developed methods for its prediction. The IrisPlex represents a validated tool for homogeneous populations, though its applicability in populations of mixed ancestry is limited, mainly regarding the prediction of intermediate eye colors. With the aim of validating the applicability of this system in an admixed population from Argentina (n = 302), we analyzed the six single nucleotide variants used in that multiplex for eye color and four additional SNPs, and evaluated its prediction ability. We also performed a genotype-phenotype association analysis. This system proved to be useful when dealing with the extreme ends of the eye color spectrum (blue and brown) but presented difficulties in determining the intermediate phenotypes (green), which were found in a large proportion of our population. We concluded that these genetic tools should be used with caution in admixed populations and that more studies are required in order to improve the prediction of intermediate phenotypes.
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Affiliation(s)
- Diana María Hohl
- Laboratorio de Diversidad Genética, Instituto Multidisciplinario de Biología Celular IMBICE (CONICET-UNLP-CIC), La Plata, Buenos Aires, Argentina
| | - Rebeca González
- Laboratorio de Diversidad Genética, Instituto Multidisciplinario de Biología Celular IMBICE (CONICET-UNLP-CIC), La Plata, Buenos Aires, Argentina
| | - Gabriela Paula Di Santo Meztler
- Centro de Investigación de Proteínas Vegetales (CIPROVE-Centro Asociado CICPBA-UNLP), Depto. de Cs. Biológicas, Facultad de Cs. Exactas, Universidad Nacional de La Plata (UNLP), La Plata, Buenos Aires, Argentina
| | - Jessica Patiño-Rico
- Centro de Ciencias Naturales, Ambientales y Antropológicas, Universidad Maimónides, Buenos Aires, Argentina
| | - Cristina Dejean
- Centro de Ciencias Naturales, Ambientales y Antropológicas, Universidad Maimónides, Buenos Aires, Argentina.,Universidad de Buenos Aires, Facultad de Filosofía y Letras, Instituto de Ciencias Antropológicas (ICA), Sección Antropología Biológica, Buenos Aires, Argentina
| | - Sergio Avena
- Centro de Ciencias Naturales, Ambientales y Antropológicas, Universidad Maimónides, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas CONICET, Buenos Aires, Argentina
| | - María De Los Ángeles Gutiérrez
- Centro de Investigaciones del Medioambiente CIM, Facultad de Ciencias Exactas-CONICET, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina
| | - Cecilia Inés Catanesi
- Laboratorio de Diversidad Genética, Instituto Multidisciplinario de Biología Celular IMBICE (CONICET-UNLP-CIC), La Plata, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas CONICET, Buenos Aires, Argentina.,Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina
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7
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Investigating the genetic architecture of eye colour in a Canadian cohort. iScience 2022; 25:104485. [PMID: 35712076 PMCID: PMC9194134 DOI: 10.1016/j.isci.2022.104485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 04/18/2022] [Accepted: 05/24/2022] [Indexed: 11/24/2022] Open
Abstract
Eye color is highly variable in populations with European ancestry, ranging from low to high quantities of melanin in the iris. Polymorphisms in the HERC2/OCA2 locus have the largest effect on eye color in these populations, although other genomic regions also influence eye color. We performed genome-wide association studies of eye color in a Canadian cohort of European ancestry (N = 5,641) and investigated candidate causal variants. We uncovered several candidate causal signals in the HERC2/OCA2 region, whereas other loci likely harbor a single causal signal. We observed colocalization of eye color signals with the expression or methylation profiles of cultured primary melanocytes. Genetic correlations of eye and hair color suggest high genome-wide pleiotropy, but locus-level differences in the genetic architecture of both traits. Overall, we provide a better picture of the polymorphisms underpinning eye color variation, which may be a consequence of specific molecular processes in the iris melanocytes. Genome-wide association studies of eye color in 5,641 participants Multiple independent candidate causal variants were identified across HERC2/OCA2 Single candidate causal variants observed on or near IRF4, SLC24A4, TYR, and TYRP1 Colocalization of eye color signals with expression and methylation profiles
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8
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Deng L, Pan Y, Wang Y, Chen H, Yuan K, Chen S, Lu D, Lu Y, Mokhtar SS, Rahman TA, Hoh BP, Xu S. Genetic connections and convergent evolution of tropical indigenous peoples in Asia. Mol Biol Evol 2021; 39:6481554. [PMID: 34940850 PMCID: PMC8826522 DOI: 10.1093/molbev/msab361] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Tropical indigenous peoples in Asia (TIA) attract much attention for their unique appearance, whereas their genetic history and adaptive evolution remain mysteries. We conducted a comprehensive study to characterize the genetic distinction and connection of broad geographical TIAs. Despite the diverse genetic makeup and large interarea genetic differentiation between the TIA groups, we identified a basal Asian ancestry (bASN) specifically shared by these populations. The bASN ancestry was relatively enriched in ancient Asian human genomes dated as early as ∼50,000 years before the present and diminished in more recent history. Notably, the bASN ancestry is unlikely to be derived from archaic hominins. Instead, we suggest it may be better modeled as a survived lineage of the initial peopling of Asia. Shared adaptations inherited from the ancient Asian ancestry were detected among the TIA groups (e.g., LIMS1 for hair morphology, and COL24A1 for bone formation), and they are enriched in neurological functions either at an identical locus (e.g., NKAIN3), or different loci in an identical gene (e.g., TENM4). The bASN ancestry could also have formed the substrate of the genetic architecture of the dark pigmentation observed in the TIA peoples. We hypothesize that phenotypic convergence of the dark pigmentation in TIAs could have resulted from parallel (e.g., DDB1/DAK) or genetic convergence driven by admixture (e.g., MTHFD1 and RAD18), new mutations (e.g., STK11), or notably purifying selection (e.g., MC1R). Our results provide new insights into the initial peopling of Asia and an advanced understanding of the phenotypic convergence of the TIA peoples.
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Affiliation(s)
- Lian Deng
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
| | - Yinan Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
| | - Sihan Chen
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai 200438, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Siti Shuhada Mokhtar
- Institute of Medical Molecular Biotechnology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, 47000 Sungai Buloh, Selangor, Malaysia
| | - Thuhairah Abdul Rahman
- Clinical Pathology Diagnostic Centre Research Laboratory, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, 47000 Sungai Buloh, Selangor, Malaysia
| | - Boon-Peng Hoh
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
- Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, UCSI Heights 56000 Cheras, Kuala Lumpur, Malaysia
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences, Shanghai 200031, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai 200438, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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9
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Zhao T, Fernando R, Cheng H. Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies. G3 (BETHESDA, MD.) 2021; 11:jkab228. [PMID: 34499126 PMCID: PMC8496266 DOI: 10.1093/g3journal/jkab228] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/22/2021] [Indexed: 01/05/2023]
Abstract
In conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. Here, we introduce a method named NN-Bayes, where "NN" stands for neural networks, and "Bayes" stands for Bayesian Alphabet models, including a collection of Bayesian regression models such as BayesA, BayesB, BayesC, and Bayesian LASSO. NN-Bayes incorporates Bayesian Alphabet models into non-linear neural networks via hidden layers between single-nucleotide polymorphisms (SNPs) and observed traits. Thus, NN-Bayes attempts to improve the performance of genome-wide prediction and GWAS by accommodating non-linear relationships between the hidden nodes and the observed trait, while maintaining genomic interpretability through the Bayesian regression models that connect the SNPs to the hidden nodes. For genomic interpretability, the posterior distribution of marker effects in NN-Bayes is inferred by Markov chain Monte Carlo approaches and used for inference of association through posterior inclusion probabilities and window posterior probability of association. In simulation studies with dominance and epistatic effects, performance of NN-Bayes was significantly better than conventional linear models for both GWAS and whole-genome prediction, and the differences on prediction accuracy were substantial in magnitude. In real-data analyses, for the soy dataset, NN-Bayes achieved significantly higher prediction accuracies than conventional linear models, and results from other four different species showed that NN-Bayes had similar prediction performance to linear models, which is potentially due to the small sample size. Our NN-Bayes is optimized for high-dimensional genomic data and implemented in an open-source package called "JWAS." NN-Bayes can lead to greater use of Bayesian neural networks to account for non-linear relationships due to its interpretability and computational performance.
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Affiliation(s)
- Tianjing Zhao
- Department of Animal Science, University of California Davis, Davis, CA 95616, USA
- Integrative Genetics and Genomics Graduate Group, University of California Davis, Davis, CA 95616, USA
| | - Rohan Fernando
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Hao Cheng
- Department of Animal Science, University of California Davis, Davis, CA 95616, USA
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10
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Feng Y, McQuillan MA, Tishkoff SA. Evolutionary genetics of skin pigmentation in African populations. Hum Mol Genet 2021; 30:R88-R97. [PMID: 33438000 PMCID: PMC8117430 DOI: 10.1093/hmg/ddab007] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 12/14/2022] Open
Abstract
Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.
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Affiliation(s)
- Yuanqing Feng
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A McQuillan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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11
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Carratto TMT, Marcorin L, do Valle-Silva G, de Oliveira MLG, Donadi EA, Simões AL, Castelli EC, Mendes-Junior CT. Prediction of eye and hair pigmentation phenotypes using the HIrisPlex system in a Brazilian admixed population sample. Int J Legal Med 2021; 135:1329-1339. [PMID: 33884487 DOI: 10.1007/s00414-021-02554-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/26/2021] [Indexed: 01/23/2023]
Abstract
Human pigmentation is a complex trait, probably involving more than 100 genes. Predicting phenotypes using SNPs present in those genes is important for forensic purpose. For this, the HIrisPlex tool was developed for eye and hair color prediction, with both models achieving high accuracy among Europeans. Its evaluation in admixed populations is important, since they present a higher frequency of intermediate phenotypes, and HIrisPlex has demonstrated limitations in such predictions; therefore, the performance of this tool may be impaired in such populations. Here, we evaluate the set of 24 markers from the HIrisPlex system in 328 individuals from Ribeirão Preto (SP) region, predicting eye and hair color and comparing the predictions with their real phenotypes. We used the HaloPlex Target Enrichment System and MiSeq Personal Sequencer platform for massively parallel sequencing. The prediction of eye and hair color was accomplished by the HIrisPlex online tool, using the default prediction settings. Ancestry was estimated using the SNPforID 34-plex to observe if and how an individual's ancestry background would affect predictions in this admixed sample. Our sample presented major European ancestry (70.5%), followed by African (21.1%) and Native American/East Asian (8.4%). HIrisPlex presented an overall sensitivity of 0.691 for hair color prediction, with sensitivities ranging from 0.547 to 0.782. The lowest sensitivity was observed for individuals with black hair, who present a reduced European contribution (48.4%). For eye color prediction, the overall sensitivity was 0.741, with sensitivities higher than 0.85 for blue and brown eyes, although it failed in predicting intermediate eye color. Such struggle in predicting this phenotype category is in accordance with what has been seen in previous studies involving HIrisPlex. Individuals with brown eye color are more admixed, with European ancestry decreasing to 62.6%; notwithstanding that, sensitivity for brown eyes was almost 100%. Overall sensitivity increases to 0.791 when a 0.7 threshold is set, though 12.5% of the individuals become undefined. When combining eye and hair prediction, hit rates between 51.3 and 68.9% were achieved. Despite the difficulties with intermediate phenotypes, we have shown that HIrisPlex results can be very helpful when interpreted with caution.
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Affiliation(s)
- Thássia Mayra Telles Carratto
- Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes, 3900, SP, 14040-901, Ribeirão Preto, Brazil
| | - Letícia Marcorin
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, 14049-900, Brazil
| | - Guilherme do Valle-Silva
- Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes, 3900, SP, 14040-901, Ribeirão Preto, Brazil
| | | | - Eduardo Antônio Donadi
- Divisão de Imunologia Clínica, Departamento de Clínica Médica, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, 14048-900, Brazil
| | - Aguinaldo Luiz Simões
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, 14049-900, Brazil
| | - Erick C Castelli
- Departamento de Patologia, Faculdade de Medicina de Botucatu, Unesp - Universidade Estadual Paulista, Botucatu, SP, 18618-970, Brazil
| | - Celso Teixeira Mendes-Junior
- Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes, 3900, SP, 14040-901, Ribeirão Preto, Brazil.
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12
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A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies. G3-GENES GENOMES GENETICS 2020; 10:4439-4448. [PMID: 33020191 PMCID: PMC7718731 DOI: 10.1534/g3.120.401618] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Bayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-Bayesian alphabet, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into multi-trait Bayesian regression methods. SEM-Bayesian alphabet provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait GWAS by performing GWAS based on indirect, direct and overall marker effects. The superior performance of SEM-Bayesian alphabet was demonstrated by comparing its GWAS results with other similar multi-trait GWAS methods on real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.
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13
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Genome-wide Analyses Identifies Known and New Markers Responsible of Chicken Plumage Color. Animals (Basel) 2020; 10:ani10030493. [PMID: 32183495 PMCID: PMC7143801 DOI: 10.3390/ani10030493] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/02/2020] [Accepted: 03/14/2020] [Indexed: 02/07/2023] Open
Abstract
Simple Summary In order to assess sources of variation related to Polverara breed plumage color (black vs. white), we carried out genome-wide analyses to identify the genomic regions involved in this trait. The present work has revealed new candidate genes involved in the phenotypic variability in local chicken populations. These results also contribute insights into the genetic basis for plumage color in poultry, and confirm the great complexity of the mechanisms that control this trait. Abstract Through the development of the high-throughput genotyping arrays, molecular markers and genes related to phenotypic traits have been identified in livestock species. In poultry, plumage color is an important qualitative trait that can be used as phenotypic marker for breed identification. In order to assess sources of genetic variation related to the Polverara chicken breed plumage colour (black vs. white), we carried out a genome-wide association study (GWAS) and a genome-wide fixation index (FST) scan to uncover the genomic regions involved. A total of 37 animals (17 white and 20 black) were genotyped with the Affymetrix 600 K Chicken single nucleotide polymorphism (SNP) Array. The combination of results from GWAS and FST revealed a total of 40 significant markers distributed on GGA 01, 03, 08, 12 and 21, and located within or near known genes. In addition to the well-known TYR, other candidate genes have been identified in this study, such as GRM5, RAB38 and NOTCH2. All these genes could explain the difference between the two Polverara breeds. Therefore, this study provides the basis for further investigation of the genetic mechanisms involved in plumage color in chicken.
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14
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Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Commun 2019; 10:5086. [PMID: 31704910 PMCID: PMC6841727 DOI: 10.1038/s41467-019-12653-0] [Citation(s) in RCA: 224] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 08/30/2019] [Indexed: 01/21/2023] Open
Abstract
Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding. Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based methods.
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15
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The Evolutionary History of Human Skin Pigmentation. J Mol Evol 2019; 88:77-87. [PMID: 31363820 DOI: 10.1007/s00239-019-09902-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 07/16/2019] [Indexed: 02/07/2023]
Abstract
Skin pigmentation is a complex, conspicuous, highly variable human trait that exhibits a remarkable correlation with latitude. The evolutionary history and genetic basis of skin color variation has been the subject of intense research in the last years. This article reviews the major hypotheses explaining skin color diversity and explores the implications of recent findings about the genes associated with skin pigmentation for understanding the evolutionary forces that have shaped the current patterns of skin color variation. A major aspect of these findings is that the genetic basis of skin color is less simple than previously thought and that geographic variation in skin pigmentation was influenced by the concerted action of different types of natural selection, rather than just by selective sweeps in a few key genes.
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16
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Lona-Durazo F, Hernandez-Pacheco N, Fan S, Zhang T, Choi J, Kovacs MA, Loftus SK, Le P, Edwards M, Fortes-Lima CA, Eng C, Huntsman S, Hu D, Gómez-Cabezas EJ, Marín-Padrón LC, Grauholm J, Mors O, Burchard EG, Norton HL, Pavan WJ, Brown KM, Tishkoff S, Pino-Yanes M, Beleza S, Marcheco-Teruel B, Parra EJ. Meta-analysis of GWA studies provides new insights on the genetic architecture of skin pigmentation in recently admixed populations. BMC Genet 2019; 20:59. [PMID: 31315583 PMCID: PMC6637524 DOI: 10.1186/s12863-019-0765-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/08/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Association studies in recently admixed populations are extremely useful to identify the genetic architecture of pigmentation, due to their high genotypic and phenotypic variation. However, to date only four Genome-Wide Association Studies (GWAS) have been carried out in these populations. RESULTS We present a GWAS of skin pigmentation in an admixed sample from Cuba (N = 762). Additionally, we conducted a meta-analysis including the Cuban sample, and admixed samples from Cape Verde, Puerto Rico and African-Americans from San Francisco. This meta-analysis is one of the largest efforts so far to characterize the genetic basis of skin pigmentation in admixed populations (N = 2,104). We identified five genome-wide significant regions in the meta-analysis, and explored if the markers observed in these regions are associated with the expression of relevant pigmentary genes in human melanocyte cultures. In three of the regions identified in the meta-analysis (SLC24A5, SLC45A2, and GRM5/TYR), the association seems to be driven by non-synonymous variants (rs1426654, rs16891982, and rs1042602, respectively). The rs16891982 polymorphism is strongly associated with the expression of the SLC45A2 gene. In the GRM5/TYR region, in addition to the rs1042602 non-synonymous SNP located on the TYR gene, variants located in the nearby GRM5 gene have an independent effect on pigmentation, possibly through regulation of gene expression of the TYR gene. We also replicated an association recently described near the MFSD12 gene on chromosome 19 (lead variant rs112332856). Additionally, our analyses support the presence of multiple signals in the OCA2/HERC2/APBA2 region on chromosome 15. A clear causal candidate is the HERC2 intronic variant rs12913832, which has a profound influence on OCA2 expression. This variant has pleiotropic effects on eye, hair, and skin pigmentation. However, conditional and haplotype-based analyses indicate the presence of other variants with independent effects on melanin levels in OCA2 and APBA2. Finally, a follow-up of genome-wide signals identified in a recent GWAS for tanning response indicates that there is a substantial overlap in the genetic factors influencing skin pigmentation and tanning response. CONCLUSIONS Our meta-analysis of skin pigmentation GWAS in recently admixed populations provides new insights about the genetic architecture of this complex trait.
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Affiliation(s)
- Frida Lona-Durazo
- Department of Anthropology, University of Toronto at Mississauga, Health Sciences Complex, room 352, Mississauga, Ontario L5L 1C6 Canada
| | - Natalia Hernandez-Pacheco
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
| | - Shaohua Fan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Tongwu Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, USA
| | - Jiyeon Choi
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, USA
| | - Michael A. Kovacs
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, USA
| | - Stacie K. Loftus
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Phuong Le
- Department of Anthropology, University of Toronto at Mississauga, Health Sciences Complex, room 352, Mississauga, Ontario L5L 1C6 Canada
| | - Melissa Edwards
- Department of Anthropology, University of Toronto at Mississauga, Health Sciences Complex, room 352, Mississauga, Ontario L5L 1C6 Canada
| | - Cesar A. Fortes-Lima
- Evolutionary Anthropology Team, Laboratory Eco-Anthropology and Ethno-Biology UMR7206, CNRS-MNHN-University Paris Diderot, Musée de l’Homme, Paris, France
- Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Celeste Eng
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - Scott Huntsman
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - Donglei Hu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | | | | | - Jonas Grauholm
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Ole Mors
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus University, Aarhus, Denmark
- Psychiatric Department, Aarhus University Hospital, Aarhus, Denmark
| | - Esteban G. Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - Heather L. Norton
- Department of Anthropology, University of Cincinnati, Cincinnati, USA
| | - William J. Pavan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Kevin M. Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, USA
| | - Sarah Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA USA
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Sandra Beleza
- Department of Genetics and Genome Biology, College of Life Sciences, University of Leicester, Leicester, UK
| | | | - Esteban J. Parra
- Department of Anthropology, University of Toronto at Mississauga, Health Sciences Complex, room 352, Mississauga, Ontario L5L 1C6 Canada
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17
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Baxter LL, Watkins-Chow DE, Pavan WJ, Loftus SK. A curated gene list for expanding the horizons of pigmentation biology. Pigment Cell Melanoma Res 2019; 32:348-358. [PMID: 30339321 PMCID: PMC10413850 DOI: 10.1111/pcmr.12743] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 09/01/2018] [Accepted: 09/29/2018] [Indexed: 12/27/2022]
Abstract
Over the past century, studies of human pigmentary disorders along with mouse and zebrafish models have shed light on the many cellular functions associated with visible pigment phenotypes. This has led to numerous genes annotated with the ontology term "pigmentation" in independent human, mouse, and zebrafish databases. Comparisons among these datasets revealed that each is individually incomplete in documenting all genes involved in integument-based pigmentation phenotypes. Additionally, each database contained inherent species-specific biases in data annotation, and the term "pigmentation" did not solely reflect integument pigmentation phenotypes. This review presents a comprehensive, cross-species list of 650 genes involved in pigmentation phenotypes that was compiled with extensive manual curation of genes annotated in OMIM, MGI, ZFIN, and GO. The resulting cross-species list of genes both intrinsic and extrinsic to integument pigment cells provides a valuable tool that can be used to expand our knowledge of complex, pigmentation-associated pathways.
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Affiliation(s)
- Laura L Baxter
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Dawn E Watkins-Chow
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - William J Pavan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Stacie K Loftus
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
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18
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Adhikari K, Mendoza-Revilla J, Sohail A, Fuentes-Guajardo M, Lampert J, Chacón-Duque JC, Hurtado M, Villegas V, Granja V, Acuña-Alonzo V, Jaramillo C, Arias W, Lozano RB, Everardo P, Gómez-Valdés J, Villamil-Ramírez H, Silva de Cerqueira CC, Hunemeier T, Ramallo V, Schuler-Faccini L, Salzano FM, Gonzalez-José R, Bortolini MC, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Tobin DJ, Fumagalli M, Balding D, Ruiz-Linares A. A GWAS in Latin Americans highlights the convergent evolution of lighter skin pigmentation in Eurasia. Nat Commun 2019; 10:358. [PMID: 30664655 PMCID: PMC6341102 DOI: 10.1038/s41467-018-08147-0] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 12/20/2018] [Indexed: 12/17/2022] Open
Abstract
We report a genome-wide association scan in >6,000 Latin Americans for pigmentation of skin and eyes. We found eighteen signals of association at twelve genomic regions. These include one novel locus for skin pigmentation (in 10q26) and three novel loci for eye pigmentation (in 1q32, 20q13 and 22q12). We demonstrate the presence of multiple independent signals of association in the 11q14 and 15q13 regions (comprising the GRM5/TYR and HERC2/OCA2 genes, respectively) and several epistatic interactions among independently associated alleles. Strongest association with skin pigmentation at 19p13 was observed for an Y182H missense variant (common only in East Asians and Native Americans) in MFSD12, a gene recently associated with skin pigmentation in Africans. We show that the frequency of the derived allele at Y182H is significantly correlated with lower solar radiation intensity in East Asia and infer that MFSD12 was under selection in East Asians, probably after their split from Europeans. Pigmentation variation in humans is influenced by complex genetic architecture in different populations. Here, the authors perform a genome-wide association analysis involving > 6,000 Latin Americans for pigmentation of skin and eyes, and identify known and novel genetic associations.
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Affiliation(s)
- Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Anood Sohail
- Department of Genetics, Cambridge University, Cambridge, CB2 3EH, UK
| | - Macarena Fuentes-Guajardo
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, 1000000, Chile
| | - Jodie Lampert
- Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Juan Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Victor Acuña-Alonzo
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,National Institute of Anthropology and History, Mexico City, 4510, Mexico
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Rodrigo Barquera Lozano
- National Institute of Anthropology and History, Mexico City, 4510, Mexico.,Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, 07745, Germany
| | - Paola Everardo
- National Institute of Anthropology and History, Mexico City, 4510, Mexico
| | - Jorge Gómez-Valdés
- National Institute of Anthropology and History, Mexico City, 4510, 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
| | - Caio C Silva de Cerqueira
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Tábita Hunemeier
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil.,Instituto Patagonico de Ciencias Sociales y Humanas, Centro Nacional Patagonico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - Lavinia Schuler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Francisco M Salzano
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Rolando Gonzalez-José
- Instituto Patagonico de Ciencias Sociales y Humanas, Centro Nacional Patagonico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - 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, Peru
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapaca, Arica, 1000000, Chile.,Programa de Genetica Humana, ICBM, Facultad de Medicina, Universidad de Chile, Santiago, 8320000, Chile
| | - Desmond J Tobin
- Centre for Skin Sciences, Faculty of Life Sciences, University of Bradford, Bradford, BD7 1DP, West Yorkshire, UK.,The Charles Institute of Dermatology, University College Dublin, Dublin, D4, Ireland
| | - Matteo Fumagalli
- Department of Life Sciences, Silwood Park campus, Imperial College London, Ascot, SL5 7PY, UK
| | - 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, 3010, Australia
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China. .,Aix-Marseille Université, CNRS, EFS, ADES, Marseille, 13005, France.
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Martin AR, Teferra S, Möller M, Hoal EG, Daly MJ. The critical needs and challenges for genetic architecture studies in Africa. Curr Opin Genet Dev 2018; 53:113-120. [PMID: 30240950 PMCID: PMC6494470 DOI: 10.1016/j.gde.2018.08.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/17/2018] [Accepted: 08/31/2018] [Indexed: 12/11/2022]
Abstract
Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date. Additionally, African investigators need to be meaningfully included, as greater inclusivity and enhanced research capacity afford enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages, challenges, and examples of genetic architecture studies of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Solomon Teferra
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, USA
| | - Marlo Möller
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, Cape Town, South Africa
| | - Eileen G Hoal
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, Cape Town, South Africa
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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20
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Zeng J, de Vlaming R, Wu Y, Robinson MR, Lloyd-Jones LR, Yengo L, Yap CX, Xue A, Sidorenko J, McRae AF, Powell JE, Montgomery GW, Metspalu A, Esko T, Gibson G, Wray NR, Visscher PM, Yang J. Signatures of negative selection in the genetic architecture of human complex traits. Nat Genet 2018; 50:746-753. [PMID: 29662166 DOI: 10.1038/s41588-018-0101-4] [Citation(s) in RCA: 195] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 03/05/2018] [Indexed: 11/09/2022]
Abstract
We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.
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Affiliation(s)
- Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Ronald de Vlaming
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Matthew R Robinson
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Luke R Lloyd-Jones
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Chloe X Yap
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Joseph E Powell
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | | | - Tonu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Greg Gibson
- School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
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21
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Crawford NG, Kelly DE, Hansen MEB, Beltrame MH, Fan S, Bowman SL, Jewett E, Ranciaro A, Thompson S, Lo Y, Pfeifer SP, Jensen JD, Campbell MC, Beggs W, Hormozdiari F, Mpoloka SW, Mokone GG, Nyambo T, Meskel DW, Belay G, Haut J, Rothschild H, Zon L, Zhou Y, Kovacs MA, Xu M, Zhang T, Bishop K, Sinclair J, Rivas C, Elliot E, Choi J, Li SA, Hicks B, Burgess S, Abnet C, Watkins-Chow DE, Oceana E, Song YS, Eskin E, Brown KM, Marks MS, Loftus SK, Pavan WJ, Yeager M, Chanock S, Tishkoff SA. Loci associated with skin pigmentation identified in African populations. Science 2017; 358:eaan8433. [PMID: 29025994 PMCID: PMC5759959 DOI: 10.1126/science.aan8433] [Citation(s) in RCA: 198] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Accepted: 10/03/2017] [Indexed: 12/13/2022]
Abstract
Despite the wide range of skin pigmentation in humans, little is known about its genetic basis in global populations. Examining ethnically diverse African genomes, we identify variants in or near SLC24A5, MFSD12, DDB1, TMEM138, OCA2, and HERC2 that are significantly associated with skin pigmentation. Genetic evidence indicates that the light pigmentation variant at SLC24A5 was introduced into East Africa by gene flow from non-Africans. At all other loci, variants associated with dark pigmentation in Africans are identical by descent in South Asian and Australo-Melanesian populations. Functional analyses indicate that MFSD12 encodes a lysosomal protein that affects melanogenesis in zebrafish and mice, and that mutations in melanocyte-specific regulatory regions near DDB1/TMEM138 correlate with expression of ultraviolet response genes under selection in Eurasians.
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Affiliation(s)
- Nicholas G Crawford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Derek E Kelly
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew E B Hansen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcia H Beltrame
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shaohua Fan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shanna L Bowman
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine and Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ethan Jewett
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94704, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA 94704, USA
| | - Alessia Ranciaro
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Simon Thompson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yancy Lo
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Michael C Campbell
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, Howard University, Washington, DC 20059, USA
| | - William Beggs
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA
| | | | - Gaonyadiwe George Mokone
- Department of Biomedical Sciences, University of Botswana School of Medicine, Gaborone, Botswana
| | - Thomas Nyambo
- Department of Biochemistry, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | | | - Gurja Belay
- Department of Biology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Jake Haut
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Harriet Rothschild
- Stem Cell Program, Division of Hematology and Oncology, Pediatric Hematology Program, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Leonard Zon
- Stem Cell Program, Division of Hematology and Oncology, Pediatric Hematology Program, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Yi Zhou
- Stem Cell Program, Division of Hematology and Oncology, Pediatric Hematology Program, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Michael A Kovacs
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mai Xu
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tongwu Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kevin Bishop
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jason Sinclair
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Cecilia Rivas
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eugene Elliot
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jiyeon Choi
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shengchao A Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA
| | - Belynda Hicks
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA
| | - Shawn Burgess
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Christian Abnet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
| | - Dawn E Watkins-Chow
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elena Oceana
- Department of Molecular Pharmacology, Physiology and Biotechnology, Brown University, Providence, RI 02912, USA
| | - Yun S Song
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94704, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA 94704, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eleazar Eskin
- Department of Computer Science and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kevin M Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael S Marks
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine and Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stacie K Loftus
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William J Pavan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
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